diff --git a/.github/workflows/build-push.yml b/.github/workflows/build-push.yml index 407bd47d9b0f8f..6daaaf5791dd24 100644 --- a/.github/workflows/build-push.yml +++ b/.github/workflows/build-push.yml @@ -125,7 +125,7 @@ jobs: with: images: ${{ env[matrix.image_name_env] }} tags: | - type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') }} + type=raw,value=latest,enable=${{ startsWith(github.ref, 'refs/tags/') && !contains(github.ref, '-') }} type=ref,event=branch type=sha,enable=true,priority=100,prefix=,suffix=,format=long type=raw,value=${{ github.ref_name }},enable=${{ startsWith(github.ref, 'refs/tags/') }} diff --git a/.github/workflows/web-tests.yml b/.github/workflows/web-tests.yml new file mode 100644 index 00000000000000..5aee64b8e6da02 --- /dev/null +++ b/.github/workflows/web-tests.yml @@ -0,0 +1,46 @@ +name: Web Tests + +on: + pull_request: + branches: + - main + paths: + - web/** + +concurrency: + group: web-tests-${{ github.head_ref || github.run_id }} + cancel-in-progress: true + +jobs: + test: + name: Web Tests + runs-on: ubuntu-latest + defaults: + run: + working-directory: ./web + + steps: + - name: Checkout code + uses: actions/checkout@v4 + + - name: Check changed files + id: changed-files + uses: tj-actions/changed-files@v45 + with: + files: web/** + + - name: Setup Node.js + uses: actions/setup-node@v4 + if: steps.changed-files.outputs.any_changed == 'true' + with: + node-version: 20 + cache: yarn + cache-dependency-path: ./web/package.json + + - name: Install dependencies + if: steps.changed-files.outputs.any_changed == 'true' + run: yarn install --frozen-lockfile + + - name: Run tests + if: steps.changed-files.outputs.any_changed == 'true' + run: yarn test diff --git a/api/.env.example b/api/.env.example index f775c1c5d34475..5b9d5281ebe39a 100644 --- a/api/.env.example +++ b/api/.env.example @@ -162,6 +162,8 @@ PGVECTOR_PORT=5433 PGVECTOR_USER=postgres PGVECTOR_PASSWORD=postgres PGVECTOR_DATABASE=postgres +PGVECTOR_MIN_CONNECTION=1 +PGVECTOR_MAX_CONNECTION=5 # Tidb Vector configuration TIDB_VECTOR_HOST=xxx.eu-central-1.xxx.aws.tidbcloud.com diff --git a/api/app.py b/api/app.py index 91a49337fccbde..1b58beee158199 100644 --- a/api/app.py +++ b/api/app.py @@ -53,11 +53,9 @@ warnings.simplefilter("ignore", ResourceWarning) -# fix windows platform -if os.name == "nt": - os.system('tzutil /s "UTC"') -else: - os.environ["TZ"] = "UTC" +os.environ["TZ"] = "UTC" +# windows platform not support tzset +if hasattr(time, "tzset"): time.tzset() diff --git a/api/commands.py b/api/commands.py index b8fc81af673afe..7ef4aed7f77664 100644 --- a/api/commands.py +++ b/api/commands.py @@ -652,7 +652,7 @@ def fix_app_site_missing(): app_was_created.send(app, account=account) except Exception as e: failed_app_ids.append(app_id) - click.echo(click.style("FFailed to fix missing site for app {}".format(app_id), fg="red")) + click.echo(click.style("Failed to fix missing site for app {}".format(app_id), fg="red")) logging.exception(f"Fix app related site missing issue failed, error: {e}") continue diff --git a/api/configs/middleware/vdb/pgvector_config.py b/api/configs/middleware/vdb/pgvector_config.py index 395dcaa4208a93..85f5dca7e23222 100644 --- a/api/configs/middleware/vdb/pgvector_config.py +++ b/api/configs/middleware/vdb/pgvector_config.py @@ -33,3 +33,13 @@ class PGVectorConfig(BaseSettings): description="Name of the PostgreSQL database to connect to", default=None, ) + + PGVECTOR_MIN_CONNECTION: PositiveInt = Field( + description="Min connection of the PostgreSQL database", + default=1, + ) + + PGVECTOR_MAX_CONNECTION: PositiveInt = Field( + description="Max connection of the PostgreSQL database", + default=5, + ) diff --git a/api/controllers/console/datasets/datasets.py b/api/controllers/console/datasets/datasets.py index 2c4e5ac60789e7..5a763b3457c695 100644 --- a/api/controllers/console/datasets/datasets.py +++ b/api/controllers/console/datasets/datasets.py @@ -563,10 +563,10 @@ def get(self): case ( VectorType.MILVUS | VectorType.RELYT - | VectorType.PGVECTOR | VectorType.TIDB_VECTOR | VectorType.CHROMA | VectorType.TENCENT + | VectorType.PGVECTO_RS ): return {"retrieval_method": [RetrievalMethod.SEMANTIC_SEARCH.value]} case ( @@ -577,6 +577,7 @@ def get(self): | VectorType.MYSCALE | VectorType.ORACLE | VectorType.ELASTICSEARCH + | VectorType.PGVECTOR ): return { "retrieval_method": [ diff --git a/api/core/app/apps/advanced_chat/generate_task_pipeline.py b/api/core/app/apps/advanced_chat/generate_task_pipeline.py index 94206a1b1cd678..897b6fd063c76b 100644 --- a/api/core/app/apps/advanced_chat/generate_task_pipeline.py +++ b/api/core/app/apps/advanced_chat/generate_task_pipeline.py @@ -231,7 +231,8 @@ def _wrapper_process_stream_response( except Exception as e: logger.error(e) break - yield MessageAudioEndStreamResponse(audio="", task_id=task_id) + if tts_publisher: + yield MessageAudioEndStreamResponse(audio="", task_id=task_id) def _process_stream_response( self, diff --git a/api/core/app/apps/base_app_generate_response_converter.py b/api/core/app/apps/base_app_generate_response_converter.py index c6855ac85494d6..62e79ec444a48a 100644 --- a/api/core/app/apps/base_app_generate_response_converter.py +++ b/api/core/app/apps/base_app_generate_response_converter.py @@ -75,10 +75,10 @@ def _get_simple_metadata(cls, metadata: dict[str, Any]): :return: """ # show_retrieve_source + updated_resources = [] if "retriever_resources" in metadata: - metadata["retriever_resources"] = [] for resource in metadata["retriever_resources"]: - metadata["retriever_resources"].append( + updated_resources.append( { "segment_id": resource["segment_id"], "position": resource["position"], @@ -87,6 +87,7 @@ def _get_simple_metadata(cls, metadata: dict[str, Any]): "content": resource["content"], } ) + metadata["retriever_resources"] = updated_resources # show annotation reply if "annotation_reply" in metadata: diff --git a/api/core/app/apps/base_app_runner.py b/api/core/app/apps/base_app_runner.py index 1b412b86396c25..203aca3384376d 100644 --- a/api/core/app/apps/base_app_runner.py +++ b/api/core/app/apps/base_app_runner.py @@ -309,7 +309,7 @@ def _handle_invoke_result_stream( if not prompt_messages: prompt_messages = result.prompt_messages - if not usage and result.delta.usage: + if result.delta.usage: usage = result.delta.usage if not usage: diff --git a/api/core/app/apps/workflow/generate_task_pipeline.py b/api/core/app/apps/workflow/generate_task_pipeline.py index 93edf8e0e882b5..798847a5070b8d 100644 --- a/api/core/app/apps/workflow/generate_task_pipeline.py +++ b/api/core/app/apps/workflow/generate_task_pipeline.py @@ -212,7 +212,8 @@ def _wrapper_process_stream_response( except Exception as e: logger.error(e) break - yield MessageAudioEndStreamResponse(audio="", task_id=task_id) + if tts_publisher: + yield MessageAudioEndStreamResponse(audio="", task_id=task_id) def _process_stream_response( self, diff --git a/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py b/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py index 8f834b6458ea4b..917649f34e769c 100644 --- a/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py +++ b/api/core/app/task_pipeline/easy_ui_based_generate_task_pipeline.py @@ -248,7 +248,8 @@ def _wrapper_process_stream_response( else: start_listener_time = time.time() yield MessageAudioStreamResponse(audio=audio.audio, task_id=task_id) - yield MessageAudioEndStreamResponse(audio="", task_id=task_id) + if publisher: + yield MessageAudioEndStreamResponse(audio="", task_id=task_id) def _process_stream_response( self, publisher: AppGeneratorTTSPublisher, trace_manager: Optional[TraceQueueManager] = None diff --git a/api/core/embedding/cached_embedding.py b/api/core/embedding/cached_embedding.py index 8ce12fd59f5761..75219051cd30cd 100644 --- a/api/core/embedding/cached_embedding.py +++ b/api/core/embedding/cached_embedding.py @@ -5,6 +5,7 @@ import numpy as np from sqlalchemy.exc import IntegrityError +from core.embedding.embedding_constant import EmbeddingInputType from core.model_manager import ModelInstance from core.model_runtime.entities.model_entities import ModelPropertyKey from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel @@ -56,7 +57,9 @@ def embed_documents(self, texts: list[str]) -> list[list[float]]: for i in range(0, len(embedding_queue_texts), max_chunks): batch_texts = embedding_queue_texts[i : i + max_chunks] - embedding_result = self._model_instance.invoke_text_embedding(texts=batch_texts, user=self._user) + embedding_result = self._model_instance.invoke_text_embedding( + texts=batch_texts, user=self._user, input_type=EmbeddingInputType.DOCUMENT + ) for vector in embedding_result.embeddings: try: @@ -100,7 +103,9 @@ def embed_query(self, text: str) -> list[float]: redis_client.expire(embedding_cache_key, 600) return list(np.frombuffer(base64.b64decode(embedding), dtype="float")) try: - embedding_result = self._model_instance.invoke_text_embedding(texts=[text], user=self._user) + embedding_result = self._model_instance.invoke_text_embedding( + texts=[text], user=self._user, input_type=EmbeddingInputType.QUERY + ) embedding_results = embedding_result.embeddings[0] embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist() diff --git a/api/core/embedding/embedding_constant.py b/api/core/embedding/embedding_constant.py new file mode 100644 index 00000000000000..9b4934646bc0e8 --- /dev/null +++ b/api/core/embedding/embedding_constant.py @@ -0,0 +1,10 @@ +from enum import Enum + + +class EmbeddingInputType(Enum): + """ + Enum for embedding input type. + """ + + DOCUMENT = "document" + QUERY = "query" diff --git a/api/core/entities/provider_configuration.py b/api/core/entities/provider_configuration.py index 4797b69b8596bb..807f09598c7607 100644 --- a/api/core/entities/provider_configuration.py +++ b/api/core/entities/provider_configuration.py @@ -119,7 +119,7 @@ def get_current_credentials(self, model_type: ModelType, model: str) -> Optional credentials = model_configuration.credentials break - if self.custom_configuration.provider: + if not credentials and self.custom_configuration.provider: credentials = self.custom_configuration.provider.credentials return credentials diff --git a/api/core/llm_generator/prompts.py b/api/core/llm_generator/prompts.py index c40b6d180804cf..e5b678451600a3 100644 --- a/api/core/llm_generator/prompts.py +++ b/api/core/llm_generator/prompts.py @@ -65,7 +65,6 @@ "Please help me predict the three most likely questions that human would ask, " "and keeping each question under 20 characters.\n" "MAKE SURE your output is the SAME language as the Assistant's latest response" - "(if the main response is written in Chinese, then the language of your output must be using Chinese.)!\n" "The output must be an array in JSON format following the specified schema:\n" '["question1","question2","question3"]\n' ) diff --git a/api/core/model_manager.py b/api/core/model_manager.py index 990efd36c609c2..74b445236268d9 100644 --- a/api/core/model_manager.py +++ b/api/core/model_manager.py @@ -3,6 +3,7 @@ from collections.abc import Callable, Generator, Sequence from typing import IO, Optional, Union, cast +from core.embedding.embedding_constant import EmbeddingInputType from core.entities.provider_configuration import ProviderConfiguration, ProviderModelBundle from core.entities.provider_entities import ModelLoadBalancingConfiguration from core.errors.error import ProviderTokenNotInitError @@ -158,12 +159,15 @@ def get_llm_num_tokens( tools=tools, ) - def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) -> TextEmbeddingResult: + def invoke_text_embedding( + self, texts: list[str], user: Optional[str] = None, input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT + ) -> TextEmbeddingResult: """ Invoke large language model :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ if not isinstance(self.model_type_instance, TextEmbeddingModel): @@ -176,6 +180,7 @@ def invoke_text_embedding(self, texts: list[str], user: Optional[str] = None) -> credentials=self.credentials, texts=texts, user=user, + input_type=input_type, ) def get_text_embedding_num_tokens(self, texts: list[str]) -> int: diff --git a/api/core/model_runtime/callbacks/base_callback.py b/api/core/model_runtime/callbacks/base_callback.py index 92da53c9a464df..6bd9325785a2da 100644 --- a/api/core/model_runtime/callbacks/base_callback.py +++ b/api/core/model_runtime/callbacks/base_callback.py @@ -1,3 +1,4 @@ +from abc import ABC, abstractmethod from typing import Optional from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk @@ -13,7 +14,7 @@ } -class Callback: +class Callback(ABC): """ Base class for callbacks. Only for LLM. @@ -21,6 +22,7 @@ class Callback: raise_error: bool = False + @abstractmethod def on_before_invoke( self, llm_instance: AIModel, @@ -48,6 +50,7 @@ def on_before_invoke( """ raise NotImplementedError() + @abstractmethod def on_new_chunk( self, llm_instance: AIModel, @@ -77,6 +80,7 @@ def on_new_chunk( """ raise NotImplementedError() + @abstractmethod def on_after_invoke( self, llm_instance: AIModel, @@ -106,6 +110,7 @@ def on_after_invoke( """ raise NotImplementedError() + @abstractmethod def on_invoke_error( self, llm_instance: AIModel, diff --git a/api/core/model_runtime/docs/en_US/customizable_model_scale_out.md b/api/core/model_runtime/docs/en_US/customizable_model_scale_out.md new file mode 100644 index 00000000000000..f5b806ade6f499 --- /dev/null +++ b/api/core/model_runtime/docs/en_US/customizable_model_scale_out.md @@ -0,0 +1,310 @@ +## Custom Integration of Pre-defined Models + +### Introduction + +After completing the vendors integration, the next step is to connect the vendor's models. To illustrate the entire connection process, we will use Xinference as an example to demonstrate a complete vendor integration. + +It is important to note that for custom models, each model connection requires a complete vendor credential. + +Unlike pre-defined models, a custom vendor integration always includes the following two parameters, which do not need to be defined in the vendor YAML file. + +![](images/index/image-3.png) + +As mentioned earlier, vendors do not need to implement validate_provider_credential. The runtime will automatically call the corresponding model layer's validate_credentials to validate the credentials based on the model type and name selected by the user. + +### Writing the Vendor YAML + +First, we need to identify the types of models supported by the vendor we are integrating. + +Currently supported model types are as follows: + +- `llm` Text Generation Models + +- `text_embedding` Text Embedding Models + +- `rerank` Rerank Models + +- `speech2text` Speech-to-Text + +- `tts` Text-to-Speech + +- `moderation` Moderation + +Xinference supports LLM, Text Embedding, and Rerank. So we will start by writing xinference.yaml. + +```yaml +provider: xinference #Define the vendor identifier +label: # Vendor display name, supports both en_US (English) and zh_Hans (Simplified Chinese). If zh_Hans is not set, it will use en_US by default. + en_US: Xorbits Inference +icon_small: # Small icon, refer to other vendors' icons stored in the _assets directory within the vendor implementation directory; follows the same language policy as the label + en_US: icon_s_en.svg +icon_large: # Large icon + en_US: icon_l_en.svg +help: # Help information + title: + en_US: How to deploy Xinference + zh_Hans: 如何部署 Xinference + url: + en_US: https://github.com/xorbitsai/inference +supported_model_types: # Supported model types. Xinference supports LLM, Text Embedding, and Rerank +- llm +- text-embedding +- rerank +configurate_methods: # Since Xinference is a locally deployed vendor with no predefined models, users need to deploy whatever models they need according to Xinference documentation. Thus, it only supports custom models. +- customizable-model +provider_credential_schema: + credential_form_schemas: +``` + + +Then, we need to determine what credentials are required to define a model in Xinference. + +- Since it supports three different types of models, we need to specify the model_type to denote the model type. Here is how we can define it: + +```yaml +provider_credential_schema: + credential_form_schemas: + - variable: model_type + type: select + label: + en_US: Model type + zh_Hans: 模型类型 + required: true + options: + - value: text-generation + label: + en_US: Language Model + zh_Hans: 语言模型 + - value: embeddings + label: + en_US: Text Embedding + - value: reranking + label: + en_US: Rerank +``` + +- Next, each model has its own model_name, so we need to define that here: + +```yaml + - variable: model_name + type: text-input + label: + en_US: Model name + zh_Hans: 模型名称 + required: true + placeholder: + zh_Hans: 填写模型名称 + en_US: Input model name +``` + +- Specify the Xinference local deployment address: + +```yaml + - variable: server_url + label: + zh_Hans: 服务器URL + en_US: Server url + type: text-input + required: true + placeholder: + zh_Hans: 在此输入Xinference的服务器地址，如 https://example.com/xxx + en_US: Enter the url of your Xinference, for example https://example.com/xxx +``` + +- Each model has a unique model_uid, so we also need to define that here: + +```yaml + - variable: model_uid + label: + zh_Hans: 模型UID + en_US: Model uid + type: text-input + required: true + placeholder: + zh_Hans: 在此输入您的Model UID + en_US: Enter the model uid +``` + +Now, we have completed the basic definition of the vendor. + +### Writing the Model Code + +Next, let's take the `llm` type as an example and write `xinference.llm.llm.py`. + +In `llm.py`, create a Xinference LLM class, we name it `XinferenceAILargeLanguageModel` (this can be arbitrary), inheriting from the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods: + +- LLM Invocation + +Implement the core method for LLM invocation, supporting both stream and synchronous responses. + +```python +def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool usage + :param stop: stop words + :param stream: is the response a stream + :param user: unique user id + :return: full response or stream response chunk generator result + """ +``` + +When implementing, ensure to use two functions to return data separately for synchronous and stream responses. This is important because Python treats functions containing the `yield` keyword as generator functions, mandating them to return `Generator` types. Here’s an example (note that the example uses simplified parameters; in real implementation, use the parameter list as defined above): + +```python +def _invoke(self, stream: bool, **kwargs) \ + -> Union[LLMResult, Generator]: + if stream: + return self._handle_stream_response(**kwargs) + return self._handle_sync_response(**kwargs) + +def _handle_stream_response(self, **kwargs) -> Generator: + for chunk in response: + yield chunk +def _handle_sync_response(self, **kwargs) -> LLMResult: + return LLMResult(**response) +``` + +- Pre-compute Input Tokens + +If the model does not provide an interface for pre-computing tokens, you can return 0 directly. + +```python +def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool usage + :return: token count + """ +``` + + +Sometimes, you might not want to return 0 directly. In such cases, you can use `self._get_num_tokens_by_gpt2(text: str)` to get pre-computed tokens. This method is provided by the `AIModel` base class, and it uses GPT2's Tokenizer for calculation. However, it should be noted that this is only a substitute and may not be fully accurate. + +- Model Credentials Validation + +Similar to vendor credentials validation, this method validates individual model credentials. + +```python +def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: None + """ +``` + +- Model Parameter Schema + +Unlike custom types, since the YAML file does not define which parameters a model supports, we need to dynamically generate the model parameter schema. + +For instance, Xinference supports `max_tokens`, `temperature`, and `top_p` parameters. + +However, some vendors may support different parameters for different models. For example, the `OpenLLM` vendor supports `top_k`, but not all models provided by this vendor support `top_k`. Let's say model A supports `top_k` but model B does not. In such cases, we need to dynamically generate the model parameter schema, as illustrated below: + +```python + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None: + """ + used to define customizable model schema + """ + rules = [ + ParameterRule( + name='temperature', type=ParameterType.FLOAT, + use_template='temperature', + label=I18nObject( + zh_Hans='温度', en_US='Temperature' + ) + ), + ParameterRule( + name='top_p', type=ParameterType.FLOAT, + use_template='top_p', + label=I18nObject( + zh_Hans='Top P', en_US='Top P' + ) + ), + ParameterRule( + name='max_tokens', type=ParameterType.INT, + use_template='max_tokens', + min=1, + default=512, + label=I18nObject( + zh_Hans='最大生成长度', en_US='Max Tokens' + ) + ) + ] + + # if model is A, add top_k to rules + if model == 'A': + rules.append( + ParameterRule( + name='top_k', type=ParameterType.INT, + use_template='top_k', + min=1, + default=50, + label=I18nObject( + zh_Hans='Top K', en_US='Top K' + ) + ) + ) + + """ + some NOT IMPORTANT code here + """ + + entity = AIModelEntity( + model=model, + label=I18nObject( + en_US=model + ), + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_type=model_type, + model_properties={ + ModelPropertyKey.MODE: ModelType.LLM, + }, + parameter_rules=rules + ) + + return entity +``` + +- Exception Error Mapping + +When a model invocation error occurs, it should be mapped to the runtime's specified `InvokeError` type, enabling Dify to handle different errors appropriately. + +Runtime Errors: + +- `InvokeConnectionError` Connection error during invocation +- `InvokeServerUnavailableError` Service provider unavailable +- `InvokeRateLimitError` Rate limit reached +- `InvokeAuthorizationError` Authorization failure +- `InvokeBadRequestError` Invalid request parameters + +```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ +``` + +For interface method details, see: [Interfaces](./interfaces.md). For specific implementations, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py). \ No newline at end of file diff --git a/api/core/model_runtime/docs/en_US/images/index/image-1.png b/api/core/model_runtime/docs/en_US/images/index/image-1.png new file mode 100644 index 00000000000000..b158d44b29dcc2 Binary files /dev/null and b/api/core/model_runtime/docs/en_US/images/index/image-1.png differ diff --git a/api/core/model_runtime/docs/en_US/images/index/image-2.png b/api/core/model_runtime/docs/en_US/images/index/image-2.png new file mode 100644 index 00000000000000..c70cd3da5eea19 Binary files /dev/null and b/api/core/model_runtime/docs/en_US/images/index/image-2.png differ diff --git a/api/core/model_runtime/docs/en_US/images/index/image-3.png b/api/core/model_runtime/docs/en_US/images/index/image-3.png new file mode 100644 index 00000000000000..bf0b9a7f47fddf Binary files /dev/null and b/api/core/model_runtime/docs/en_US/images/index/image-3.png differ diff --git a/api/core/model_runtime/docs/en_US/images/index/image.png b/api/core/model_runtime/docs/en_US/images/index/image.png new file mode 100644 index 00000000000000..eb63d107e1c385 Binary files /dev/null and b/api/core/model_runtime/docs/en_US/images/index/image.png differ diff --git a/api/core/model_runtime/docs/en_US/predefined_model_scale_out.md b/api/core/model_runtime/docs/en_US/predefined_model_scale_out.md new file mode 100644 index 00000000000000..3e16257452c7a0 --- /dev/null +++ b/api/core/model_runtime/docs/en_US/predefined_model_scale_out.md @@ -0,0 +1,173 @@ +## Predefined Model Integration + +After completing the vendor integration, the next step is to integrate the models from the vendor. + +First, we need to determine the type of model to be integrated and create the corresponding model type `module` under the respective vendor's directory. + +Currently supported model types are: + +- `llm` Text Generation Model +- `text_embedding` Text Embedding Model +- `rerank` Rerank Model +- `speech2text` Speech-to-Text +- `tts` Text-to-Speech +- `moderation` Moderation + +Continuing with `Anthropic` as an example, `Anthropic` only supports LLM, so create a `module` named `llm` under `model_providers.anthropic`. + +For predefined models, we first need to create a YAML file named after the model under the `llm` `module`, such as `claude-2.1.yaml`. + +### Prepare Model YAML + +```yaml +model: claude-2.1 # Model identifier +# Display name of the model, which can be set to en_US English or zh_Hans Chinese. If zh_Hans is not set, it will default to en_US. +# This can also be omitted, in which case the model identifier will be used as the label +label: + en_US: claude-2.1 +model_type: llm # Model type, claude-2.1 is an LLM +features: # Supported features, agent-thought supports Agent reasoning, vision supports image understanding +- agent-thought +model_properties: # Model properties + mode: chat # LLM mode, complete for text completion models, chat for conversation models + context_size: 200000 # Maximum context size +parameter_rules: # Parameter rules for the model call; only LLM requires this +- name: temperature # Parameter variable name + # Five default configuration templates are provided: temperature/top_p/max_tokens/presence_penalty/frequency_penalty + # The template variable name can be set directly in use_template, which will use the default configuration in entities.defaults.PARAMETER_RULE_TEMPLATE + # Additional configuration parameters will override the default configuration if set + use_template: temperature +- name: top_p + use_template: top_p +- name: top_k + label: # Display name of the parameter + zh_Hans: 取样数量 + en_US: Top k + type: int # Parameter type, supports float/int/string/boolean + help: # Help information, describing the parameter's function + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false # Whether the parameter is mandatory; can be omitted +- name: max_tokens_to_sample + use_template: max_tokens + default: 4096 # Default value of the parameter + min: 1 # Minimum value of the parameter, applicable to float/int only + max: 4096 # Maximum value of the parameter, applicable to float/int only +pricing: # Pricing information + input: '8.00' # Input unit price, i.e., prompt price + output: '24.00' # Output unit price, i.e., response content price + unit: '0.000001' # Price unit, meaning the above prices are per 100K + currency: USD # Price currency +``` + +It is recommended to prepare all model configurations before starting the implementation of the model code. + +You can also refer to the YAML configuration information under the corresponding model type directories of other vendors in the `model_providers` directory. For the complete YAML rules, refer to: [Schema](schema.md#aimodelentity). + +### Implement the Model Call Code + +Next, create a Python file named `llm.py` under the `llm` `module` to write the implementation code. + +Create an Anthropic LLM class named `AnthropicLargeLanguageModel` (or any other name), inheriting from the `__base.large_language_model.LargeLanguageModel` base class, and implement the following methods: + +- LLM Call + +Implement the core method for calling the LLM, supporting both streaming and synchronous responses. + +```python + def _invoke(self, model: str, credentials: dict, + prompt_messages: list[PromptMessage], model_parameters: dict, + tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None, + stream: bool = True, user: Optional[str] = None) \ + -> Union[LLMResult, Generator]: + """ + Invoke large language model + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param model_parameters: model parameters + :param tools: tools for tool calling + :param stop: stop words + :param stream: is stream response + :param user: unique user id + :return: full response or stream response chunk generator result + """ +``` + +Ensure to use two functions for returning data, one for synchronous returns and the other for streaming returns, because Python identifies functions containing the `yield` keyword as generator functions, fixing the return type to `Generator`. Thus, synchronous and streaming returns need to be implemented separately, as shown below (note that the example uses simplified parameters, for actual implementation follow the above parameter list): + +```python + def _invoke(self, stream: bool, **kwargs) \ + -> Union[LLMResult, Generator]: + if stream: + return self._handle_stream_response(**kwargs) + return self._handle_sync_response(**kwargs) + + def _handle_stream_response(self, **kwargs) -> Generator: + for chunk in response: + yield chunk + def _handle_sync_response(self, **kwargs) -> LLMResult: + return LLMResult(**response) +``` + +- Pre-compute Input Tokens + +If the model does not provide an interface to precompute tokens, return 0 directly. + +```python + def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param prompt_messages: prompt messages + :param tools: tools for tool calling + :return: + """ +``` + +- Validate Model Credentials + +Similar to vendor credential validation, but specific to a single model. + +```python + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ +``` + +- Map Invoke Errors + +When a model call fails, map it to a specific `InvokeError` type as required by Runtime, allowing Dify to handle different errors accordingly. + +Runtime Errors: + +- `InvokeConnectionError` Connection error + +- `InvokeServerUnavailableError` Service provider unavailable +- `InvokeRateLimitError` Rate limit reached +- `InvokeAuthorizationError` Authorization failed +- `InvokeBadRequestError` Parameter error + +```python + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ +``` + +For interface method explanations, see: [Interfaces](./interfaces.md). For detailed implementation, refer to: [llm.py](https://github.com/langgenius/dify-runtime/blob/main/lib/model_providers/anthropic/llm/llm.py). \ No newline at end of file diff --git a/api/core/model_runtime/docs/en_US/provider_scale_out.md b/api/core/model_runtime/docs/en_US/provider_scale_out.md index ba356c5cab63d0..07be5811d30137 100644 --- a/api/core/model_runtime/docs/en_US/provider_scale_out.md +++ b/api/core/model_runtime/docs/en_US/provider_scale_out.md @@ -58,7 +58,7 @@ provider_credential_schema: # Provider credential rules, as Anthropic only supp en_US: Enter your API URL ``` -You can also refer to the YAML configuration information under other provider directories in `model_providers`. The complete YAML rules are available at: [Schema](schema.md#Provider). +You can also refer to the YAML configuration information under other provider directories in `model_providers`. The complete YAML rules are available at: [Schema](schema.md#provider). ### Implementing Provider Code diff --git a/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md b/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md index b34544c789fa76..78aad8876f4b84 100644 --- a/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md +++ b/api/core/model_runtime/docs/zh_Hans/provider_scale_out.md @@ -117,7 +117,7 @@ model_credential_schema: en_US: Enter your API Base ``` -也可以参考 `model_providers` 目录下其他供应商目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#Provider)。 +也可以参考 `model_providers` 目录下其他供应商目录下的 YAML 配置信息，完整的 YAML 规则见：[Schema](schema.md#provider)。 #### 实现供应商代码 diff --git a/api/core/model_runtime/model_providers/__base/text_embedding_model.py b/api/core/model_runtime/model_providers/__base/text_embedding_model.py index 54a44860236918..a948dca20d69a4 100644 --- a/api/core/model_runtime/model_providers/__base/text_embedding_model.py +++ b/api/core/model_runtime/model_providers/__base/text_embedding_model.py @@ -4,6 +4,7 @@ from pydantic import ConfigDict +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult from core.model_runtime.model_providers.__base.ai_model import AIModel @@ -20,35 +21,47 @@ class TextEmbeddingModel(AIModel): model_config = ConfigDict(protected_namespaces=()) def invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ - Invoke large language model + Invoke text embedding model :param model: model name :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ self.started_at = time.perf_counter() try: - return self._invoke(model, credentials, texts, user) + return self._invoke(model, credentials, texts, user, input_type) except Exception as e: raise self._transform_invoke_error(e) @abstractmethod def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ - Invoke large language model + Invoke text embedding model :param model: model name :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ raise NotImplementedError diff --git a/api/core/model_runtime/model_providers/_position.yaml b/api/core/model_runtime/model_providers/_position.yaml index 1f5f64019a1663..89fccef6598fdd 100644 --- a/api/core/model_runtime/model_providers/_position.yaml +++ b/api/core/model_runtime/model_providers/_position.yaml @@ -38,3 +38,6 @@ - perfxcloud - zhinao - fireworks +- mixedbread +- nomic +- voyage diff --git a/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py index d9cff8ecbbadb9..8701a3805002eb 100644 --- a/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/azure_openai/text_embedding/text_embedding.py @@ -7,6 +7,7 @@ import tiktoken from openai import AzureOpenAI +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import AIModelEntity, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.validate import CredentialsValidateFailedError @@ -17,8 +18,23 @@ class AzureOpenAITextEmbeddingModel(_CommonAzureOpenAI, TextEmbeddingModel): def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ base_model_name = credentials["base_model_name"] credentials_kwargs = self._to_credential_kwargs(credentials) client = AzureOpenAI(**credentials_kwargs) diff --git a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py index 779dfbb6081638..56b9be1c365340 100644 --- a/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/baichuan/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ from requests import post +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -35,7 +36,12 @@ class BaichuanTextEmbeddingModel(TextEmbeddingModel): api_base: str = "http://api.baichuan-ai.com/v1/embeddings" def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -44,6 +50,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ api_key = credentials["api_key"] diff --git a/api/core/model_runtime/model_providers/bedrock/llm/_position.yaml b/api/core/model_runtime/model_providers/bedrock/llm/_position.yaml index 86c8061deefac8..47e2b020fd09a3 100644 --- a/api/core/model_runtime/model_providers/bedrock/llm/_position.yaml +++ b/api/core/model_runtime/model_providers/bedrock/llm/_position.yaml @@ -6,6 +6,8 @@ - anthropic.claude-v2:1 - anthropic.claude-3-sonnet-v1:0 - anthropic.claude-3-haiku-v1:0 +- ai21.jamba-1-5-large-v1:0 +- ai21.jamba-1-5-mini-v1:0 - cohere.command-light-text-v14 - cohere.command-text-v14 - cohere.command-r-plus-v1.0 @@ -15,6 +17,10 @@ - meta.llama3-1-405b-instruct-v1:0 - meta.llama3-8b-instruct-v1:0 - meta.llama3-70b-instruct-v1:0 +- us.meta.llama3-2-1b-instruct-v1:0 +- us.meta.llama3-2-3b-instruct-v1:0 +- us.meta.llama3-2-11b-instruct-v1:0 +- us.meta.llama3-2-90b-instruct-v1:0 - meta.llama2-13b-chat-v1 - meta.llama2-70b-chat-v1 - mistral.mistral-large-2407-v1:0 diff --git a/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-large-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-large-v1.0.yaml new file mode 100644 index 00000000000000..276c7312cee008 --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-large-v1.0.yaml @@ -0,0 +1,26 @@ +model: ai21.jamba-1-5-large-v1:0 +label: + en_US: Jamba 1.5 Large +model_type: llm +model_properties: + mode: completion + context_size: 256000 +parameter_rules: + - name: temperature + use_template: temperature + default: 1 + min: 0.0 + max: 2.0 + - name: top_p + use_template: top_p + - name: max_gen_len + use_template: max_tokens + required: true + default: 4096 + min: 1 + max: 4096 +pricing: + input: '0.002' + output: '0.008' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-mini-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-mini-v1.0.yaml new file mode 100644 index 00000000000000..3461d8ab71329d --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/ai21.jamba-1-5-mini-v1.0.yaml @@ -0,0 +1,26 @@ +model: ai21.jamba-1-5-mini-v1:0 +label: + en_US: Jamba 1.5 Mini +model_type: llm +model_properties: + mode: completion + context_size: 256000 +parameter_rules: + - name: temperature + use_template: temperature + default: 1 + min: 0.0 + max: 2.0 + - name: top_p + use_template: top_p + - name: max_gen_len + use_template: max_tokens + required: true + default: 4096 + min: 1 + max: 4096 +pricing: + input: '0.0002' + output: '0.0004' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/llm/llm.py b/api/core/model_runtime/model_providers/bedrock/llm/llm.py index 77bab0c2945887..d1961784f2e841 100644 --- a/api/core/model_runtime/model_providers/bedrock/llm/llm.py +++ b/api/core/model_runtime/model_providers/bedrock/llm/llm.py @@ -63,6 +63,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel): {"prefix": "us.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True}, {"prefix": "eu.anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True}, {"prefix": "anthropic.claude-3", "support_system_prompts": True, "support_tool_use": True}, + {"prefix": "us.meta.llama3-2", "support_system_prompts": True, "support_tool_use": True}, {"prefix": "meta.llama", "support_system_prompts": True, "support_tool_use": False}, {"prefix": "mistral.mistral-7b-instruct", "support_system_prompts": False, "support_tool_use": False}, {"prefix": "mistral.mixtral-8x7b-instruct", "support_system_prompts": False, "support_tool_use": False}, @@ -70,6 +71,7 @@ class BedrockLargeLanguageModel(LargeLanguageModel): {"prefix": "mistral.mistral-small", "support_system_prompts": True, "support_tool_use": True}, {"prefix": "cohere.command-r", "support_system_prompts": True, "support_tool_use": True}, {"prefix": "amazon.titan", "support_system_prompts": False, "support_tool_use": False}, + {"prefix": "ai21.jamba-1-5", "support_system_prompts": True, "support_tool_use": False}, ] @staticmethod diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-11b-instruct-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-11b-instruct-v1.0.yaml new file mode 100644 index 00000000000000..029f428776e0be --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-11b-instruct-v1.0.yaml @@ -0,0 +1,29 @@ +model: us.meta.llama3-2-11b-instruct-v1:0 +label: + en_US: US Meta Llama 3.2 11B Instruct +model_type: llm +features: + - vision + - tool-call +model_properties: + mode: completion + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.5 + min: 0.0 + max: 1 + - name: top_p + use_template: top_p + - name: max_gen_len + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 2048 +pricing: + input: '0.00035' + output: '0.00035' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-1b-instruct-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-1b-instruct-v1.0.yaml new file mode 100644 index 00000000000000..51c8474e54846d --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-1b-instruct-v1.0.yaml @@ -0,0 +1,26 @@ +model: us.meta.llama3-2-1b-instruct-v1:0 +label: + en_US: US Meta Llama 3.2 1B Instruct +model_type: llm +model_properties: + mode: completion + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.5 + min: 0.0 + max: 1 + - name: top_p + use_template: top_p + - name: max_gen_len + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 2048 +pricing: + input: '0.0001' + output: '0.0001' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-3b-instruct-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-3b-instruct-v1.0.yaml new file mode 100644 index 00000000000000..472cc7403e2d3e --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-3b-instruct-v1.0.yaml @@ -0,0 +1,26 @@ +model: us.meta.llama3-2-3b-instruct-v1:0 +label: + en_US: US Meta Llama 3.2 3B Instruct +model_type: llm +model_properties: + mode: completion + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.5 + min: 0.0 + max: 1 + - name: top_p + use_template: top_p + - name: max_gen_len + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 2048 +pricing: + input: '0.00015' + output: '0.00015' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-90b-instruct-v1.0.yaml b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-90b-instruct-v1.0.yaml new file mode 100644 index 00000000000000..cecd0236ca9e31 --- /dev/null +++ b/api/core/model_runtime/model_providers/bedrock/llm/us.meta.llama3-2-90b-instruct-v1.0.yaml @@ -0,0 +1,31 @@ +model: us.meta.llama3-2-90b-instruct-v1:0 +label: + en_US: US Meta Llama 3.2 90B Instruct +model_type: llm +features: + - tool-call +model_properties: + mode: completion + context_size: 128000 +parameter_rules: + - name: temperature + use_template: temperature + default: 0.5 + min: 0.0 + max: 1 + - name: top_p + use_template: top_p + default: 0.9 + min: 0 + max: 1 + - name: max_gen_len + use_template: max_tokens + required: true + default: 512 + min: 1 + max: 2048 +pricing: + input: '0.002' + output: '0.002' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py index 251170d1aec492..d9c57265921133 100644 --- a/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/bedrock/text_embedding/text_embedding.py @@ -13,6 +13,7 @@ UnknownServiceError, ) +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -30,7 +31,12 @@ class BedrockTextEmbeddingModel(TextEmbeddingModel): def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -39,6 +45,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ client_config = Config(region_name=credentials["aws_region"]) diff --git a/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py index a1c5e98118e4c6..4da20806904ba0 100644 --- a/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/cohere/text_embedding/text_embedding.py @@ -5,6 +5,7 @@ import numpy as np from cohere.core import RequestOptions +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -25,7 +26,12 @@ class CohereTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -34,6 +40,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ # get model properties diff --git a/api/core/model_runtime/model_providers/fireworks/fireworks.yaml b/api/core/model_runtime/model_providers/fireworks/fireworks.yaml index f886fa23b5bd82..cdb87a55e94660 100644 --- a/api/core/model_runtime/model_providers/fireworks/fireworks.yaml +++ b/api/core/model_runtime/model_providers/fireworks/fireworks.yaml @@ -15,6 +15,7 @@ help: en_US: https://fireworks.ai/account/api-keys supported_model_types: - llm + - text-embedding configurate_methods: - predefined-model provider_credential_schema: diff --git a/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml new file mode 100644 index 00000000000000..31415a24fa8b7e --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-11b-vision-instruct.yaml @@ -0,0 +1,46 @@ +model: accounts/fireworks/models/llama-v3p2-11b-vision-instruct +label: + zh_Hans: Llama 3.2 11B Vision Instruct + en_US: Llama 3.2 11B Vision Instruct +model_type: llm +features: + - agent-thought + - tool-call +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + - name: max_tokens + use_template: max_tokens + - name: context_length_exceeded_behavior + default: None + label: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + help: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + type: string + options: + - None + - truncate + - error + - name: response_format + use_template: response_format +pricing: + input: '0.2' + output: '0.2' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml new file mode 100644 index 00000000000000..c2fd77d2568d29 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-1b-instruct.yaml @@ -0,0 +1,46 @@ +model: accounts/fireworks/models/llama-v3p2-1b-instruct +label: + zh_Hans: Llama 3.2 1B Instruct + en_US: Llama 3.2 1B Instruct +model_type: llm +features: + - agent-thought + - tool-call +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + - name: max_tokens + use_template: max_tokens + - name: context_length_exceeded_behavior + default: None + label: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + help: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + type: string + options: + - None + - truncate + - error + - name: response_format + use_template: response_format +pricing: + input: '0.1' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml new file mode 100644 index 00000000000000..4b3c459c7bf2fc --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-3b-instruct.yaml @@ -0,0 +1,46 @@ +model: accounts/fireworks/models/llama-v3p2-3b-instruct +label: + zh_Hans: Llama 3.2 3B Instruct + en_US: Llama 3.2 3B Instruct +model_type: llm +features: + - agent-thought + - tool-call +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + - name: max_tokens + use_template: max_tokens + - name: context_length_exceeded_behavior + default: None + label: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + help: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + type: string + options: + - None + - truncate + - error + - name: response_format + use_template: response_format +pricing: + input: '0.1' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml new file mode 100644 index 00000000000000..0aece7455d6254 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/llm/llama-v3p2-90b-vision-instruct.yaml @@ -0,0 +1,46 @@ +model: accounts/fireworks/models/llama-v3p2-90b-vision-instruct +label: + zh_Hans: Llama 3.2 90B Vision Instruct + en_US: Llama 3.2 90B Vision Instruct +model_type: llm +features: + - agent-thought + - tool-call +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + - name: max_tokens + use_template: max_tokens + - name: context_length_exceeded_behavior + default: None + label: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + help: + zh_Hans: 上下文长度超出行为 + en_US: Context Length Exceeded Behavior + type: string + options: + - None + - truncate + - error + - name: response_format + use_template: response_format +pricing: + input: '0.9' + output: '0.9' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/UAE-Large-V1.yaml b/api/core/model_runtime/model_providers/fireworks/text_embedding/UAE-Large-V1.yaml new file mode 100644 index 00000000000000..d7c11691cf9bbc --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/UAE-Large-V1.yaml @@ -0,0 +1,12 @@ +model: WhereIsAI/UAE-Large-V1 +label: + zh_Hans: UAE-Large-V1 + en_US: UAE-Large-V1 +model_type: text-embedding +model_properties: + context_size: 512 + max_chunks: 1 +pricing: + input: '0.008' + unit: '0.000001' + currency: 'USD' diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/__init__.py b/api/core/model_runtime/model_providers/fireworks/text_embedding/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-base.yaml b/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-base.yaml new file mode 100644 index 00000000000000..d09bafb4d312f9 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-base.yaml @@ -0,0 +1,12 @@ +model: thenlper/gte-base +label: + zh_Hans: GTE-base + en_US: GTE-base +model_type: text-embedding +model_properties: + context_size: 512 + max_chunks: 1 +pricing: + input: '0.008' + unit: '0.000001' + currency: 'USD' diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-large.yaml b/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-large.yaml new file mode 100644 index 00000000000000..c41fa2f9d32361 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/gte-large.yaml @@ -0,0 +1,12 @@ +model: thenlper/gte-large +label: + zh_Hans: GTE-large + en_US: GTE-large +model_type: text-embedding +model_properties: + context_size: 512 + max_chunks: 1 +pricing: + input: '0.008' + unit: '0.000001' + currency: 'USD' diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.5.yaml b/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.5.yaml new file mode 100644 index 00000000000000..c9098503d96529 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.5.yaml @@ -0,0 +1,12 @@ +model: nomic-ai/nomic-embed-text-v1.5 +label: + zh_Hans: nomic-embed-text-v1.5 + en_US: nomic-embed-text-v1.5 +model_type: text-embedding +model_properties: + context_size: 8192 + max_chunks: 16 +pricing: + input: '0.008' + unit: '0.000001' + currency: 'USD' diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.yaml b/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.yaml new file mode 100644 index 00000000000000..89078d3ff69f93 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/nomic-embed-text-v1.yaml @@ -0,0 +1,12 @@ +model: nomic-ai/nomic-embed-text-v1 +label: + zh_Hans: nomic-embed-text-v1 + en_US: nomic-embed-text-v1 +model_type: text-embedding +model_properties: + context_size: 8192 + max_chunks: 16 +pricing: + input: '0.008' + unit: '0.000001' + currency: 'USD' diff --git a/api/core/model_runtime/model_providers/fireworks/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/fireworks/text_embedding/text_embedding.py new file mode 100644 index 00000000000000..cdce69ff380338 --- /dev/null +++ b/api/core/model_runtime/model_providers/fireworks/text_embedding/text_embedding.py @@ -0,0 +1,151 @@ +import time +from collections.abc import Mapping +from typing import Optional, Union + +import numpy as np +from openai import OpenAI + +from core.embedding.embedding_constant import EmbeddingInputType +from core.model_runtime.entities.model_entities import PriceType +from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel +from core.model_runtime.model_providers.fireworks._common import _CommonFireworks + + +class FireworksTextEmbeddingModel(_CommonFireworks, TextEmbeddingModel): + """ + Model class for Fireworks text embedding model. + """ + + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + + credentials_kwargs = self._to_credential_kwargs(credentials) + client = OpenAI(**credentials_kwargs) + + extra_model_kwargs = {} + if user: + extra_model_kwargs["user"] = user + + extra_model_kwargs["encoding_format"] = "float" + + context_size = self._get_context_size(model, credentials) + max_chunks = self._get_max_chunks(model, credentials) + + inputs = [] + indices = [] + used_tokens = 0 + + for i, text in enumerate(texts): + # Here token count is only an approximation based on the GPT2 tokenizer + # TODO: Optimize for better token estimation and chunking + num_tokens = self._get_num_tokens_by_gpt2(text) + + if num_tokens >= context_size: + cutoff = int(np.floor(len(text) * (context_size / num_tokens))) + # if num tokens is larger than context length, only use the start + inputs.append(text[0:cutoff]) + else: + inputs.append(text) + indices += [i] + + batched_embeddings = [] + _iter = range(0, len(inputs), max_chunks) + + for i in _iter: + embeddings_batch, embedding_used_tokens = self._embedding_invoke( + model=model, + client=client, + texts=inputs[i : i + max_chunks], + extra_model_kwargs=extra_model_kwargs, + ) + used_tokens += embedding_used_tokens + batched_embeddings += embeddings_batch + + usage = self._calc_response_usage(model=model, credentials=credentials, tokens=used_tokens) + return TextEmbeddingResult(embeddings=batched_embeddings, usage=usage, model=model) + + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + return sum(self._get_num_tokens_by_gpt2(text) for text in texts) + + def validate_credentials(self, model: str, credentials: Mapping) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + # transform credentials to kwargs for model instance + credentials_kwargs = self._to_credential_kwargs(credentials) + client = OpenAI(**credentials_kwargs) + + # call embedding model + self._embedding_invoke(model=model, client=client, texts=["ping"], extra_model_kwargs={}) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + def _embedding_invoke( + self, model: str, client: OpenAI, texts: Union[list[str], str], extra_model_kwargs: dict + ) -> tuple[list[list[float]], int]: + """ + Invoke embedding model + :param model: model name + :param client: model client + :param texts: texts to embed + :param extra_model_kwargs: extra model kwargs + :return: embeddings and used tokens + """ + response = client.embeddings.create(model=model, input=texts, **extra_model_kwargs) + return [data.embedding for data in response.data], response.usage.total_tokens + + def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param tokens: input tokens + :return: usage + """ + input_price_info = self.get_price( + model=model, credentials=credentials, tokens=tokens, price_type=PriceType.INPUT + ) + + usage = EmbeddingUsage( + tokens=tokens, + total_tokens=tokens, + unit_price=input_price_info.unit_price, + price_unit=input_price_info.unit, + total_price=input_price_info.total_amount, + currency=input_price_info.currency, + latency=time.perf_counter() - self.started_at, + ) + + return usage diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml new file mode 100644 index 00000000000000..d84e9937e0b661 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-001.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-flash-001 +label: + en_US: Gemini 1.5 Flash 001 +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml new file mode 100644 index 00000000000000..2ff70564b2a951 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-002.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-flash-002 +label: + en_US: Gemini 1.5 Flash 002 +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml index bbc697e934e055..4e0209890a336a 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0827.yaml @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml new file mode 100644 index 00000000000000..2aea8149f4c794 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-8b-exp-0924.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-flash-8b-exp-0924 +label: + en_US: Gemini 1.5 Flash 8B 0924 +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml index c5695e5dda8eb0..faabc5e4d13a73 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-exp-0827.yaml @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml index 24b1c5af8a3fd8..a22fcca9419b91 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash-latest.yaml @@ -1,6 +1,6 @@ model: gemini-1.5-flash-latest label: - en_US: Gemini 1.5 Flash + en_US: Gemini 1.5 Flash Latest model_type: llm features: - agent-thought @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml new file mode 100644 index 00000000000000..dfd55c3a949c97 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-flash.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-flash +label: + en_US: Gemini 1.5 Flash +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml new file mode 100644 index 00000000000000..a1feff171d48c2 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-001.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-pro-001 +label: + en_US: Gemini 1.5 Pro 001 +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 2097152 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml new file mode 100644 index 00000000000000..9ae07a06c5118b --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-002.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-pro-002 +label: + en_US: Gemini 1.5 Pro 002 +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 2097152 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml index 0a918e0d7b1ac3..97c68f7a18d91e 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0801.yaml @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml index 7452ce46e7dcb6..860e4816a163cc 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-exp-0827.yaml @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml index b3e1ecf3aff379..d1bf7d269de765 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml @@ -1,6 +1,6 @@ model: gemini-1.5-pro-latest label: - en_US: Gemini 1.5 Pro + en_US: Gemini 1.5 Pro Latest model_type: llm features: - agent-thought @@ -32,6 +32,15 @@ parameter_rules: max: 8192 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml new file mode 100644 index 00000000000000..bdd70b34a21dc5 --- /dev/null +++ b/api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro.yaml @@ -0,0 +1,48 @@ +model: gemini-1.5-pro +label: + en_US: Gemini 1.5 Pro +model_type: llm +features: + - agent-thought + - vision + - tool-call + - stream-tool-call +model_properties: + mode: chat + context_size: 2097152 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + help: + zh_Hans: 仅从每个后续标记的前 K 个选项中采样。 + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: max_tokens_to_sample + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 + - name: response_format + use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml index 075e484e469308..2d213d56adb9c7 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-pro-vision.yaml @@ -27,6 +27,15 @@ parameter_rules: default: 4096 min: 1 max: 4096 + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml b/api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml index 4e9f59e7da94f0..e2f487c1ee9219 100644 --- a/api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml +++ b/api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml @@ -31,6 +31,15 @@ parameter_rules: max: 2048 - name: response_format use_template: response_format + - name: stream + label: + zh_Hans: 流式输出 + en_US: Stream + type: boolean + help: + zh_Hans: 流式输出允许模型在生成文本的过程中逐步返回结果，而不是一次性生成全部结果后再返回。 + en_US: Streaming output allows the model to return results incrementally as it generates text, rather than generating all the results at once. + default: false pricing: input: '0.00' output: '0.00' diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py index 3fc6787a444e41..e686ad08d9d355 100644 --- a/api/core/model_runtime/model_providers/google/llm/llm.py +++ b/api/core/model_runtime/model_providers/google/llm/llm.py @@ -9,8 +9,8 @@ import google.generativeai as genai import requests from google.api_core import exceptions -from google.generativeai import client -from google.generativeai.types import ContentType, GenerateContentResponse, HarmBlockThreshold, HarmCategory +from google.generativeai.client import _ClientManager +from google.generativeai.types import ContentType, GenerateContentResponse from google.generativeai.types.content_types import to_part from PIL import Image @@ -200,24 +200,16 @@ def _generate( history.append(content) # Create a new ClientManager with tenant's API key - new_client_manager = client._ClientManager() + new_client_manager = _ClientManager() new_client_manager.configure(api_key=credentials["google_api_key"]) new_custom_client = new_client_manager.make_client("generative") google_model._client = new_custom_client - safety_settings = { - HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, - } - response = google_model.generate_content( contents=history, generation_config=genai.types.GenerationConfig(**config_kwargs), stream=stream, - safety_settings=safety_settings, tools=self._convert_tools_to_glm_tool(tools) if tools else None, request_options={"timeout": 600}, ) diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml new file mode 100644 index 00000000000000..019d45372361d3 --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-11b-text-preview.yaml @@ -0,0 +1,25 @@ +model: llama-3.2-11b-text-preview +label: + zh_Hans: Llama 3.2 11B Text (Preview) + en_US: Llama 3.2 11B Text (Preview) +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml new file mode 100644 index 00000000000000..a44e4ff508eb82 --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-1b-preview.yaml @@ -0,0 +1,25 @@ +model: llama-3.2-1b-preview +label: + zh_Hans: Llama 3.2 1B Text (Preview) + en_US: Llama 3.2 1B Text (Preview) +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml new file mode 100644 index 00000000000000..f2fdd0a05e027a --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-3b-preview.yaml @@ -0,0 +1,25 @@ +model: llama-3.2-3b-preview +label: + zh_Hans: Llama 3.2 3B Text (Preview) + en_US: Llama 3.2 3B Text (Preview) +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml new file mode 100644 index 00000000000000..3b34e7c07996bd --- /dev/null +++ b/api/core/model_runtime/model_providers/groq/llm/llama-3.2-90b-text-preview.yaml @@ -0,0 +1,25 @@ +model: llama-3.2-90b-text-preview +label: + zh_Hans: Llama 3.2 90B Text (Preview) + en_US: Llama 3.2 90B Text (Preview) +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: max_tokens + use_template: max_tokens + default: 512 + min: 1 + max: 8192 +pricing: + input: '0.05' + output: '0.1' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py index 4ad96c4233fb1a..b2e6d1b6520c72 100644 --- a/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/huggingface_hub/text_embedding/text_embedding.py @@ -6,6 +6,7 @@ import requests from huggingface_hub import HfApi, InferenceClient +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -18,8 +19,23 @@ class HuggingfaceHubTextEmbeddingModel(_CommonHuggingfaceHub, TextEmbeddingModel): def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ client = InferenceClient(token=credentials["huggingfacehub_api_token"]) execute_model = model diff --git a/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py index 55f3c25804ea09..b8ff3ca549a63b 100644 --- a/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/huggingface_tei/text_embedding/text_embedding.py @@ -1,6 +1,7 @@ import time from typing import Optional +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -23,7 +24,12 @@ class HuggingfaceTeiTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -38,6 +44,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ server_url = credentials["server_url"] diff --git a/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py index 1396e59e188bca..75701ebc54a749 100644 --- a/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/hunyuan/text_embedding/text_embedding.py @@ -9,6 +9,7 @@ from tencentcloud.common.profile.http_profile import HttpProfile from tencentcloud.hunyuan.v20230901 import hunyuan_client, models +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -26,7 +27,12 @@ class HunyuanTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -35,6 +41,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/jina/jina.yaml b/api/core/model_runtime/model_providers/jina/jina.yaml index 23e18ad75f6885..970b22965b5d29 100644 --- a/api/core/model_runtime/model_providers/jina/jina.yaml +++ b/api/core/model_runtime/model_providers/jina/jina.yaml @@ -1,6 +1,6 @@ provider: jina label: - en_US: Jina + en_US: Jina AI description: en_US: Embedding and Rerank Model Supported icon_small: @@ -11,7 +11,7 @@ background: "#EFFDFD" help: title: en_US: Get your API key from Jina AI - zh_Hans: 从 Jina 获取 API Key + zh_Hans: 从 Jina AI 获取 API Key url: en_US: https://jina.ai/ supported_model_types: diff --git a/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py index ceb79567d54ca2..b39712951256c8 100644 --- a/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/jina/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ from requests import post +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -27,8 +28,37 @@ class JinaTextEmbeddingModel(TextEmbeddingModel): api_base: str = "https://api.jina.ai/v1" + def _to_payload(self, model: str, texts: list[str], credentials: dict, input_type: EmbeddingInputType) -> dict: + """ + Parse model credentials + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: parsed credentials + """ + + def transform_jina_input_text(model, text): + if model == "jina-clip-v1": + return {"text": text} + return text + + data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]} + + # model specific parameters + if model == "jina-embeddings-v3": + # set `task` type according to input type for the best performance + data["task"] = "retrieval.query" if input_type == EmbeddingInputType.QUERY else "retrieval.passage" + + return data + def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -37,6 +67,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ api_key = credentials["api_key"] @@ -49,15 +80,7 @@ def _invoke( url = base_url + "/embeddings" headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"} - def transform_jina_input_text(model, text): - if model == "jina-clip-v1": - return {"text": text} - return text - - data = {"model": model, "input": [transform_jina_input_text(model, text) for text in texts]} - - if model == "jina-embeddings-v3": - data["task"] = "text-matching" + data = self._to_payload(model=model, texts=texts, credentials=credentials, input_type=input_type) try: response = post(url, headers=headers, data=dumps(data)) diff --git a/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py index 7d258be81e0580..ab8ca76c2f0971 100644 --- a/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py @@ -5,6 +5,7 @@ from requests import post from yarl import URL +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -22,11 +23,16 @@ class LocalAITextEmbeddingModel(TextEmbeddingModel): """ - Model class for Jina text embedding model. + Model class for LocalAI text embedding model. """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -35,6 +41,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ if len(texts) != 1: diff --git a/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py index 76fd1342bdb929..74d2a221d1b57e 100644 --- a/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/minimax/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ from requests import post +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -34,7 +35,12 @@ class MinimaxTextEmbeddingModel(TextEmbeddingModel): api_base: str = "https://api.minimax.chat/v1/embeddings" def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -43,6 +49,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ api_key = credentials["minimax_api_key"] diff --git a/api/core/model_runtime/model_providers/mixedbread/__init__.py b/api/core/model_runtime/model_providers/mixedbread/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/mixedbread/_assets/icon_l_en.png b/api/core/model_runtime/model_providers/mixedbread/_assets/icon_l_en.png new file mode 100644 index 00000000000000..2027611bd5e8b4 Binary files /dev/null and b/api/core/model_runtime/model_providers/mixedbread/_assets/icon_l_en.png differ diff --git a/api/core/model_runtime/model_providers/mixedbread/_assets/icon_s_en.png b/api/core/model_runtime/model_providers/mixedbread/_assets/icon_s_en.png new file mode 100644 index 00000000000000..5c357bddbddb15 Binary files /dev/null and b/api/core/model_runtime/model_providers/mixedbread/_assets/icon_s_en.png differ diff --git a/api/core/model_runtime/model_providers/mixedbread/mixedbread.py b/api/core/model_runtime/model_providers/mixedbread/mixedbread.py new file mode 100644 index 00000000000000..3c78150e6f806e --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/mixedbread.py @@ -0,0 +1,27 @@ +import logging + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.model_provider import ModelProvider + +logger = logging.getLogger(__name__) + + +class MixedBreadProvider(ModelProvider): + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + try: + model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING) + + # Use `mxbai-embed-large-v1` model for validate, + model_instance.validate_credentials(model="mxbai-embed-large-v1", credentials=credentials) + except CredentialsValidateFailedError as ex: + raise ex + except Exception as ex: + logger.exception(f"{self.get_provider_schema().provider} credentials validate failed") + raise ex diff --git a/api/core/model_runtime/model_providers/mixedbread/mixedbread.yaml b/api/core/model_runtime/model_providers/mixedbread/mixedbread.yaml new file mode 100644 index 00000000000000..2f43aea6ade2c6 --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/mixedbread.yaml @@ -0,0 +1,31 @@ +provider: mixedbread +label: + en_US: MixedBread +description: + en_US: Embedding and Rerank Model Supported +icon_small: + en_US: icon_s_en.png +icon_large: + en_US: icon_l_en.png +background: "#EFFDFD" +help: + title: + en_US: Get your API key from MixedBread AI + zh_Hans: 从 MixedBread 获取 API Key + url: + en_US: https://www.mixedbread.ai/ +supported_model_types: + - text-embedding + - rerank +configurate_methods: + - predefined-model +provider_credential_schema: + credential_form_schemas: + - variable: api_key + label: + en_US: API Key + type: secret-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key diff --git a/api/core/model_runtime/model_providers/mixedbread/rerank/__init__.py b/api/core/model_runtime/model_providers/mixedbread/rerank/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/mixedbread/rerank/mxbai-rerank-large-v1-en.yaml b/api/core/model_runtime/model_providers/mixedbread/rerank/mxbai-rerank-large-v1-en.yaml new file mode 100644 index 00000000000000..beda2199537450 --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/rerank/mxbai-rerank-large-v1-en.yaml @@ -0,0 +1,4 @@ +model: mxbai-rerank-large-v1 +model_type: rerank +model_properties: + context_size: 512 diff --git a/api/core/model_runtime/model_providers/mixedbread/rerank/rerank.py b/api/core/model_runtime/model_providers/mixedbread/rerank/rerank.py new file mode 100644 index 00000000000000..bf3c12fd86dc35 --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/rerank/rerank.py @@ -0,0 +1,125 @@ +from typing import Optional + +import httpx + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType +from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.rerank_model import RerankModel + + +class MixedBreadRerankModel(RerankModel): + """ + Model class for MixedBread rerank model. + """ + + def _invoke( + self, + model: str, + credentials: dict, + query: str, + docs: list[str], + score_threshold: Optional[float] = None, + top_n: Optional[int] = None, + user: Optional[str] = None, + ) -> RerankResult: + """ + Invoke rerank model + + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n documents to return + :param user: unique user id + :return: rerank result + """ + if len(docs) == 0: + return RerankResult(model=model, docs=[]) + + base_url = credentials.get("base_url", "https://api.mixedbread.ai/v1") + base_url = base_url.removesuffix("/") + + try: + response = httpx.post( + base_url + "/reranking", + json={"model": model, "query": query, "input": docs, "top_k": top_n, "return_input": True}, + headers={"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"}, + ) + response.raise_for_status() + results = response.json() + + rerank_documents = [] + for result in results["data"]: + rerank_document = RerankDocument( + index=result["index"], + text=result["input"], + score=result["score"], + ) + if score_threshold is None or result["score"] >= score_threshold: + rerank_documents.append(rerank_document) + + return RerankResult(model=model, docs=rerank_documents) + except httpx.HTTPStatusError as e: + raise InvokeServerUnavailableError(str(e)) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + self._invoke( + model=model, + credentials=credentials, + query="What is the capital of the United States?", + docs=[ + "Carson City is the capital city of the American state of Nevada. At the 2010 United States " + "Census, Carson City had a population of 55,274.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that " + "are a political division controlled by the United States. Its capital is Saipan.", + ], + score_threshold=0.8, + ) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + """ + return { + InvokeConnectionError: [httpx.ConnectError], + InvokeServerUnavailableError: [httpx.RemoteProtocolError], + InvokeRateLimitError: [], + InvokeAuthorizationError: [httpx.HTTPStatusError], + InvokeBadRequestError: [httpx.RequestError], + } + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.RERANK, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "512"))}, + ) + + return entity diff --git a/api/core/model_runtime/model_providers/mixedbread/text_embedding/__init__.py b/api/core/model_runtime/model_providers/mixedbread/text_embedding/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-2d-large-v1-en.yaml b/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-2d-large-v1-en.yaml new file mode 100644 index 00000000000000..0c3c863d06b89a --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-2d-large-v1-en.yaml @@ -0,0 +1,8 @@ +model: mxbai-embed-2d-large-v1 +model_type: text-embedding +model_properties: + context_size: 512 +pricing: + input: '0.0001' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-large-v1-en.yaml b/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-large-v1-en.yaml new file mode 100644 index 00000000000000..0c5cda2a72a99e --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/text_embedding/mxbai-embed-large-v1-en.yaml @@ -0,0 +1,8 @@ +model: mxbai-embed-large-v1 +model_type: text-embedding +model_properties: + context_size: 512 +pricing: + input: '0.0001' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/mixedbread/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/mixedbread/text_embedding/text_embedding.py new file mode 100644 index 00000000000000..68b7b448bfec75 --- /dev/null +++ b/api/core/model_runtime/model_providers/mixedbread/text_embedding/text_embedding.py @@ -0,0 +1,170 @@ +import time +from json import JSONDecodeError, dumps +from typing import Optional + +import requests + +from core.embedding.embedding_constant import EmbeddingInputType +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType +from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel + + +class MixedBreadTextEmbeddingModel(TextEmbeddingModel): + """ + Model class for MixedBread text embedding model. + """ + + api_base: str = "https://api.mixedbread.ai/v1" + + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + api_key = credentials["api_key"] + if not api_key: + raise CredentialsValidateFailedError("api_key is required") + + base_url = credentials.get("base_url", self.api_base) + base_url = base_url.removesuffix("/") + + url = base_url + "/embeddings" + headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"} + + data = {"model": model, "input": texts} + + try: + response = requests.post(url, headers=headers, data=dumps(data)) + except Exception as e: + raise InvokeConnectionError(str(e)) + + if response.status_code != 200: + try: + resp = response.json() + msg = resp["detail"] + if response.status_code == 401: + raise InvokeAuthorizationError(msg) + elif response.status_code == 429: + raise InvokeRateLimitError(msg) + elif response.status_code == 500: + raise InvokeServerUnavailableError(msg) + else: + raise InvokeBadRequestError(msg) + except JSONDecodeError as e: + raise InvokeServerUnavailableError( + f"Failed to convert response to json: {e} with text: {response.text}" + ) + + try: + resp = response.json() + embeddings = resp["data"] + usage = resp["usage"] + except Exception as e: + raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}") + + usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"]) + + result = TextEmbeddingResult( + model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage + ) + + return result + + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + return sum(self._get_num_tokens_by_gpt2(text) for text in texts) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + self._invoke(model=model, credentials=credentials, texts=["ping"]) + except Exception as e: + raise CredentialsValidateFailedError(f"Credentials validation failed: {e}") + + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + return { + InvokeConnectionError: [InvokeConnectionError], + InvokeServerUnavailableError: [InvokeServerUnavailableError], + InvokeRateLimitError: [InvokeRateLimitError], + InvokeAuthorizationError: [InvokeAuthorizationError], + InvokeBadRequestError: [KeyError, InvokeBadRequestError], + } + + def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param tokens: input tokens + :return: usage + """ + # get input price info + input_price_info = self.get_price( + model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens + ) + + # transform usage + usage = EmbeddingUsage( + tokens=tokens, + total_tokens=tokens, + unit_price=input_price_info.unit_price, + price_unit=input_price_info.unit, + total_price=input_price_info.total_amount, + currency=input_price_info.currency, + latency=time.perf_counter() - self.started_at, + ) + + return usage + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.TEXT_EMBEDDING, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "512"))}, + ) + + return entity diff --git a/api/core/model_runtime/model_providers/nomic/__init__.py b/api/core/model_runtime/model_providers/nomic/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/nomic/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/nomic/_assets/icon_l_en.svg new file mode 100644 index 00000000000000..6c4a1058ab9c70 --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/_assets/icon_l_en.svg @@ -0,0 +1,13 @@ + + + + + + + + + + + + + diff --git a/api/core/model_runtime/model_providers/nomic/_assets/icon_s_en.png b/api/core/model_runtime/model_providers/nomic/_assets/icon_s_en.png new file mode 100644 index 00000000000000..3eba3b82bc1e3f Binary files /dev/null and b/api/core/model_runtime/model_providers/nomic/_assets/icon_s_en.png differ diff --git a/api/core/model_runtime/model_providers/nomic/_common.py b/api/core/model_runtime/model_providers/nomic/_common.py new file mode 100644 index 00000000000000..406577dcd7e701 --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/_common.py @@ -0,0 +1,28 @@ +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) + + +class _CommonNomic: + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + The key is the error type thrown to the caller + The value is the error type thrown by the model, + which needs to be converted into a unified error type for the caller. + + :return: Invoke error mapping + """ + return { + InvokeConnectionError: [InvokeConnectionError], + InvokeServerUnavailableError: [InvokeServerUnavailableError], + InvokeRateLimitError: [InvokeRateLimitError], + InvokeAuthorizationError: [InvokeAuthorizationError], + InvokeBadRequestError: [KeyError, InvokeBadRequestError], + } diff --git a/api/core/model_runtime/model_providers/nomic/nomic.py b/api/core/model_runtime/model_providers/nomic/nomic.py new file mode 100644 index 00000000000000..d4e5da2e98ec97 --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/nomic.py @@ -0,0 +1,26 @@ +import logging + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.model_provider import ModelProvider + +logger = logging.getLogger(__name__) + + +class NomicAtlasProvider(ModelProvider): + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + try: + model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING) + model_instance.validate_credentials(model="nomic-embed-text-v1.5", credentials=credentials) + except CredentialsValidateFailedError as ex: + raise ex + except Exception as ex: + logger.exception(f"{self.get_provider_schema().provider} credentials validate failed") + raise ex diff --git a/api/core/model_runtime/model_providers/nomic/nomic.yaml b/api/core/model_runtime/model_providers/nomic/nomic.yaml new file mode 100644 index 00000000000000..60dcf1facb475d --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/nomic.yaml @@ -0,0 +1,29 @@ +provider: nomic +label: + zh_Hans: Nomic Atlas + en_US: Nomic Atlas +icon_small: + en_US: icon_s_en.png +icon_large: + en_US: icon_l_en.svg +background: "#EFF1FE" +help: + title: + en_US: Get your API key from Nomic Atlas + zh_Hans: 从Nomic Atlas获取 API Key + url: + en_US: https://atlas.nomic.ai/data +supported_model_types: + - text-embedding +configurate_methods: + - predefined-model +provider_credential_schema: + credential_form_schemas: + - variable: nomic_api_key + label: + en_US: API Key + type: secret-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key diff --git a/api/core/model_runtime/model_providers/nomic/text_embedding/__init__.py b/api/core/model_runtime/model_providers/nomic/text_embedding/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.5.yaml b/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.5.yaml new file mode 100644 index 00000000000000..111452df579f8f --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.5.yaml @@ -0,0 +1,8 @@ +model: nomic-embed-text-v1.5 +model_type: text-embedding +model_properties: + context_size: 8192 +pricing: + input: "0.1" + unit: "0.000001" + currency: USD diff --git a/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.yaml b/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.yaml new file mode 100644 index 00000000000000..ac59f106ed2928 --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/text_embedding/nomic-embed-text-v1.yaml @@ -0,0 +1,8 @@ +model: nomic-embed-text-v1 +model_type: text-embedding +model_properties: + context_size: 8192 +pricing: + input: "0.1" + unit: "0.000001" + currency: USD diff --git a/api/core/model_runtime/model_providers/nomic/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/nomic/text_embedding/text_embedding.py new file mode 100644 index 00000000000000..857dfb5f41e2f5 --- /dev/null +++ b/api/core/model_runtime/model_providers/nomic/text_embedding/text_embedding.py @@ -0,0 +1,165 @@ +import time +from functools import wraps +from typing import Optional + +from nomic import embed +from nomic import login as nomic_login + +from core.embedding.embedding_constant import EmbeddingInputType +from core.model_runtime.entities.model_entities import PriceType +from core.model_runtime.entities.text_embedding_entities import ( + EmbeddingUsage, + TextEmbeddingResult, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.text_embedding_model import ( + TextEmbeddingModel, +) +from core.model_runtime.model_providers.nomic._common import _CommonNomic + + +def nomic_login_required(func): + @wraps(func) + def wrapper(*args, **kwargs): + try: + if not kwargs.get("credentials"): + raise ValueError("missing credentials parameters") + credentials = kwargs.get("credentials") + if "nomic_api_key" not in credentials: + raise ValueError("missing nomic_api_key in credentials parameters") + # nomic login + nomic_login(credentials["nomic_api_key"]) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + return func(*args, **kwargs) + + return wrapper + + +class NomicTextEmbeddingModel(_CommonNomic, TextEmbeddingModel): + """ + Model class for nomic text embedding model. + """ + + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + embeddings, prompt_tokens, total_tokens = self.embed_text( + model=model, + credentials=credentials, + texts=texts, + ) + + # calc usage + usage = self._calc_response_usage( + model=model, credentials=credentials, tokens=prompt_tokens, total_tokens=total_tokens + ) + return TextEmbeddingResult(embeddings=embeddings, usage=usage, model=model) + + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + return sum(self._get_num_tokens_by_gpt2(text) for text in texts) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + # call embedding model + self.embed_text(model=model, credentials=credentials, texts=["ping"]) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + @nomic_login_required + def embed_text(self, model: str, credentials: dict, texts: list[str]) -> tuple[list[list[float]], int, int]: + """Call out to Nomic's embedding endpoint. + + Args: + model: The model to use for embedding. + texts: The list of texts to embed. + + Returns: + List of embeddings, one for each text, and tokens usage. + """ + embeddings: list[list[float]] = [] + prompt_tokens = 0 + total_tokens = 0 + + response = embed.text( + model=model, + texts=texts, + ) + + if not (response and "embeddings" in response): + raise ValueError("Embedding data is missing in the response.") + + if not (response and "usage" in response): + raise ValueError("Response usage is missing.") + + if "prompt_tokens" not in response["usage"]: + raise ValueError("Response usage does not contain prompt tokens.") + + if "total_tokens" not in response["usage"]: + raise ValueError("Response usage does not contain total tokens.") + + embeddings = [list(map(float, e)) for e in response["embeddings"]] + total_tokens = response["usage"]["total_tokens"] + prompt_tokens = response["usage"]["prompt_tokens"] + return embeddings, prompt_tokens, total_tokens + + def _calc_response_usage(self, model: str, credentials: dict, tokens: int, total_tokens: int) -> EmbeddingUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param tokens: prompt tokens + :param total_tokens: total tokens + :return: usage + """ + # get input price info + input_price_info = self.get_price( + model=model, + credentials=credentials, + price_type=PriceType.INPUT, + tokens=tokens, + ) + + # transform usage + usage = EmbeddingUsage( + tokens=tokens, + total_tokens=total_tokens, + unit_price=input_price_info.unit_price, + price_unit=input_price_info.unit, + total_price=input_price_info.total_amount, + currency=input_price_info.currency, + latency=time.perf_counter() - self.started_at, + ) + + return usage diff --git a/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py index 00cec265d5ed98..936ceb8dd2c60e 100644 --- a/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/nvidia/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ from requests import post +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -27,7 +28,12 @@ class NvidiaTextEmbeddingModel(TextEmbeddingModel): models: list[str] = ["NV-Embed-QA"] def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -36,6 +42,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ api_key = credentials["api_key"] diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py index 80ad2be9f56872..4de9296ccaa92d 100644 --- a/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py @@ -6,6 +6,7 @@ import numpy as np import oci +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -41,7 +42,12 @@ class OCITextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -50,6 +56,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ # get model properties diff --git a/api/core/model_runtime/model_providers/ollama/llm/llm.py b/api/core/model_runtime/model_providers/ollama/llm/llm.py index ff732e69259e2b..a7ea53e0e99c5f 100644 --- a/api/core/model_runtime/model_providers/ollama/llm/llm.py +++ b/api/core/model_runtime/model_providers/ollama/llm/llm.py @@ -364,14 +364,21 @@ def create_final_llm_result_chunk( if chunk_json["done"]: # calculate num tokens - if "prompt_eval_count" in chunk_json and "eval_count" in chunk_json: - # transform usage + if "prompt_eval_count" in chunk_json: prompt_tokens = chunk_json["prompt_eval_count"] - completion_tokens = chunk_json["eval_count"] else: - # calculate num tokens - prompt_tokens = self._get_num_tokens_by_gpt2(prompt_messages[0].content) - completion_tokens = self._get_num_tokens_by_gpt2(full_text) + prompt_message_content = prompt_messages[0].content + if isinstance(prompt_message_content, str): + prompt_tokens = self._get_num_tokens_by_gpt2(prompt_message_content) + else: + content_text = "" + for message_content in prompt_message_content: + if message_content.type == PromptMessageContentType.TEXT: + message_content = cast(TextPromptMessageContent, message_content) + content_text += message_content.data + prompt_tokens = self._get_num_tokens_by_gpt2(content_text) + + completion_tokens = chunk_json.get("eval_count", self._get_num_tokens_by_gpt2(full_text)) # transform usage usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) diff --git a/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py index b4c61d8a6dc790..5cf3f1c6fa87f3 100644 --- a/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/ollama/text_embedding/text_embedding.py @@ -8,6 +8,7 @@ import numpy as np import requests +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import ( AIModelEntity, @@ -38,7 +39,12 @@ class OllamaEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -47,6 +53,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py index 535d8388bc7d7d..16f1a0cfa1117b 100644 --- a/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/openai/text_embedding/text_embedding.py @@ -6,6 +6,7 @@ import tiktoken from openai import OpenAI +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.validate import CredentialsValidateFailedError @@ -19,7 +20,12 @@ class OpenAITextEmbeddingModel(_CommonOpenAI, TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -28,6 +34,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ # transform credentials to kwargs for model instance diff --git a/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py index e83cfdf873cb4b..64fa6aaa3c5a71 100644 --- a/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/openai_api_compatible/text_embedding/text_embedding.py @@ -7,6 +7,7 @@ import numpy as np import requests +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import ( AIModelEntity, @@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -37,6 +43,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py index 00e583cc797dcb..c5d43309127822 100644 --- a/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/openllm/text_embedding/text_embedding.py @@ -5,6 +5,7 @@ from requests import post from requests.exceptions import ConnectionError, InvalidSchema, MissingSchema +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import ( @@ -25,7 +26,12 @@ class OpenLLMTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -34,6 +40,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ server_url = credentials["server_url"] diff --git a/api/core/model_runtime/model_providers/perfxcloud/llm/_position.yaml b/api/core/model_runtime/model_providers/perfxcloud/llm/_position.yaml index 37bf400f1e3475..c6930e54f50aa4 100644 --- a/api/core/model_runtime/model_providers/perfxcloud/llm/_position.yaml +++ b/api/core/model_runtime/model_providers/perfxcloud/llm/_position.yaml @@ -1,24 +1,23 @@ - Qwen2.5-72B-Instruct - Qwen2.5-7B-Instruct +- Qwen2-72B-Instruct +- Qwen2-72B-Instruct-AWQ-int4 +- Qwen2-72B-Instruct-GPTQ-Int4 +- Qwen2-7B-Instruct +- Qwen2-7B +- Qwen1.5-110B-Chat-GPTQ-Int4 +- Qwen1.5-72B-Chat-GPTQ-Int4 +- Qwen1.5-7B +- Qwen-14B-Chat-Int4 - Yi-Coder-1.5B-Chat - Yi-Coder-9B-Chat -- Qwen2-72B-Instruct-AWQ-int4 - Yi-1_5-9B-Chat-16K -- Qwen2-7B-Instruct - Reflection-Llama-3.1-70B -- Qwen2-72B-Instruct - Meta-Llama-3.1-8B-Instruct - - Meta-Llama-3.1-405B-Instruct-AWQ-INT4 - Meta-Llama-3-70B-Instruct-GPTQ-Int4 -- chatglm3-6b - Meta-Llama-3-8B-Instruct - Llama3-Chinese_v2 - deepseek-v2-lite-chat -- Qwen2-72B-Instruct-GPTQ-Int4 -- Qwen2-7B -- Qwen-14B-Chat-Int4 -- Qwen1.5-72B-Chat-GPTQ-Int4 -- Qwen1.5-7B -- Qwen1.5-110B-Chat-GPTQ-Int4 - deepseek-v2-chat +- chatglm3-6b diff --git a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/_position.yaml b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/_position.yaml new file mode 100644 index 00000000000000..99163d42931b16 --- /dev/null +++ b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/_position.yaml @@ -0,0 +1,4 @@ +- gte-Qwen2-7B-instruct +- BAAI/bge-large-en-v1.5 +- BAAI/bge-large-zh-v1.5 +- BAAI/bge-m3 diff --git a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/gte-Qwen2-7B-instruct.yaml b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/gte-Qwen2-7B-instruct.yaml index 03db0d8bce8500..161d5ea9a2657e 100644 --- a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/gte-Qwen2-7B-instruct.yaml +++ b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/gte-Qwen2-7B-instruct.yaml @@ -2,3 +2,4 @@ model: gte-Qwen2-7B-instruct model_type: text-embedding model_properties: context_size: 2048 +deprecated: true diff --git a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py index b62a2d2aaf06fe..1e86f351c8ae57 100644 --- a/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/perfxcloud/text_embedding/text_embedding.py @@ -7,6 +7,7 @@ import numpy as np import requests +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import ( AIModelEntity, @@ -28,7 +29,12 @@ class OAICompatEmbeddingModel(_CommonOaiApiCompat, TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -37,6 +43,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py index 71b6fb99c4a1b2..9f724a77ac040b 100644 --- a/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/replicate/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ from replicate import Client as ReplicateClient +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -14,8 +15,23 @@ class ReplicateEmbeddingModel(_CommonReplicate, TextEmbeddingModel): def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ client = ReplicateClient(api_token=credentials["replicate_api_token"], timeout=30) if "model_version" in credentials: diff --git a/api/core/model_runtime/model_providers/sagemaker/llm/llm.py b/api/core/model_runtime/model_providers/sagemaker/llm/llm.py index 2edd13d56d4d87..97b76920443840 100644 --- a/api/core/model_runtime/model_providers/sagemaker/llm/llm.py +++ b/api/core/model_runtime/model_providers/sagemaker/llm/llm.py @@ -84,9 +84,9 @@ class SageMakerLargeLanguageModel(LargeLanguageModel): Model class for Cohere large language model. """ - sagemaker_client: Any = None - sagemaker_sess: Any = None + sagemaker_session: Any = None predictor: Any = None + sagemaker_endpoint: str = None def _handle_chat_generate_response( self, @@ -212,27 +212,29 @@ def _invoke( :param user: unique user id :return: full response or stream response chunk generator result """ - if not self.sagemaker_client: - access_key = credentials.get("access_key") - secret_key = credentials.get("secret_key") + if not self.sagemaker_session: + access_key = credentials.get("aws_access_key_id") + secret_key = credentials.get("aws_secret_access_key") aws_region = credentials.get("aws_region") + boto_session = None if aws_region: if access_key and secret_key: - self.sagemaker_client = boto3.client( - "sagemaker-runtime", - aws_access_key_id=access_key, - aws_secret_access_key=secret_key, - region_name=aws_region, + boto_session = boto3.Session( + aws_access_key_id=access_key, aws_secret_access_key=secret_key, region_name=aws_region ) else: - self.sagemaker_client = boto3.client("sagemaker-runtime", region_name=aws_region) + boto_session = boto3.Session(region_name=aws_region) else: - self.sagemaker_client = boto3.client("sagemaker-runtime") + boto_session = boto3.Session() - sagemaker_session = Session(sagemaker_runtime_client=self.sagemaker_client) + sagemaker_client = boto_session.client("sagemaker") + self.sagemaker_session = Session(boto_session=boto_session, sagemaker_client=sagemaker_client) + + if self.sagemaker_endpoint != credentials.get("sagemaker_endpoint"): + self.sagemaker_endpoint = credentials.get("sagemaker_endpoint") self.predictor = Predictor( - endpoint_name=credentials.get("sagemaker_endpoint"), - sagemaker_session=sagemaker_session, + endpoint_name=self.sagemaker_endpoint, + sagemaker_session=self.sagemaker_session, serializer=serializers.JSONSerializer(), ) diff --git a/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py index d55144f8a79be9..8f993ce6722522 100644 --- a/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/sagemaker/text_embedding/text_embedding.py @@ -6,6 +6,7 @@ import boto3 +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -53,7 +54,12 @@ def _sagemaker_embedding(self, sm_client, endpoint_name, content_list: list[str] return embeddings def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -62,6 +68,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ # get model properties diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml index 43db4aed11916b..8d1df82140b79f 100644 --- a/api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml +++ b/api/core/model_runtime/model_providers/siliconflow/llm/_position.yaml @@ -1,25 +1,28 @@ -- Qwen/Qwen2.5-7B-Instruct -- Qwen/Qwen2.5-14B-Instruct -- Qwen/Qwen2.5-32B-Instruct - Qwen/Qwen2.5-72B-Instruct +- Qwen/Qwen2.5-32B-Instruct +- Qwen/Qwen2.5-14B-Instruct +- Qwen/Qwen2.5-7B-Instruct +- Qwen/Qwen2.5-Coder-7B-Instruct +- Qwen/Qwen2.5-Math-72B-Instruct - Qwen/Qwen2-72B-Instruct - Qwen/Qwen2-57B-A14B-Instruct - Qwen/Qwen2-7B-Instruct - Qwen/Qwen2-1.5B-Instruct -- 01-ai/Yi-1.5-34B-Chat -- 01-ai/Yi-1.5-9B-Chat-16K -- 01-ai/Yi-1.5-6B-Chat -- THUDM/glm-4-9b-chat - deepseek-ai/DeepSeek-V2.5 - deepseek-ai/DeepSeek-V2-Chat - deepseek-ai/DeepSeek-Coder-V2-Instruct +- THUDM/glm-4-9b-chat +- 01-ai/Yi-1.5-34B-Chat-16K +- 01-ai/Yi-1.5-9B-Chat-16K +- 01-ai/Yi-1.5-6B-Chat +- internlm/internlm2_5-20b-chat - internlm/internlm2_5-7b-chat -- google/gemma-2-27b-it -- google/gemma-2-9b-it -- meta-llama/Meta-Llama-3-70B-Instruct -- meta-llama/Meta-Llama-3-8B-Instruct - meta-llama/Meta-Llama-3.1-405B-Instruct - meta-llama/Meta-Llama-3.1-70B-Instruct - meta-llama/Meta-Llama-3.1-8B-Instruct -- mistralai/Mixtral-8x7B-Instruct-v0.1 +- meta-llama/Meta-Llama-3-70B-Instruct +- meta-llama/Meta-Llama-3-8B-Instruct +- google/gemma-2-27b-it +- google/gemma-2-9b-it - mistralai/Mistral-7B-Instruct-v0.2 +- mistralai/Mixtral-8x7B-Instruct-v0.1 diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/internlm2_5-20b-chat.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/internlm2_5-20b-chat.yaml new file mode 100644 index 00000000000000..d9663582e5ca26 --- /dev/null +++ b/api/core/model_runtime/model_providers/siliconflow/llm/internlm2_5-20b-chat.yaml @@ -0,0 +1,30 @@ +model: internlm/internlm2_5-20b-chat +label: + en_US: internlm/internlm2_5-20b-chat +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 32768 +parameter_rules: + - name: temperature + use_template: temperature + - name: max_tokens + use_template: max_tokens + type: int + default: 512 + min: 1 + max: 4096 + help: + zh_Hans: 指定生成结果长度的上限。如果生成结果截断，可以调大该参数。 + en_US: Specifies the upper limit on the length of generated results. If the generated results are truncated, you can increase this parameter. + - name: top_p + use_template: top_p + - name: frequency_penalty + use_template: frequency_penalty +pricing: + input: '1' + output: '1' + unit: '0.000001' + currency: RMB diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml index 27664eab6c817a..89fb153ba09ac9 100644 --- a/api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml +++ b/api/core/model_runtime/model_providers/siliconflow/llm/mistral-7b-instruct-v0.2.yaml @@ -28,3 +28,4 @@ pricing: output: '0' unit: '0.000001' currency: RMB +deprecated: true diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml index fd7aada42848aa..2785e7496fb060 100644 --- a/api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml +++ b/api/core/model_runtime/model_providers/siliconflow/llm/mistral-8x7b-instruct-v0.1.yaml @@ -28,3 +28,4 @@ pricing: output: '1.26' unit: '0.000001' currency: RMB +deprecated: true diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-coder-7b-instruct.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-coder-7b-instruct.yaml new file mode 100644 index 00000000000000..76526200ccdccc --- /dev/null +++ b/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-coder-7b-instruct.yaml @@ -0,0 +1,74 @@ +model: Qwen/Qwen2.5-Coder-7B-Instruct +label: + en_US: Qwen/Qwen2.5-Coder-7B-Instruct +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 131072 +parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. + - name: max_tokens + use_template: max_tokens + type: int + default: 8192 + min: 1 + max: 8192 + help: + zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。 + en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time. + - name: top_p + use_template: top_p + type: float + default: 0.8 + min: 0.1 + max: 0.9 + help: + zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。 + en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated. + - name: top_k + type: int + min: 0 + max: 99 + label: + zh_Hans: 取样数量 + en_US: Top k + help: + zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。 + en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. + - name: seed + required: false + type: int + default: 1234 + label: + zh_Hans: 随机种子 + en_US: Random seed + help: + zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。 + en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format +pricing: + input: '0' + output: '0' + unit: '0.000001' + currency: RMB diff --git a/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-math-72b-instruct.yaml b/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-math-72b-instruct.yaml new file mode 100644 index 00000000000000..90afa0cfd5b96a --- /dev/null +++ b/api/core/model_runtime/model_providers/siliconflow/llm/qwen2.5-math-72b-instruct.yaml @@ -0,0 +1,74 @@ +model: Qwen/Qwen2.5-Math-72B-Instruct +label: + en_US: Qwen/Qwen2.5-Math-72B-Instruct +model_type: llm +features: + - agent-thought +model_properties: + mode: chat + context_size: 4096 +parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. + - name: max_tokens + use_template: max_tokens + type: int + default: 2000 + min: 1 + max: 2000 + help: + zh_Hans: 用于指定模型在生成内容时token的最大数量，它定义了生成的上限，但不保证每次都会生成到这个数量。 + en_US: It is used to specify the maximum number of tokens when the model generates content. It defines the upper limit of generation, but does not guarantee that this number will be generated every time. + - name: top_p + use_template: top_p + type: float + default: 0.8 + min: 0.1 + max: 0.9 + help: + zh_Hans: 生成过程中核采样方法概率阈值，例如，取值为0.8时，仅保留概率加起来大于等于0.8的最可能token的最小集合作为候选集。取值范围为（0,1.0)，取值越大，生成的随机性越高；取值越低，生成的确定性越高。 + en_US: The probability threshold of the kernel sampling method during the generation process. For example, when the value is 0.8, only the smallest set of the most likely tokens with a sum of probabilities greater than or equal to 0.8 is retained as the candidate set. The value range is (0,1.0). The larger the value, the higher the randomness generated; the lower the value, the higher the certainty generated. + - name: top_k + type: int + min: 0 + max: 99 + label: + zh_Hans: 取样数量 + en_US: Top k + help: + zh_Hans: 生成时，采样候选集的大小。例如，取值为50时，仅将单次生成中得分最高的50个token组成随机采样的候选集。取值越大，生成的随机性越高；取值越小，生成的确定性越高。 + en_US: The size of the sample candidate set when generated. For example, when the value is 50, only the 50 highest-scoring tokens in a single generation form a randomly sampled candidate set. The larger the value, the higher the randomness generated; the smaller the value, the higher the certainty generated. + - name: seed + required: false + type: int + default: 1234 + label: + zh_Hans: 随机种子 + en_US: Random seed + help: + zh_Hans: 生成时使用的随机数种子，用户控制模型生成内容的随机性。支持无符号64位整数，默认值为 1234。在使用seed时，模型将尽可能生成相同或相似的结果，但目前不保证每次生成的结果完全相同。 + en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format +pricing: + input: '4.13' + output: '4.13' + unit: '0.000001' + currency: RMB diff --git a/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py index 6cdf4933b47c73..c5dcc126107aa2 100644 --- a/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/siliconflow/text_embedding/text_embedding.py @@ -1,5 +1,6 @@ from typing import Optional +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult from core.model_runtime.model_providers.openai_api_compatible.text_embedding.text_embedding import ( OAICompatEmbeddingModel, @@ -16,8 +17,23 @@ def validate_credentials(self, model: str, credentials: dict) -> None: super().validate_credentials(model, credentials) def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ self._add_custom_parameters(credentials) return super()._invoke(model, credentials, texts, user) diff --git a/api/core/model_runtime/model_providers/spark/llm/_client.py b/api/core/model_runtime/model_providers/spark/llm/_client.py index b99a657e715edb..48911f657a52e3 100644 --- a/api/core/model_runtime/model_providers/spark/llm/_client.py +++ b/api/core/model_runtime/model_providers/spark/llm/_client.py @@ -25,6 +25,7 @@ def __init__(self, model: str, app_id: str, api_key: str, api_secret: str, api_d "spark-pro": {"version": "v3.1", "chat_domain": "generalv3"}, "spark-pro-128k": {"version": "pro-128k", "chat_domain": "pro-128k"}, "spark-max": {"version": "v3.5", "chat_domain": "generalv3.5"}, + "spark-max-32k": {"version": "max-32k", "chat_domain": "max-32k"}, "spark-4.0-ultra": {"version": "v4.0", "chat_domain": "4.0Ultra"}, } @@ -32,7 +33,7 @@ def __init__(self, model: str, app_id: str, api_key: str, api_secret: str, api_d self.chat_domain = model_api_configs[model]["chat_domain"] - if model == "spark-pro-128k": + if model in ["spark-pro-128k", "spark-max-32k"]: self.api_base = f"wss://{domain}/{endpoint}/{api_version}" else: self.api_base = f"wss://{domain}/{api_version}/{endpoint}" diff --git a/api/core/model_runtime/model_providers/spark/llm/_position.yaml b/api/core/model_runtime/model_providers/spark/llm/_position.yaml index 458397f2aaf1c6..73f39cb1197b48 100644 --- a/api/core/model_runtime/model_providers/spark/llm/_position.yaml +++ b/api/core/model_runtime/model_providers/spark/llm/_position.yaml @@ -1,3 +1,4 @@ +- spark-max-32k - spark-4.0-ultra - spark-max - spark-pro-128k diff --git a/api/core/model_runtime/model_providers/spark/llm/llm.py b/api/core/model_runtime/model_providers/spark/llm/llm.py index 57193dc0316b72..1181ba699af886 100644 --- a/api/core/model_runtime/model_providers/spark/llm/llm.py +++ b/api/core/model_runtime/model_providers/spark/llm/llm.py @@ -213,18 +213,21 @@ def _handle_generate_stream_response( :param prompt_messages: prompt messages :return: llm response chunk generator result """ + completion = "" for index, content in enumerate(client.subscribe()): if isinstance(content, dict): delta = content["data"] else: delta = content - + completion += delta assistant_prompt_message = AssistantPromptMessage( content=delta or "", ) - + temp_assistant_prompt_message = AssistantPromptMessage( + content=completion, + ) prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages) - completion_tokens = self.get_num_tokens(model, credentials, [assistant_prompt_message]) + completion_tokens = self.get_num_tokens(model, credentials, [temp_assistant_prompt_message]) # transform usage usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens) diff --git a/api/core/model_runtime/model_providers/spark/llm/spark-max-32k.yaml b/api/core/model_runtime/model_providers/spark/llm/spark-max-32k.yaml new file mode 100644 index 00000000000000..1a1ab6844c69c5 --- /dev/null +++ b/api/core/model_runtime/model_providers/spark/llm/spark-max-32k.yaml @@ -0,0 +1,33 @@ +model: spark-max-32k +label: + en_US: Spark Max-32K +model_type: llm +model_properties: + mode: chat +parameter_rules: + - name: temperature + use_template: temperature + default: 0.5 + help: + zh_Hans: 核采样阈值。用于决定结果随机性，取值越高随机性越强即相同的问题得到的不同答案的可能性越高。 + en_US: Kernel sampling threshold. Used to determine the randomness of the results. The higher the value, the stronger the randomness, that is, the higher the possibility of getting different answers to the same question. + - name: max_tokens + use_template: max_tokens + default: 4096 + min: 1 + max: 8192 + help: + zh_Hans: 模型回答的tokens的最大长度。 + en_US: Maximum length of tokens for the model response. + - name: top_k + label: + zh_Hans: 取样数量 + en_US: Top k + type: int + default: 4 + min: 1 + max: 6 + help: + zh_Hans: 从 k 个候选中随机选择一个（非等概率）。 + en_US: Randomly select one from k candidates (non-equal probability). + required: false diff --git a/api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml b/api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml index aad07f56736e52..34a57d1fc0c9a5 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/farui-plus.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: farui-plus label: en_US: farui-plus @@ -62,16 +63,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/llm.py b/api/core/model_runtime/model_providers/tongyi/llm/llm.py index f90c7f075fe86c..3e3585b30ae33d 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/llm.py +++ b/api/core/model_runtime/model_providers/tongyi/llm/llm.py @@ -18,7 +18,7 @@ UnsupportedModel, ) -from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta +from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta from core.model_runtime.entities.message_entities import ( AssistantPromptMessage, ImagePromptMessageContent, @@ -35,6 +35,7 @@ FetchFrom, I18nObject, ModelFeature, + ModelPropertyKey, ModelType, ParameterRule, ParameterType, @@ -97,6 +98,11 @@ def get_num_tokens( :param tools: tools for tool calling :return: """ + # Check if the model was added via get_customizable_model_schema + if self.get_customizable_model_schema(model, credentials) is not None: + # For custom models, tokens are not calculated. + return 0 + if model in {"qwen-turbo-chat", "qwen-plus-chat"}: model = model.replace("-chat", "") if model == "farui-plus": @@ -537,55 +543,51 @@ def get_customizable_model_schema(self, model: str, credentials: dict) -> AIMode :param credentials: model credentials :return: AIModelEntity or None """ - rules = [ - ParameterRule( - name="temperature", - type=ParameterType.FLOAT, - use_template="temperature", - label=I18nObject(zh_Hans="温度", en_US="Temperature"), - ), - ParameterRule( - name="top_p", - type=ParameterType.FLOAT, - use_template="top_p", - label=I18nObject(zh_Hans="Top P", en_US="Top P"), - ), - ParameterRule( - name="top_k", - type=ParameterType.INT, - min=0, - max=99, - label=I18nObject(zh_Hans="top_k", en_US="top_k"), - ), - ParameterRule( - name="max_tokens", - type=ParameterType.INT, - min=1, - max=128000, - default=1024, - label=I18nObject(zh_Hans="最大生成长度", en_US="Max Tokens"), - ), - ParameterRule( - name="seed", - type=ParameterType.INT, - default=1234, - label=I18nObject(zh_Hans="随机种子", en_US="Random Seed"), - ), - ParameterRule( - name="repetition_penalty", - type=ParameterType.FLOAT, - default=1.1, - label=I18nObject(zh_Hans="重复惩罚", en_US="Repetition Penalty"), - ), - ] - - entity = AIModelEntity( + return AIModelEntity( model=model, - label=I18nObject(en_US=model), - fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + label=I18nObject(en_US=model, zh_Hans=model), model_type=ModelType.LLM, - model_properties={}, - parameter_rules=rules, + features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL, ModelFeature.STREAM_TOOL_CALL] + if credentials.get("function_calling_type") == "tool_call" + else [], + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ + ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", 8000)), + ModelPropertyKey.MODE: LLMMode.CHAT.value, + }, + parameter_rules=[ + ParameterRule( + name="temperature", + use_template="temperature", + label=I18nObject(en_US="Temperature", zh_Hans="温度"), + type=ParameterType.FLOAT, + ), + ParameterRule( + name="max_tokens", + use_template="max_tokens", + default=512, + min=1, + max=int(credentials.get("max_tokens", 1024)), + label=I18nObject(en_US="Max Tokens", zh_Hans="最大标记"), + type=ParameterType.INT, + ), + ParameterRule( + name="top_p", + use_template="top_p", + label=I18nObject(en_US="Top P", zh_Hans="Top P"), + type=ParameterType.FLOAT, + ), + ParameterRule( + name="top_k", + use_template="top_k", + label=I18nObject(en_US="Top K", zh_Hans="Top K"), + type=ParameterType.FLOAT, + ), + ParameterRule( + name="frequency_penalty", + use_template="frequency_penalty", + label=I18nObject(en_US="Frequency Penalty", zh_Hans="重复惩罚"), + type=ParameterType.FLOAT, + ), + ], ) - - return entity diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml index ebba565d572aec..64a3f331336bc0 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-0919.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-coder-turbo-0919 label: en_US: qwen-coder-turbo-0919 @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml index 361e2c2373d652..a4c93f7047ff58 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo-latest.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-coder-turbo-latest label: en_US: qwen-coder-turbo-latest @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml index f4032a4dd316d8..ff68faed80810b 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-coder-turbo.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-coder-turbo label: en_US: qwen-coder-turbo @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml index dbe7d024a50f17..c3dbb3616fb961 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-long.yaml @@ -1,4 +1,4 @@ -# model docs: https://help.aliyun.com/zh/model-studio/getting-started/models#27b2b3a15d5c6 +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-long label: en_US: qwen-long @@ -63,16 +63,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml index 89d1302abef70d..42fe1f68623bc4 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0816.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-plus-0816 label: en_US: qwen-math-plus-0816 @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml index 032b3c970d7f46..9b6567b8cda4d7 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-0919.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-plus-0919 label: en_US: qwen-math-plus-0919 @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml index 31dd9f69725760..b2a2393b365fcb 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus-latest.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-plus-latest label: en_US: qwen-math-plus-latest @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml index 1a51d57f7814ba..63f4b7ff0a0879 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-plus.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-plus label: en_US: qwen-math-plus @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml index 1894eea417a43d..4da90eec3eddfd 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-0919.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-turbo-0919 label: en_US: qwen-math-turbo-0919 @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml index b8365618b0499f..d29f8851dd3909 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo-latest.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-turbo-latest label: en_US: qwen-math-turbo-latest @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml index 8d346d691e6b2f..2a8f7f725e9366 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-math-turbo.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-math-turbo label: en_US: qwen-math-turbo @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml index c0ad12b85e5469..ef1841b5173bc5 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0107.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-0107 label: en_US: qwen-max-0107 @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml index b00fb44d29fa72..a2ea5df130f379 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0403.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max-0403, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-0403 label: en_US: qwen-max-0403 @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml index 1848dcc07d1853..a467665f118a68 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0428.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max-0428, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-0428 label: en_US: qwen-max-0428 @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml index 238882bb121898..78661eaea065f2 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-0919.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max-0919, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-0919 label: en_US: qwen-max-0919 @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml index dc234783cde63b..6f4674576b4426 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-1201.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-1201 label: en_US: qwen-max-1201 @@ -66,12 +68,6 @@ parameter_rules: help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml index 9d7d3c2fcbf67e..8b5f0054733455 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-latest.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-latest label: en_US: qwen-max-latest @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml index a7bdc42f7347e5..098494ff95012d 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max-longcontext.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max-longcontext label: en_US: qwen-max-longcontext @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml index 57888406afa0a3..9d0d3f8db39c23 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-max.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-max, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#cf6cc4aa2aokf) model: qwen-max label: en_US: qwen-max @@ -62,6 +64,7 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 @@ -69,6 +72,9 @@ parameter_rules: - name: enable_search type: boolean default: false + label: + zh_Hans: 联网搜索 + en_US: Web Search help: zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml index 1e0b81661731a1..0b1a6f81df80c0 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0206.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-0206, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-0206 label: en_US: qwen-plus-0206 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml index f70c373922fb3a..7706005bb535cd 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0624.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-0624, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-0624 label: en_US: qwen-plus-0624 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml index c6007e9164ca2c..348276fc08f98c 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0723.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-0723, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-0723 label: en_US: qwen-plus-0723 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml index 2f53c433366988..29f125135eaa3f 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0806.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-0806, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-0806 label: en_US: qwen-plus-0806 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml index 90b54ca52e2d44..905fa1e1028bbf 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-0919.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-0919, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-0919 label: en_US: qwen-plus-0919 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml index 59e8851240b0ac..c7a3549727ce8e 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-chat.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-chat label: en_US: qwen-plus-chat @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml index 2a821dbcfe0011..608f52c2964ea3 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus-latest.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus-latest, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus-latest label: en_US: qwen-plus-latest @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml index 626884f4b29542..9089e57255bb70 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-plus.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-plus, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#bb0ffee88bwnk) model: qwen-plus label: en_US: qwen-plus @@ -62,6 +64,7 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 @@ -69,6 +72,9 @@ parameter_rules: - name: enable_search type: boolean default: false + label: + zh_Hans: 联网搜索 + en_US: Web Search help: zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml index 844fced77a684a..7ee0d44f2f2834 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0206.yaml @@ -1,3 +1,6 @@ +# this model corresponds to qwen-turbo-0206, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) + model: qwen-turbo-0206 label: en_US: qwen-turbo-0206 @@ -60,16 +63,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml index 0152f75579e5c9..20a3f7eb6460f3 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0624.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-turbo-0624, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) model: qwen-turbo-0624 label: en_US: qwen-turbo-0624 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml index 19c6c8d293f4e3..ba73dec3631fb5 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-0919.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-turbo-0919, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) model: qwen-turbo-0919 label: en_US: qwen-turbo-0919 @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-chat.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-chat.yaml index f557f311ef9c6d..d785b7fe857878 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-chat.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-chat.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-turbo, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) model: qwen-turbo-chat label: en_US: qwen-turbo-chat @@ -62,16 +64,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-latest.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-latest.yaml index be2475847ef22c..fe38a4283c2d1e 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-latest.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo-latest.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-turbo-latest, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) model: qwen-turbo-latest label: en_US: qwen-turbo-latest @@ -60,16 +62,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml index 90f13dc19f5ae3..215c9ec5fc96aa 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-turbo.yaml @@ -1,3 +1,5 @@ +# this model corresponds to qwen-turbo, for more details +# please refer to (https://help.aliyun.com/zh/model-studio/getting-started/models#ff492e2c10lub) model: qwen-turbo label: en_US: qwen-turbo @@ -62,6 +64,7 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 @@ -69,6 +72,9 @@ parameter_rules: - name: enable_search type: boolean default: false + label: + zh_Hans: 联网搜索 + en_US: Web Search help: zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0201.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0201.yaml index 63b6074d0d7c1c..d80168ffc3fb55 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0201.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0201.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-max-0201 label: en_US: qwen-vl-max-0201 diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0809.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0809.yaml index 41d45966e9d628..50e10226a5f5c4 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0809.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max-0809.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-max-0809 label: en_US: qwen-vl-max-0809 @@ -9,6 +10,15 @@ model_properties: mode: chat context_size: 32000 parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. - name: top_p use_template: top_p type: float @@ -50,6 +60,18 @@ parameter_rules: en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. - name: response_format use_template: response_format + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format pricing: input: '0.02' output: '0.02' diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max.yaml index 78d0509374131e..21b127f56c47d9 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-max.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-max label: en_US: qwen-vl-max @@ -9,6 +10,15 @@ model_properties: mode: chat context_size: 32000 parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. - name: top_p use_template: top_p type: float @@ -50,6 +60,18 @@ parameter_rules: en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. - name: response_format use_template: response_format + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format pricing: input: '0.02' output: '0.02' diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0201.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0201.yaml index 8944388b1ee5a2..03cb039d15a7dd 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0201.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0201.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-plus-0201 label: en_US: qwen-vl-plus-0201 @@ -9,6 +10,15 @@ model_properties: mode: chat context_size: 8000 parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. - name: top_p use_template: top_p type: float @@ -50,6 +60,18 @@ parameter_rules: en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. - name: response_format use_template: response_format + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format pricing: input: '0.02' output: '0.02' diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0809.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0809.yaml index 869e0ea71c01b1..67b2b2ebddc616 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0809.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus-0809.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-plus-0809 label: en_US: qwen-vl-plus-0809 @@ -9,6 +10,15 @@ model_properties: mode: chat context_size: 32768 parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. - name: top_p use_template: top_p type: float @@ -50,6 +60,18 @@ parameter_rules: en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. - name: response_format use_template: response_format + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format pricing: input: '0.008' output: '0.008' diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus.yaml index da11bacc646c87..f55764c6c05500 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen-vl-plus.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen-vl-plus label: en_US: qwen-vl-plus @@ -9,6 +10,15 @@ model_properties: mode: chat context_size: 8000 parameter_rules: + - name: temperature + use_template: temperature + type: float + default: 0.3 + min: 0.0 + max: 2.0 + help: + zh_Hans: 用于控制随机性和多样性的程度。具体来说，temperature值控制了生成文本时对每个候选词的概率分布进行平滑的程度。较高的temperature值会降低概率分布的峰值，使得更多的低概率词被选择，生成结果更加多样化；而较低的temperature值则会增强概率分布的峰值，使得高概率词更容易被选择，生成结果更加确定。 + en_US: Used to control the degree of randomness and diversity. Specifically, the temperature value controls the degree to which the probability distribution of each candidate word is smoothed when generating text. A higher temperature value will reduce the peak value of the probability distribution, allowing more low-probability words to be selected, and the generated results will be more diverse; while a lower temperature value will enhance the peak value of the probability distribution, making it easier for high-probability words to be selected. , the generated results are more certain. - name: top_p use_template: top_p type: float @@ -50,6 +60,18 @@ parameter_rules: en_US: The random number seed used when generating, the user controls the randomness of the content generated by the model. Supports unsigned 64-bit integers, default value is 1234. When using seed, the model will try its best to generate the same or similar results, but there is currently no guarantee that the results will be exactly the same every time. - name: response_format use_template: response_format + - name: repetition_penalty + required: false + type: float + default: 1.1 + label: + zh_Hans: 重复惩罚 + en_US: Repetition penalty + help: + zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 + en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. + - name: response_format + use_template: response_format pricing: input: '0.008' output: '0.008' diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-1.5b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-1.5b-instruct.yaml index cfe4b5a6662a37..ea157f42ded914 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-1.5b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-1.5b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2-math-1.5b-instruct label: en_US: qwen2-math-1.5b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-72b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-72b-instruct.yaml index e541c197b0ff4f..37052a923317d9 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-72b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-72b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2-math-72b-instruct label: en_US: qwen2-math-72b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-7b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-7b-instruct.yaml index ba4514e3d6eadb..e182f1c27f7ea9 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-7b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2-math-7b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2-math-7b-instruct label: en_US: qwen2-math-7b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-0.5b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-0.5b-instruct.yaml index e5596041af6f21..9e75ccc1f210d9 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-0.5b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-0.5b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-0.5b-instruct label: en_US: qwen2.5-0.5b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-1.5b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-1.5b-instruct.yaml index 4004c59417107a..67c9d312432af7 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-1.5b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-1.5b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-1.5b-instruct label: en_US: qwen2.5-1.5b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-14b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-14b-instruct.yaml index d8f53666ced415..2a38be921cf3fd 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-14b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-14b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-14b-instruct label: en_US: qwen2.5-14b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-32b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-32b-instruct.yaml index 890f7e6e4e5d96..e6e4fbf97808be 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-32b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-32b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-32b-instruct label: en_US: qwen2.5-32b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-3b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-3b-instruct.yaml index 6d3d2dd5bb5e61..8f250379a788ab 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-3b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-3b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-3b-instruct label: en_US: qwen2.5-3b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-72b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-72b-instruct.yaml index 17d0eb5b351d85..bb3cdd6141f1ea 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-72b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-72b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-72b-instruct label: en_US: qwen2.5-72b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-7b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-7b-instruct.yaml index 435b3f90a2be53..fdcd3d42757edb 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-7b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-7b-instruct.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models model: qwen2.5-7b-instruct label: en_US: qwen2.5-7b-instruct @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-coder-7b-instruct.yaml b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-coder-7b-instruct.yaml index 435b3f90a2be53..7ebeec395393c7 100644 --- a/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-coder-7b-instruct.yaml +++ b/api/core/model_runtime/model_providers/tongyi/llm/qwen2.5-coder-7b-instruct.yaml @@ -1,6 +1,7 @@ -model: qwen2.5-7b-instruct +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models +model: qwen2.5-coder-7b-instruct label: - en_US: qwen2.5-7b-instruct + en_US: qwen2.5-coder-7b-instruct model_type: llm features: - agent-thought @@ -60,16 +61,11 @@ parameter_rules: type: float default: 1.1 label: + zh_Hans: 重复惩罚 en_US: Repetition penalty help: zh_Hans: 用于控制模型生成时的重复度。提高repetition_penalty时可以降低模型生成的重复度。1.0表示不做惩罚。 en_US: Used to control the repeatability when generating models. Increasing repetition_penalty can reduce the duplication of model generation. 1.0 means no punishment. - - name: enable_search - type: boolean - default: false - help: - zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 - en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. - name: response_format use_template: response_format pricing: diff --git a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml index f4303c53d38b80..52e35d8b50afd8 100644 --- a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml +++ b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v1.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw model: text-embedding-v1 model_type: text-embedding model_properties: diff --git a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml index f6be3544ed8f65..5bb6a8f4243d53 100644 --- a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml +++ b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v2.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw model: text-embedding-v2 model_type: text-embedding model_properties: diff --git a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v3.yaml b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v3.yaml index 171a379ee23f77..d8af0e2b63565d 100644 --- a/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v3.yaml +++ b/api/core/model_runtime/model_providers/tongyi/text_embedding/text-embedding-v3.yaml @@ -1,3 +1,4 @@ +# for more details, please refer to https://help.aliyun.com/zh/model-studio/getting-started/models#3383780daf8hw model: text-embedding-v3 model_type: text-embedding model_properties: diff --git a/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py index 5783d2e383e2de..736cd44df8888f 100644 --- a/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/tongyi/text_embedding/text_embedding.py @@ -4,6 +4,7 @@ import dashscope import numpy as np +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import ( EmbeddingUsage, @@ -27,6 +28,7 @@ def _invoke( credentials: dict, texts: list[str], user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -35,6 +37,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ credentials_kwargs = self._to_credential_kwargs(credentials) diff --git a/api/core/model_runtime/model_providers/tongyi/tongyi.yaml b/api/core/model_runtime/model_providers/tongyi/tongyi.yaml index fabe6d90e6b206..1a09c20fd93f61 100644 --- a/api/core/model_runtime/model_providers/tongyi/tongyi.yaml +++ b/api/core/model_runtime/model_providers/tongyi/tongyi.yaml @@ -37,14 +37,51 @@ model_credential_schema: en_US: Model Name zh_Hans: 模型名称 placeholder: - en_US: Enter full model name - zh_Hans: 输入模型全称 + en_US: Enter your model name + zh_Hans: 输入模型名称 credential_form_schemas: - variable: dashscope_api_key - required: true label: en_US: API Key type: secret-input + required: true placeholder: zh_Hans: 在此输入您的 API Key en_US: Enter your API Key + - variable: context_size + label: + zh_Hans: 模型上下文长度 + en_US: Model context size + required: true + type: text-input + default: '4096' + placeholder: + zh_Hans: 在此输入您的模型上下文长度 + en_US: Enter your Model context size + - variable: max_tokens + label: + zh_Hans: 最大 token 上限 + en_US: Upper bound for max tokens + default: '4096' + type: text-input + show_on: + - variable: __model_type + value: llm + - variable: function_calling_type + label: + en_US: Function calling + type: select + required: false + default: no_call + options: + - value: no_call + label: + en_US: Not Support + zh_Hans: 不支持 + - value: function_call + label: + en_US: Support + zh_Hans: 支持 + show_on: + - variable: __model_type + value: llm diff --git a/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py index edd4a36d98f587..b6509cd26cfa28 100644 --- a/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/upstage/text_embedding/text_embedding.py @@ -7,6 +7,7 @@ from openai import OpenAI from tokenizers import Tokenizer +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.validate import CredentialsValidateFailedError @@ -22,7 +23,14 @@ class UpstageTextEmbeddingModel(_CommonUpstage, TextEmbeddingModel): def _get_tokenizer(self) -> Tokenizer: return Tokenizer.from_pretrained("upstage/solar-1-mini-tokenizer") - def _invoke(self, model: str, credentials: dict, texts: list[str], user: str | None = None) -> TextEmbeddingResult: + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: str | None = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -30,6 +38,7 @@ def _invoke(self, model: str, credentials: dict, texts: list[str], user: str | N :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-001.yaml similarity index 96% rename from api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash.yaml rename to api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-001.yaml index c308f0a322fddd..f5386be06da6be 100644 --- a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash.yaml +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-001.yaml @@ -1,6 +1,6 @@ model: gemini-1.5-flash-001 label: - en_US: Gemini 1.5 Flash + en_US: Gemini 1.5 Flash 001 model_type: llm features: - agent-thought diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-002.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-002.yaml new file mode 100644 index 00000000000000..97bd44f06b5145 --- /dev/null +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-flash-002.yaml @@ -0,0 +1,37 @@ +model: gemini-1.5-flash-002 +label: + en_US: Gemini 1.5 Flash 002 +model_type: llm +features: + - agent-thought + - vision +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + en_US: Top k + type: int + help: + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_output_tokens + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-001.yaml similarity index 96% rename from api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro.yaml rename to api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-001.yaml index 744863e7731e15..5e08f2294e2ebf 100644 --- a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro.yaml +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-001.yaml @@ -1,6 +1,6 @@ model: gemini-1.5-pro-001 label: - en_US: Gemini 1.5 Pro + en_US: Gemini 1.5 Pro 001 model_type: llm features: - agent-thought diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-002.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-002.yaml new file mode 100644 index 00000000000000..8f327ea2f3d37e --- /dev/null +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-1.5-pro-002.yaml @@ -0,0 +1,37 @@ +model: gemini-1.5-pro-002 +label: + en_US: Gemini 1.5 Pro 002 +model_type: llm +features: + - agent-thought + - vision +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + en_US: Top k + type: int + help: + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_output_tokens + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-flash-experimental.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-flash-experimental.yaml new file mode 100644 index 00000000000000..0f5eb34c0cdf03 --- /dev/null +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-flash-experimental.yaml @@ -0,0 +1,37 @@ +model: gemini-flash-experimental +label: + en_US: Gemini Flash Experimental +model_type: llm +features: + - agent-thought + - vision +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + en_US: Top k + type: int + help: + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_output_tokens + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-pro-experimental.yaml b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-pro-experimental.yaml new file mode 100644 index 00000000000000..fa31cabb85abb0 --- /dev/null +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/gemini-pro-experimental.yaml @@ -0,0 +1,37 @@ +model: gemini-pro-experimental +label: + en_US: Gemini Pro Experimental +model_type: llm +features: + - agent-thought + - vision +model_properties: + mode: chat + context_size: 1048576 +parameter_rules: + - name: temperature + use_template: temperature + - name: top_p + use_template: top_p + - name: top_k + label: + en_US: Top k + type: int + help: + en_US: Only sample from the top K options for each subsequent token. + required: false + - name: presence_penalty + use_template: presence_penalty + - name: frequency_penalty + use_template: frequency_penalty + - name: max_output_tokens + use_template: max_tokens + required: true + default: 8192 + min: 1 + max: 8192 +pricing: + input: '0.00' + output: '0.00' + unit: '0.000001' + currency: USD diff --git a/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py b/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py index da69b7cdf382de..1dd785d5454082 100644 --- a/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py +++ b/api/core/model_runtime/model_providers/vertex_ai/llm/llm.py @@ -2,6 +2,7 @@ import io import json import logging +import time from collections.abc import Generator from typing import Optional, Union, cast @@ -20,7 +21,6 @@ from google.cloud import aiplatform from google.oauth2 import service_account from PIL import Image -from vertexai.generative_models import HarmBlockThreshold, HarmCategory from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta, LLMUsage from core.model_runtime.entities.message_entities import ( @@ -34,6 +34,7 @@ ToolPromptMessage, UserPromptMessage, ) +from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.errors.invoke import ( InvokeAuthorizationError, InvokeBadRequestError, @@ -503,20 +504,12 @@ def _generate( else: history.append(content) - safety_settings = { - HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE, - HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE, - } - google_model = glm.GenerativeModel(model_name=model, system_instruction=system_instruction) response = google_model.generate_content( contents=history, generation_config=glm.GenerationConfig(**config_kwargs), stream=stream, - safety_settings=safety_settings, tools=self._convert_tools_to_glm_tool(tools) if tools else None, ) diff --git a/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py index 519373a7f31a35..fce9544df0a414 100644 --- a/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/vertex_ai/text_embedding/text_embedding.py @@ -9,6 +9,7 @@ from google.oauth2 import service_account from vertexai.language_models import TextEmbeddingModel as VertexTextEmbeddingModel +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import ( AIModelEntity, @@ -30,7 +31,12 @@ class VertexAiTextEmbeddingModel(_CommonVertexAi, TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -38,6 +44,8 @@ def _invoke( :param model: model name :param credentials: model credentials :param texts: texts to embed + :param user: unique user id + :param input_type: input type :return: embeddings result """ service_account_info = json.loads(base64.b64decode(credentials["vertex_service_account_key"])) diff --git a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py index 9cba2cb8794184..0dd4037c958567 100644 --- a/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/volcengine_maas/text_embedding/text_embedding.py @@ -2,6 +2,7 @@ from decimal import Decimal from typing import Optional +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import ( AIModelEntity, @@ -41,7 +42,12 @@ class VolcengineMaaSTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -50,6 +56,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ if ArkClientV3.is_legacy(credentials): diff --git a/api/core/model_runtime/model_providers/voyage/__init__.py b/api/core/model_runtime/model_providers/voyage/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/voyage/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/voyage/_assets/icon_l_en.svg new file mode 100644 index 00000000000000..a961f5e4355eea --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/_assets/icon_l_en.svg @@ -0,0 +1,21 @@ + \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/voyage/_assets/icon_s_en.svg b/api/core/model_runtime/model_providers/voyage/_assets/icon_s_en.svg new file mode 100644 index 00000000000000..2c4e121dd71f0b --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/_assets/icon_s_en.svg @@ -0,0 +1,8 @@ + + + voyage + + + + + \ No newline at end of file diff --git a/api/core/model_runtime/model_providers/voyage/rerank/__init__.py b/api/core/model_runtime/model_providers/voyage/rerank/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/voyage/rerank/rerank-1.yaml b/api/core/model_runtime/model_providers/voyage/rerank/rerank-1.yaml new file mode 100644 index 00000000000000..9c894eda85203b --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/rerank/rerank-1.yaml @@ -0,0 +1,4 @@ +model: rerank-1 +model_type: rerank +model_properties: + context_size: 8000 diff --git a/api/core/model_runtime/model_providers/voyage/rerank/rerank-lite-1.yaml b/api/core/model_runtime/model_providers/voyage/rerank/rerank-lite-1.yaml new file mode 100644 index 00000000000000..b052d6f00028cb --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/rerank/rerank-lite-1.yaml @@ -0,0 +1,4 @@ +model: rerank-lite-1 +model_type: rerank +model_properties: + context_size: 4000 diff --git a/api/core/model_runtime/model_providers/voyage/rerank/rerank.py b/api/core/model_runtime/model_providers/voyage/rerank/rerank.py new file mode 100644 index 00000000000000..33fdebbb45ef36 --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/rerank/rerank.py @@ -0,0 +1,123 @@ +from typing import Optional + +import httpx + +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType +from core.model_runtime.entities.rerank_entities import RerankDocument, RerankResult +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.rerank_model import RerankModel + + +class VoyageRerankModel(RerankModel): + """ + Model class for Voyage rerank model. + """ + + def _invoke( + self, + model: str, + credentials: dict, + query: str, + docs: list[str], + score_threshold: Optional[float] = None, + top_n: Optional[int] = None, + user: Optional[str] = None, + ) -> RerankResult: + """ + Invoke rerank model + :param model: model name + :param credentials: model credentials + :param query: search query + :param docs: docs for reranking + :param score_threshold: score threshold + :param top_n: top n documents to return + :param user: unique user id + :return: rerank result + """ + if len(docs) == 0: + return RerankResult(model=model, docs=[]) + + base_url = credentials.get("base_url", "https://api.voyageai.com/v1") + base_url = base_url.removesuffix("/") + + try: + response = httpx.post( + base_url + "/rerank", + json={"model": model, "query": query, "documents": docs, "top_k": top_n, "return_documents": True}, + headers={"Authorization": f"Bearer {credentials.get('api_key')}", "Content-Type": "application/json"}, + ) + response.raise_for_status() + results = response.json() + + rerank_documents = [] + for result in results["data"]: + rerank_document = RerankDocument( + index=result["index"], + text=result["document"], + score=result["relevance_score"], + ) + if score_threshold is None or result["relevance_score"] >= score_threshold: + rerank_documents.append(rerank_document) + + return RerankResult(model=model, docs=rerank_documents) + except httpx.HTTPStatusError as e: + raise InvokeServerUnavailableError(str(e)) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + :param model: model name + :param credentials: model credentials + :return: + """ + try: + self._invoke( + model=model, + credentials=credentials, + query="What is the capital of the United States?", + docs=[ + "Carson City is the capital city of the American state of Nevada. At the 2010 United States " + "Census, Carson City had a population of 55,274.", + "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific Ocean that " + "are a political division controlled by the United States. Its capital is Saipan.", + ], + score_threshold=0.8, + ) + except Exception as ex: + raise CredentialsValidateFailedError(str(ex)) + + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + """ + Map model invoke error to unified error + """ + return { + InvokeConnectionError: [httpx.ConnectError], + InvokeServerUnavailableError: [httpx.RemoteProtocolError], + InvokeRateLimitError: [], + InvokeAuthorizationError: [httpx.HTTPStatusError], + InvokeBadRequestError: [httpx.RequestError], + } + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.RERANK, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size", "8000"))}, + ) + + return entity diff --git a/api/core/model_runtime/model_providers/voyage/text_embedding/__init__.py b/api/core/model_runtime/model_providers/voyage/text_embedding/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/core/model_runtime/model_providers/voyage/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/voyage/text_embedding/text_embedding.py new file mode 100644 index 00000000000000..a8a4d3c15bbb13 --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/text_embedding/text_embedding.py @@ -0,0 +1,172 @@ +import time +from json import JSONDecodeError, dumps +from typing import Optional + +import requests + +from core.embedding.embedding_constant import EmbeddingInputType +from core.model_runtime.entities.common_entities import I18nObject +from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType +from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult +from core.model_runtime.errors.invoke import ( + InvokeAuthorizationError, + InvokeBadRequestError, + InvokeConnectionError, + InvokeError, + InvokeRateLimitError, + InvokeServerUnavailableError, +) +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel + + +class VoyageTextEmbeddingModel(TextEmbeddingModel): + """ + Model class for Voyage text embedding model. + """ + + api_base: str = "https://api.voyageai.com/v1" + + def _invoke( + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, + ) -> TextEmbeddingResult: + """ + Invoke text embedding model + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :param user: unique user id + :param input_type: input type + :return: embeddings result + """ + api_key = credentials["api_key"] + if not api_key: + raise CredentialsValidateFailedError("api_key is required") + + base_url = credentials.get("base_url", self.api_base) + base_url = base_url.removesuffix("/") + + url = base_url + "/embeddings" + headers = {"Authorization": "Bearer " + api_key, "Content-Type": "application/json"} + voyage_input_type = "null" + if input_type is not None: + voyage_input_type = input_type.value + data = {"model": model, "input": texts, "input_type": voyage_input_type} + + try: + response = requests.post(url, headers=headers, data=dumps(data)) + except Exception as e: + raise InvokeConnectionError(str(e)) + + if response.status_code != 200: + try: + resp = response.json() + msg = resp["detail"] + if response.status_code == 401: + raise InvokeAuthorizationError(msg) + elif response.status_code == 429: + raise InvokeRateLimitError(msg) + elif response.status_code == 500: + raise InvokeServerUnavailableError(msg) + else: + raise InvokeBadRequestError(msg) + except JSONDecodeError as e: + raise InvokeServerUnavailableError( + f"Failed to convert response to json: {e} with text: {response.text}" + ) + + try: + resp = response.json() + embeddings = resp["data"] + usage = resp["usage"] + except Exception as e: + raise InvokeServerUnavailableError(f"Failed to convert response to json: {e} with text: {response.text}") + + usage = self._calc_response_usage(model=model, credentials=credentials, tokens=usage["total_tokens"]) + + result = TextEmbeddingResult( + model=model, embeddings=[[float(data) for data in x["embedding"]] for x in embeddings], usage=usage + ) + + return result + + def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int: + """ + Get number of tokens for given prompt messages + + :param model: model name + :param credentials: model credentials + :param texts: texts to embed + :return: + """ + return sum(self._get_num_tokens_by_gpt2(text) for text in texts) + + def validate_credentials(self, model: str, credentials: dict) -> None: + """ + Validate model credentials + + :param model: model name + :param credentials: model credentials + :return: + """ + try: + self._invoke(model=model, credentials=credentials, texts=["ping"]) + except Exception as e: + raise CredentialsValidateFailedError(f"Credentials validation failed: {e}") + + @property + def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: + return { + InvokeConnectionError: [InvokeConnectionError], + InvokeServerUnavailableError: [InvokeServerUnavailableError], + InvokeRateLimitError: [InvokeRateLimitError], + InvokeAuthorizationError: [InvokeAuthorizationError], + InvokeBadRequestError: [KeyError, InvokeBadRequestError], + } + + def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage: + """ + Calculate response usage + + :param model: model name + :param credentials: model credentials + :param tokens: input tokens + :return: usage + """ + # get input price info + input_price_info = self.get_price( + model=model, credentials=credentials, price_type=PriceType.INPUT, tokens=tokens + ) + + # transform usage + usage = EmbeddingUsage( + tokens=tokens, + total_tokens=tokens, + unit_price=input_price_info.unit_price, + price_unit=input_price_info.unit, + total_price=input_price_info.total_amount, + currency=input_price_info.currency, + latency=time.perf_counter() - self.started_at, + ) + + return usage + + def get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity: + """ + generate custom model entities from credentials + """ + entity = AIModelEntity( + model=model, + label=I18nObject(en_US=model), + model_type=ModelType.TEXT_EMBEDDING, + fetch_from=FetchFrom.CUSTOMIZABLE_MODEL, + model_properties={ModelPropertyKey.CONTEXT_SIZE: int(credentials.get("context_size"))}, + ) + + return entity diff --git a/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3-lite.yaml b/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3-lite.yaml new file mode 100644 index 00000000000000..a06bb7639feacd --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3-lite.yaml @@ -0,0 +1,8 @@ +model: voyage-3-lite +model_type: text-embedding +model_properties: + context_size: 32000 +pricing: + input: '0.00002' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3.yaml b/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3.yaml new file mode 100644 index 00000000000000..117afbcaf3c808 --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/text_embedding/voyage-3.yaml @@ -0,0 +1,8 @@ +model: voyage-3 +model_type: text-embedding +model_properties: + context_size: 32000 +pricing: + input: '0.00006' + unit: '0.001' + currency: USD diff --git a/api/core/model_runtime/model_providers/voyage/voyage.py b/api/core/model_runtime/model_providers/voyage/voyage.py new file mode 100644 index 00000000000000..3e33b45e110d56 --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/voyage.py @@ -0,0 +1,28 @@ +import logging + +from core.model_runtime.entities.model_entities import ModelType +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.__base.model_provider import ModelProvider + +logger = logging.getLogger(__name__) + + +class VoyageProvider(ModelProvider): + def validate_provider_credentials(self, credentials: dict) -> None: + """ + Validate provider credentials + if validate failed, raise exception + + :param credentials: provider credentials, credentials form defined in `provider_credential_schema`. + """ + try: + model_instance = self.get_model_instance(ModelType.TEXT_EMBEDDING) + + # Use `voyage-3` model for validate, + # no matter what model you pass in, text completion model or chat model + model_instance.validate_credentials(model="voyage-3", credentials=credentials) + except CredentialsValidateFailedError as ex: + raise ex + except Exception as ex: + logger.exception(f"{self.get_provider_schema().provider} credentials validate failed") + raise ex diff --git a/api/core/model_runtime/model_providers/voyage/voyage.yaml b/api/core/model_runtime/model_providers/voyage/voyage.yaml new file mode 100644 index 00000000000000..c64707800eebe0 --- /dev/null +++ b/api/core/model_runtime/model_providers/voyage/voyage.yaml @@ -0,0 +1,31 @@ +provider: voyage +label: + en_US: Voyage +description: + en_US: Embedding and Rerank Model Supported +icon_small: + en_US: icon_s_en.svg +icon_large: + en_US: icon_l_en.svg +background: "#EFFDFD" +help: + title: + en_US: Get your API key from Voyage AI + zh_Hans: 从 Voyage 获取 API Key + url: + en_US: https://dash.voyageai.com/ +supported_model_types: + - text-embedding + - rerank +configurate_methods: + - predefined-model +provider_credential_schema: + credential_form_schemas: + - variable: api_key + label: + en_US: API Key + type: secret-input + required: true + placeholder: + zh_Hans: 在此输入您的 API Key + en_US: Enter your API Key diff --git a/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py index 4d6f6dccd0cf72..c21d0c055277f7 100644 --- a/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/wenxin/text_embedding/text_embedding.py @@ -7,6 +7,7 @@ import numpy as np from requests import Response, post +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.invoke import InvokeError @@ -70,7 +71,12 @@ def _create_text_embedding(self, api_key: str, secret_key: str) -> TextEmbedding return WenxinTextEmbedding(api_key, secret_key) def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -79,6 +85,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ diff --git a/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py index 8043af1d6cf11e..16272391320d55 100644 --- a/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/xinference/text_embedding/text_embedding.py @@ -3,6 +3,7 @@ from xinference_client.client.restful.restful_client import Client, RESTfulEmbeddingModelHandle +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.common_entities import I18nObject from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult @@ -25,7 +26,12 @@ class XinferenceTextEmbeddingModel(TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -40,6 +46,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ server_url = credentials["server_url"] diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml index b1f9b7485cd9dd..7fcf6922023fbf 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-0520.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.1' output: '0.1' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml index 4e7d5fd3cc9775..fcd7c7768c7179 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-air.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.001' output: '0.001' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml index 14f17db5d6c06a..c9ae5abf196360 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-airx.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.01' output: '0.01' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml index 3361474d737df6..98c4f72c7237f7 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm-4-flash.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0' output: '0' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml index bf0135d1985481..0b5391ce2f83fd 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_3_turbo.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 8192 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.001' output: '0.001' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml index ab4b32dd826b00..62f453fb775b9b 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.1' output: '0.1' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml index d1b01731f54632..350b080c3fc11b 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_long.yaml @@ -49,6 +49,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.001' output: '0.001' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml index 9ede308f18d55a..2d7ebd71cf26e1 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4_plus.yaml @@ -46,6 +46,15 @@ parameter_rules: default: 1024 min: 1 max: 4095 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.05' output: '0.05' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml index 28286580a7ec1b..3a1120ff375c19 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v.yaml @@ -44,6 +44,15 @@ parameter_rules: default: 1024 min: 1 max: 1024 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.05' output: '0.05' diff --git a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml index 4c5fa2403413ea..14b9623e5a8c44 100644 --- a/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml +++ b/api/core/model_runtime/model_providers/zhipuai/llm/glm_4v_plus.yaml @@ -44,6 +44,15 @@ parameter_rules: default: 1024 min: 1 max: 1024 + - name: web_search + type: boolean + label: + zh_Hans: 联网搜索 + en_US: Web Search + default: false + help: + zh_Hans: 模型内置了互联网搜索服务，该参数控制模型在生成文本时是否参考使用互联网搜索结果。启用互联网搜索，模型会将搜索结果作为文本生成过程中的参考信息，但模型会基于其内部逻辑“自行判断”是否使用互联网搜索结果。 + en_US: The model has a built-in Internet search service. This parameter controls whether the model refers to Internet search results when generating text. When Internet search is enabled, the model will use the search results as reference information in the text generation process, but the model will "judge" whether to use Internet search results based on its internal logic. pricing: input: '0.01' output: '0.01' diff --git a/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py index ee20954381053d..14a529dddf82d1 100644 --- a/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py +++ b/api/core/model_runtime/model_providers/zhipuai/text_embedding/text_embedding.py @@ -1,6 +1,7 @@ import time from typing import Optional +from core.embedding.embedding_constant import EmbeddingInputType from core.model_runtime.entities.model_entities import PriceType from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult from core.model_runtime.errors.validate import CredentialsValidateFailedError @@ -15,7 +16,12 @@ class ZhipuAITextEmbeddingModel(_CommonZhipuaiAI, TextEmbeddingModel): """ def _invoke( - self, model: str, credentials: dict, texts: list[str], user: Optional[str] = None + self, + model: str, + credentials: dict, + texts: list[str], + user: Optional[str] = None, + input_type: EmbeddingInputType = EmbeddingInputType.DOCUMENT, ) -> TextEmbeddingResult: """ Invoke text embedding model @@ -24,6 +30,7 @@ def _invoke( :param credentials: model credentials :param texts: texts to embed :param user: unique user id + :param input_type: input type :return: embeddings result """ credentials_kwargs = self._to_credential_kwargs(credentials) diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py index d8ecc310644d17..05510a3ec421d0 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/async_completions.py @@ -57,7 +57,7 @@ def create( if temperature <= 0: do_sample = False temperature = 0.01 - # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间，do_sample重写为:false（参数top_p temperture不生效）") # noqa: E501 + # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间，do_sample重写为:false（参数top_p temperature不生效）") # noqa: E501 if temperature >= 1: temperature = 0.99 # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间") diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py index 1c23473a03ae32..8e5bb454e6ce7e 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/api_resource/chat/completions.py @@ -60,7 +60,7 @@ def create( if temperature <= 0: do_sample = False temperature = 0.01 - # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间，do_sample重写为:false（参数top_p temperture不生效）") # noqa: E501 + # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间，do_sample重写为:false（参数top_p temperature不生效）") # noqa: E501 if temperature >= 1: temperature = 0.99 # logger.warning("temperature:取值范围是：(0.0, 1.0) 开区间") diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_models.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_models.py index 5e9a7e0a987e28..69b1d3a83dfef3 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_models.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_base_models.py @@ -48,7 +48,7 @@ ) if TYPE_CHECKING: - from pydantic_core.core_schema import LiteralSchema, ModelField, ModelFieldsSchema + from pydantic_core.core_schema import ModelField __all__ = ["BaseModel", "GenericModel"] _BaseModelT = TypeVar("_BaseModelT", bound="BaseModel") @@ -630,8 +630,7 @@ def validate_type(*, type_: type[_T], value: object) -> _T: return cast(_T, _validate_non_model_type(type_=type_, value=value)) -# our use of subclasssing here causes weirdness for type checkers, -# so we just pretend that we don't subclass +# Subclassing here confuses type checkers, so we treat this class as non-inheriting. if TYPE_CHECKING: GenericModel = BaseModel else: diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py index d0f933d8141389..ffdafb85d581fe 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_http_client.py @@ -169,7 +169,7 @@ def _set_private_attributes( # Pydantic uses a custom `__iter__` method to support casting BaseModels # to dictionaries. e.g. dict(model). # As we want to support `for item in page`, this is inherently incompatible - # with the default pydantic behaviour. It is not possible to support both + # with the default pydantic behavior. It is not possible to support both # use cases at once. Fortunately, this is not a big deal as all other pydantic # methods should continue to work as expected as there is an alternative method # to cast a model to a dictionary, model.dict(), which is used internally @@ -356,16 +356,16 @@ def _build_request(self, options: FinalRequestOptions) -> httpx.Request: **kwargs, ) - def _object_to_formfata(self, key: str, value: Data | Mapping[object, object]) -> list[tuple[str, str]]: + def _object_to_formdata(self, key: str, value: Data | Mapping[object, object]) -> list[tuple[str, str]]: items = [] if isinstance(value, Mapping): for k, v in value.items(): - items.extend(self._object_to_formfata(f"{key}[{k}]", v)) + items.extend(self._object_to_formdata(f"{key}[{k}]", v)) return items if isinstance(value, list | tuple): for v in value: - items.extend(self._object_to_formfata(key + "[]", v)) + items.extend(self._object_to_formdata(key + "[]", v)) return items def _primitive_value_to_str(val) -> str: @@ -385,7 +385,7 @@ def _primitive_value_to_str(val) -> str: return [(key, str_data)] def _make_multipartform(self, data: Mapping[object, object]) -> dict[str, object]: - items = flatten(list(starmap(self._object_to_formfata, data.items()))) + items = flatten(list(starmap(self._object_to_formdata, data.items()))) serialized: dict[str, object] = {} for key, value in items: @@ -620,7 +620,7 @@ def _process_response( stream: bool, stream_cls: type[StreamResponse] | None, ) -> ResponseT: - # _legacy_response with raw_response_header to paser method + # _legacy_response with raw_response_header to parser method if response.request.headers.get(RAW_RESPONSE_HEADER) == "true": return cast( ResponseT, diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_legacy_response.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_legacy_response.py index 47183b9eee9c0d..51bf21bcdc17a8 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_legacy_response.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_legacy_response.py @@ -87,7 +87,7 @@ def parse(self, *, to: type[_T] | None = None) -> R | _T: For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`. - You can customise the type that the response is parsed into through + You can customize the type that the response is parsed into through the `to` argument, e.g. ```py diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py index 45443da662d57e..92e601805569f3 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_response.py @@ -252,7 +252,7 @@ def parse(self, *, to: type[_T] | None = None) -> R | _T: For lower-level control, see `.read()`, `.json()`, `.iter_bytes()`. - You can customise the type that the response is parsed into through + You can customize the type that the response is parsed into through the `to` argument, e.g. ```py @@ -363,7 +363,7 @@ class StreamAlreadyConsumed(ZhipuAIError): # noqa: N818 # ^ error ``` - If you want this behaviour you'll need to either manually accumulate the response + If you want this behavior you'll need to either manually accumulate the response content or call `await response.read()` before iterating over the stream. """ diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_utils/_utils.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_utils/_utils.py index ce5e7786aa2937..3a7b234ab0c067 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_utils/_utils.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/core/_utils/_utils.py @@ -248,7 +248,7 @@ def inner(func: CallableT) -> CallableT: @functools.wraps(func) def wrapper(*args: object, **kwargs: object) -> object: given_params: set[str] = set() - for i, _ in enumerate(args): + for i in range(len(args)): try: given_params.add(positional[i]) except IndexError: diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/__init__.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/__init__.py index 32e23e6dab3076..59cb41d7124a7f 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/__init__.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/__init__.py @@ -1,8 +1,8 @@ -from .document import DocumentData, DocumentFailedInfo, DocumentObject, DocumentSuccessinfo +from .document import DocumentData, DocumentFailedInfo, DocumentObject, DocumentSuccessInfo __all__ = [ "DocumentData", "DocumentObject", - "DocumentSuccessinfo", + "DocumentSuccessInfo", "DocumentFailedInfo", ] diff --git a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/document.py b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/document.py index b9a1646391ece8..980bc6f4a7c40d 100644 --- a/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/document.py +++ b/api/core/model_runtime/model_providers/zhipuai/zhipuai_sdk/types/knowledge/document/document.py @@ -2,10 +2,10 @@ from ....core import BaseModel -__all__ = ["DocumentData", "DocumentObject", "DocumentSuccessinfo", "DocumentFailedInfo"] +__all__ = ["DocumentData", "DocumentObject", "DocumentSuccessInfo", "DocumentFailedInfo"] -class DocumentSuccessinfo(BaseModel): +class DocumentSuccessInfo(BaseModel): documentId: Optional[str] = None """文件id""" filename: Optional[str] = None @@ -24,7 +24,7 @@ class DocumentFailedInfo(BaseModel): class DocumentObject(BaseModel): """文档信息""" - successInfos: Optional[list[DocumentSuccessinfo]] = None + successInfos: Optional[list[DocumentSuccessInfo]] = None """上传成功的文件信息""" failedInfos: Optional[list[DocumentFailedInfo]] = None """上传失败的文件信息""" diff --git a/api/core/rag/datasource/keyword/jieba/jieba.py b/api/core/rag/datasource/keyword/jieba/jieba.py index 3073100746d360..a0153c1e58a1a8 100644 --- a/api/core/rag/datasource/keyword/jieba/jieba.py +++ b/api/core/rag/datasource/keyword/jieba/jieba.py @@ -45,7 +45,7 @@ def add_texts(self, texts: list[Document], **kwargs): keyword_table_handler = JiebaKeywordTableHandler() keyword_table = self._get_dataset_keyword_table() - keywords_list = kwargs.get("keywords_list", None) + keywords_list = kwargs.get("keywords_list") for i in range(len(texts)): text = texts[i] if keywords_list: diff --git a/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py b/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py index 612542dab1df11..6dcd98dcfd14f6 100644 --- a/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py +++ b/api/core/rag/datasource/vdb/analyticdb/analyticdb_vector.py @@ -40,19 +40,8 @@ def to_analyticdb_client_params(self): class AnalyticdbVector(BaseVector): - _instance = None - _init = False - - def __new__(cls, *args, **kwargs): - if cls._instance is None: - cls._instance = super().__new__(cls) - return cls._instance - def __init__(self, collection_name: str, config: AnalyticdbConfig): - # collection_name must be updated every time self._collection_name = collection_name.lower() - if AnalyticdbVector._init: - return try: from alibabacloud_gpdb20160503.client import Client from alibabacloud_tea_openapi import models as open_api_models @@ -62,7 +51,6 @@ def __init__(self, collection_name: str, config: AnalyticdbConfig): self._client_config = open_api_models.Config(user_agent="dify", **config.to_analyticdb_client_params()) self._client = Client(self._client_config) self._initialize() - AnalyticdbVector._init = True def _initialize(self) -> None: cache_key = f"vector_indexing_{self.config.instance_id}" @@ -257,11 +245,14 @@ def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Doc documents = [] for match in response.body.matches.match: if match.score > score_threshold: + metadata = json.loads(match.metadata.get("metadata_")) + metadata["score"] = match.score doc = Document( page_content=match.metadata.get("page_content"), - metadata=json.loads(match.metadata.get("metadata_")), + metadata=metadata, ) documents.append(doc) + documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True) return documents def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]: @@ -286,12 +277,14 @@ def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]: for match in response.body.matches.match: if match.score > score_threshold: metadata = json.loads(match.metadata.get("metadata_")) + metadata["score"] = match.score doc = Document( page_content=match.metadata.get("page_content"), vector=match.metadata.get("vector"), metadata=metadata, ) documents.append(doc) + documents = sorted(documents, key=lambda x: x.metadata["score"], reverse=True) return documents def delete(self) -> None: diff --git a/api/core/rag/datasource/vdb/pgvector/pgvector.py b/api/core/rag/datasource/vdb/pgvector/pgvector.py index 79879d4f6394b1..d90707ebcd5cbd 100644 --- a/api/core/rag/datasource/vdb/pgvector/pgvector.py +++ b/api/core/rag/datasource/vdb/pgvector/pgvector.py @@ -23,6 +23,8 @@ class PGVectorConfig(BaseModel): user: str password: str database: str + min_connection: int + max_connection: int @model_validator(mode="before") @classmethod @@ -37,6 +39,12 @@ def validate_config(cls, values: dict) -> dict: raise ValueError("config PGVECTOR_PASSWORD is required") if not values["database"]: raise ValueError("config PGVECTOR_DATABASE is required") + if not values["min_connection"]: + raise ValueError("config PGVECTOR_MIN_CONNECTION is required") + if not values["max_connection"]: + raise ValueError("config PGVECTOR_MAX_CONNECTION is required") + if values["min_connection"] > values["max_connection"]: + raise ValueError("config PGVECTOR_MIN_CONNECTION should less than PGVECTOR_MAX_CONNECTION") return values @@ -61,8 +69,8 @@ def get_type(self) -> str: def _create_connection_pool(self, config: PGVectorConfig): return psycopg2.pool.SimpleConnectionPool( - 1, - 5, + config.min_connection, + config.max_connection, host=config.host, port=config.port, user=config.user, @@ -213,5 +221,7 @@ def init_vector(self, dataset: Dataset, attributes: list, embeddings: Embeddings user=dify_config.PGVECTOR_USER, password=dify_config.PGVECTOR_PASSWORD, database=dify_config.PGVECTOR_DATABASE, + min_connection=dify_config.PGVECTOR_MIN_CONNECTION, + max_connection=dify_config.PGVECTOR_MAX_CONNECTION, ), ) diff --git a/api/core/rag/datasource/vdb/tencent/tencent_vector.py b/api/core/rag/datasource/vdb/tencent/tencent_vector.py index faa373017becf9..39e3a7f6cfea42 100644 --- a/api/core/rag/datasource/vdb/tencent/tencent_vector.py +++ b/api/core/rag/datasource/vdb/tencent/tencent_vector.py @@ -56,7 +56,7 @@ def _init_database(self): return self._client.create_database(database_name=self._client_config.database) def get_type(self) -> str: - return "tencent" + return VectorType.TENCENT def to_index_struct(self) -> dict: return {"type": self.get_type(), "vector_store": {"class_prefix": self._collection_name}} diff --git a/api/core/rag/datasource/vdb/vector_base.py b/api/core/rag/datasource/vdb/vector_base.py index 1a0dc7f48b8031..22e191340d3a47 100644 --- a/api/core/rag/datasource/vdb/vector_base.py +++ b/api/core/rag/datasource/vdb/vector_base.py @@ -45,6 +45,7 @@ def search_by_vector(self, query_vector: list[float], **kwargs: Any) -> list[Doc def search_by_full_text(self, query: str, **kwargs: Any) -> list[Document]: raise NotImplementedError + @abstractmethod def delete(self) -> None: raise NotImplementedError diff --git a/api/core/rag/extractor/extract_processor.py b/api/core/rag/extractor/extract_processor.py index fe7eaa32e62cb6..0ffc89b214c2d9 100644 --- a/api/core/rag/extractor/extract_processor.py +++ b/api/core/rag/extractor/extract_processor.py @@ -124,7 +124,7 @@ def extract( extractor = UnstructuredPPTXExtractor(file_path, unstructured_api_url) elif file_extension == ".xml": extractor = UnstructuredXmlExtractor(file_path, unstructured_api_url) - elif file_extension == "epub": + elif file_extension == ".epub": extractor = UnstructuredEpubExtractor(file_path, unstructured_api_url) else: # txt @@ -146,7 +146,7 @@ def extract( extractor = WordExtractor(file_path, upload_file.tenant_id, upload_file.created_by) elif file_extension == ".csv": extractor = CSVExtractor(file_path, autodetect_encoding=True) - elif file_extension == "epub": + elif file_extension == ".epub": extractor = UnstructuredEpubExtractor(file_path) else: # txt diff --git a/api/core/tools/entities/common_entities.py b/api/core/tools/entities/common_entities.py index b52119fdc4759b..924e6fc0cf9f17 100644 --- a/api/core/tools/entities/common_entities.py +++ b/api/core/tools/entities/common_entities.py @@ -1,6 +1,6 @@ from typing import Optional -from pydantic import BaseModel +from pydantic import BaseModel, Field class I18nObject(BaseModel): @@ -8,19 +8,16 @@ class I18nObject(BaseModel): Model class for i18n object. """ - zh_Hans: Optional[str] = None - pt_BR: Optional[str] = None - ja_JP: Optional[str] = None en_US: str + zh_Hans: Optional[str] = Field(default=None) + pt_BR: Optional[str] = Field(default=None) + ja_JP: Optional[str] = Field(default=None) def __init__(self, **data): super().__init__(**data) - if not self.zh_Hans: - self.zh_Hans = self.en_US - if not self.pt_BR: - self.pt_BR = self.en_US - if not self.ja_JP: - self.ja_JP = self.en_US + self.zh_Hans = self.zh_Hans or self.en_US + self.pt_BR = self.pt_BR or self.en_US + self.ja_JP = self.ja_JP or self.en_US def to_dict(self) -> dict: return {"zh_Hans": self.zh_Hans, "en_US": self.en_US, "pt_BR": self.pt_BR, "ja_JP": self.ja_JP} diff --git a/api/core/tools/provider/_position.yaml b/api/core/tools/provider/_position.yaml index 40c3356116770b..e0a8e7511e59ba 100644 --- a/api/core/tools/provider/_position.yaml +++ b/api/core/tools/provider/_position.yaml @@ -34,5 +34,9 @@ - feishu_base - feishu_document - feishu_message +- feishu_wiki +- feishu_task +- feishu_calendar +- feishu_spreadsheet - slack - tianditu diff --git a/api/core/tools/provider/builtin/comfyui/comfyui.yaml b/api/core/tools/provider/builtin/comfyui/comfyui.yaml index 066fd853082817..3891eebf3ac7e4 100644 --- a/api/core/tools/provider/builtin/comfyui/comfyui.yaml +++ b/api/core/tools/provider/builtin/comfyui/comfyui.yaml @@ -39,4 +39,4 @@ credentials_for_provider: en_US: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors zh_Hans: ComfyUI服务器的模型名称, 比如 xxx.safetensors pt_BR: The checkpoint name of the ComfyUI server, e.g. xxx.safetensors - url: https://docs.dify.ai/tutorials/tool-configuration/comfyui + url: https://github.com/comfyanonymous/ComfyUI#installing diff --git a/api/core/tools/provider/builtin/feishu_calendar/_assets/icon.png b/api/core/tools/provider/builtin/feishu_calendar/_assets/icon.png new file mode 100644 index 00000000000000..2a934747a98c66 Binary files /dev/null and b/api/core/tools/provider/builtin/feishu_calendar/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.py b/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.py new file mode 100644 index 00000000000000..a46a9fa9e80cab --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.py @@ -0,0 +1,7 @@ +from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController +from core.tools.utils.feishu_api_utils import auth + + +class FeishuCalendarProvider(BuiltinToolProviderController): + def _validate_credentials(self, credentials: dict) -> None: + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.yaml b/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.yaml new file mode 100644 index 00000000000000..db5bab5c1081d9 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/feishu_calendar.yaml @@ -0,0 +1,36 @@ +identity: + author: Doug Lea + name: feishu_calendar + label: + en_US: Feishu Calendar + zh_Hans: 飞书日历 + description: + en_US: | + Feishu calendar, requires the following permissions: calendar:calendar:read、calendar:calendar、contact:user.id:readonly. + zh_Hans: | + 飞书日历，需要开通以下权限: calendar:calendar:read、calendar:calendar、contact:user.id:readonly。 + icon: icon.png + tags: + - social + - productivity +credentials_for_provider: + app_id: + type: text-input + required: true + label: + en_US: APP ID + placeholder: + en_US: Please input your feishu app id + zh_Hans: 请输入你的飞书 app id + help: + en_US: Get your app_id and app_secret from Feishu + zh_Hans: 从飞书获取您的 app_id 和 app_secret + url: https://open.larkoffice.com/app + app_secret: + type: secret-input + required: true + label: + en_US: APP Secret + placeholder: + en_US: Please input your app secret + zh_Hans: 请输入你的飞书 app secret diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.py b/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.py new file mode 100644 index 00000000000000..8f83aea5abbe3d --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.py @@ -0,0 +1,20 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class AddEventAttendeesTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + event_id = tool_parameters.get("event_id") + attendee_phone_or_email = tool_parameters.get("attendee_phone_or_email") + need_notification = tool_parameters.get("need_notification", True) + + res = client.add_event_attendees(event_id, attendee_phone_or_email, need_notification) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.yaml new file mode 100644 index 00000000000000..b7744499b07344 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/add_event_attendees.yaml @@ -0,0 +1,54 @@ +identity: + name: add_event_attendees + author: Doug Lea + label: + en_US: Add Event Attendees + zh_Hans: 添加日程参会人 +description: + human: + en_US: Add Event Attendees + zh_Hans: 添加日程参会人 + llm: A tool for adding attendees to events in Feishu. (在飞书中添加日程参会人) +parameters: + - name: event_id + type: string + required: true + label: + en_US: Event ID + zh_Hans: 日程 ID + human_description: + en_US: | + The ID of the event, which will be returned when the event is created. For example: fb2a6406-26d6-4c8d-a487-6f0246c94d2f_0. + zh_Hans: | + 创建日程时会返回日程 ID。例如: fb2a6406-26d6-4c8d-a487-6f0246c94d2f_0。 + llm_description: | + 日程 ID，创建日程时会返回日程 ID。例如: fb2a6406-26d6-4c8d-a487-6f0246c94d2f_0。 + form: llm + + - name: need_notification + type: boolean + required: false + default: true + label: + en_US: Need Notification + zh_Hans: 是否需要通知 + human_description: + en_US: | + Whether to send a Bot notification to attendees. true: send, false: do not send. + zh_Hans: | + 是否给参与人发送 Bot 通知，true: 发送，false: 不发送。 + llm_description: | + 是否给参与人发送 Bot 通知，true: 发送，false: 不发送。 + form: form + + - name: attendee_phone_or_email + type: string + required: true + label: + en_US: Attendee Phone or Email + zh_Hans: 参会人电话或邮箱 + human_description: + en_US: The list of attendee emails or phone numbers, separated by commas. + zh_Hans: 日程参会人邮箱或者手机号列表，使用逗号分隔。 + llm_description: 日程参会人邮箱或者手机号列表，使用逗号分隔。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.py b/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.py new file mode 100644 index 00000000000000..8820bebdbed922 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.py @@ -0,0 +1,26 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class CreateEventTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + summary = tool_parameters.get("summary") + description = tool_parameters.get("description") + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + attendee_ability = tool_parameters.get("attendee_ability") + need_notification = tool_parameters.get("need_notification", True) + auto_record = tool_parameters.get("auto_record", False) + + res = client.create_event( + summary, description, start_time, end_time, attendee_ability, need_notification, auto_record + ) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.yaml new file mode 100644 index 00000000000000..f0784221ce7965 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/create_event.yaml @@ -0,0 +1,119 @@ +identity: + name: create_event + author: Doug Lea + label: + en_US: Create Event + zh_Hans: 创建日程 +description: + human: + en_US: Create Event + zh_Hans: 创建日程 + llm: A tool for creating events in Feishu.(创建飞书日程) +parameters: + - name: summary + type: string + required: false + label: + en_US: Summary + zh_Hans: 日程标题 + human_description: + en_US: The title of the event. If not filled, the event title will display (No Subject). + zh_Hans: 日程标题，若不填则日程标题显示 (无主题)。 + llm_description: 日程标题，若不填则日程标题显示 (无主题)。 + form: llm + + - name: description + type: string + required: false + label: + en_US: Description + zh_Hans: 日程描述 + human_description: + en_US: The description of the event. + zh_Hans: 日程描述。 + llm_description: 日程描述。 + form: llm + + - name: need_notification + type: boolean + required: false + default: true + label: + en_US: Need Notification + zh_Hans: 是否发送通知 + human_description: + en_US: | + Whether to send a bot message when the event is created, true: send, false: do not send. + zh_Hans: 创建日程时是否发送 bot 消息，true：发送，false：不发送。 + llm_description: 创建日程时是否发送 bot 消息，true：发送，false：不发送。 + form: form + + - name: start_time + type: string + required: true + label: + en_US: Start Time + zh_Hans: 开始时间 + human_description: + en_US: | + The start time of the event, format: 2006-01-02 15:04:05. + zh_Hans: 日程开始时间，格式：2006-01-02 15:04:05。 + llm_description: 日程开始时间，格式：2006-01-02 15:04:05。 + form: llm + + - name: end_time + type: string + required: true + label: + en_US: End Time + zh_Hans: 结束时间 + human_description: + en_US: | + The end time of the event, format: 2006-01-02 15:04:05. + zh_Hans: 日程结束时间，格式：2006-01-02 15:04:05。 + llm_description: 日程结束时间，格式：2006-01-02 15:04:05。 + form: llm + + - name: attendee_ability + type: select + required: false + options: + - value: none + label: + en_US: none + zh_Hans: 无 + - value: can_see_others + label: + en_US: can_see_others + zh_Hans: 可以查看参与人列表 + - value: can_invite_others + label: + en_US: can_invite_others + zh_Hans: 可以邀请其它参与人 + - value: can_modify_event + label: + en_US: can_modify_event + zh_Hans: 可以编辑日程 + default: "none" + label: + en_US: attendee_ability + zh_Hans: 参会人权限 + human_description: + en_US: Attendee ability, optional values are none, can_see_others, can_invite_others, can_modify_event, with a default value of none. + zh_Hans: 参会人权限，可选值有无、可以查看参与人列表、可以邀请其它参与人、可以编辑日程，默认值为无。 + llm_description: 参会人权限，可选值有无、可以查看参与人列表、可以邀请其它参与人、可以编辑日程，默认值为无。 + form: form + + - name: auto_record + type: boolean + required: false + default: false + label: + en_US: Auto Record + zh_Hans: 自动录制 + human_description: + en_US: | + Whether to enable automatic recording, true: enabled, automatically record when the meeting starts; false: not enabled. + zh_Hans: 是否开启自动录制，true：开启，会议开始后自动录制；false：不开启。 + llm_description: 是否开启自动录制，true：开启，会议开始后自动录制；false：不开启。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.py b/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.py new file mode 100644 index 00000000000000..144889692f9055 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.py @@ -0,0 +1,19 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class DeleteEventTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + event_id = tool_parameters.get("event_id") + need_notification = tool_parameters.get("need_notification", True) + + res = client.delete_event(event_id, need_notification) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.yaml new file mode 100644 index 00000000000000..54fdb04acc3371 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/delete_event.yaml @@ -0,0 +1,38 @@ +identity: + name: delete_event + author: Doug Lea + label: + en_US: Delete Event + zh_Hans: 删除日程 +description: + human: + en_US: Delete Event + zh_Hans: 删除日程 + llm: A tool for deleting events in Feishu.(在飞书中删除日程) +parameters: + - name: event_id + type: string + required: true + label: + en_US: Event ID + zh_Hans: 日程 ID + human_description: + en_US: | + The ID of the event, for example: e8b9791c-39ae-4908-8ad8-66b13159b9fb_0. + zh_Hans: 日程 ID，例如：e8b9791c-39ae-4908-8ad8-66b13159b9fb_0。 + llm_description: 日程 ID，例如：e8b9791c-39ae-4908-8ad8-66b13159b9fb_0。 + form: llm + + - name: need_notification + type: boolean + required: false + default: true + label: + en_US: Need Notification + zh_Hans: 是否需要通知 + human_description: + en_US: | + Indicates whether to send bot notifications to event participants upon deletion. true: send, false: do not send. + zh_Hans: 删除日程是否给日程参与人发送 bot 通知，true：发送，false：不发送。 + llm_description: 删除日程是否给日程参与人发送 bot 通知，true：发送，false：不发送。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.py b/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.py new file mode 100644 index 00000000000000..a2cd5a8b17d0af --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.py @@ -0,0 +1,18 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class GetPrimaryCalendarTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + user_id_type = tool_parameters.get("user_id_type", "open_id") + + res = client.get_primary_calendar(user_id_type) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.yaml new file mode 100644 index 00000000000000..3440c85d4a9733 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/get_primary_calendar.yaml @@ -0,0 +1,37 @@ +identity: + name: get_primary_calendar + author: Doug Lea + label: + en_US: Get Primary Calendar + zh_Hans: 查询主日历信息 +description: + human: + en_US: Get Primary Calendar + zh_Hans: 查询主日历信息 + llm: A tool for querying primary calendar information in Feishu.(在飞书中查询主日历信息) +parameters: + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.py b/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.py new file mode 100644 index 00000000000000..8815b4c9c871cd --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.py @@ -0,0 +1,21 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class ListEventsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + page_token = tool_parameters.get("page_token") + page_size = tool_parameters.get("page_size") + + res = client.list_events(start_time, end_time, page_token, page_size) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.yaml new file mode 100644 index 00000000000000..f4a5bfe6bab948 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/list_events.yaml @@ -0,0 +1,62 @@ +identity: + name: list_events + author: Doug Lea + label: + en_US: List Events + zh_Hans: 获取日程列表 +description: + human: + en_US: List Events + zh_Hans: 获取日程列表 + llm: A tool for listing events in Feishu.(在飞书中获取日程列表) +parameters: + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 开始时间 + human_description: + en_US: | + The start time, defaults to 0:00 of the current day if not provided, format: 2006-01-02 15:04:05. + zh_Hans: 开始时间，不传值时默认当天 0 点时间，格式为:2006-01-02 15:04:05。 + llm_description: 开始时间，不传值时默认当天 0 点时间，格式为:2006-01-02 15:04:05。 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 结束时间 + human_description: + en_US: | + The end time, defaults to 23:59 of the current day if not provided, format: 2006-01-02 15:04:05. + zh_Hans: 结束时间，不传值时默认当天 23:59 分时间，格式为:2006-01-02 15:04:05。 + llm_description: 结束时间，不传值时默认当天 23:59 分时间，格式为:2006-01-02 15:04:05。 + form: llm + + - name: page_size + type: number + required: false + default: 50 + label: + en_US: Page Size + zh_Hans: 分页大小 + human_description: + en_US: The page size, i.e., the number of data entries returned in a single request. The default value is 50, and the value range is [50,1000]. + zh_Hans: 分页大小，即单次请求所返回的数据条目数。默认值为 50，取值范围为 [50,1000]。 + llm_description: 分页大小，即单次请求所返回的数据条目数。默认值为 50，取值范围为 [50,1000]。 + form: llm + + - name: page_token + type: string + required: false + label: + en_US: Page Token + zh_Hans: 分页标记 + human_description: + en_US: The pagination token. Leave it blank for the first request, indicating to start traversing from the beginning; when the pagination query result has more items, a new page_token will be returned simultaneously, which can be used to obtain the query result in the next traversal. + zh_Hans: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.py b/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.py new file mode 100644 index 00000000000000..dc365205a4cffa --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class SearchEventsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + query = tool_parameters.get("query") + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + page_token = tool_parameters.get("page_token") + user_id_type = tool_parameters.get("user_id_type", "open_id") + page_size = tool_parameters.get("page_size", 20) + + res = client.search_events(query, start_time, end_time, page_token, user_id_type, page_size) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.yaml new file mode 100644 index 00000000000000..e92a282091efcc --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/search_events.yaml @@ -0,0 +1,100 @@ +identity: + name: search_events + author: Doug Lea + label: + en_US: Search Events + zh_Hans: 搜索日程 +description: + human: + en_US: Search Events + zh_Hans: 搜索日程 + llm: A tool for searching events in Feishu.(在飞书中搜索日程) +parameters: + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form + + - name: query + type: string + required: true + label: + en_US: Query + zh_Hans: 搜索关键字 + human_description: + en_US: The search keyword used for fuzzy searching event names, with a maximum input of 200 characters. + zh_Hans: 用于模糊查询日程名称的搜索关键字，最大输入 200 字符。 + llm_description: 用于模糊查询日程名称的搜索关键字，最大输入 200 字符。 + form: llm + + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 开始时间 + human_description: + en_US: | + The start time, defaults to 0:00 of the current day if not provided, format: 2006-01-02 15:04:05. + zh_Hans: 开始时间，不传值时默认当天 0 点时间，格式为:2006-01-02 15:04:05。 + llm_description: 开始时间，不传值时默认当天 0 点时间，格式为:2006-01-02 15:04:05。 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 结束时间 + human_description: + en_US: | + The end time, defaults to 23:59 of the current day if not provided, format: 2006-01-02 15:04:05. + zh_Hans: 结束时间，不传值时默认当天 23:59 分时间，格式为:2006-01-02 15:04:05。 + llm_description: 结束时间，不传值时默认当天 23:59 分时间，格式为:2006-01-02 15:04:05。 + form: llm + + - name: page_size + type: number + required: false + default: 20 + label: + en_US: Page Size + zh_Hans: 分页大小 + human_description: + en_US: The page size, i.e., the number of data entries returned in a single request. The default value is 20, and the value range is [10,100]. + zh_Hans: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [10,100]。 + llm_description: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [10,100]。 + form: llm + + - name: page_token + type: string + required: false + label: + en_US: Page Token + zh_Hans: 分页标记 + human_description: + en_US: The pagination token. Leave it blank for the first request, indicating to start traversing from the beginning; when the pagination query result has more items, a new page_token will be returned simultaneously, which can be used to obtain the query result in the next traversal. + zh_Hans: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.py b/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.py new file mode 100644 index 00000000000000..85bcb1d3f63847 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.py @@ -0,0 +1,24 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class UpdateEventTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + event_id = tool_parameters.get("event_id") + summary = tool_parameters.get("summary") + description = tool_parameters.get("description") + need_notification = tool_parameters.get("need_notification", True) + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + auto_record = tool_parameters.get("auto_record", False) + + res = client.update_event(event_id, summary, description, need_notification, start_time, end_time, auto_record) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.yaml b/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.yaml new file mode 100644 index 00000000000000..4d60dbf8c8e1b0 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_calendar/tools/update_event.yaml @@ -0,0 +1,100 @@ +identity: + name: update_event + author: Doug Lea + label: + en_US: Update Event + zh_Hans: 更新日程 +description: + human: + en_US: Update Event + zh_Hans: 更新日程 + llm: A tool for updating events in Feishu.(更新飞书中的日程) +parameters: + - name: event_id + type: string + required: true + label: + en_US: Event ID + zh_Hans: 日程 ID + human_description: + en_US: | + The ID of the event, for example: e8b9791c-39ae-4908-8ad8-66b13159b9fb_0. + zh_Hans: 日程 ID，例如：e8b9791c-39ae-4908-8ad8-66b13159b9fb_0。 + llm_description: 日程 ID，例如：e8b9791c-39ae-4908-8ad8-66b13159b9fb_0。 + form: llm + + - name: summary + type: string + required: false + label: + en_US: Summary + zh_Hans: 日程标题 + human_description: + en_US: The title of the event. + zh_Hans: 日程标题。 + llm_description: 日程标题。 + form: llm + + - name: description + type: string + required: false + label: + en_US: Description + zh_Hans: 日程描述 + human_description: + en_US: The description of the event. + zh_Hans: 日程描述。 + llm_description: 日程描述。 + form: llm + + - name: need_notification + type: boolean + required: false + label: + en_US: Need Notification + zh_Hans: 是否发送通知 + human_description: + en_US: | + Whether to send a bot message when the event is updated, true: send, false: do not send. + zh_Hans: 更新日程时是否发送 bot 消息，true：发送，false：不发送。 + llm_description: 更新日程时是否发送 bot 消息，true：发送，false：不发送。 + form: form + + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 开始时间 + human_description: + en_US: | + The start time of the event, format: 2006-01-02 15:04:05. + zh_Hans: 日程开始时间，格式：2006-01-02 15:04:05。 + llm_description: 日程开始时间，格式：2006-01-02 15:04:05。 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 结束时间 + human_description: + en_US: | + The end time of the event, format: 2006-01-02 15:04:05. + zh_Hans: 日程结束时间，格式：2006-01-02 15:04:05。 + llm_description: 日程结束时间，格式：2006-01-02 15:04:05。 + form: llm + + - name: auto_record + type: boolean + required: false + label: + en_US: Auto Record + zh_Hans: 自动录制 + human_description: + en_US: | + Whether to enable automatic recording, true: enabled, automatically record when the meeting starts; false: not enabled. + zh_Hans: 是否开启自动录制，true：开启，会议开始后自动录制；false：不开启。 + llm_description: 是否开启自动录制，true：开启，会议开始后自动录制；false：不开启。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_document/feishu_document.py b/api/core/tools/provider/builtin/feishu_document/feishu_document.py index b0a1e393eb8116..217ae52082b82c 100644 --- a/api/core/tools/provider/builtin/feishu_document/feishu_document.py +++ b/api/core/tools/provider/builtin/feishu_document/feishu_document.py @@ -1,15 +1,7 @@ -from core.tools.errors import ToolProviderCredentialValidationError from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController -from core.tools.utils.feishu_api_utils import FeishuRequest +from core.tools.utils.feishu_api_utils import auth class FeishuDocumentProvider(BuiltinToolProviderController): def _validate_credentials(self, credentials: dict) -> None: - app_id = credentials.get("app_id") - app_secret = credentials.get("app_secret") - if not app_id or not app_secret: - raise ToolProviderCredentialValidationError("app_id and app_secret is required") - try: - assert FeishuRequest(app_id, app_secret).tenant_access_token is not None - except Exception as e: - raise ToolProviderCredentialValidationError(str(e)) + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_document/feishu_document.yaml b/api/core/tools/provider/builtin/feishu_document/feishu_document.yaml index 8eaa6b27049c6b..8f9afa6149445c 100644 --- a/api/core/tools/provider/builtin/feishu_document/feishu_document.yaml +++ b/api/core/tools/provider/builtin/feishu_document/feishu_document.yaml @@ -5,8 +5,10 @@ identity: en_US: Lark Cloud Document zh_Hans: 飞书云文档 description: - en_US: Lark Cloud Document - zh_Hans: 飞书云文档 + en_US: | + Lark cloud document, requires the following permissions: docx:document、drive:drive、docs:document.content:read. + zh_Hans: | + 飞书云文档，需要开通以下权限: docx:document、drive:drive、docs:document.content:read。 icon: icon.svg tags: - social @@ -23,7 +25,7 @@ credentials_for_provider: help: en_US: Get your app_id and app_secret from Feishu zh_Hans: 从飞书获取您的 app_id 和 app_secret - url: https://open.feishu.cn + url: https://open.larkoffice.com/app app_secret: type: secret-input required: true diff --git a/api/core/tools/provider/builtin/feishu_document/tools/create_document.yaml b/api/core/tools/provider/builtin/feishu_document/tools/create_document.yaml index ddf2729f0e4b5c..85382e9d8e8d1f 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/create_document.yaml +++ b/api/core/tools/provider/builtin/feishu_document/tools/create_document.yaml @@ -7,7 +7,7 @@ identity: description: human: en_US: Create Lark document - zh_Hans: 创建飞书文档，支持创建空文档和带内容的文档，支持 markdown 语法创建。 + zh_Hans: 创建飞书文档，支持创建空文档和带内容的文档，支持 markdown 语法创建。应用需要开启机器人能力(https://open.feishu.cn/document/faq/trouble-shooting/how-to-enable-bot-ability)。 llm: A tool for creating Feishu documents. parameters: - name: title @@ -41,7 +41,8 @@ parameters: en_US: folder_token zh_Hans: 文档所在文件夹的 Token human_description: - en_US: The token of the folder where the document is located. If it is not passed or is empty, it means the root directory. - zh_Hans: 文档所在文件夹的 Token，不传或传空表示根目录。 - llm_description: 文档所在文件夹的 Token，不传或传空表示根目录。 + en_US: | + The token of the folder where the document is located. If it is not passed or is empty, it means the root directory. For Example: https://svi136aogf123.feishu.cn/drive/folder/JgR9fiG9AlPt8EdsSNpcGjIInbf + zh_Hans: 文档所在文件夹的 Token，不传或传空表示根目录。例如：https://svi136aogf123.feishu.cn/drive/folder/JgR9fiG9AlPt8EdsSNpcGjIInbf。 + llm_description: 文档所在文件夹的 Token，不传或传空表示根目录。例如：https://svi136aogf123.feishu.cn/drive/folder/JgR9fiG9AlPt8EdsSNpcGjIInbf。 form: llm diff --git a/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py index c94a5f70ed7e34..e67a017facc8d4 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py +++ b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.py @@ -12,8 +12,8 @@ def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMe client = FeishuRequest(app_id, app_secret) document_id = tool_parameters.get("document_id") - mode = tool_parameters.get("mode") - lang = tool_parameters.get("lang", 0) + mode = tool_parameters.get("mode", "markdown") + lang = tool_parameters.get("lang", "0") res = client.get_document_content(document_id, mode, lang) return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml index 51eda73a60095c..15e827cde91ee6 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml +++ b/api/core/tools/provider/builtin/feishu_document/tools/get_document_content.yaml @@ -23,8 +23,18 @@ parameters: form: llm - name: mode - type: string + type: select required: false + options: + - value: text + label: + en_US: text + zh_Hans: text + - value: markdown + label: + en_US: markdown + zh_Hans: markdown + default: "markdown" label: en_US: mode zh_Hans: 文档返回格式 @@ -32,18 +42,29 @@ parameters: en_US: Format of the document return, optional values are text, markdown, can be empty, default is markdown. zh_Hans: 文档返回格式，可选值有 text、markdown，可以为空，默认值为 markdown。 llm_description: 文档返回格式，可选值有 text、markdown，可以为空，默认值为 markdown。 - form: llm + form: form - name: lang - type: number + type: select required: false - default: 0 + options: + - value: "0" + label: + en_US: User's default name + zh_Hans: 用户的默认名称 + - value: "1" + label: + en_US: User's English name + zh_Hans: 用户的英文名称 + default: "0" label: en_US: lang zh_Hans: 指定@用户的语言 human_description: en_US: | Specifies the language for MentionUser, optional values are [0, 1]. 0: User's default name, 1: User's English name, default is 0. - zh_Hans: 指定返回的 MentionUser，即 @用户 的语言，可选值有 [0,1]。0：该用户的默认名称，1：该用户的英文名称，默认值为 0。 - llm_description: 指定返回的 MentionUser，即 @用户 的语言，可选值有 [0,1]。0：该用户的默认名称，1：该用户的英文名称，默认值为 0。 - form: llm + zh_Hans: | + 指定返回的 MentionUser，即@用户的语言，可选值有 [0,1]。0: 该用户的默认名称，1: 该用户的英文名称，默认值为 0。 + llm_description: | + 指定返回的 MentionUser，即@用户的语言，可选值有 [0,1]。0: 该用户的默认名称，1: 该用户的英文名称，默认值为 0。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py index 572a7abf284193..dd57c6870d0ba9 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py +++ b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.py @@ -12,8 +12,9 @@ def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMe client = FeishuRequest(app_id, app_secret) document_id = tool_parameters.get("document_id") - page_size = tool_parameters.get("page_size", 500) page_token = tool_parameters.get("page_token", "") + user_id_type = tool_parameters.get("user_id_type", "open_id") + page_size = tool_parameters.get("page_size", 500) - res = client.list_document_blocks(document_id, page_token, page_size) + res = client.list_document_blocks(document_id, page_token, user_id_type, page_size) return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml index 019ac983906ff1..5b8ef7d53c23f4 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml +++ b/api/core/tools/provider/builtin/feishu_document/tools/list_document_blocks.yaml @@ -46,12 +46,12 @@ parameters: en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 - form: llm + form: form - name: page_size type: number required: false - default: "500" + default: 500 label: en_US: page_size zh_Hans: 分页大小 diff --git a/api/core/tools/provider/builtin/feishu_document/tools/write_document.py b/api/core/tools/provider/builtin/feishu_document/tools/write_document.py index 6061250e48e136..59f08f53dc68de 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/write_document.py +++ b/api/core/tools/provider/builtin/feishu_document/tools/write_document.py @@ -13,7 +13,7 @@ def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMe document_id = tool_parameters.get("document_id") content = tool_parameters.get("content") - position = tool_parameters.get("position") + position = tool_parameters.get("position", "end") res = client.write_document(document_id, content, position) return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml b/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml index 4282e3dcf3977f..de70f4e7726a28 100644 --- a/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml +++ b/api/core/tools/provider/builtin/feishu_document/tools/write_document.yaml @@ -35,25 +35,23 @@ parameters: form: llm - name: position - type: string + type: select required: false - label: - en_US: position - zh_Hans: 添加位置 - human_description: - en_US: | - Enumeration values: start or end. Use 'start' to add content at the beginning of the document, and 'end' to add content at the end. The default value is 'end'. - zh_Hans: 枚举值：start 或 end。使用 'start' 在文档开头添加内容，使用 'end' 在文档结尾添加内容，默认值为 'end'。 - llm_description: | - 枚举值 start、end，start: 在文档开头添加内容；end: 在文档结尾添加内容，默认值为 end。 - form: llm options: - value: start label: - en_US: start - zh_Hans: 在文档开头添加内容 + en_US: document start + zh_Hans: 文档开始 - value: end label: - en_US: end - zh_Hans: 在文档结尾添加内容 - default: start + en_US: document end + zh_Hans: 文档结束 + default: "end" + label: + en_US: position + zh_Hans: 内容添加位置 + human_description: + en_US: Content insertion position, optional values are start, end. 'start' means adding content at the beginning of the document; 'end' means adding content at the end of the document. The default value is end. + zh_Hans: 内容添加位置，可选值有 start、end。start 表示在文档开头添加内容；end 表示在文档结尾添加内容，默认值为 end。 + llm_description: 内容添加位置，可选值有 start、end。start 表示在文档开头添加内容；end 表示在文档结尾添加内容，默认值为 end。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_message/feishu_message.py b/api/core/tools/provider/builtin/feishu_message/feishu_message.py index 7b3adb9293750b..a3b54737691c9c 100644 --- a/api/core/tools/provider/builtin/feishu_message/feishu_message.py +++ b/api/core/tools/provider/builtin/feishu_message/feishu_message.py @@ -1,15 +1,7 @@ -from core.tools.errors import ToolProviderCredentialValidationError from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController -from core.tools.utils.feishu_api_utils import FeishuRequest +from core.tools.utils.feishu_api_utils import auth class FeishuMessageProvider(BuiltinToolProviderController): def _validate_credentials(self, credentials: dict) -> None: - app_id = credentials.get("app_id") - app_secret = credentials.get("app_secret") - if not app_id or not app_secret: - raise ToolProviderCredentialValidationError("app_id and app_secret is required") - try: - assert FeishuRequest(app_id, app_secret).tenant_access_token is not None - except Exception as e: - raise ToolProviderCredentialValidationError(str(e)) + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_message/feishu_message.yaml b/api/core/tools/provider/builtin/feishu_message/feishu_message.yaml index 1bd8953dddcb24..56683ec1680f40 100644 --- a/api/core/tools/provider/builtin/feishu_message/feishu_message.yaml +++ b/api/core/tools/provider/builtin/feishu_message/feishu_message.yaml @@ -5,8 +5,10 @@ identity: en_US: Lark Message zh_Hans: 飞书消息 description: - en_US: Lark Message - zh_Hans: 飞书消息 + en_US: | + Lark message, requires the following permissions: im:message、im:message.group_msg. + zh_Hans: | + 飞书消息，需要开通以下权限: im:message、im:message.group_msg。 icon: icon.svg tags: - social @@ -23,7 +25,7 @@ credentials_for_provider: help: en_US: Get your app_id and app_secret from Feishu zh_Hans: 从飞书获取您的 app_id 和 app_secret - url: https://open.feishu.cn + url: https://open.larkoffice.com/app app_secret: type: secret-input required: true diff --git a/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.py b/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.py new file mode 100644 index 00000000000000..7eb29230b2ceb0 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class GetChatMessagesTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + container_id = tool_parameters.get("container_id") + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + page_token = tool_parameters.get("page_token") + sort_type = tool_parameters.get("sort_type", "ByCreateTimeAsc") + page_size = tool_parameters.get("page_size", 20) + + res = client.get_chat_messages(container_id, start_time, end_time, page_token, sort_type, page_size) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.yaml b/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.yaml new file mode 100644 index 00000000000000..153c8c80e58db4 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_message/tools/get_chat_messages.yaml @@ -0,0 +1,96 @@ +identity: + name: get_chat_messages + author: Doug Lea + label: + en_US: Get Chat Messages + zh_Hans: 获取指定单聊、群聊的消息历史 +description: + human: + en_US: Get Chat Messages + zh_Hans: 获取指定单聊、群聊的消息历史 + llm: A tool for getting chat messages from specific one-on-one chats or group chats.(获取指定单聊、群聊的消息历史) +parameters: + - name: container_id + type: string + required: true + label: + en_US: Container Id + zh_Hans: 群聊或单聊的 ID + human_description: + en_US: The ID of the group chat or single chat. Refer to the group ID description for how to obtain it. https://open.feishu.cn/document/server-docs/group/chat/chat-id-description + zh_Hans: 群聊或单聊的 ID，获取方式参见群 ID 说明。https://open.feishu.cn/document/server-docs/group/chat/chat-id-description + llm_description: 群聊或单聊的 ID，获取方式参见群 ID 说明。https://open.feishu.cn/document/server-docs/group/chat/chat-id-description + form: llm + + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 起始时间 + human_description: + en_US: The start time for querying historical messages, formatted as "2006-01-02 15:04:05". + zh_Hans: 待查询历史信息的起始时间，格式为 "2006-01-02 15:04:05"。 + llm_description: 待查询历史信息的起始时间，格式为 "2006-01-02 15:04:05"。 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 结束时间 + human_description: + en_US: The end time for querying historical messages, formatted as "2006-01-02 15:04:05". + zh_Hans: 待查询历史信息的结束时间，格式为 "2006-01-02 15:04:05"。 + llm_description: 待查询历史信息的结束时间，格式为 "2006-01-02 15:04:05"。 + form: llm + + - name: sort_type + type: select + required: false + options: + - value: ByCreateTimeAsc + label: + en_US: ByCreateTimeAsc + zh_Hans: ByCreateTimeAsc + - value: ByCreateTimeDesc + label: + en_US: ByCreateTimeDesc + zh_Hans: ByCreateTimeDesc + default: "ByCreateTimeAsc" + label: + en_US: Sort Type + zh_Hans: 排序方式 + human_description: + en_US: | + The message sorting method. Optional values are ByCreateTimeAsc: sorted in ascending order by message creation time; ByCreateTimeDesc: sorted in descending order by message creation time. The default value is ByCreateTimeAsc. Note: When using page_token for pagination requests, the sorting method (sort_type) is consistent with the first request and cannot be changed midway. + zh_Hans: | + 消息排序方式，可选值有 ByCreateTimeAsc：按消息创建时间升序排列；ByCreateTimeDesc：按消息创建时间降序排列。默认值为：ByCreateTimeAsc。注意：使用 page_token 分页请求时，排序方式（sort_type）均与第一次请求一致，不支持中途改换排序方式。 + llm_description: 消息排序方式，可选值有 ByCreateTimeAsc：按消息创建时间升序排列；ByCreateTimeDesc：按消息创建时间降序排列。默认值为：ByCreateTimeAsc。注意：使用 page_token 分页请求时，排序方式（sort_type）均与第一次请求一致，不支持中途改换排序方式。 + form: form + + - name: page_size + type: number + required: false + default: 20 + label: + en_US: Page Size + zh_Hans: 分页大小 + human_description: + en_US: The page size, i.e., the number of data entries returned in a single request. The default value is 20, and the value range is [1,50]. + zh_Hans: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [1,50]。 + llm_description: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [1,50]。 + form: llm + + - name: page_token + type: string + required: false + label: + en_US: Page Token + zh_Hans: 分页标记 + human_description: + en_US: The pagination token. Leave it blank for the first request, indicating to start traversing from the beginning; when the pagination query result has more items, a new page_token will be returned simultaneously, which can be used to obtain the query result in the next traversal. + zh_Hans: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.py b/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.py new file mode 100644 index 00000000000000..3b14f46e0048a8 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.py @@ -0,0 +1,21 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class GetChatMessagesTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + container_id = tool_parameters.get("container_id") + page_token = tool_parameters.get("page_token") + sort_type = tool_parameters.get("sort_type", "ByCreateTimeAsc") + page_size = tool_parameters.get("page_size", 20) + + res = client.get_thread_messages(container_id, page_token, sort_type, page_size) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.yaml b/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.yaml new file mode 100644 index 00000000000000..8d5fed9d0bba24 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_message/tools/get_thread_messages.yaml @@ -0,0 +1,72 @@ +identity: + name: get_thread_messages + author: Doug Lea + label: + en_US: Get Thread Messages + zh_Hans: 获取指定话题的消息历史 +description: + human: + en_US: Get Thread Messages + zh_Hans: 获取指定话题的消息历史 + llm: A tool for getting chat messages from specific threads.(获取指定话题的消息历史) +parameters: + - name: container_id + type: string + required: true + label: + en_US: Thread Id + zh_Hans: 话题 ID + human_description: + en_US: The ID of the thread. Refer to the thread overview on how to obtain the thread_id. https://open.feishu.cn/document/uAjLw4CM/ukTMukTMukTM/reference/im-v1/message/thread-introduction + zh_Hans: 话题 ID，获取方式参见话题概述的如何获取 thread_id 章节。https://open.feishu.cn/document/uAjLw4CM/ukTMukTMukTM/reference/im-v1/message/thread-introduction + llm_description: 话题 ID，获取方式参见话题概述的如何获取 thread_id 章节。https://open.feishu.cn/document/uAjLw4CM/ukTMukTMukTM/reference/im-v1/message/thread-introduction + form: llm + + - name: sort_type + type: select + required: false + options: + - value: ByCreateTimeAsc + label: + en_US: ByCreateTimeAsc + zh_Hans: ByCreateTimeAsc + - value: ByCreateTimeDesc + label: + en_US: ByCreateTimeDesc + zh_Hans: ByCreateTimeDesc + default: "ByCreateTimeAsc" + label: + en_US: Sort Type + zh_Hans: 排序方式 + human_description: + en_US: | + The message sorting method. Optional values are ByCreateTimeAsc: sorted in ascending order by message creation time; ByCreateTimeDesc: sorted in descending order by message creation time. The default value is ByCreateTimeAsc. Note: When using page_token for pagination requests, the sorting method (sort_type) is consistent with the first request and cannot be changed midway. + zh_Hans: | + 消息排序方式，可选值有 ByCreateTimeAsc：按消息创建时间升序排列；ByCreateTimeDesc：按消息创建时间降序排列。默认值为：ByCreateTimeAsc。注意：使用 page_token 分页请求时，排序方式（sort_type）均与第一次请求一致，不支持中途改换排序方式。 + llm_description: 消息排序方式，可选值有 ByCreateTimeAsc：按消息创建时间升序排列；ByCreateTimeDesc：按消息创建时间降序排列。默认值为：ByCreateTimeAsc。注意：使用 page_token 分页请求时，排序方式（sort_type）均与第一次请求一致，不支持中途改换排序方式。 + form: form + + - name: page_size + type: number + required: false + default: 20 + label: + en_US: Page Size + zh_Hans: 分页大小 + human_description: + en_US: The page size, i.e., the number of data entries returned in a single request. The default value is 20, and the value range is [1,50]. + zh_Hans: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [1,50]。 + llm_description: 分页大小，即单次请求所返回的数据条目数。默认值为 20，取值范围为 [1,50]。 + form: llm + + - name: page_token + type: string + required: false + label: + en_US: Page Token + zh_Hans: 分页标记 + human_description: + en_US: The pagination token. Leave it blank for the first request, indicating to start traversing from the beginning; when the pagination query result has more items, a new page_token will be returned simultaneously, which can be used to obtain the query result in the next traversal. + zh_Hans: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.yaml b/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.yaml index 6e398b18ab3aee..4f7f65a8a74fc0 100644 --- a/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.yaml +++ b/api/core/tools/provider/builtin/feishu_message/tools/send_bot_message.yaml @@ -10,53 +10,53 @@ description: zh_Hans: 发送飞书应用消息 llm: A tool for sending Feishu application messages. parameters: + - name: receive_id + type: string + required: true + label: + en_US: receive_id + zh_Hans: 消息接收者的 ID + human_description: + en_US: The ID of the message receiver, the ID type is consistent with the value of the query parameter receive_id_type. + zh_Hans: 消息接收者的 ID，ID 类型与查询参数 receive_id_type 的取值一致。 + llm_description: 消息接收者的 ID，ID 类型与查询参数 receive_id_type 的取值一致。 + form: llm + - name: receive_id_type type: select required: true options: - value: open_id label: - en_US: open id - zh_Hans: open id + en_US: open_id + zh_Hans: open_id - value: union_id label: - en_US: union id - zh_Hans: union id + en_US: union_id + zh_Hans: union_id - value: user_id label: - en_US: user id - zh_Hans: user id + en_US: user_id + zh_Hans: user_id - value: email label: en_US: email zh_Hans: email - value: chat_id label: - en_US: chat id - zh_Hans: chat id + en_US: chat_id + zh_Hans: chat_id label: - en_US: User ID Type - zh_Hans: 用户 ID 类型 + en_US: receive_id_type + zh_Hans: 消息接收者的 ID 类型 human_description: - en_US: User ID Type - zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id。 - llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id。 - form: llm - - - name: receive_id - type: string - required: true - label: - en_US: Receive Id - zh_Hans: 消息接收者的 ID - human_description: - en_US: The ID of the message receiver. The ID type should correspond to the query parameter receive_id_type. - zh_Hans: 消息接收者的 ID，ID 类型应与查询参数 receive_id_type 对应。 - llm_description: 消息接收者的 ID，ID 类型应与查询参数 receive_id_type 对应。 - form: llm + en_US: The ID type of the message receiver, optional values are open_id, union_id, user_id, email, chat_id, with a default value of open_id. + zh_Hans: 消息接收者的 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id，默认值为 open_id。 + llm_description: 消息接收者的 ID 类型，可选值有 open_id、union_id、user_id、email、chat_id，默认值为 open_id。 + form: form - name: msg_type - type: string + type: select required: true options: - value: text @@ -65,27 +65,61 @@ parameters: zh_Hans: 文本 - value: interactive label: - en_US: message card - zh_Hans: 消息卡片 + en_US: interactive + zh_Hans: 卡片 + - value: post + label: + en_US: post + zh_Hans: 富文本 + - value: image + label: + en_US: image + zh_Hans: 图片 + - value: file + label: + en_US: file + zh_Hans: 文件 + - value: audio + label: + en_US: audio + zh_Hans: 语音 + - value: media + label: + en_US: media + zh_Hans: 视频 + - value: sticker + label: + en_US: sticker + zh_Hans: 表情包 + - value: share_chat + label: + en_US: share_chat + zh_Hans: 分享群名片 + - value: share_user + label: + en_US: share_user + zh_Hans: 分享个人名片 + - value: system + label: + en_US: system + zh_Hans: 系统消息 label: - en_US: Message type + en_US: msg_type zh_Hans: 消息类型 human_description: - en_US: Message type, optional values are, text (text), interactive (message card). - zh_Hans: 消息类型，可选值有：text(文本)、interactive(消息卡片)。 - llm_description: 消息类型，可选值有：text(文本)、interactive(消息卡片)。 - form: llm + en_US: Message type. Optional values are text, post, image, file, audio, media, sticker, interactive, share_chat, share_user, system. For detailed introduction of different message types, refer to the message content(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json). + zh_Hans: 消息类型。可选值有：text、post、image、file、audio、media、sticker、interactive、share_chat、share_user、system。不同消息类型的详细介绍，参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + llm_description: 消息类型。可选值有：text、post、image、file、audio、media、sticker、interactive、share_chat、share_user、system。不同消息类型的详细介绍，参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + form: form - name: content type: string required: true label: - en_US: Message content + en_US: content zh_Hans: 消息内容 human_description: - en_US: Message content - zh_Hans: | - 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容， - 具体格式说明参考：https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json - llm_description: 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容。 + en_US: Message content, a JSON structure serialized string. The value of this parameter corresponds to msg_type. For example, if msg_type is text, this parameter needs to pass in text type content. To understand the format and usage limitations of different message types, refer to the message content(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json). + zh_Hans: 消息内容，JSON 结构序列化后的字符串。该参数的取值与 msg_type 对应，例如 msg_type 取值为 text，则该参数需要传入文本类型的内容。了解不同类型的消息内容格式、使用限制，可参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + llm_description: 消息内容，JSON 结构序列化后的字符串。该参数的取值与 msg_type 对应，例如 msg_type 取值为 text，则该参数需要传入文本类型的内容。了解不同类型的消息内容格式、使用限制，可参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 form: llm diff --git a/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.yaml b/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.yaml index 8b39ce4874d506..eeeae8b29cd935 100644 --- a/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.yaml +++ b/api/core/tools/provider/builtin/feishu_message/tools/send_webhook_message.yaml @@ -15,15 +15,18 @@ parameters: required: true label: en_US: webhook - zh_Hans: webhook 的地址 + zh_Hans: webhook human_description: - en_US: The address of the webhook - zh_Hans: webhook 的地址 - llm_description: webhook 的地址 + en_US: | + The address of the webhook, the format of the webhook address corresponding to the bot is as follows: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxxxx. For details, please refer to: Feishu Custom Bot Usage Guide(https://open.larkoffice.com/document/client-docs/bot-v3/add-custom-bot) + zh_Hans: | + webhook 的地址，机器人对应的 webhook 地址格式如下: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxxxx，详情可参考: 飞书自定义机器人使用指南(https://open.larkoffice.com/document/client-docs/bot-v3/add-custom-bot) + llm_description: | + webhook 的地址，机器人对应的 webhook 地址格式如下: https://open.feishu.cn/open-apis/bot/v2/hook/xxxxxxxxxxxxxxxxx，详情可参考: 飞书自定义机器人使用指南(https://open.larkoffice.com/document/client-docs/bot-v3/add-custom-bot) form: llm - name: msg_type - type: string + type: select required: true options: - value: text @@ -32,27 +35,34 @@ parameters: zh_Hans: 文本 - value: interactive label: - en_US: message card - zh_Hans: 消息卡片 + en_US: interactive + zh_Hans: 卡片 + - value: image + label: + en_US: image + zh_Hans: 图片 + - value: share_chat + label: + en_US: share_chat + zh_Hans: 分享群名片 label: - en_US: Message type + en_US: msg_type zh_Hans: 消息类型 human_description: - en_US: Message type, optional values are, text (text), interactive (message card). - zh_Hans: 消息类型，可选值有：text(文本)、interactive(消息卡片)。 - llm_description: 消息类型，可选值有：text(文本)、interactive(消息卡片)。 - form: llm + en_US: Message type. Optional values are text, image, interactive, share_chat. For detailed introduction of different message types, refer to the message content(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json). + zh_Hans: 消息类型。可选值有：text、image、interactive、share_chat。不同消息类型的详细介绍，参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + llm_description: 消息类型。可选值有：text、image、interactive、share_chat。不同消息类型的详细介绍，参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + form: form + - name: content type: string required: true label: - en_US: Message content + en_US: content zh_Hans: 消息内容 human_description: - en_US: Message content - zh_Hans: | - 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容， - 具体格式说明参考：https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json - llm_description: 消息内容，JSON 结构序列化后的字符串。不同 msg_type 对应不同内容。 + en_US: Message content, a JSON structure serialized string. The value of this parameter corresponds to msg_type. For example, if msg_type is text, this parameter needs to pass in text type content. To understand the format and usage limitations of different message types, refer to the message content(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json). + zh_Hans: 消息内容，JSON 结构序列化后的字符串。该参数的取值与 msg_type 对应，例如 msg_type 取值为 text，则该参数需要传入文本类型的内容。了解不同类型的消息内容格式、使用限制，可参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 + llm_description: 消息内容，JSON 结构序列化后的字符串。该参数的取值与 msg_type 对应，例如 msg_type 取值为 text，则该参数需要传入文本类型的内容。了解不同类型的消息内容格式、使用限制，可参见发送消息内容(https://open.larkoffice.com/document/server-docs/im-v1/message-content-description/create_json)。 form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/_assets/icon.png b/api/core/tools/provider/builtin/feishu_spreadsheet/_assets/icon.png new file mode 100644 index 00000000000000..258b361261d4e3 Binary files /dev/null and b/api/core/tools/provider/builtin/feishu_spreadsheet/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.py b/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.py new file mode 100644 index 00000000000000..a3b54737691c9c --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.py @@ -0,0 +1,7 @@ +from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController +from core.tools.utils.feishu_api_utils import auth + + +class FeishuMessageProvider(BuiltinToolProviderController): + def _validate_credentials(self, credentials: dict) -> None: + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.yaml new file mode 100644 index 00000000000000..29e448d730f745 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/feishu_spreadsheet.yaml @@ -0,0 +1,36 @@ +identity: + author: Doug Lea + name: feishu_spreadsheet + label: + en_US: Feishu Spreadsheet + zh_Hans: 飞书电子表格 + description: + en_US: | + Feishu Spreadsheet, requires the following permissions: sheets:spreadsheet. + zh_Hans: | + 飞书电子表格，需要开通以下权限: sheets:spreadsheet。 + icon: icon.png + tags: + - social + - productivity +credentials_for_provider: + app_id: + type: text-input + required: true + label: + en_US: APP ID + placeholder: + en_US: Please input your feishu app id + zh_Hans: 请输入你的飞书 app id + help: + en_US: Get your app_id and app_secret from Feishu + zh_Hans: 从飞书获取您的 app_id 和 app_secret + url: https://open.larkoffice.com/app + app_secret: + type: secret-input + required: true + label: + en_US: APP Secret + placeholder: + en_US: Please input your app secret + zh_Hans: 请输入你的飞书 app secret diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.py new file mode 100644 index 00000000000000..44d062f9bdded2 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.py @@ -0,0 +1,22 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class AddColsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + sheet_id = tool_parameters.get("sheet_id") + sheet_name = tool_parameters.get("sheet_name") + length = tool_parameters.get("length") + values = tool_parameters.get("values") + + res = client.add_cols(spreadsheet_token, sheet_id, sheet_name, length, values) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.yaml new file mode 100644 index 00000000000000..ef457f8e009b2c --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_cols.yaml @@ -0,0 +1,72 @@ +identity: + name: add_cols + author: Doug Lea + label: + en_US: Add Cols + zh_Hans: 新增多列至工作表最后 +description: + human: + en_US: Add Cols + zh_Hans: 新增多列至工作表最后 + llm: A tool for adding multiple columns to the end of a spreadsheet. (新增多列至工作表最后) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: spreadsheet_token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 url。 + llm_description: 电子表格 token，支持输入电子表格 url。 + form: llm + + - name: sheet_id + type: string + required: false + label: + en_US: sheet_id + zh_Hans: 工作表 ID + human_description: + en_US: Sheet ID, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表 ID，与 sheet_name 二者其一必填。 + llm_description: 工作表 ID，与 sheet_name 二者其一必填。 + form: llm + + - name: sheet_name + type: string + required: false + label: + en_US: sheet_name + zh_Hans: 工作表名称 + human_description: + en_US: Sheet name, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表名称，与 sheet_id 二者其一必填。 + llm_description: 工作表名称，与 sheet_id 二者其一必填。 + form: llm + + - name: length + type: number + required: true + label: + en_US: length + zh_Hans: 要增加的列数 + human_description: + en_US: Number of columns to add, range (0-5000]. + zh_Hans: 要增加的列数，范围(0-5000]。 + llm_description: 要增加的列数，范围(0-5000]。 + form: llm + + - name: values + type: string + required: false + label: + en_US: values + zh_Hans: 新增列的单元格内容 + human_description: + en_US: | + Content of the new columns, array of objects in string format, each array represents a row of table data, format like: [ [ "ID","Name","Age" ],[ 1,"Zhang San",10 ],[ 2,"Li Si",11 ] ]. + zh_Hans: 新增列的单元格内容，数组对象字符串，每个数组一行表格数据，格式：[["编号","姓名","年龄"],[1,"张三",10],[2,"李四",11]]。 + llm_description: 新增列的单元格内容，数组对象字符串，每个数组一行表格数据，格式：[["编号","姓名","年龄"],[1,"张三",10],[2,"李四",11]]。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.py new file mode 100644 index 00000000000000..3a85b7b46ccb93 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.py @@ -0,0 +1,22 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class AddRowsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + sheet_id = tool_parameters.get("sheet_id") + sheet_name = tool_parameters.get("sheet_name") + length = tool_parameters.get("length") + values = tool_parameters.get("values") + + res = client.add_rows(spreadsheet_token, sheet_id, sheet_name, length, values) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.yaml new file mode 100644 index 00000000000000..37653325aeb16b --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/add_rows.yaml @@ -0,0 +1,72 @@ +identity: + name: add_rows + author: Doug Lea + label: + en_US: Add Rows + zh_Hans: 新增多行至工作表最后 +description: + human: + en_US: Add Rows + zh_Hans: 新增多行至工作表最后 + llm: A tool for adding multiple rows to the end of a spreadsheet. (新增多行至工作表最后) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: spreadsheet_token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 url。 + llm_description: 电子表格 token，支持输入电子表格 url。 + form: llm + + - name: sheet_id + type: string + required: false + label: + en_US: sheet_id + zh_Hans: 工作表 ID + human_description: + en_US: Sheet ID, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表 ID，与 sheet_name 二者其一必填。 + llm_description: 工作表 ID，与 sheet_name 二者其一必填。 + form: llm + + - name: sheet_name + type: string + required: false + label: + en_US: sheet_name + zh_Hans: 工作表名称 + human_description: + en_US: Sheet name, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表名称，与 sheet_id 二者其一必填。 + llm_description: 工作表名称，与 sheet_id 二者其一必填。 + form: llm + + - name: length + type: number + required: true + label: + en_US: length + zh_Hans: 要增加行数 + human_description: + en_US: Number of rows to add, range (0-5000]. + zh_Hans: 要增加行数，范围(0-5000]。 + llm_description: 要增加行数，范围(0-5000]。 + form: llm + + - name: values + type: string + required: false + label: + en_US: values + zh_Hans: 新增行的表格内容 + human_description: + en_US: | + Content of the new rows, array of objects in string format, each array represents a row of table data, format like: [ [ "ID","Name","Age" ],[ 1,"Zhang San",10 ],[ 2,"Li Si",11 ] ]. + zh_Hans: 新增行的表格内容，数组对象字符串，每个数组一行表格数据，格式,如：[["编号","姓名","年龄"],[1,"张三",10],[2,"李四",11]]。 + llm_description: 新增行的表格内容，数组对象字符串，每个数组一行表格数据，格式,如：[["编号","姓名","年龄"],[1,"张三",10],[2,"李四",11]]。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.py new file mode 100644 index 00000000000000..647364fab0a966 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.py @@ -0,0 +1,19 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class CreateSpreadsheetTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + title = tool_parameters.get("title") + folder_token = tool_parameters.get("folder_token") + + res = client.create_spreadsheet(title, folder_token) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.yaml new file mode 100644 index 00000000000000..931310e63172d4 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/create_spreadsheet.yaml @@ -0,0 +1,35 @@ +identity: + name: create_spreadsheet + author: Doug Lea + label: + en_US: Create Spreadsheet + zh_Hans: 创建电子表格 +description: + human: + en_US: Create Spreadsheet + zh_Hans: 创建电子表格 + llm: A tool for creating spreadsheets. (创建电子表格) +parameters: + - name: title + type: string + required: false + label: + en_US: Spreadsheet Title + zh_Hans: 电子表格标题 + human_description: + en_US: The title of the spreadsheet + zh_Hans: 电子表格的标题 + llm_description: 电子表格的标题 + form: llm + + - name: folder_token + type: string + required: false + label: + en_US: Folder Token + zh_Hans: 文件夹 token + human_description: + en_US: The token of the folder, supports folder URL input, e.g., https://bytedance.larkoffice.com/drive/folder/CxHEf4DCSlNkL2dUTCJcPRgentg + zh_Hans: 文件夹 token，支持文件夹 URL 输入，如：https://bytedance.larkoffice.com/drive/folder/CxHEf4DCSlNkL2dUTCJcPRgentg + llm_description: 文件夹 token，支持文件夹 URL 输入，如：https://bytedance.larkoffice.com/drive/folder/CxHEf4DCSlNkL2dUTCJcPRgentg + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.py new file mode 100644 index 00000000000000..dda8c59daffabf --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.py @@ -0,0 +1,19 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class GetSpreadsheetTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + user_id_type = tool_parameters.get("user_id_type", "open_id") + + res = client.get_spreadsheet(spreadsheet_token, user_id_type) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.yaml new file mode 100644 index 00000000000000..c519938617ba8c --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/get_spreadsheet.yaml @@ -0,0 +1,49 @@ +identity: + name: get_spreadsheet + author: Doug Lea + label: + en_US: Get Spreadsheet + zh_Hans: 获取电子表格信息 +description: + human: + en_US: Get Spreadsheet + zh_Hans: 获取电子表格信息 + llm: A tool for getting information from spreadsheets. (获取电子表格信息) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: Spreadsheet Token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 URL。 + llm_description: 电子表格 token，支持输入电子表格 URL。 + form: llm + + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.py new file mode 100644 index 00000000000000..98497791c0fa1e --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.py @@ -0,0 +1,18 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class ListSpreadsheetSheetsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + + res = client.list_spreadsheet_sheets(spreadsheet_token) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.yaml new file mode 100644 index 00000000000000..c6a7ef45d46589 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/list_spreadsheet_sheets.yaml @@ -0,0 +1,23 @@ +identity: + name: list_spreadsheet_sheets + author: Doug Lea + label: + en_US: List Spreadsheet Sheets + zh_Hans: 列出电子表格所有工作表 +description: + human: + en_US: List Spreadsheet Sheets + zh_Hans: 列出电子表格所有工作表 + llm: A tool for listing all sheets in a spreadsheet. (列出电子表格所有工作表) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: Spreadsheet Token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 URL。 + llm_description: 电子表格 token，支持输入电子表格 URL。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.py new file mode 100644 index 00000000000000..ebe3f619d091d1 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class ReadColsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + sheet_id = tool_parameters.get("sheet_id") + sheet_name = tool_parameters.get("sheet_name") + start_col = tool_parameters.get("start_col") + num_cols = tool_parameters.get("num_cols") + user_id_type = tool_parameters.get("user_id_type", "open_id") + + res = client.read_cols(spreadsheet_token, sheet_id, sheet_name, start_col, num_cols, user_id_type) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.yaml new file mode 100644 index 00000000000000..3273857b709bf9 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_cols.yaml @@ -0,0 +1,97 @@ +identity: + name: read_cols + author: Doug Lea + label: + en_US: Read Cols + zh_Hans: 读取工作表列数据 +description: + human: + en_US: Read Cols + zh_Hans: 读取工作表列数据 + llm: A tool for reading column data from a spreadsheet. (读取工作表列数据) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: spreadsheet_token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 url。 + llm_description: 电子表格 token，支持输入电子表格 url。 + form: llm + + - name: sheet_id + type: string + required: false + label: + en_US: sheet_id + zh_Hans: 工作表 ID + human_description: + en_US: Sheet ID, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表 ID，与 sheet_name 二者其一必填。 + llm_description: 工作表 ID，与 sheet_name 二者其一必填。 + form: llm + + - name: sheet_name + type: string + required: false + label: + en_US: sheet_name + zh_Hans: 工作表名称 + human_description: + en_US: Sheet name, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表名称，与 sheet_id 二者其一必填。 + llm_description: 工作表名称，与 sheet_id 二者其一必填。 + form: llm + + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form + + - name: start_col + type: number + required: false + label: + en_US: start_col + zh_Hans: 起始列号 + human_description: + en_US: Starting column number, starting from 1. + zh_Hans: 起始列号，从 1 开始。 + llm_description: 起始列号，从 1 开始。 + form: llm + + - name: num_cols + type: number + required: true + label: + en_US: num_cols + zh_Hans: 读取列数 + human_description: + en_US: Number of columns to read. + zh_Hans: 读取列数 + llm_description: 读取列数 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.py new file mode 100644 index 00000000000000..86b91b104b7029 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class ReadRowsTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + sheet_id = tool_parameters.get("sheet_id") + sheet_name = tool_parameters.get("sheet_name") + start_row = tool_parameters.get("start_row") + num_rows = tool_parameters.get("num_rows") + user_id_type = tool_parameters.get("user_id_type", "open_id") + + res = client.read_rows(spreadsheet_token, sheet_id, sheet_name, start_row, num_rows, user_id_type) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.yaml new file mode 100644 index 00000000000000..3e9206e8ef70fe --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_rows.yaml @@ -0,0 +1,97 @@ +identity: + name: read_rows + author: Doug Lea + label: + en_US: Read Rows + zh_Hans: 读取工作表行数据 +description: + human: + en_US: Read Rows + zh_Hans: 读取工作表行数据 + llm: A tool for reading row data from a spreadsheet. (读取工作表行数据) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: spreadsheet_token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 url。 + llm_description: 电子表格 token，支持输入电子表格 url。 + form: llm + + - name: sheet_id + type: string + required: false + label: + en_US: sheet_id + zh_Hans: 工作表 ID + human_description: + en_US: Sheet ID, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表 ID，与 sheet_name 二者其一必填。 + llm_description: 工作表 ID，与 sheet_name 二者其一必填。 + form: llm + + - name: sheet_name + type: string + required: false + label: + en_US: sheet_name + zh_Hans: 工作表名称 + human_description: + en_US: Sheet name, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表名称，与 sheet_id 二者其一必填。 + llm_description: 工作表名称，与 sheet_id 二者其一必填。 + form: llm + + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form + + - name: start_row + type: number + required: false + label: + en_US: start_row + zh_Hans: 起始行号 + human_description: + en_US: Starting row number, starting from 1. + zh_Hans: 起始行号，从 1 开始。 + llm_description: 起始行号，从 1 开始。 + form: llm + + - name: num_rows + type: number + required: true + label: + en_US: num_rows + zh_Hans: 读取行数 + human_description: + en_US: Number of rows to read. + zh_Hans: 读取行数 + llm_description: 读取行数 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.py b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.py new file mode 100644 index 00000000000000..ddd607d87838f4 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class ReadTableTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + spreadsheet_token = tool_parameters.get("spreadsheet_token") + sheet_id = tool_parameters.get("sheet_id") + sheet_name = tool_parameters.get("sheet_name") + num_range = tool_parameters.get("num_range") + query = tool_parameters.get("query") + user_id_type = tool_parameters.get("user_id_type", "open_id") + + res = client.read_table(spreadsheet_token, sheet_id, sheet_name, num_range, query, user_id_type) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.yaml b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.yaml new file mode 100644 index 00000000000000..e3dc80e1eb0b4f --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_spreadsheet/tools/read_table.yaml @@ -0,0 +1,122 @@ +identity: + name: read_table + author: Doug Lea + label: + en_US: Read Table + zh_Hans: 自定义读取电子表格行列数据 +description: + human: + en_US: Read Table + zh_Hans: 自定义读取电子表格行列数据 + llm: A tool for custom reading of row and column data from a spreadsheet. (自定义读取电子表格行列数据) +parameters: + - name: spreadsheet_token + type: string + required: true + label: + en_US: spreadsheet_token + zh_Hans: 电子表格 token + human_description: + en_US: Spreadsheet token, supports input of spreadsheet URL. + zh_Hans: 电子表格 token，支持输入电子表格 url。 + llm_description: 电子表格 token，支持输入电子表格 url。 + form: llm + + - name: sheet_id + type: string + required: false + label: + en_US: sheet_id + zh_Hans: 工作表 ID + human_description: + en_US: Sheet ID, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表 ID，与 sheet_name 二者其一必填。 + llm_description: 工作表 ID，与 sheet_name 二者其一必填。 + form: llm + + - name: sheet_name + type: string + required: false + label: + en_US: sheet_name + zh_Hans: 工作表名称 + human_description: + en_US: Sheet name, either sheet_id or sheet_name must be filled. + zh_Hans: 工作表名称，与 sheet_id 二者其一必填。 + llm_description: 工作表名称，与 sheet_id 二者其一必填。 + form: llm + + - name: user_id_type + type: select + required: false + options: + - value: open_id + label: + en_US: open_id + zh_Hans: open_id + - value: union_id + label: + en_US: union_id + zh_Hans: union_id + - value: user_id + label: + en_US: user_id + zh_Hans: user_id + default: "open_id" + label: + en_US: user_id_type + zh_Hans: 用户 ID 类型 + human_description: + en_US: User ID type, optional values are open_id, union_id, user_id, with a default value of open_id. + zh_Hans: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + llm_description: 用户 ID 类型，可选值有 open_id、union_id、user_id，默认值为 open_id。 + form: form + + - name: start_row + type: number + required: false + label: + en_US: start_row + zh_Hans: 起始行号 + human_description: + en_US: Starting row number, starting from 1. + zh_Hans: 起始行号，从 1 开始。 + llm_description: 起始行号，从 1 开始。 + form: llm + + - name: num_rows + type: number + required: false + label: + en_US: num_rows + zh_Hans: 读取行数 + human_description: + en_US: Number of rows to read. + zh_Hans: 读取行数 + llm_description: 读取行数 + form: llm + + - name: range + type: string + required: false + label: + en_US: range + zh_Hans: 取数范围 + human_description: + en_US: | + Data range, format like: A1:B2, can be empty when query=all. + zh_Hans: 取数范围,格式如:A1:B2，query=all 时可为空。 + llm_description: 取数范围,格式如:A1:B2，query=all 时可为空。 + form: llm + + - name: query + type: string + required: false + label: + en_US: query + zh_Hans: 查询 + human_description: + en_US: Pass "all" to query all data in the table, but no more than 100 columns. + zh_Hans: 传 all，表示查询表格所有数据，但最多查询 100 列数据。 + llm_description: 传 all，表示查询表格所有数据，但最多查询 100 列数据。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_task/_assets/icon.png b/api/core/tools/provider/builtin/feishu_task/_assets/icon.png new file mode 100644 index 00000000000000..3485be0d0fbd85 Binary files /dev/null and b/api/core/tools/provider/builtin/feishu_task/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/feishu_task/feishu_task.py b/api/core/tools/provider/builtin/feishu_task/feishu_task.py new file mode 100644 index 00000000000000..6df05968d8f176 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/feishu_task.py @@ -0,0 +1,7 @@ +from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController +from core.tools.utils.feishu_api_utils import auth + + +class FeishuTaskProvider(BuiltinToolProviderController): + def _validate_credentials(self, credentials: dict) -> None: + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_task/feishu_task.yaml b/api/core/tools/provider/builtin/feishu_task/feishu_task.yaml new file mode 100644 index 00000000000000..88736f79a02e87 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/feishu_task.yaml @@ -0,0 +1,36 @@ +identity: + author: Doug Lea + name: feishu_task + label: + en_US: Feishu Task + zh_Hans: 飞书任务 + description: + en_US: | + Feishu Task, requires the following permissions: task:task:write、contact:user.id:readonly. + zh_Hans: | + 飞书任务，需要开通以下权限: task:task:write、contact:user.id:readonly。 + icon: icon.png + tags: + - social + - productivity +credentials_for_provider: + app_id: + type: text-input + required: true + label: + en_US: APP ID + placeholder: + en_US: Please input your feishu app id + zh_Hans: 请输入你的飞书 app id + help: + en_US: Get your app_id and app_secret from Feishu + zh_Hans: 从飞书获取您的 app_id 和 app_secret + url: https://open.larkoffice.com/app + app_secret: + type: secret-input + required: true + label: + en_US: APP Secret + placeholder: + en_US: Please input your app secret + zh_Hans: 请输入你的飞书 app secret diff --git a/api/core/tools/provider/builtin/feishu_task/tools/add_members.py b/api/core/tools/provider/builtin/feishu_task/tools/add_members.py new file mode 100644 index 00000000000000..e58ed22e0f4797 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/add_members.py @@ -0,0 +1,20 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class AddMembersTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + task_guid = tool_parameters.get("task_guid") + member_phone_or_email = tool_parameters.get("member_phone_or_email") + member_role = tool_parameters.get("member_role", "follower") + + res = client.add_members(task_guid, member_phone_or_email, member_role) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_task/tools/add_members.yaml b/api/core/tools/provider/builtin/feishu_task/tools/add_members.yaml new file mode 100644 index 00000000000000..063c0f7f04956c --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/add_members.yaml @@ -0,0 +1,58 @@ +identity: + name: add_members + author: Doug Lea + label: + en_US: Add Members + zh_Hans: 添加任务成员 +description: + human: + en_US: Add Members + zh_Hans: 添加任务成员 + llm: A tool for adding members to a Feishu task.(添加任务成员) +parameters: + - name: task_guid + type: string + required: true + label: + en_US: Task GUID + zh_Hans: 任务 GUID + human_description: + en_US: | + The GUID of the task to be added, supports passing either the Task ID or the Task link URL. Example of Task ID: 8b5425ec-9f2a-43bd-a3ab-01912f50282b; Example of Task link URL: https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + zh_Hans: 要添加的任务的 GUID，支持传任务 ID 和任务链接 URL。任务 ID 示例：8b5425ec-9f2a-43bd-a3ab-01912f50282b；任务链接 URL 示例：https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + llm_description: 要添加的任务的 GUID，支持传任务 ID 和任务链接 URL。任务 ID 示例：8b5425ec-9f2a-43bd-a3ab-01912f50282b；任务链接 URL 示例：https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + form: llm + + - name: member_phone_or_email + type: string + required: true + label: + en_US: Task Member Phone Or Email + zh_Hans: 任务成员的电话或邮箱 + human_description: + en_US: A list of member emails or phone numbers, separated by commas. + zh_Hans: 任务成员邮箱或者手机号列表，使用逗号分隔。 + llm_description: 任务成员邮箱或者手机号列表，使用逗号分隔。 + form: llm + + - name: member_role + type: select + required: true + options: + - value: assignee + label: + en_US: assignee + zh_Hans: 负责人 + - value: follower + label: + en_US: follower + zh_Hans: 关注人 + default: "follower" + label: + en_US: member_role + zh_Hans: 成员的角色 + human_description: + en_US: Member role, optional values are "assignee" (responsible person) and "follower" (observer), with a default value of "assignee". + zh_Hans: 成员的角色，可选值有 "assignee"（负责人）和 "follower"（关注人），默认值为 "assignee"。 + llm_description: 成员的角色，可选值有 "assignee"（负责人）和 "follower"（关注人），默认值为 "assignee"。 + form: form diff --git a/api/core/tools/provider/builtin/feishu_task/tools/create_task.py b/api/core/tools/provider/builtin/feishu_task/tools/create_task.py new file mode 100644 index 00000000000000..96cdcd71f6d2ec --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/create_task.py @@ -0,0 +1,22 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class CreateTaskTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + summary = tool_parameters.get("summary") + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + completed_time = tool_parameters.get("completed_time") + description = tool_parameters.get("description") + + res = client.create_task(summary, start_time, end_time, completed_time, description) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_task/tools/create_task.yaml b/api/core/tools/provider/builtin/feishu_task/tools/create_task.yaml new file mode 100644 index 00000000000000..7eb4af168bf740 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/create_task.yaml @@ -0,0 +1,74 @@ +identity: + name: create_task + author: Doug Lea + label: + en_US: Create Task + zh_Hans: 创建飞书任务 +description: + human: + en_US: Create Feishu Task + zh_Hans: 创建飞书任务 + llm: A tool for creating tasks in Feishu.(创建飞书任务) +parameters: + - name: summary + type: string + required: true + label: + en_US: Task Title + zh_Hans: 任务标题 + human_description: + en_US: The title of the task. + zh_Hans: 任务标题 + llm_description: 任务标题 + form: llm + + - name: description + type: string + required: false + label: + en_US: Task Description + zh_Hans: 任务备注 + human_description: + en_US: The description or notes for the task. + zh_Hans: 任务备注 + llm_description: 任务备注 + form: llm + + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 任务开始时间 + human_description: + en_US: | + The start time of the task, in the format: 2006-01-02 15:04:05 + zh_Hans: 任务开始时间，格式为：2006-01-02 15:04:05 + llm_description: 任务开始时间，格式为：2006-01-02 15:04:05 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 任务结束时间 + human_description: + en_US: | + The end time of the task, in the format: 2006-01-02 15:04:05 + zh_Hans: 任务结束时间，格式为：2006-01-02 15:04:05 + llm_description: 任务结束时间，格式为：2006-01-02 15:04:05 + form: llm + + - name: completed_time + type: string + required: false + label: + en_US: Completed Time + zh_Hans: 任务完成时间 + human_description: + en_US: | + The completion time of the task, in the format: 2006-01-02 15:04:05. Leave empty to create an incomplete task; fill in a specific time to create a completed task. + zh_Hans: 任务完成时间，格式为：2006-01-02 15:04:05，不填写表示创建一个未完成任务；填写一个具体的时间表示创建一个已完成任务。 + llm_description: 任务完成时间，格式为：2006-01-02 15:04:05，不填写表示创建一个未完成任务；填写一个具体的时间表示创建一个已完成任务。 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_task/tools/delete_task.py b/api/core/tools/provider/builtin/feishu_task/tools/delete_task.py new file mode 100644 index 00000000000000..dee036fee5203a --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/delete_task.py @@ -0,0 +1,18 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class UpdateTaskTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + task_guid = tool_parameters.get("task_guid") + + res = client.delete_task(task_guid) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_task/tools/delete_task.yaml b/api/core/tools/provider/builtin/feishu_task/tools/delete_task.yaml new file mode 100644 index 00000000000000..d3f97413676624 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/delete_task.yaml @@ -0,0 +1,24 @@ +identity: + name: delete_task + author: Doug Lea + label: + en_US: Delete Task + zh_Hans: 删除飞书任务 +description: + human: + en_US: Delete Task + zh_Hans: 删除飞书任务 + llm: A tool for deleting tasks in Feishu.(删除飞书任务) +parameters: + - name: task_guid + type: string + required: true + label: + en_US: Task GUID + zh_Hans: 任务 GUID + human_description: + en_US: | + The GUID of the task to be deleted, supports passing either the Task ID or the Task link URL. Example of Task ID: 8b5425ec-9f2a-43bd-a3ab-01912f50282b; Example of Task link URL: https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + zh_Hans: 要删除的任务的 GUID，支持传任务 ID 和任务链接 URL。任务 ID 示例：8b5425ec-9f2a-43bd-a3ab-01912f50282b；任务链接 URL 示例：https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + llm_description: 要删除的任务的 GUID，支持传任务 ID 和任务链接 URL。任务 ID 示例：8b5425ec-9f2a-43bd-a3ab-01912f50282b；任务链接 URL 示例：https://applink.feishu-pre.net/client/todo/detail?guid=8c6bf822-e4da-449a-b82a-dc44020f9be9&suite_entity_num=t21587362 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_task/tools/update_task.py b/api/core/tools/provider/builtin/feishu_task/tools/update_task.py new file mode 100644 index 00000000000000..4a48cd283abf1d --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/update_task.py @@ -0,0 +1,23 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class UpdateTaskTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + task_guid = tool_parameters.get("task_guid") + summary = tool_parameters.get("summary") + start_time = tool_parameters.get("start_time") + end_time = tool_parameters.get("end_time") + completed_time = tool_parameters.get("completed_time") + description = tool_parameters.get("description") + + res = client.update_task(task_guid, summary, start_time, end_time, completed_time, description) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_task/tools/update_task.yaml b/api/core/tools/provider/builtin/feishu_task/tools/update_task.yaml new file mode 100644 index 00000000000000..83c9bcb1c443ac --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_task/tools/update_task.yaml @@ -0,0 +1,89 @@ +identity: + name: update_task + author: Doug Lea + label: + en_US: Update Task + zh_Hans: 更新飞书任务 +description: + human: + en_US: Update Feishu Task + zh_Hans: 更新飞书任务 + llm: A tool for updating tasks in Feishu.(更新飞书任务) +parameters: + - name: task_guid + type: string + required: true + label: + en_US: Task GUID + zh_Hans: 任务 GUID + human_description: + en_US: | + The task ID, supports inputting either the Task ID or the Task link URL. Example of Task ID: 42cad8a0-f8c8-4344-9be2-d1d7e8e91b64; Example of Task link URL: https://applink.feishu-pre.net/client/todo/detail?guid=42cad8a0-f8c8-4344-9be2-d1d7e8e91b64&suite_entity_num=t21700217 + zh_Hans: | + 任务ID，支持传入任务 ID 和任务链接 URL。任务 ID 示例: 42cad8a0-f8c8-4344-9be2-d1d7e8e91b64；任务链接 URL 示例: https://applink.feishu-pre.net/client/todo/detail?guid=42cad8a0-f8c8-4344-9be2-d1d7e8e91b64&suite_entity_num=t21700217 + llm_description: | + 任务ID，支持传入任务 ID 和任务链接 URL。任务 ID 示例: 42cad8a0-f8c8-4344-9be2-d1d7e8e91b64；任务链接 URL 示例: https://applink.feishu-pre.net/client/todo/detail?guid=42cad8a0-f8c8-4344-9be2-d1d7e8e91b64&suite_entity_num=t21700217 + form: llm + + - name: summary + type: string + required: true + label: + en_US: Task Title + zh_Hans: 任务标题 + human_description: + en_US: The title of the task. + zh_Hans: 任务标题 + llm_description: 任务标题 + form: llm + + - name: description + type: string + required: false + label: + en_US: Task Description + zh_Hans: 任务备注 + human_description: + en_US: The description or notes for the task. + zh_Hans: 任务备注 + llm_description: 任务备注 + form: llm + + - name: start_time + type: string + required: false + label: + en_US: Start Time + zh_Hans: 任务开始时间 + human_description: + en_US: | + The start time of the task, in the format: 2006-01-02 15:04:05 + zh_Hans: 任务开始时间，格式为：2006-01-02 15:04:05 + llm_description: 任务开始时间，格式为：2006-01-02 15:04:05 + form: llm + + - name: end_time + type: string + required: false + label: + en_US: End Time + zh_Hans: 任务结束时间 + human_description: + en_US: | + The end time of the task, in the format: 2006-01-02 15:04:05 + zh_Hans: 任务结束时间，格式为：2006-01-02 15:04:05 + llm_description: 任务结束时间，格式为：2006-01-02 15:04:05 + form: llm + + - name: completed_time + type: string + required: false + label: + en_US: Completed Time + zh_Hans: 任务完成时间 + human_description: + en_US: | + The completion time of the task, in the format: 2006-01-02 15:04:05 + zh_Hans: 任务完成时间，格式为：2006-01-02 15:04:05 + llm_description: 任务完成时间，格式为：2006-01-02 15:04:05 + form: llm diff --git a/api/core/tools/provider/builtin/feishu_wiki/_assets/icon.png b/api/core/tools/provider/builtin/feishu_wiki/_assets/icon.png new file mode 100644 index 00000000000000..878672c9ae5a51 Binary files /dev/null and b/api/core/tools/provider/builtin/feishu_wiki/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.py b/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.py new file mode 100644 index 00000000000000..6c5fccb1a31d0d --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.py @@ -0,0 +1,7 @@ +from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController +from core.tools.utils.feishu_api_utils import auth + + +class FeishuWikiProvider(BuiltinToolProviderController): + def _validate_credentials(self, credentials: dict) -> None: + auth(credentials) diff --git a/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.yaml b/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.yaml new file mode 100644 index 00000000000000..1fb5f71cbc5169 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_wiki/feishu_wiki.yaml @@ -0,0 +1,36 @@ +identity: + author: Doug Lea + name: feishu_wiki + label: + en_US: Feishu Wiki + zh_Hans: 飞书知识库 + description: + en_US: | + Feishu Wiki, requires the following permissions: wiki:wiki:readonly. + zh_Hans: | + 飞书知识库，需要开通以下权限: wiki:wiki:readonly。 + icon: icon.png + tags: + - social + - productivity +credentials_for_provider: + app_id: + type: text-input + required: true + label: + en_US: APP ID + placeholder: + en_US: Please input your feishu app id + zh_Hans: 请输入你的飞书 app id + help: + en_US: Get your app_id and app_secret from Feishu + zh_Hans: 从飞书获取您的 app_id 和 app_secret + url: https://open.larkoffice.com/app + app_secret: + type: secret-input + required: true + label: + en_US: APP Secret + placeholder: + en_US: Please input your app secret + zh_Hans: 请输入你的飞书 app secret diff --git a/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.py b/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.py new file mode 100644 index 00000000000000..374b4c9a7d1492 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.py @@ -0,0 +1,21 @@ +from typing import Any + +from core.tools.entities.tool_entities import ToolInvokeMessage +from core.tools.tool.builtin_tool import BuiltinTool +from core.tools.utils.feishu_api_utils import FeishuRequest + + +class GetWikiNodesTool(BuiltinTool): + def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMessage: + app_id = self.runtime.credentials.get("app_id") + app_secret = self.runtime.credentials.get("app_secret") + client = FeishuRequest(app_id, app_secret) + + space_id = tool_parameters.get("space_id") + parent_node_token = tool_parameters.get("parent_node_token") + page_token = tool_parameters.get("page_token") + page_size = tool_parameters.get("page_size") + + res = client.get_wiki_nodes(space_id, parent_node_token, page_token, page_size) + + return self.create_json_message(res) diff --git a/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.yaml b/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.yaml new file mode 100644 index 00000000000000..7d6ac3c8248772 --- /dev/null +++ b/api/core/tools/provider/builtin/feishu_wiki/tools/get_wiki_nodes.yaml @@ -0,0 +1,63 @@ +identity: + name: get_wiki_nodes + author: Doug Lea + label: + en_US: Get Wiki Nodes + zh_Hans: 获取知识空间子节点列表 +description: + human: + en_US: | + Get the list of child nodes in Wiki, make sure the app/bot is a member of the wiki space. See How to add an app as a wiki base administrator (member). https://open.feishu.cn/document/server-docs/docs/wiki-v2/wiki-qa + zh_Hans: | + 获取知识库全部子节点列表，请确保应用/机器人为知识空间成员。参阅如何将应用添加为知识库管理员（成员）。https://open.feishu.cn/document/server-docs/docs/wiki-v2/wiki-qa + llm: A tool for getting all sub-nodes of a knowledge base.(获取知识空间子节点列表) +parameters: + - name: space_id + type: string + required: true + label: + en_US: Space Id + zh_Hans: 知识空间 ID + human_description: + en_US: | + The ID of the knowledge space. Supports space link URL, for example: https://svi136aogf123.feishu.cn/wiki/settings/7166950623940706332 + zh_Hans: 知识空间 ID，支持空间链接 URL，例如：https://svi136aogf123.feishu.cn/wiki/settings/7166950623940706332 + llm_description: 知识空间 ID，支持空间链接 URL，例如：https://svi136aogf123.feishu.cn/wiki/settings/7166950623940706332 + form: llm + + - name: page_size + type: number + required: false + default: 10 + label: + en_US: Page Size + zh_Hans: 分页大小 + human_description: + en_US: The size of each page, with a maximum value of 50. + zh_Hans: 分页大小，最大值 50。 + llm_description: 分页大小，最大值 50。 + form: llm + + - name: page_token + type: string + required: false + label: + en_US: Page Token + zh_Hans: 分页标记 + human_description: + en_US: The pagination token. Leave empty for the first request to start from the beginning; if the paginated query result has more items, a new page_token will be returned, which can be used to get the next set of results. + zh_Hans: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + llm_description: 分页标记，第一次请求不填，表示从头开始遍历；分页查询结果还有更多项时会同时返回新的 page_token，下次遍历可采用该 page_token 获取查询结果。 + form: llm + + - name: parent_node_token + type: string + required: false + label: + en_US: Parent Node Token + zh_Hans: 父节点 token + human_description: + en_US: The token of the parent node. + zh_Hans: 父节点 token + llm_description: 父节点 token + form: llm diff --git a/api/core/tools/provider/builtin/firecrawl/tools/crawl.py b/api/core/tools/provider/builtin/firecrawl/tools/crawl.py index 9675b8eb913351..15ab510c6c889c 100644 --- a/api/core/tools/provider/builtin/firecrawl/tools/crawl.py +++ b/api/core/tools/provider/builtin/firecrawl/tools/crawl.py @@ -35,10 +35,10 @@ def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> ToolInvokeMe scrapeOptions["excludeTags"] = get_array_params(tool_parameters, "excludeTags") scrapeOptions["onlyMainContent"] = tool_parameters.get("onlyMainContent", False) scrapeOptions["waitFor"] = tool_parameters.get("waitFor", 0) - scrapeOptions = {k: v for k, v in scrapeOptions.items() if v not in {None, ""}} + scrapeOptions = {k: v for k, v in scrapeOptions.items() if v not in (None, "")} payload["scrapeOptions"] = scrapeOptions or None - payload = {k: v for k, v in payload.items() if v not in {None, ""}} + payload = {k: v for k, v in payload.items() if v not in (None, "")} crawl_result = app.crawl_url(url=tool_parameters["url"], wait=wait_for_results, **payload) diff --git a/api/core/tools/provider/builtin/firecrawl/tools/scrape.py b/api/core/tools/provider/builtin/firecrawl/tools/scrape.py index 538b4a1fcbf056..f00a9b31ce8c2c 100644 --- a/api/core/tools/provider/builtin/firecrawl/tools/scrape.py +++ b/api/core/tools/provider/builtin/firecrawl/tools/scrape.py @@ -29,10 +29,10 @@ def _invoke(self, user_id: str, tool_parameters: dict[str, Any]) -> list[ToolInv extract["schema"] = get_json_params(tool_parameters, "schema") extract["systemPrompt"] = tool_parameters.get("systemPrompt") extract["prompt"] = tool_parameters.get("prompt") - extract = {k: v for k, v in extract.items() if v not in {None, ""}} + extract = {k: v for k, v in extract.items() if v not in (None, "")} payload["extract"] = extract or None - payload = {k: v for k, v in payload.items() if v not in {None, ""}} + payload = {k: v for k, v in payload.items() if v not in (None, "")} crawl_result = app.scrape_url(url=tool_parameters["url"], **payload) markdown_result = crawl_result.get("data", {}).get("markdown", "") diff --git a/api/core/tools/provider/builtin/jina/jina.yaml b/api/core/tools/provider/builtin/jina/jina.yaml index 06f23382d92a3a..346175c41fbe17 100644 --- a/api/core/tools/provider/builtin/jina/jina.yaml +++ b/api/core/tools/provider/builtin/jina/jina.yaml @@ -2,9 +2,9 @@ identity: author: Dify name: jina label: - en_US: Jina - zh_Hans: Jina - pt_BR: Jina + en_US: Jina AI + zh_Hans: Jina AI + pt_BR: Jina AI description: en_US: Convert any URL to an LLM-friendly input or perform searches on the web for grounding information. Experience improved output for your agent and RAG systems at no cost. zh_Hans: 将任何URL转换为LLM易读的输入或在网页上搜索引擎上搜索引擎。 @@ -22,11 +22,11 @@ credentials_for_provider: zh_Hans: API 密钥（可留空） pt_BR: Chave API (deixe vazio se você não tiver uma) placeholder: - en_US: Please enter your Jina API key - zh_Hans: 请输入你的 Jina API 密钥 - pt_BR: Por favor, insira sua chave de API do Jina + en_US: Please enter your Jina AI API key + zh_Hans: 请输入你的 Jina AI API 密钥 + pt_BR: Por favor, insira sua chave de API do Jina AI help: - en_US: Get your Jina API key from Jina (optional, but you can get a higher rate) - zh_Hans: 从 Jina 获取您的 Jina API 密钥（非必须，能得到更高的速率） - pt_BR: Obtenha sua chave de API do Jina na Jina (opcional, mas você pode obter uma taxa mais alta) + en_US: Get your Jina AI API key from Jina AI (optional, but you can get a higher rate) + zh_Hans: 从 Jina AI 获取您的 Jina AI API 密钥（非必须，能得到更高的速率） + pt_BR: Obtenha sua chave de API do Jina AI na Jina AI (opcional, mas você pode obter uma taxa mais alta) url: https://jina.ai diff --git a/api/core/tools/provider/builtin/jina/tools/jina_reader.yaml b/api/core/tools/provider/builtin/jina/tools/jina_reader.yaml index 58ad6d8694222d..589bc3433d9478 100644 --- a/api/core/tools/provider/builtin/jina/tools/jina_reader.yaml +++ b/api/core/tools/provider/builtin/jina/tools/jina_reader.yaml @@ -2,14 +2,14 @@ identity: name: jina_reader author: Dify label: - en_US: JinaReader - zh_Hans: JinaReader - pt_BR: JinaReader + en_US: Fetch Single Page + zh_Hans: 获取单页面 + pt_BR: Fetch Single Page description: human: - en_US: Convert any URL to an LLM-friendly input. Experience improved output for your agent and RAG systems at no cost. - zh_Hans: 将任何 URL 转换为 LLM 友好的输入。无需付费即可体验为您的 Agent 和 RAG 系统提供的改进输出。 - pt_BR: Converta qualquer URL em uma entrada amigável ao LLM. Experimente uma saída aprimorada para seus sistemas de agente e RAG sem custo. + en_US: Fetch the target URL (can be a PDF) and convert it into a LLM-friendly markdown. + zh_Hans: 获取目标网址（可以是 PDF），并将其转换为适合大模型处理的 Markdown 格式。 + pt_BR: Busque a URL de destino (que pode ser um PDF) e converta em um Markdown LLM-friendly. llm: A tool for scraping webpages. Input should be a URL. parameters: - name: url @@ -17,13 +17,13 @@ parameters: required: true label: en_US: URL - zh_Hans: 网页链接 + zh_Hans: 网址 pt_BR: URL human_description: - en_US: used for linking to webpages - zh_Hans: 用于链接到网页 - pt_BR: used for linking to webpages - llm_description: url for scraping + en_US: Web link + zh_Hans: 网页链接 + pt_BR: URL da web + llm_description: url para scraping form: llm - name: request_params type: string @@ -31,14 +31,14 @@ parameters: label: en_US: Request params zh_Hans: 请求参数 - pt_BR: Request params + pt_BR: Parâmetros de solicitação human_description: en_US: | request parameters, format: {"key1": "value1", "key2": "value2"} zh_Hans: | 请求参数，格式：{"key1": "value1", "key2": "value2"} pt_BR: | - request parameters, format: {"key1": "value1", "key2": "value2"} + parâmetros de solicitação, formato: {"key1": "value1", "key2": "value2"} llm_description: request parameters form: llm - name: target_selector @@ -51,7 +51,7 @@ parameters: human_description: en_US: css selector for scraping specific elements zh_Hans: css 选择器用于抓取特定元素 - pt_BR: css selector for scraping specific elements + pt_BR: css selector para scraping de elementos específicos llm_description: css selector of the target element to scrape form: form - name: wait_for_selector @@ -64,7 +64,7 @@ parameters: human_description: en_US: css selector for waiting for specific elements zh_Hans: css 选择器用于等待特定元素 - pt_BR: css selector for waiting for specific elements + pt_BR: css selector para aguardar elementos específicos llm_description: css selector of the target element to wait for form: form - name: image_caption @@ -77,8 +77,8 @@ parameters: pt_BR: Legenda da imagem human_description: en_US: "Captions all images at the specified URL, adding 'Image [idx]: [caption]' as an alt tag for those without one. This allows downstream LLMs to interact with the images in activities such as reasoning and summarizing." - zh_Hans: "为指定 URL 上的所有图像添加标题，为没有标题的图像添加“Image [idx]: [caption]”作为 alt 标签。这允许下游 LLM 在推理和总结等活动中与图像进行交互。" - pt_BR: "Captions all images at the specified URL, adding 'Image [idx]: [caption]' as an alt tag for those without one. This allows downstream LLMs to interact with the images in activities such as reasoning and summarizing." + zh_Hans: "为指定 URL 上的所有图像添加标题，为没有标题的图像添加“Image [idx]: [caption]”作为 alt 标签，以支持下游模型的图像交互。" + pt_BR: "Adiciona legendas a todas as imagens na URL especificada, adicionando 'Imagem [idx]: [legenda]' como uma tag alt para aquelas que não têm uma. Isso permite que os modelos LLM inferiores interajam com as imagens em atividades como raciocínio e resumo." llm_description: Captions all images at the specified URL form: form - name: gather_all_links_at_the_end @@ -91,8 +91,8 @@ parameters: pt_BR: Coletar todos os links ao final human_description: en_US: A "Buttons & Links" section will be created at the end. This helps the downstream LLMs or web agents navigating the page or take further actions. - zh_Hans: 最后会创建一个“按钮和链接”部分。这可以帮助下游 LLM 或 Web 代理浏览页面或采取进一步的行动。 - pt_BR: A "Buttons & Links" section will be created at the end. This helps the downstream LLMs or web agents navigating the page or take further actions. + zh_Hans: 末尾将添加“按钮和链接”部分，方便下游模型或网络代理做页面导航或执行进一步操作。 + pt_BR: Um "Botões & Links" section will be created at the end. This helps the downstream LLMs or web agents navigating the page or take further actions. llm_description: Gather all links at the end form: form - name: gather_all_images_at_the_end @@ -105,8 +105,8 @@ parameters: pt_BR: Coletar todas as imagens ao final human_description: en_US: An "Images" section will be created at the end. This gives the downstream LLMs an overview of all visuals on the page, which may improve reasoning. - zh_Hans: 最后会创建一个“图像”部分。这可以让下游的 LLM 概览页面上的所有视觉效果，从而提高推理能力。 - pt_BR: An "Images" section will be created at the end. This gives the downstream LLMs an overview of all visuals on the page, which may improve reasoning. + zh_Hans: 末尾会新增“图片”部分，方便下游模型全面了解页面的视觉内容，提升推理效果。 + pt_BR: Um "Imagens" section will be created at the end. This gives the downstream LLMs an overview of all visuals on the page, which may improve reasoning. llm_description: Gather all images at the end form: form - name: proxy_server diff --git a/api/core/tools/provider/builtin/jina/tools/jina_search.yaml b/api/core/tools/provider/builtin/jina/tools/jina_search.yaml index 2bc70e1be1934d..e58c639e5690d0 100644 --- a/api/core/tools/provider/builtin/jina/tools/jina_search.yaml +++ b/api/core/tools/provider/builtin/jina/tools/jina_search.yaml @@ -2,13 +2,14 @@ identity: name: jina_search author: Dify label: - en_US: JinaSearch - zh_Hans: JinaSearch - pt_BR: JinaSearch + en_US: Search the web + zh_Hans: 联网搜索 + pt_BR: Search the web description: human: - en_US: Search on the web and get the top 5 results. Useful for grounding using information from the web. - zh_Hans: 在网络上搜索返回前 5 个结果。 + en_US: Search on the public web of a given query and return the top results as LLM-friendly markdown. + zh_Hans: 针对给定的查询在互联网上进行搜索，并以适合大模型处理的 Markdown 格式返回最相关的结果。 + pt_BR: Procurar na web pública de uma consulta fornecida e retornar os melhores resultados como markdown para LLMs. llm: A tool for searching results on the web for grounding. Input should be a simple question. parameters: - name: query @@ -16,11 +17,13 @@ parameters: required: true label: en_US: Question (Query) - zh_Hans: 信息查询 + zh_Hans: 查询 + pt_BR: Pergunta (Consulta) human_description: en_US: used to find information on the web zh_Hans: 在网络上搜索信息 - llm_description: simple question to ask on the web + pt_BR: Usado para encontrar informações na web + llm_description: Pergunta simples para fazer na web form: llm - name: image_caption type: boolean @@ -32,7 +35,7 @@ parameters: pt_BR: Legenda da imagem human_description: en_US: "Captions all images at the specified URL, adding 'Image [idx]: [caption]' as an alt tag for those without one. This allows downstream LLMs to interact with the images in activities such as reasoning and summarizing." - zh_Hans: "为指定 URL 上的所有图像添加标题，为没有标题的图像添加“Image [idx]: [caption]”作为 alt 标签。这允许下游 LLM 在推理和总结等活动中与图像进行交互。" + zh_Hans: "为指定 URL 上的所有图像添加标题，为没有标题的图像添加“Image [idx]: [caption]”作为 alt 标签，以支持下游模型的图像交互。" pt_BR: "Captions all images at the specified URL, adding 'Image [idx]: [caption]' as an alt tag for those without one. This allows downstream LLMs to interact with the images in activities such as reasoning and summarizing." llm_description: Captions all images at the specified URL form: form @@ -46,8 +49,8 @@ parameters: pt_BR: Coletar todos os links ao final human_description: en_US: A "Buttons & Links" section will be created at the end. This helps the downstream LLMs or web agents navigating the page or take further actions. - zh_Hans: 最后会创建一个“按钮和链接”部分。这可以帮助下游 LLM 或 Web 代理浏览页面或采取进一步的行动。 - pt_BR: A "Buttons & Links" section will be created at the end. This helps the downstream LLMs or web agents navigating the page or take further actions. + zh_Hans: 末尾将添加“按钮和链接”部分，汇总页面上的所有链接。方便下游模型或网络代理做页面导航或执行进一步操作。 + pt_BR: Um "Botão & Links" seção será criada no final. Isso ajuda os LLMs ou agentes da web navegando pela página ou executar ações adicionais. llm_description: Gather all links at the end form: form - name: gather_all_images_at_the_end @@ -60,8 +63,8 @@ parameters: pt_BR: Coletar todas as imagens ao final human_description: en_US: An "Images" section will be created at the end. This gives the downstream LLMs an overview of all visuals on the page, which may improve reasoning. - zh_Hans: 最后会创建一个“图像”部分。这可以让下游的 LLM 概览页面上的所有视觉效果，从而提高推理能力。 - pt_BR: An "Images" section will be created at the end. This gives the downstream LLMs an overview of all visuals on the page, which may improve reasoning. + zh_Hans: 末尾会新增“图片”部分，汇总页面上的所有图片。方便下游模型概览页面的视觉内容，提升推理效果。 + pt_BR: Um "Imagens" seção será criada no final. Isso fornece uma visão geral de todas as imagens na página para os LLMs, que pode melhorar a razão. llm_description: Gather all images at the end form: form - name: proxy_server @@ -74,7 +77,7 @@ parameters: human_description: en_US: Use proxy to access URLs zh_Hans: 利用代理访问 URL - pt_BR: Use proxy to access URLs + pt_BR: Usar proxy para acessar URLs llm_description: Use proxy to access URLs form: form - name: no_cache @@ -83,7 +86,7 @@ parameters: default: false label: en_US: Bypass the Cache - zh_Hans: 绕过缓存 + zh_Hans: 是否绕过缓存 pt_BR: Ignorar o cache human_description: en_US: Bypass the Cache diff --git a/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.yaml b/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.yaml index 62a5c7e7bacd75..74885cdf9a7048 100644 --- a/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.yaml +++ b/api/core/tools/provider/builtin/jina/tools/jina_tokenizer.yaml @@ -2,11 +2,14 @@ identity: name: jina_tokenizer author: hjlarry label: - en_US: JinaTokenizer + en_US: Segment + zh_Hans: 切分器 + pt_BR: Segment description: human: - en_US: Free API to tokenize text and segment long text into chunks. - zh_Hans: 免费的API可以将文本tokenize，也可以将长文本分割成多个部分。 + en_US: Split long text into chunks and do tokenization. + zh_Hans: 将长文本拆分成小段落，并做分词处理。 + pt_BR: Dividir o texto longo em pedaços e fazer tokenização. llm: Free API to tokenize text and segment long text into chunks. parameters: - name: content @@ -15,6 +18,7 @@ parameters: label: en_US: Content zh_Hans: 内容 + pt_BR: Conteúdo llm_description: the content which need to tokenize or segment form: llm - name: return_tokens @@ -23,18 +27,22 @@ parameters: label: en_US: Return the tokens zh_Hans: 是否返回tokens + pt_BR: Retornar os tokens human_description: en_US: Return the tokens and their corresponding ids in the response. zh_Hans: 返回tokens及其对应的ids。 + pt_BR: Retornar os tokens e seus respectivos ids na resposta. form: form - name: return_chunks type: boolean label: en_US: Return the chunks zh_Hans: 是否分块 + pt_BR: Retornar os chunks human_description: en_US: Chunking the input into semantically meaningful segments while handling a wide variety of text types and edge cases based on common structural cues. - zh_Hans: 将输入分块为具有语义意义的片段，同时根据常见的结构线索处理各种文本类型和边缘情况。 + zh_Hans: 将输入文本分块为语义有意义的片段，同时基于常见的结构线索处理各种文本类型和特殊情况。 + pt_BR: Dividir o texto de entrada em segmentos semanticamente significativos, enquanto lida com uma ampla variedade de tipos de texto e casos de borda com base em pistas estruturais comuns. form: form - name: tokenizer type: select diff --git a/api/core/tools/provider/builtin/stepfun/stepfun.py b/api/core/tools/provider/builtin/stepfun/stepfun.py index b24f730c95a7c2..239db85b1118b0 100644 --- a/api/core/tools/provider/builtin/stepfun/stepfun.py +++ b/api/core/tools/provider/builtin/stepfun/stepfun.py @@ -16,7 +16,7 @@ def _validate_credentials(self, credentials: dict[str, Any]) -> None: user_id="", tool_parameters={ "prompt": "cute girl, blue eyes, white hair, anime style", - "size": "1024x1024", + "size": "256x256", "n": 1, }, ) diff --git a/api/core/tools/provider/builtin/stepfun/stepfun.yaml b/api/core/tools/provider/builtin/stepfun/stepfun.yaml index 1f841ec369b5c3..e8139a4d7d6cfd 100644 --- a/api/core/tools/provider/builtin/stepfun/stepfun.yaml +++ b/api/core/tools/provider/builtin/stepfun/stepfun.yaml @@ -4,11 +4,9 @@ identity: label: en_US: Image-1X zh_Hans: 阶跃星辰绘画 - pt_BR: Image-1X description: en_US: Image-1X zh_Hans: 阶跃星辰绘画 - pt_BR: Image-1X icon: icon.png tags: - image @@ -20,27 +18,16 @@ credentials_for_provider: label: en_US: Stepfun API key zh_Hans: 阶跃星辰API key - pt_BR: Stepfun API key - help: - en_US: Please input your stepfun API key - zh_Hans: 请输入你的阶跃星辰 API key - pt_BR: Please input your stepfun API key placeholder: - en_US: Please input your stepfun API key + en_US: Please input your Stepfun API key zh_Hans: 请输入你的阶跃星辰 API key - pt_BR: Please input your stepfun API key + url: https://platform.stepfun.com/interface-key stepfun_base_url: type: text-input required: false label: en_US: Stepfun base URL zh_Hans: 阶跃星辰 base URL - pt_BR: Stepfun base URL - help: - en_US: Please input your Stepfun base URL - zh_Hans: 请输入你的阶跃星辰 base URL - pt_BR: Please input your Stepfun base URL placeholder: en_US: Please input your Stepfun base URL zh_Hans: 请输入你的阶跃星辰 base URL - pt_BR: Please input your Stepfun base URL diff --git a/api/core/tools/provider/builtin/stepfun/tools/image.py b/api/core/tools/provider/builtin/stepfun/tools/image.py index 0b92b122bf59bc..eb55dae5183c41 100644 --- a/api/core/tools/provider/builtin/stepfun/tools/image.py +++ b/api/core/tools/provider/builtin/stepfun/tools/image.py @@ -1,4 +1,3 @@ -import random from typing import Any, Union from openai import OpenAI @@ -19,7 +18,7 @@ def _invoke( """ invoke tools """ - base_url = self.runtime.credentials.get("stepfun_base_url", "https://api.stepfun.com") + base_url = self.runtime.credentials.get("stepfun_base_url") or "https://api.stepfun.com" base_url = str(URL(base_url) / "v1") client = OpenAI( @@ -28,9 +27,7 @@ def _invoke( ) extra_body = {} - model = tool_parameters.get("model", "step-1x-medium") - if not model: - return self.create_text_message("Please input model name") + model = "step-1x-medium" # prompt prompt = tool_parameters.get("prompt", "") if not prompt: @@ -67,9 +64,3 @@ def _invoke( ) ) return result - - @staticmethod - def _generate_random_id(length=8): - characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789" - random_id = "".join(random.choices(characters, k=length)) - return random_id diff --git a/api/core/tools/provider/builtin/stepfun/tools/image.yaml b/api/core/tools/provider/builtin/stepfun/tools/image.yaml index dcc5bd2db2f5ba..8d7c9b6586b6cd 100644 --- a/api/core/tools/provider/builtin/stepfun/tools/image.yaml +++ b/api/core/tools/provider/builtin/stepfun/tools/image.yaml @@ -29,35 +29,6 @@ parameters: pt_BR: Image prompt, you can check the official documentation of step-1x llm_description: Image prompt of step-1x you should describe the image you want to generate as a list of words as possible as detailed form: llm - - name: model - type: select - required: false - human_description: - en_US: used for selecting the model name - zh_Hans: 用于选择模型的名字 - pt_BR: used for selecting the model name - label: - en_US: Model Name - zh_Hans: 模型名字 - pt_BR: Model Name - form: form - options: - - value: step-1x-turbo - label: - en_US: turbo - zh_Hans: turbo - pt_BR: turbo - - value: step-1x-medium - label: - en_US: medium - zh_Hans: medium - pt_BR: medium - - value: step-1x-large - label: - en_US: large - zh_Hans: large - pt_BR: large - default: step-1x-medium - name: size type: select required: false diff --git a/api/core/tools/provider/builtin/tavily/tavily.yaml b/api/core/tools/provider/builtin/tavily/tavily.yaml index 7b25a8184857ca..95820f4d18b051 100644 --- a/api/core/tools/provider/builtin/tavily/tavily.yaml +++ b/api/core/tools/provider/builtin/tavily/tavily.yaml @@ -28,4 +28,4 @@ credentials_for_provider: en_US: Get your Tavily API key from Tavily zh_Hans: 从 TavilyApi 获取您的 Tavily API key pt_BR: Get your Tavily API key from Tavily - url: https://docs.tavily.com/docs/tavily-api/introduction + url: https://docs.tavily.com/docs/welcome diff --git a/api/core/tools/provider/builtin/xinference/_assets/icon.png b/api/core/tools/provider/builtin/xinference/_assets/icon.png new file mode 100644 index 00000000000000..e58cacbd123b58 Binary files /dev/null and b/api/core/tools/provider/builtin/xinference/_assets/icon.png differ diff --git a/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.py b/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.py new file mode 100644 index 00000000000000..847f2730f240c4 --- /dev/null +++ b/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.py @@ -0,0 +1,412 @@ +import io +import json +from base64 import b64decode, b64encode +from copy import deepcopy +from typing import Any, Union + +from httpx import get, post +from PIL import Image +from yarl import URL + +from core.tools.entities.common_entities import I18nObject +from core.tools.entities.tool_entities import ( + ToolInvokeMessage, + ToolParameter, + ToolParameterOption, +) +from core.tools.errors import ToolProviderCredentialValidationError +from core.tools.tool.builtin_tool import BuiltinTool + +# All commented out parameters default to null +DRAW_TEXT_OPTIONS = { + # Prompts + "prompt": "", + "negative_prompt": "", + # "styles": [], + # Seeds + "seed": -1, + "subseed": -1, + "subseed_strength": 0, + "seed_resize_from_h": -1, + "seed_resize_from_w": -1, + # Samplers + "sampler_name": "DPM++ 2M", + # "scheduler": "", + # "sampler_index": "Automatic", + # Latent Space Options + "batch_size": 1, + "n_iter": 1, + "steps": 10, + "cfg_scale": 7, + "width": 512, + "height": 512, + # "restore_faces": True, + # "tiling": True, + "do_not_save_samples": False, + "do_not_save_grid": False, + # "eta": 0, + # "denoising_strength": 0.75, + # "s_min_uncond": 0, + # "s_churn": 0, + # "s_tmax": 0, + # "s_tmin": 0, + # "s_noise": 0, + "override_settings": {}, + "override_settings_restore_afterwards": True, + # Refinement Options + "refiner_checkpoint": "", + "refiner_switch_at": 0, + "disable_extra_networks": False, + # "firstpass_image": "", + # "comments": "", + # High-Resolution Options + "enable_hr": False, + "firstphase_width": 0, + "firstphase_height": 0, + "hr_scale": 2, + # "hr_upscaler": "", + "hr_second_pass_steps": 0, + "hr_resize_x": 0, + "hr_resize_y": 0, + # "hr_checkpoint_name": "", + # "hr_sampler_name": "", + # "hr_scheduler": "", + "hr_prompt": "", + "hr_negative_prompt": "", + # Task Options + # "force_task_id": "", + # Script Options + # "script_name": "", + "script_args": [], + # Output Options + "send_images": True, + "save_images": False, + "alwayson_scripts": {}, + # "infotext": "", +} + + +class StableDiffusionTool(BuiltinTool): + def _invoke( + self, user_id: str, tool_parameters: dict[str, Any] + ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]: + """ + invoke tools + """ + # base url + base_url = self.runtime.credentials.get("base_url", None) + if not base_url: + return self.create_text_message("Please input base_url") + + if tool_parameters.get("model"): + self.runtime.credentials["model"] = tool_parameters["model"] + + model = self.runtime.credentials.get("model", None) + if not model: + return self.create_text_message("Please input model") + + # set model + try: + url = str(URL(base_url) / "sdapi" / "v1" / "options") + response = post( + url, + json={"sd_model_checkpoint": model}, + headers={"Authorization": f"Bearer {self.runtime.credentials['api_key']}"}, + ) + if response.status_code != 200: + raise ToolProviderCredentialValidationError("Failed to set model, please tell user to set model") + except Exception as e: + raise ToolProviderCredentialValidationError("Failed to set model, please tell user to set model") + + # get image id and image variable + image_id = tool_parameters.get("image_id", "") + image_variable = self.get_default_image_variable() + # Return text2img if there's no image ID or no image variable + if not image_id or not image_variable: + return self.text2img(base_url=base_url, tool_parameters=tool_parameters) + + # Proceed with image-to-image generation + return self.img2img(base_url=base_url, tool_parameters=tool_parameters) + + def validate_models(self): + """ + validate models + """ + try: + base_url = self.runtime.credentials.get("base_url", None) + if not base_url: + raise ToolProviderCredentialValidationError("Please input base_url") + model = self.runtime.credentials.get("model", None) + if not model: + raise ToolProviderCredentialValidationError("Please input model") + + api_url = str(URL(base_url) / "sdapi" / "v1" / "sd-models") + response = get(url=api_url, timeout=10) + if response.status_code == 404: + # try draw a picture + self._invoke( + user_id="test", + tool_parameters={ + "prompt": "a cat", + "width": 1024, + "height": 1024, + "steps": 1, + "lora": "", + }, + ) + elif response.status_code != 200: + raise ToolProviderCredentialValidationError("Failed to get models") + else: + models = [d["model_name"] for d in response.json()] + if len([d for d in models if d == model]) > 0: + return self.create_text_message(json.dumps(models)) + else: + raise ToolProviderCredentialValidationError(f"model {model} does not exist") + except Exception as e: + raise ToolProviderCredentialValidationError(f"Failed to get models, {e}") + + def get_sd_models(self) -> list[str]: + """ + get sd models + """ + try: + base_url = self.runtime.credentials.get("base_url", None) + if not base_url: + return [] + api_url = str(URL(base_url) / "sdapi" / "v1" / "sd-models") + response = get(url=api_url, timeout=120) + if response.status_code != 200: + return [] + else: + return [d["model_name"] for d in response.json()] + except Exception as e: + return [] + + def get_sample_methods(self) -> list[str]: + """ + get sample method + """ + try: + base_url = self.runtime.credentials.get("base_url", None) + if not base_url: + return [] + api_url = str(URL(base_url) / "sdapi" / "v1" / "samplers") + response = get(url=api_url, timeout=120) + if response.status_code != 200: + return [] + else: + return [d["name"] for d in response.json()] + except Exception as e: + return [] + + def img2img( + self, base_url: str, tool_parameters: dict[str, Any] + ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]: + """ + generate image + """ + + # Fetch the binary data of the image + image_variable = self.get_default_image_variable() + image_binary = self.get_variable_file(image_variable.name) + if not image_binary: + return self.create_text_message("Image not found, please request user to generate image firstly.") + + # Convert image to RGB and save as PNG + try: + with Image.open(io.BytesIO(image_binary)) as image, io.BytesIO() as buffer: + image.convert("RGB").save(buffer, format="PNG") + image_binary = buffer.getvalue() + except Exception as e: + return self.create_text_message(f"Failed to process the image: {str(e)}") + + # copy draw options + draw_options = deepcopy(DRAW_TEXT_OPTIONS) + # set image options + model = tool_parameters.get("model", "") + draw_options_image = { + "init_images": [b64encode(image_binary).decode("utf-8")], + "denoising_strength": 0.9, + "restore_faces": False, + "script_args": [], + "override_settings": {"sd_model_checkpoint": model}, + "resize_mode": 0, + "image_cfg_scale": 0, + # "mask": None, + "mask_blur_x": 4, + "mask_blur_y": 4, + "mask_blur": 0, + "mask_round": True, + "inpainting_fill": 0, + "inpaint_full_res": True, + "inpaint_full_res_padding": 0, + "inpainting_mask_invert": 0, + "initial_noise_multiplier": 0, + # "latent_mask": None, + "include_init_images": True, + } + # update key and values + draw_options.update(draw_options_image) + draw_options.update(tool_parameters) + + # get prompt lora model + prompt = tool_parameters.get("prompt", "") + lora = tool_parameters.get("lora", "") + model = tool_parameters.get("model", "") + if lora: + draw_options["prompt"] = f"{lora},{prompt}" + else: + draw_options["prompt"] = prompt + + try: + url = str(URL(base_url) / "sdapi" / "v1" / "img2img") + response = post( + url, + json=draw_options, + timeout=120, + headers={"Authorization": f"Bearer {self.runtime.credentials['api_key']}"}, + ) + if response.status_code != 200: + return self.create_text_message("Failed to generate image") + + image = response.json()["images"][0] + + return self.create_blob_message( + blob=b64decode(image), + meta={"mime_type": "image/png"}, + save_as=self.VariableKey.IMAGE.value, + ) + + except Exception as e: + return self.create_text_message("Failed to generate image") + + def text2img( + self, base_url: str, tool_parameters: dict[str, Any] + ) -> Union[ToolInvokeMessage, list[ToolInvokeMessage]]: + """ + generate image + """ + # copy draw options + draw_options = deepcopy(DRAW_TEXT_OPTIONS) + draw_options.update(tool_parameters) + # get prompt lora model + prompt = tool_parameters.get("prompt", "") + lora = tool_parameters.get("lora", "") + model = tool_parameters.get("model", "") + if lora: + draw_options["prompt"] = f"{lora},{prompt}" + else: + draw_options["prompt"] = prompt + draw_options["override_settings"]["sd_model_checkpoint"] = model + + try: + url = str(URL(base_url) / "sdapi" / "v1" / "txt2img") + response = post( + url, + json=draw_options, + timeout=120, + headers={"Authorization": f"Bearer {self.runtime.credentials['api_key']}"}, + ) + if response.status_code != 200: + return self.create_text_message("Failed to generate image") + + image = response.json()["images"][0] + + return self.create_blob_message( + blob=b64decode(image), + meta={"mime_type": "image/png"}, + save_as=self.VariableKey.IMAGE.value, + ) + + except Exception as e: + return self.create_text_message("Failed to generate image") + + def get_runtime_parameters(self) -> list[ToolParameter]: + parameters = [ + ToolParameter( + name="prompt", + label=I18nObject(en_US="Prompt", zh_Hans="Prompt"), + human_description=I18nObject( + en_US="Image prompt, you can check the official documentation of Stable Diffusion", + zh_Hans="图像提示词，您可以查看 Stable Diffusion 的官方文档", + ), + type=ToolParameter.ToolParameterType.STRING, + form=ToolParameter.ToolParameterForm.LLM, + llm_description="Image prompt of Stable Diffusion, you should describe the image you want to generate" + " as a list of words as possible as detailed, the prompt must be written in English.", + required=True, + ), + ] + if len(self.list_default_image_variables()) != 0: + parameters.append( + ToolParameter( + name="image_id", + label=I18nObject(en_US="image_id", zh_Hans="image_id"), + human_description=I18nObject( + en_US="Image id of the image you want to generate based on, if you want to generate image based" + " on the default image, you can leave this field empty.", + zh_Hans="您想要生成的图像的图像 ID，如果您想要基于默认图像生成图像，则可以将此字段留空。", + ), + type=ToolParameter.ToolParameterType.STRING, + form=ToolParameter.ToolParameterForm.LLM, + llm_description="Image id of the original image, you can leave this field empty if you want to" + " generate a new image.", + required=True, + options=[ + ToolParameterOption(value=i.name, label=I18nObject(en_US=i.name, zh_Hans=i.name)) + for i in self.list_default_image_variables() + ], + ) + ) + + if self.runtime.credentials: + try: + models = self.get_sd_models() + if len(models) != 0: + parameters.append( + ToolParameter( + name="model", + label=I18nObject(en_US="Model", zh_Hans="Model"), + human_description=I18nObject( + en_US="Model of Stable Diffusion, you can check the official documentation" + " of Stable Diffusion", + zh_Hans="Stable Diffusion 的模型，您可以查看 Stable Diffusion 的官方文档", + ), + type=ToolParameter.ToolParameterType.SELECT, + form=ToolParameter.ToolParameterForm.FORM, + llm_description="Model of Stable Diffusion, you can check the official documentation" + " of Stable Diffusion", + required=True, + default=models[0], + options=[ + ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in models + ], + ) + ) + + except: + pass + + sample_methods = self.get_sample_methods() + if len(sample_methods) != 0: + parameters.append( + ToolParameter( + name="sampler_name", + label=I18nObject(en_US="Sampling method", zh_Hans="Sampling method"), + human_description=I18nObject( + en_US="Sampling method of Stable Diffusion, you can check the official documentation" + " of Stable Diffusion", + zh_Hans="Stable Diffusion 的Sampling method，您可以查看 Stable Diffusion 的官方文档", + ), + type=ToolParameter.ToolParameterType.SELECT, + form=ToolParameter.ToolParameterForm.FORM, + llm_description="Sampling method of Stable Diffusion, you can check the official documentation" + " of Stable Diffusion", + required=True, + default=sample_methods[0], + options=[ + ToolParameterOption(value=i, label=I18nObject(en_US=i, zh_Hans=i)) for i in sample_methods + ], + ) + ) + return parameters diff --git a/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.yaml b/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.yaml new file mode 100644 index 00000000000000..4f1d17f175c567 --- /dev/null +++ b/api/core/tools/provider/builtin/xinference/tools/stable_diffusion.yaml @@ -0,0 +1,87 @@ +identity: + name: stable_diffusion + author: xinference + label: + en_US: Stable Diffusion + zh_Hans: Stable Diffusion +description: + human: + en_US: Generate images using Stable Diffusion models. + zh_Hans: 使用 Stable Diffusion 模型生成图片。 + llm: draw the image you want based on your prompt. +parameters: + - name: prompt + type: string + required: true + label: + en_US: Prompt + zh_Hans: 提示词 + human_description: + en_US: Image prompt + zh_Hans: 图像提示词 + llm_description: Image prompt of Stable Diffusion, you should describe the image you want to generate as a list of words as possible as detailed, the prompt must be written in English. + form: llm + - name: model + type: string + required: false + label: + en_US: Model Name + zh_Hans: 模型名称 + human_description: + en_US: Model Name + zh_Hans: 模型名称 + form: form + - name: lora + type: string + required: false + label: + en_US: Lora + zh_Hans: Lora + human_description: + en_US: Lora + zh_Hans: Lora + form: form + - name: steps + type: number + required: false + label: + en_US: Steps + zh_Hans: Steps + human_description: + en_US: Steps + zh_Hans: Steps + form: form + default: 10 + - name: width + type: number + required: false + label: + en_US: Width + zh_Hans: Width + human_description: + en_US: Width + zh_Hans: Width + form: form + default: 1024 + - name: height + type: number + required: false + label: + en_US: Height + zh_Hans: Height + human_description: + en_US: Height + zh_Hans: Height + form: form + default: 1024 + - name: negative_prompt + type: string + required: false + label: + en_US: Negative prompt + zh_Hans: Negative prompt + human_description: + en_US: Negative prompt + zh_Hans: Negative prompt + form: form + default: bad art, ugly, deformed, watermark, duplicated, discontinuous lines diff --git a/api/core/tools/provider/builtin/xinference/xinference.py b/api/core/tools/provider/builtin/xinference/xinference.py new file mode 100644 index 00000000000000..7c2428cc00534e --- /dev/null +++ b/api/core/tools/provider/builtin/xinference/xinference.py @@ -0,0 +1,18 @@ +import requests + +from core.tools.errors import ToolProviderCredentialValidationError +from core.tools.provider.builtin_tool_provider import BuiltinToolProviderController + + +class XinferenceProvider(BuiltinToolProviderController): + def _validate_credentials(self, credentials: dict) -> None: + base_url = credentials.get("base_url") + api_key = credentials.get("api_key") + model = credentials.get("model") + res = requests.post( + f"{base_url}/sdapi/v1/options", + headers={"Authorization": f"Bearer {api_key}"}, + json={"sd_model_checkpoint": model}, + ) + if res.status_code != 200: + raise ToolProviderCredentialValidationError("Xinference API key is invalid") diff --git a/api/core/tools/provider/builtin/xinference/xinference.yaml b/api/core/tools/provider/builtin/xinference/xinference.yaml new file mode 100644 index 00000000000000..19aaf5cbd1398d --- /dev/null +++ b/api/core/tools/provider/builtin/xinference/xinference.yaml @@ -0,0 +1,40 @@ +identity: + author: xinference + name: xinference + label: + en_US: Xinference + zh_Hans: Xinference + description: + zh_Hans: Xinference 提供的兼容 Stable Diffusion web ui 的图片生成 API。 + en_US: Stable Diffusion web ui compatible API provided by Xinference. + icon: icon.png + tags: + - image +credentials_for_provider: + base_url: + type: secret-input + required: true + label: + en_US: Base URL + zh_Hans: Xinference 服务器的 Base URL + placeholder: + en_US: Please input Xinference server's Base URL + zh_Hans: 请输入 Xinference 服务器的 Base URL + model: + type: text-input + required: true + label: + en_US: Model + zh_Hans: 模型 + placeholder: + en_US: Please input your model name + zh_Hans: 请输入你的模型名称 + api_key: + type: secret-input + required: true + label: + en_US: API Key + zh_Hans: Xinference 服务器的 API Key + placeholder: + en_US: Please input Xinference server's API Key + zh_Hans: 请输入 Xinference 服务器的 API Key diff --git a/api/core/tools/provider/builtin/youtube/youtube.py b/api/core/tools/provider/builtin/youtube/youtube.py index aad876491c85dc..07e430bcbf27e1 100644 --- a/api/core/tools/provider/builtin/youtube/youtube.py +++ b/api/core/tools/provider/builtin/youtube/youtube.py @@ -13,7 +13,7 @@ def _validate_credentials(self, credentials: dict) -> None: ).invoke( user_id="", tool_parameters={ - "channel": "TOKYO GIRLS COLLECTION", + "channel": "UC2JZCsZSOudXA08cMMRCL9g", "start_date": "2020-01-01", "end_date": "2024-12-31", }, diff --git a/api/core/tools/provider/tool_provider.py b/api/core/tools/provider/tool_provider.py index 05c88b904e4a6d..321b21201414e8 100644 --- a/api/core/tools/provider/tool_provider.py +++ b/api/core/tools/provider/tool_provider.py @@ -153,6 +153,9 @@ def validate_credentials_format(self, credentials: dict[str, Any]) -> None: # check type credential_schema = credentials_need_to_validate[credential_name] + if not credential_schema.required and credentials[credential_name] is None: + continue + if credential_schema.type in { ToolProviderCredentials.CredentialsType.SECRET_INPUT, ToolProviderCredentials.CredentialsType.TEXT_INPUT, diff --git a/api/core/tools/utils/feishu_api_utils.py b/api/core/tools/utils/feishu_api_utils.py index ffdb06498fd519..ce1fd7dc19e808 100644 --- a/api/core/tools/utils/feishu_api_utils.py +++ b/api/core/tools/utils/feishu_api_utils.py @@ -1,9 +1,23 @@ import httpx +from core.tools.errors import ToolProviderCredentialValidationError from extensions.ext_redis import redis_client +def auth(credentials): + app_id = credentials.get("app_id") + app_secret = credentials.get("app_secret") + if not app_id or not app_secret: + raise ToolProviderCredentialValidationError("app_id and app_secret is required") + try: + assert FeishuRequest(app_id, app_secret).tenant_access_token is not None + except Exception as e: + raise ToolProviderCredentialValidationError(str(e)) + + class FeishuRequest: + API_BASE_URL = "https://lark-plugin-api.solutionsuite.cn/lark-plugin" + def __init__(self, app_id: str, app_secret: str): self.app_id = app_id self.app_secret = app_secret @@ -42,7 +56,7 @@ def get_tenant_access_token(self, app_id: str, app_secret: str) -> dict: "expire": 7200 } """ - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/access_token/get_tenant_access_token" + url = f"{self.API_BASE_URL}/access_token/get_tenant_access_token" payload = {"app_id": app_id, "app_secret": app_secret} res = self._send_request(url, require_token=False, payload=payload) return res @@ -63,7 +77,7 @@ def create_document(self, title: str, content: str, folder_token: str) -> dict: "msg": "创建飞书文档成功，请查看" } """ - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/create_document" + url = f"{self.API_BASE_URL}/document/create_document" payload = { "title": title, "content": content, @@ -72,13 +86,13 @@ def create_document(self, title: str, content: str, folder_token: str) -> dict: res = self._send_request(url, payload=payload) return res.get("data") - def write_document(self, document_id: str, content: str, position: str = "start") -> dict: - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/write_document" + def write_document(self, document_id: str, content: str, position: str = "end") -> dict: + url = f"{self.API_BASE_URL}/document/write_document" payload = {"document_id": document_id, "content": content, "position": position} res = self._send_request(url, payload=payload) return res - def get_document_content(self, document_id: str, mode: str, lang: int = 0) -> dict: + def get_document_content(self, document_id: str, mode: str = "markdown", lang: str = "0") -> dict: """ API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/raw_content Example Response: @@ -95,45 +109,404 @@ def get_document_content(self, document_id: str, mode: str, lang: int = 0) -> di "mode": mode, "lang": lang, } - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/get_document_content" - res = self._send_request(url, method="get", params=params) + url = f"{self.API_BASE_URL}/document/get_document_content" + res = self._send_request(url, method="GET", params=params) return res.get("data").get("content") - def list_document_blocks(self, document_id: str, page_token: str, page_size: int = 500) -> dict: + def list_document_blocks( + self, document_id: str, page_token: str, user_id_type: str = "open_id", page_size: int = 500 + ) -> dict: """ API url: https://open.larkoffice.com/document/server-docs/docs/docs/docx-v1/document/list """ - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/document/list_document_blocks" params = { + "user_id_type": user_id_type, "document_id": document_id, "page_size": page_size, "page_token": page_token, } - res = self._send_request(url, method="get", params=params) + url = f"{self.API_BASE_URL}/document/list_document_blocks" + res = self._send_request(url, method="GET", params=params) return res.get("data") def send_bot_message(self, receive_id_type: str, receive_id: str, msg_type: str, content: str) -> dict: """ API url: https://open.larkoffice.com/document/server-docs/im-v1/message/create """ - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_bot_message" + url = f"{self.API_BASE_URL}/message/send_bot_message" params = { "receive_id_type": receive_id_type, } payload = { "receive_id": receive_id, "msg_type": msg_type, - "content": content, + "content": content.strip('"').replace(r"\"", '"').replace(r"\\", "\\"), } res = self._send_request(url, params=params, payload=payload) return res.get("data") def send_webhook_message(self, webhook: str, msg_type: str, content: str) -> dict: - url = "https://lark-plugin-api.solutionsuite.cn/lark-plugin/message/send_webhook_message" + url = f"{self.API_BASE_URL}/message/send_webhook_message" payload = { "webhook": webhook, "msg_type": msg_type, - "content": content, + "content": content.strip('"').replace(r"\"", '"').replace(r"\\", "\\"), } res = self._send_request(url, require_token=False, payload=payload) return res + + def get_chat_messages( + self, + container_id: str, + start_time: str, + end_time: str, + page_token: str, + sort_type: str = "ByCreateTimeAsc", + page_size: int = 20, + ) -> dict: + """ + API url: https://open.larkoffice.com/document/server-docs/im-v1/message/list + """ + url = f"{self.API_BASE_URL}/message/get_chat_messages" + params = { + "container_id": container_id, + "start_time": start_time, + "end_time": end_time, + "sort_type": sort_type, + "page_token": page_token, + "page_size": page_size, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def get_thread_messages( + self, container_id: str, page_token: str, sort_type: str = "ByCreateTimeAsc", page_size: int = 20 + ) -> dict: + """ + API url: https://open.larkoffice.com/document/server-docs/im-v1/message/list + """ + url = f"{self.API_BASE_URL}/message/get_thread_messages" + params = { + "container_id": container_id, + "sort_type": sort_type, + "page_token": page_token, + "page_size": page_size, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def create_task(self, summary: str, start_time: str, end_time: str, completed_time: str, description: str) -> dict: + # 创建任务 + url = f"{self.API_BASE_URL}/task/create_task" + payload = { + "summary": summary, + "start_time": start_time, + "end_time": end_time, + "completed_at": completed_time, + "description": description, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def update_task( + self, task_guid: str, summary: str, start_time: str, end_time: str, completed_time: str, description: str + ) -> dict: + # 更新任务 + url = f"{self.API_BASE_URL}/task/update_task" + payload = { + "task_guid": task_guid, + "summary": summary, + "start_time": start_time, + "end_time": end_time, + "completed_time": completed_time, + "description": description, + } + res = self._send_request(url, method="PATCH", payload=payload) + return res.get("data") + + def delete_task(self, task_guid: str) -> dict: + # 删除任务 + url = f"{self.API_BASE_URL}/task/delete_task" + payload = { + "task_guid": task_guid, + } + res = self._send_request(url, method="DELETE", payload=payload) + return res + + def add_members(self, task_guid: str, member_phone_or_email: str, member_role: str) -> dict: + # 删除任务 + url = f"{self.API_BASE_URL}/task/add_members" + payload = { + "task_guid": task_guid, + "member_phone_or_email": member_phone_or_email, + "member_role": member_role, + } + res = self._send_request(url, payload=payload) + return res + + def get_wiki_nodes(self, space_id: str, parent_node_token: str, page_token: str, page_size: int = 20) -> dict: + # 获取知识库全部子节点列表 + url = f"{self.API_BASE_URL}/wiki/get_wiki_nodes" + payload = { + "space_id": space_id, + "parent_node_token": parent_node_token, + "page_token": page_token, + "page_size": page_size, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def get_primary_calendar(self, user_id_type: str = "open_id") -> dict: + url = f"{self.API_BASE_URL}/calendar/get_primary_calendar" + params = { + "user_id_type": user_id_type, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def create_event( + self, + summary: str, + description: str, + start_time: str, + end_time: str, + attendee_ability: str, + need_notification: bool = True, + auto_record: bool = False, + ) -> dict: + url = f"{self.API_BASE_URL}/calendar/create_event" + payload = { + "summary": summary, + "description": description, + "need_notification": need_notification, + "start_time": start_time, + "end_time": end_time, + "auto_record": auto_record, + "attendee_ability": attendee_ability, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def update_event( + self, + event_id: str, + summary: str, + description: str, + need_notification: bool, + start_time: str, + end_time: str, + auto_record: bool, + ) -> dict: + url = f"{self.API_BASE_URL}/calendar/update_event/{event_id}" + payload = {} + if summary: + payload["summary"] = summary + if description: + payload["description"] = description + if start_time: + payload["start_time"] = start_time + if end_time: + payload["end_time"] = end_time + if need_notification: + payload["need_notification"] = need_notification + if auto_record: + payload["auto_record"] = auto_record + res = self._send_request(url, method="PATCH", payload=payload) + return res + + def delete_event(self, event_id: str, need_notification: bool = True) -> dict: + url = f"{self.API_BASE_URL}/calendar/delete_event/{event_id}" + params = { + "need_notification": need_notification, + } + res = self._send_request(url, method="DELETE", params=params) + return res + + def list_events(self, start_time: str, end_time: str, page_token: str, page_size: int = 50) -> dict: + url = f"{self.API_BASE_URL}/calendar/list_events" + params = { + "start_time": start_time, + "end_time": end_time, + "page_token": page_token, + "page_size": page_size, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def search_events( + self, + query: str, + start_time: str, + end_time: str, + page_token: str, + user_id_type: str = "open_id", + page_size: int = 20, + ) -> dict: + url = f"{self.API_BASE_URL}/calendar/search_events" + payload = { + "query": query, + "start_time": start_time, + "end_time": end_time, + "page_token": page_token, + "user_id_type": user_id_type, + "page_size": page_size, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def add_event_attendees(self, event_id: str, attendee_phone_or_email: str, need_notification: bool = True) -> dict: + # 参加日程参会人 + url = f"{self.API_BASE_URL}/calendar/add_event_attendees" + payload = { + "event_id": event_id, + "attendee_phone_or_email": attendee_phone_or_email, + "need_notification": need_notification, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def create_spreadsheet( + self, + title: str, + folder_token: str, + ) -> dict: + # 创建电子表格 + url = f"{self.API_BASE_URL}/spreadsheet/create_spreadsheet" + payload = { + "title": title, + "folder_token": folder_token, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def get_spreadsheet( + self, + spreadsheet_token: str, + user_id_type: str = "open_id", + ) -> dict: + # 获取电子表格信息 + url = f"{self.API_BASE_URL}/spreadsheet/get_spreadsheet" + params = { + "spreadsheet_token": spreadsheet_token, + "user_id_type": user_id_type, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def list_spreadsheet_sheets( + self, + spreadsheet_token: str, + ) -> dict: + # 列出电子表格的所有工作表 + url = f"{self.API_BASE_URL}/spreadsheet/list_spreadsheet_sheets" + params = { + "spreadsheet_token": spreadsheet_token, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def add_rows( + self, + spreadsheet_token: str, + sheet_id: str, + sheet_name: str, + length: int, + values: str, + ) -> dict: + # 增加行,在工作表最后添加 + url = f"{self.API_BASE_URL}/spreadsheet/add_rows" + payload = { + "spreadsheet_token": spreadsheet_token, + "sheet_id": sheet_id, + "sheet_name": sheet_name, + "length": length, + "values": values, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def add_cols( + self, + spreadsheet_token: str, + sheet_id: str, + sheet_name: str, + length: int, + values: str, + ) -> dict: + # 增加列,在工作表最后添加 + url = f"{self.API_BASE_URL}/spreadsheet/add_cols" + payload = { + "spreadsheet_token": spreadsheet_token, + "sheet_id": sheet_id, + "sheet_name": sheet_name, + "length": length, + "values": values, + } + res = self._send_request(url, payload=payload) + return res.get("data") + + def read_rows( + self, + spreadsheet_token: str, + sheet_id: str, + sheet_name: str, + start_row: int, + num_rows: int, + user_id_type: str = "open_id", + ) -> dict: + # 读取工作表行数据 + url = f"{self.API_BASE_URL}/spreadsheet/read_rows" + params = { + "spreadsheet_token": spreadsheet_token, + "sheet_id": sheet_id, + "sheet_name": sheet_name, + "start_row": start_row, + "num_rows": num_rows, + "user_id_type": user_id_type, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def read_cols( + self, + spreadsheet_token: str, + sheet_id: str, + sheet_name: str, + start_col: int, + num_cols: int, + user_id_type: str = "open_id", + ) -> dict: + # 读取工作表列数据 + url = f"{self.API_BASE_URL}/spreadsheet/read_cols" + params = { + "spreadsheet_token": spreadsheet_token, + "sheet_id": sheet_id, + "sheet_name": sheet_name, + "start_col": start_col, + "num_cols": num_cols, + "user_id_type": user_id_type, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") + + def read_table( + self, + spreadsheet_token: str, + sheet_id: str, + sheet_name: str, + num_range: str, + query: str, + user_id_type: str = "open_id", + ) -> dict: + # 自定义读取行列数据 + url = f"{self.API_BASE_URL}/spreadsheet/read_table" + params = { + "spreadsheet_token": spreadsheet_token, + "sheet_id": sheet_id, + "sheet_name": sheet_name, + "range": num_range, + "query": query, + "user_id_type": user_id_type, + } + res = self._send_request(url, method="GET", params=params) + return res.get("data") diff --git a/api/core/workflow/nodes/end/end_stream_processor.py b/api/core/workflow/nodes/end/end_stream_processor.py index 0366d7965d7c17..1aecf863ac5fb9 100644 --- a/api/core/workflow/nodes/end/end_stream_processor.py +++ b/api/core/workflow/nodes/end/end_stream_processor.py @@ -22,8 +22,8 @@ def __init__(self, graph: Graph, variable_pool: VariablePool) -> None: for end_node_id, _ in self.end_stream_param.end_stream_variable_selector_mapping.items(): self.route_position[end_node_id] = 0 self.current_stream_chunk_generating_node_ids: dict[str, list[str]] = {} - self.has_outputed = False - self.outputed_node_ids = set() + self.has_output = False + self.output_node_ids = set() def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generator[GraphEngineEvent, None, None]: for event in generator: @@ -34,11 +34,11 @@ def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generat yield event elif isinstance(event, NodeRunStreamChunkEvent): if event.in_iteration_id: - if self.has_outputed and event.node_id not in self.outputed_node_ids: + if self.has_output and event.node_id not in self.output_node_ids: event.chunk_content = "\n" + event.chunk_content - self.outputed_node_ids.add(event.node_id) - self.has_outputed = True + self.output_node_ids.add(event.node_id) + self.has_output = True yield event continue @@ -53,11 +53,11 @@ def process(self, generator: Generator[GraphEngineEvent, None, None]) -> Generat ) if stream_out_end_node_ids: - if self.has_outputed and event.node_id not in self.outputed_node_ids: + if self.has_output and event.node_id not in self.output_node_ids: event.chunk_content = "\n" + event.chunk_content - self.outputed_node_ids.add(event.node_id) - self.has_outputed = True + self.output_node_ids.add(event.node_id) + self.has_output = True yield event elif isinstance(event, NodeRunSucceededEvent): yield event @@ -124,11 +124,11 @@ def _generate_stream_outputs_when_node_finished( if text: current_node_id = value_selector[0] - if self.has_outputed and current_node_id not in self.outputed_node_ids: + if self.has_output and current_node_id not in self.output_node_ids: text = "\n" + text - self.outputed_node_ids.add(current_node_id) - self.has_outputed = True + self.output_node_ids.add(current_node_id) + self.has_output = True yield NodeRunStreamChunkEvent( id=event.id, node_id=event.node_id, diff --git a/api/events/event_handlers/create_document_index.py b/api/events/event_handlers/create_document_index.py index 54f6a76e167231..5af45e1e5026df 100644 --- a/api/events/event_handlers/create_document_index.py +++ b/api/events/event_handlers/create_document_index.py @@ -14,7 +14,7 @@ @document_index_created.connect def handle(sender, **kwargs): dataset_id = sender - document_ids = kwargs.get("document_ids", None) + document_ids = kwargs.get("document_ids") documents = [] start_at = time.perf_counter() for document_id in document_ids: diff --git a/api/poetry.lock b/api/poetry.lock index 78816683d8d24d..85c68cd75f1292 100644 --- a/api/poetry.lock +++ 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"eb7ef7be5c7790e214f37f17f92b69407ad557cb80055ef7e49e36eb51b3fca6" +content-hash = "c4580c22e2b220c8c80dbc3f765060a09e14874ed29b690c13a533bf0365e789" diff --git a/api/pyproject.toml b/api/pyproject.toml index 506f379aaf116f..e737761f3b2c0b 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -28,7 +28,6 @@ select = [ "PLR0402", # manual-from-import "PLR1711", # useless-return "PLR1714", # repeated-equality-comparison - "PLR6201", # literal-membership "RUF019", # unnecessary-key-check "RUF100", # unused-noqa "RUF101", # redirected-noqa @@ -101,6 +100,7 @@ exclude = [ OPENAI_API_KEY = "sk-IamNotARealKeyJustForMockTestKawaiiiiiiiiii" UPSTAGE_API_KEY = "up-aaaaaaaaaaaaaaaaaaaa" FIREWORKS_API_KEY = "fw_aaaaaaaaaaaaaaaaaaaa" +NOMIC_API_KEY = "nk-aaaaaaaaaaaaaaaaaaaa" AZURE_OPENAI_API_BASE = "https://difyai-openai.openai.azure.com" AZURE_OPENAI_API_KEY = "xxxxb1707exxxxxxxxxxaaxxxxxf94" ANTHROPIC_API_KEY = "sk-ant-api11-IamNotARealKeyJustForMockTestKawaiiiiiiiiii-NotBaka-ASkksz" @@ -122,6 +122,8 @@ CODE_EXECUTION_API_KEY = "dify-sandbox" FIRECRAWL_API_KEY = "fc-" TEI_EMBEDDING_SERVER_URL = "http://a.abc.com:11451" TEI_RERANK_SERVER_URL = "http://a.abc.com:11451" +MIXEDBREAD_API_KEY = "mk-aaaaaaaaaaaaaaaaaaaa" +VOYAGE_API_KEY = "va-aaaaaaaaaaaaaaaaaaaa" [tool.poetry] name = "dify-api" @@ -218,6 +220,7 @@ azure-ai-inference = "^1.0.0b3" volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"} oci = "^2.133.0" tos = "^2.7.1" +nomic = "^3.1.2" [tool.poetry.group.indriect.dependencies] kaleido = "0.2.1" rank-bm25 = "~0.2.2" @@ -251,7 +254,7 @@ alibabacloud_gpdb20160503 = "~3.8.0" alibabacloud_tea_openapi = "~0.3.9" chromadb = "0.5.1" clickhouse-connect = "~0.7.16" -elasticsearch = "8.14.0" +elasticsearch = "~8.15.1" oracledb = "~2.2.1" pgvecto-rs = { version = "~0.2.1", extras = ['sqlalchemy'] } pgvector = "0.2.5" @@ -284,4 +287,4 @@ optional = true [tool.poetry.group.lint.dependencies] dotenv-linter = "~0.5.0" -ruff = "~0.6.5" +ruff = "~0.6.8" diff --git a/api/services/dataset_service.py b/api/services/dataset_service.py index 30c010ef29f623..e96f06ed40fa4f 100644 --- a/api/services/dataset_service.py +++ b/api/services/dataset_service.py @@ -1100,8 +1100,8 @@ def document_create_args_validate(cls, args: dict): DocumentService.data_source_args_validate(args) DocumentService.process_rule_args_validate(args) else: - if ("data_source" not in args and not args["data_source"]) and ( - "process_rule" not in args and not args["process_rule"] + if ("data_source" not in args or not args["data_source"]) and ( + "process_rule" not in args or not args["process_rule"] ): raise ValueError("Data source or Process rule is required") else: diff --git a/api/tests/integration_tests/model_runtime/__mock/nomic_embeddings.py b/api/tests/integration_tests/model_runtime/__mock/nomic_embeddings.py new file mode 100644 index 00000000000000..281e866e45c2e9 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/__mock/nomic_embeddings.py @@ -0,0 +1,59 @@ +import os +from collections.abc import Callable +from typing import Any, Literal, Union + +import pytest + +# import monkeypatch +from _pytest.monkeypatch import MonkeyPatch +from nomic import embed + + +def create_embedding(texts: list[str], model: str, **kwargs: Any) -> dict: + texts_len = len(texts) + + foo_embedding_sample = 0.123456 + + combined = { + "embeddings": [[foo_embedding_sample for _ in range(768)] for _ in range(texts_len)], + "usage": {"prompt_tokens": texts_len, "total_tokens": texts_len}, + "model": model, + "inference_mode": "remote", + } + + return combined + + +def mock_nomic( + monkeypatch: MonkeyPatch, + methods: list[Literal["text_embedding"]], +) -> Callable[[], None]: + """ + mock nomic module + + :param monkeypatch: pytest monkeypatch fixture + :return: unpatch function + """ + + def unpatch() -> None: + monkeypatch.undo() + + if "text_embedding" in methods: + monkeypatch.setattr(embed, "text", create_embedding) + + return unpatch + + +MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true" + + +@pytest.fixture +def setup_nomic_mock(request, monkeypatch): + methods = request.param if hasattr(request, "param") else [] + if MOCK: + unpatch = mock_nomic(monkeypatch, methods=methods) + + yield + + if MOCK: + unpatch() diff --git a/api/tests/integration_tests/model_runtime/fireworks/test_text_embedding.py b/api/tests/integration_tests/model_runtime/fireworks/test_text_embedding.py new file mode 100644 index 00000000000000..7bf723b3a93742 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/fireworks/test_text_embedding.py @@ -0,0 +1,54 @@ +import os + +import pytest + +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.fireworks.text_embedding.text_embedding import FireworksTextEmbeddingModel +from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock + + +@pytest.mark.parametrize("setup_openai_mock", [["text_embedding"]], indirect=True) +def test_validate_credentials(setup_openai_mock): + model = FireworksTextEmbeddingModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials( + model="nomic-ai/nomic-embed-text-v1.5", credentials={"fireworks_api_key": "invalid_key"} + ) + + model.validate_credentials( + model="nomic-ai/nomic-embed-text-v1.5", credentials={"fireworks_api_key": os.environ.get("FIREWORKS_API_KEY")} + ) + + +@pytest.mark.parametrize("setup_openai_mock", [["text_embedding"]], indirect=True) +def test_invoke_model(setup_openai_mock): + model = FireworksTextEmbeddingModel() + + result = model.invoke( + model="nomic-ai/nomic-embed-text-v1.5", + credentials={ + "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY"), + }, + texts=["hello", "world", " ".join(["long_text"] * 100), " ".join(["another_long_text"] * 100)], + user="foo", + ) + + assert isinstance(result, TextEmbeddingResult) + assert len(result.embeddings) == 4 + assert result.usage.total_tokens == 2 + + +def test_get_num_tokens(): + model = FireworksTextEmbeddingModel() + + num_tokens = model.get_num_tokens( + model="nomic-ai/nomic-embed-text-v1.5", + credentials={ + "fireworks_api_key": os.environ.get("FIREWORKS_API_KEY"), + }, + texts=["hello", "world"], + ) + + assert num_tokens == 2 diff --git a/api/tests/integration_tests/model_runtime/mixedbread/__init__.py b/api/tests/integration_tests/model_runtime/mixedbread/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/tests/integration_tests/model_runtime/mixedbread/test_provider.py b/api/tests/integration_tests/model_runtime/mixedbread/test_provider.py new file mode 100644 index 00000000000000..25c9f3ce8dffa9 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/mixedbread/test_provider.py @@ -0,0 +1,28 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.mixedbread.mixedbread import MixedBreadProvider + + +def test_validate_provider_credentials(): + provider = MixedBreadProvider() + + with pytest.raises(CredentialsValidateFailedError): + provider.validate_provider_credentials(credentials={"api_key": "hahahaha"}) + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "usage": {"prompt_tokens": 3, "total_tokens": 3}, + "model": "mixedbread-ai/mxbai-embed-large-v1", + "data": [{"embedding": [0.23333 for _ in range(1024)], "index": 0, "object": "embedding"}], + "object": "list", + "normalized": "true", + "encoding_format": "float", + "dimensions": 1024, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + provider.validate_provider_credentials(credentials={"api_key": os.environ.get("MIXEDBREAD_API_KEY")}) diff --git a/api/tests/integration_tests/model_runtime/mixedbread/test_rerank.py b/api/tests/integration_tests/model_runtime/mixedbread/test_rerank.py new file mode 100644 index 00000000000000..b65aab74aa96d3 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/mixedbread/test_rerank.py @@ -0,0 +1,100 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.entities.rerank_entities import RerankResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.mixedbread.rerank.rerank import MixedBreadRerankModel + + +def test_validate_credentials(): + model = MixedBreadRerankModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials( + model="mxbai-rerank-large-v1", + credentials={"api_key": "invalid_key"}, + ) + with patch("httpx.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "usage": {"prompt_tokens": 86, "total_tokens": 86}, + "model": "mixedbread-ai/mxbai-rerank-large-v1", + "data": [ + { + "index": 0, + "score": 0.06762695, + "input": "Carson City is the capital city of the American state of Nevada. At the 2010 United " + "States Census, Carson City had a population of 55,274.", + "object": "text_document", + }, + { + "index": 1, + "score": 0.057403564, + "input": "The Commonwealth of the Northern Mariana Islands is a group of islands in the Pacific " + "Ocean that are a political division controlled by the United States. Its capital is " + "Saipan.", + "object": "text_document", + }, + ], + "object": "list", + "top_k": 2, + "return_input": True, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + model.validate_credentials( + model="mxbai-rerank-large-v1", + credentials={ + "api_key": os.environ.get("MIXEDBREAD_API_KEY"), + }, + ) + + +def test_invoke_model(): + model = MixedBreadRerankModel() + with patch("httpx.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "usage": {"prompt_tokens": 56, "total_tokens": 56}, + "model": "mixedbread-ai/mxbai-rerank-large-v1", + "data": [ + { + "index": 0, + "score": 0.6044922, + "input": "Kasumi is a girl name of Japanese origin meaning mist.", + "object": "text_document", + }, + { + "index": 1, + "score": 0.0703125, + "input": "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she leads a " + "team named PopiParty.", + "object": "text_document", + }, + ], + "object": "list", + "top_k": 2, + "return_input": "true", + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + result = model.invoke( + model="mxbai-rerank-large-v1", + credentials={ + "api_key": os.environ.get("MIXEDBREAD_API_KEY"), + }, + query="Who is Kasumi?", + docs=[ + "Kasumi is a girl name of Japanese origin meaning mist.", + "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she leads a team named " + "PopiParty.", + ], + score_threshold=0.5, + ) + + assert isinstance(result, RerankResult) + assert len(result.docs) == 1 + assert result.docs[0].index == 0 + assert result.docs[0].score >= 0.5 diff --git a/api/tests/integration_tests/model_runtime/mixedbread/test_text_embedding.py b/api/tests/integration_tests/model_runtime/mixedbread/test_text_embedding.py new file mode 100644 index 00000000000000..ca97a1895113f0 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/mixedbread/test_text_embedding.py @@ -0,0 +1,78 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.mixedbread.text_embedding.text_embedding import MixedBreadTextEmbeddingModel + + +def test_validate_credentials(): + model = MixedBreadTextEmbeddingModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials(model="mxbai-embed-large-v1", credentials={"api_key": "invalid_key"}) + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "usage": {"prompt_tokens": 3, "total_tokens": 3}, + "model": "mixedbread-ai/mxbai-embed-large-v1", + "data": [{"embedding": [0.23333 for _ in range(1024)], "index": 0, "object": "embedding"}], + "object": "list", + "normalized": "true", + "encoding_format": "float", + "dimensions": 1024, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + model.validate_credentials( + model="mxbai-embed-large-v1", credentials={"api_key": os.environ.get("MIXEDBREAD_API_KEY")} + ) + + +def test_invoke_model(): + model = MixedBreadTextEmbeddingModel() + + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "usage": {"prompt_tokens": 6, "total_tokens": 6}, + "model": "mixedbread-ai/mxbai-embed-large-v1", + "data": [ + {"embedding": [0.23333 for _ in range(1024)], "index": 0, "object": "embedding"}, + {"embedding": [0.23333 for _ in range(1024)], "index": 1, "object": "embedding"}, + ], + "object": "list", + "normalized": "true", + "encoding_format": "float", + "dimensions": 1024, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + result = model.invoke( + model="mxbai-embed-large-v1", + credentials={ + "api_key": os.environ.get("MIXEDBREAD_API_KEY"), + }, + texts=["hello", "world"], + user="abc-123", + ) + + assert isinstance(result, TextEmbeddingResult) + assert len(result.embeddings) == 2 + assert result.usage.total_tokens == 6 + + +def test_get_num_tokens(): + model = MixedBreadTextEmbeddingModel() + + num_tokens = model.get_num_tokens( + model="mxbai-embed-large-v1", + credentials={ + "api_key": os.environ.get("MIXEDBREAD_API_KEY"), + }, + texts=["ping"], + ) + + assert num_tokens == 1 diff --git a/api/tests/integration_tests/model_runtime/nomic/__init__.py b/api/tests/integration_tests/model_runtime/nomic/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/tests/integration_tests/model_runtime/nomic/test_embeddings.py b/api/tests/integration_tests/model_runtime/nomic/test_embeddings.py new file mode 100644 index 00000000000000..52dc96ee95c1bc --- /dev/null +++ b/api/tests/integration_tests/model_runtime/nomic/test_embeddings.py @@ -0,0 +1,62 @@ +import os + +import pytest + +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.nomic.text_embedding.text_embedding import NomicTextEmbeddingModel +from tests.integration_tests.model_runtime.__mock.nomic_embeddings import setup_nomic_mock + + +@pytest.mark.parametrize("setup_nomic_mock", [["text_embedding"]], indirect=True) +def test_validate_credentials(setup_nomic_mock): + model = NomicTextEmbeddingModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials( + model="nomic-embed-text-v1.5", + credentials={ + "nomic_api_key": "invalid_key", + }, + ) + + model.validate_credentials( + model="nomic-embed-text-v1.5", + credentials={ + "nomic_api_key": os.environ.get("NOMIC_API_KEY"), + }, + ) + + +@pytest.mark.parametrize("setup_nomic_mock", [["text_embedding"]], indirect=True) +def test_invoke_model(setup_nomic_mock): + model = NomicTextEmbeddingModel() + + result = model.invoke( + model="nomic-embed-text-v1.5", + credentials={ + "nomic_api_key": os.environ.get("NOMIC_API_KEY"), + }, + texts=["hello", "world"], + user="foo", + ) + + assert isinstance(result, TextEmbeddingResult) + assert result.model == "nomic-embed-text-v1.5" + assert len(result.embeddings) == 2 + assert result.usage.total_tokens == 2 + + +@pytest.mark.parametrize("setup_nomic_mock", [["text_embedding"]], indirect=True) +def test_get_num_tokens(setup_nomic_mock): + model = NomicTextEmbeddingModel() + + num_tokens = model.get_num_tokens( + model="nomic-embed-text-v1.5", + credentials={ + "nomic_api_key": os.environ.get("NOMIC_API_KEY"), + }, + texts=["hello", "world"], + ) + + assert num_tokens == 2 diff --git a/api/tests/integration_tests/model_runtime/nomic/test_provider.py b/api/tests/integration_tests/model_runtime/nomic/test_provider.py new file mode 100644 index 00000000000000..6cad400c069555 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/nomic/test_provider.py @@ -0,0 +1,22 @@ +import os + +import pytest + +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.nomic.nomic import NomicAtlasProvider +from core.model_runtime.model_providers.nomic.text_embedding.text_embedding import NomicTextEmbeddingModel +from tests.integration_tests.model_runtime.__mock.nomic_embeddings import setup_nomic_mock + + +@pytest.mark.parametrize("setup_nomic_mock", [["text_embedding"]], indirect=True) +def test_validate_provider_credentials(setup_nomic_mock): + provider = NomicAtlasProvider() + + with pytest.raises(CredentialsValidateFailedError): + provider.validate_provider_credentials(credentials={}) + + provider.validate_provider_credentials( + credentials={ + "nomic_api_key": os.environ.get("NOMIC_API_KEY"), + }, + ) diff --git a/api/tests/integration_tests/model_runtime/voyage/__init__.py b/api/tests/integration_tests/model_runtime/voyage/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/api/tests/integration_tests/model_runtime/voyage/test_provider.py b/api/tests/integration_tests/model_runtime/voyage/test_provider.py new file mode 100644 index 00000000000000..08978c88a961e7 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/voyage/test_provider.py @@ -0,0 +1,25 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.voyage.voyage import VoyageProvider + + +def test_validate_provider_credentials(): + provider = VoyageProvider() + + with pytest.raises(CredentialsValidateFailedError): + provider.validate_provider_credentials(credentials={"api_key": "hahahaha"}) + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "object": "list", + "data": [{"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0}], + "model": "voyage-3", + "usage": {"total_tokens": 1}, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + provider.validate_provider_credentials(credentials={"api_key": os.environ.get("VOYAGE_API_KEY")}) diff --git a/api/tests/integration_tests/model_runtime/voyage/test_rerank.py b/api/tests/integration_tests/model_runtime/voyage/test_rerank.py new file mode 100644 index 00000000000000..e97a9e4c811c82 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/voyage/test_rerank.py @@ -0,0 +1,92 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.entities.rerank_entities import RerankResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.voyage.rerank.rerank import VoyageRerankModel + + +def test_validate_credentials(): + model = VoyageRerankModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials( + model="rerank-lite-1", + credentials={"api_key": "invalid_key"}, + ) + with patch("httpx.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "object": "list", + "data": [ + { + "relevance_score": 0.546875, + "index": 0, + "document": "Carson City is the capital city of the American state of Nevada. At the 2010 United " + "States Census, Carson City had a population of 55,274.", + }, + { + "relevance_score": 0.4765625, + "index": 1, + "document": "The Commonwealth of the Northern Mariana Islands is a group of islands in the " + "Pacific Ocean that are a political division controlled by the United States. Its " + "capital is Saipan.", + }, + ], + "model": "rerank-lite-1", + "usage": {"total_tokens": 96}, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + model.validate_credentials( + model="rerank-lite-1", + credentials={ + "api_key": os.environ.get("VOYAGE_API_KEY"), + }, + ) + + +def test_invoke_model(): + model = VoyageRerankModel() + with patch("httpx.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "object": "list", + "data": [ + { + "relevance_score": 0.84375, + "index": 0, + "document": "Kasumi is a girl name of Japanese origin meaning mist.", + }, + { + "relevance_score": 0.4765625, + "index": 1, + "document": "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she " + "leads a team named PopiParty.", + }, + ], + "model": "rerank-lite-1", + "usage": {"total_tokens": 59}, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + result = model.invoke( + model="rerank-lite-1", + credentials={ + "api_key": os.environ.get("VOYAGE_API_KEY"), + }, + query="Who is Kasumi?", + docs=[ + "Kasumi is a girl name of Japanese origin meaning mist.", + "Her music is a kawaii bass, a mix of future bass, pop, and kawaii music and she leads a team named " + "PopiParty.", + ], + score_threshold=0.5, + ) + + assert isinstance(result, RerankResult) + assert len(result.docs) == 1 + assert result.docs[0].index == 0 + assert result.docs[0].score >= 0.5 diff --git a/api/tests/integration_tests/model_runtime/voyage/test_text_embedding.py b/api/tests/integration_tests/model_runtime/voyage/test_text_embedding.py new file mode 100644 index 00000000000000..75719672a9ecc9 --- /dev/null +++ b/api/tests/integration_tests/model_runtime/voyage/test_text_embedding.py @@ -0,0 +1,70 @@ +import os +from unittest.mock import Mock, patch + +import pytest + +from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult +from core.model_runtime.errors.validate import CredentialsValidateFailedError +from core.model_runtime.model_providers.voyage.text_embedding.text_embedding import VoyageTextEmbeddingModel + + +def test_validate_credentials(): + model = VoyageTextEmbeddingModel() + + with pytest.raises(CredentialsValidateFailedError): + model.validate_credentials(model="voyage-3", credentials={"api_key": "invalid_key"}) + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "object": "list", + "data": [{"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0}], + "model": "voyage-3", + "usage": {"total_tokens": 1}, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + model.validate_credentials(model="voyage-3", credentials={"api_key": os.environ.get("VOYAGE_API_KEY")}) + + +def test_invoke_model(): + model = VoyageTextEmbeddingModel() + + with patch("requests.post") as mock_post: + mock_response = Mock() + mock_response.json.return_value = { + "object": "list", + "data": [ + {"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 0}, + {"object": "embedding", "embedding": [0.23333 for _ in range(1024)], "index": 1}, + ], + "model": "voyage-3", + "usage": {"total_tokens": 2}, + } + mock_response.status_code = 200 + mock_post.return_value = mock_response + result = model.invoke( + model="voyage-3", + credentials={ + "api_key": os.environ.get("VOYAGE_API_KEY"), + }, + texts=["hello", "world"], + user="abc-123", + ) + + assert isinstance(result, TextEmbeddingResult) + assert len(result.embeddings) == 2 + assert result.usage.total_tokens == 2 + + +def test_get_num_tokens(): + model = VoyageTextEmbeddingModel() + + num_tokens = model.get_num_tokens( + model="voyage-3", + credentials={ + "api_key": os.environ.get("VOYAGE_API_KEY"), + }, + texts=["ping"], + ) + + assert num_tokens == 1 diff --git a/api/tests/integration_tests/tools/__mock/http.py b/api/tests/integration_tests/tools/__mock/http.py index d3c1f3101c54bc..42cf87e317ab6a 100644 --- a/api/tests/integration_tests/tools/__mock/http.py +++ b/api/tests/integration_tests/tools/__mock/http.py @@ -17,7 +17,7 @@ def httpx_request( request = httpx.Request( method, url, params=kwargs.get("params"), headers=kwargs.get("headers"), cookies=kwargs.get("cookies") ) - data = kwargs.get("data", None) + data = kwargs.get("data") resp = json.dumps(data).encode("utf-8") if data else b"OK" response = httpx.Response( status_code=200, diff --git a/api/tests/integration_tests/vdb/pgvector/test_pgvector.py b/api/tests/integration_tests/vdb/pgvector/test_pgvector.py index c5a986b7479259..72efdc2780eca0 100644 --- a/api/tests/integration_tests/vdb/pgvector/test_pgvector.py +++ b/api/tests/integration_tests/vdb/pgvector/test_pgvector.py @@ -18,6 +18,8 @@ def __init__(self): user="postgres", password="difyai123456", database="dify", + min_connection=1, + max_connection=5, ), ) diff --git a/api/tests/integration_tests/workflow/nodes/__mock/http.py b/api/tests/integration_tests/workflow/nodes/__mock/http.py index f1ab23b0026aab..ec013183b7601d 100644 --- a/api/tests/integration_tests/workflow/nodes/__mock/http.py +++ b/api/tests/integration_tests/workflow/nodes/__mock/http.py @@ -22,8 +22,8 @@ def httpx_request( return response # get data, files - data = kwargs.get("data", None) - files = kwargs.get("files", None) + data = kwargs.get("data") + files = kwargs.get("files") if data is not None: resp = dumps(data).encode("utf-8") elif files is not None: diff --git a/dev/pytest/pytest_model_runtime.sh b/dev/pytest/pytest_model_runtime.sh index 4c1c6bf4f3ab19..63891eb9f8d13f 100755 --- a/dev/pytest/pytest_model_runtime.sh +++ b/dev/pytest/pytest_model_runtime.sh @@ -7,4 +7,7 @@ pytest api/tests/integration_tests/model_runtime/anthropic \ api/tests/integration_tests/model_runtime/google api/tests/integration_tests/model_runtime/xinference \ api/tests/integration_tests/model_runtime/huggingface_hub/test_llm.py \ api/tests/integration_tests/model_runtime/upstage \ - api/tests/integration_tests/model_runtime/fireworks + api/tests/integration_tests/model_runtime/fireworks \ + api/tests/integration_tests/model_runtime/nomic \ + api/tests/integration_tests/model_runtime/mixedbread \ + api/tests/integration_tests/model_runtime/voyage \ No newline at end of file diff --git a/docker/.env.example b/docker/.env.example index 3a4cccad757600..a370f2a77ef933 100644 --- a/docker/.env.example +++ b/docker/.env.example @@ -346,7 +346,7 @@ VOLCENGINE_TOS_REGION=your-region # ------------------------------ # The type of vector store to use. -# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`. +# Supported values are `weaviate`, `qdrant`, `milvus`, `myscale`, `relyt`, `pgvector`, `pgvecto-rs`, ``chroma`, `opensearch`, `tidb_vector`, `oracle`, `tencent`, `elasticsearch`, `analyticdb`. VECTOR_STORE=weaviate # The Weaviate endpoint URL. Only available when VECTOR_STORE is `weaviate`. @@ -385,12 +385,31 @@ MYSCALE_PASSWORD= MYSCALE_DATABASE=dify MYSCALE_FTS_PARAMS= -# pgvector configurations, only available when VECTOR_STORE is `pgvecto-rs or pgvector` +# pgvector configurations, only available when VECTOR_STORE is `pgvector` PGVECTOR_HOST=pgvector PGVECTOR_PORT=5432 PGVECTOR_USER=postgres PGVECTOR_PASSWORD=difyai123456 PGVECTOR_DATABASE=dify +PGVECTOR_MIN_CONNECTION=1 +PGVECTOR_MAX_CONNECTION=5 + +# pgvecto-rs configurations, only available when VECTOR_STORE is `pgvecto-rs` +PGVECTO_RS_HOST=pgvecto-rs +PGVECTO_RS_PORT=5432 +PGVECTO_RS_USER=postgres +PGVECTO_RS_PASSWORD=difyai123456 +PGVECTO_RS_DATABASE=dify + +# analyticdb configurations, only available when VECTOR_STORE is `analyticdb` +ANALYTICDB_KEY_ID=your-ak +ANALYTICDB_KEY_SECRET=your-sk +ANALYTICDB_REGION_ID=cn-hangzhou +ANALYTICDB_INSTANCE_ID=gp-ab123456 +ANALYTICDB_ACCOUNT=testaccount +ANALYTICDB_PASSWORD=testpassword +ANALYTICDB_NAMESPACE=dify +ANALYTICDB_NAMESPACE_PASSWORD=difypassword # TiDB vector configurations, only available when VECTOR_STORE is `tidb` TIDB_VECTOR_HOST=tidb @@ -567,6 +586,10 @@ WORKFLOW_MAX_EXECUTION_STEPS=500 WORKFLOW_MAX_EXECUTION_TIME=1200 WORKFLOW_CALL_MAX_DEPTH=5 +# HTTP request node in workflow configuration +HTTP_REQUEST_NODE_MAX_BINARY_SIZE=10485760 +HTTP_REQUEST_NODE_MAX_TEXT_SIZE=1048576 + # SSRF Proxy server HTTP URL SSRF_PROXY_HTTP_URL=http://ssrf_proxy:3128 # SSRF Proxy server HTTPS URL diff --git a/docker/docker-compose.yaml b/docker/docker-compose.yaml index 16bef279bcc688..95e271a0e9e4fb 100644 --- a/docker/docker-compose.yaml +++ b/docker/docker-compose.yaml @@ -207,6 +207,8 @@ x-shared-env: &shared-api-worker-env WORKFLOW_CALL_MAX_DEPTH: ${WORKFLOW_MAX_EXECUTION_TIME:-5} SSRF_PROXY_HTTP_URL: ${SSRF_PROXY_HTTP_URL:-http://ssrf_proxy:3128} SSRF_PROXY_HTTPS_URL: ${SSRF_PROXY_HTTPS_URL:-http://ssrf_proxy:3128} + HTTP_REQUEST_NODE_MAX_BINARY_SIZE: ${HTTP_REQUEST_NODE_MAX_BINARY_SIZE:-10485760} + HTTP_REQUEST_NODE_MAX_TEXT_SIZE: ${HTTP_REQUEST_NODE_MAX_TEXT_SIZE:-1048576} services: # API service @@ -628,7 +630,7 @@ services: # https://www.elastic.co/guide/en/elasticsearch/reference/current/settings.html # https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html#docker-prod-prerequisites elasticsearch: - image: docker.elastic.co/elasticsearch/elasticsearch:8.14.3 + image: docker.elastic.co/elasticsearch/elasticsearch:8.15.1 container_name: elasticsearch profiles: - elasticsearch @@ -655,7 +657,7 @@ services: # https://www.elastic.co/guide/en/kibana/current/docker.html # https://www.elastic.co/guide/en/kibana/current/settings.html kibana: - image: docker.elastic.co/kibana/kibana:8.14.3 + image: docker.elastic.co/kibana/kibana:8.15.1 container_name: kibana profiles: - elasticsearch diff --git a/sdks/python-client/dify_client/client.py b/sdks/python-client/dify_client/client.py index 2be079bdf381ce..5e42507a42abc7 100644 --- a/sdks/python-client/dify_client/client.py +++ b/sdks/python-client/dify_client/client.py @@ -1,103 +1,80 @@ import json + import requests class DifyClient: - def __init__(self, api_key, base_url: str = 'https://api.dify.ai/v1'): + def __init__(self, api_key, base_url: str = "https://api.dify.ai/v1"): self.api_key = api_key self.base_url = base_url def _send_request(self, method, endpoint, json=None, params=None, stream=False): - headers = { - "Authorization": f"Bearer {self.api_key}", - "Content-Type": "application/json" - } + headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"} url = f"{self.base_url}{endpoint}" response = requests.request(method, url, json=json, params=params, headers=headers, stream=stream) return response - def _send_request_with_files(self, method, endpoint, data, files): - headers = { - "Authorization": f"Bearer {self.api_key}" - } + headers = {"Authorization": f"Bearer {self.api_key}"} url = f"{self.base_url}{endpoint}" response = requests.request(method, url, data=data, headers=headers, files=files) return response - + def message_feedback(self, message_id, rating, user): - data = { - "rating": rating, - "user": user - } + data = {"rating": rating, "user": user} return self._send_request("POST", f"/messages/{message_id}/feedbacks", data) - + def get_application_parameters(self, user): params = {"user": user} return self._send_request("GET", "/parameters", params=params) - + def file_upload(self, user, files): - data = { - "user": user - } + data = {"user": user} return self._send_request_with_files("POST", "/files/upload", data=data, files=files) - def text_to_audio(self, text:str, user:str, streaming:bool=False): - data = { - "text": text, - "user": user, - "streaming": streaming - } + def text_to_audio(self, text: str, user: str, streaming: bool = False): + data = {"text": text, "user": user, "streaming": streaming} return self._send_request("POST", "/text-to-audio", data=data) - - def get_meta(self,user): - params = { "user": user} - return self._send_request("GET", f"/meta", params=params) + + def get_meta(self, user): + params = {"user": user} + return self._send_request("GET", "/meta", params=params) class CompletionClient(DifyClient): def create_completion_message(self, inputs, response_mode, user, files=None): - data = { - "inputs": inputs, - "response_mode": response_mode, - "user": user, - "files": files - } - return self._send_request("POST", "/completion-messages", data, - stream=True if response_mode == "streaming" else False) + data = {"inputs": inputs, "response_mode": response_mode, "user": user, "files": files} + return self._send_request( + "POST", "/completion-messages", data, stream=True if response_mode == "streaming" else False + ) class ChatClient(DifyClient): def create_chat_message(self, inputs, query, user, response_mode="blocking", conversation_id=None, files=None): - data = { - "inputs": inputs, - "query": query, - "user": user, - "response_mode": response_mode, - "files": files - } + data = {"inputs": inputs, "query": query, "user": user, "response_mode": response_mode, "files": files} if conversation_id: data["conversation_id"] = conversation_id - return self._send_request("POST", "/chat-messages", data, - stream=True if response_mode == "streaming" else False) - - def get_suggested(self, message_id, user:str): + return self._send_request( + "POST", "/chat-messages", data, stream=True if response_mode == "streaming" else False + ) + + def get_suggested(self, message_id, user: str): params = {"user": user} return self._send_request("GET", f"/messages/{message_id}/suggested", params=params) - + def stop_message(self, task_id, user): data = {"user": user} - return self._send_request("POST", f"/chat-messages/{task_id}/stop", data) + return self._send_request("POST", f"/chat-messages/{task_id}/stop", data) def get_conversations(self, user, last_id=None, limit=None, pinned=None): params = {"user": user, "last_id": last_id, "limit": limit, "pinned": pinned} return self._send_request("GET", "/conversations", params=params) - + def get_conversation_messages(self, user, conversation_id=None, first_id=None, limit=None): params = {"user": user} @@ -109,15 +86,15 @@ def get_conversation_messages(self, user, conversation_id=None, first_id=None, l params["limit"] = limit return self._send_request("GET", "/messages", params=params) - - def rename_conversation(self, conversation_id, name,auto_generate:bool, user:str): - data = {"name": name, "auto_generate": auto_generate,"user": user} + + def rename_conversation(self, conversation_id, name, auto_generate: bool, user: str): + data = {"name": name, "auto_generate": auto_generate, "user": user} return self._send_request("POST", f"/conversations/{conversation_id}/name", data) def delete_conversation(self, conversation_id, user): data = {"user": user} return self._send_request("DELETE", f"/conversations/{conversation_id}", data) - + def audio_to_text(self, audio_file, user): data = {"user": user} files = {"audio_file": audio_file} @@ -125,10 +102,10 @@ def audio_to_text(self, audio_file, user): class WorkflowClient(DifyClient): - def run(self, inputs:dict, response_mode:str="streaming", user:str="abc-123"): + def run(self, inputs: dict, response_mode: str = "streaming", user: str = "abc-123"): data = {"inputs": inputs, "response_mode": response_mode, "user": user} return self._send_request("POST", "/workflows/run", data) - + def stop(self, task_id, user): data = {"user": user} return self._send_request("POST", f"/workflows/tasks/{task_id}/stop", data) @@ -137,10 +114,8 @@ def get_result(self, workflow_run_id): return self._send_request("GET", f"/workflows/run/{workflow_run_id}") - class KnowledgeBaseClient(DifyClient): - - def __init__(self, api_key, base_url: str = 'https://api.dify.ai/v1', dataset_id: str = None): + def __init__(self, api_key, base_url: str = "https://api.dify.ai/v1", dataset_id: str = None): """ Construct a KnowledgeBaseClient object. @@ -150,10 +125,7 @@ def __init__(self, api_key, base_url: str = 'https://api.dify.ai/v1', dataset_id dataset_id (str, optional): ID of the dataset. Defaults to None. You don't need this if you just want to create a new dataset. or list datasets. otherwise you need to set this. """ - super().__init__( - api_key=api_key, - base_url=base_url - ) + super().__init__(api_key=api_key, base_url=base_url) self.dataset_id = dataset_id def _get_dataset_id(self): @@ -162,10 +134,10 @@ def _get_dataset_id(self): return self.dataset_id def create_dataset(self, name: str, **kwargs): - return self._send_request('POST', '/datasets', {'name': name}, **kwargs) + return self._send_request("POST", "/datasets", {"name": name}, **kwargs) def list_datasets(self, page: int = 1, page_size: int = 20, **kwargs): - return self._send_request('GET', f'/datasets?page={page}&limit={page_size}', **kwargs) + return self._send_request("GET", f"/datasets?page={page}&limit={page_size}", **kwargs) def create_document_by_text(self, name, text, extra_params: dict = None, **kwargs): """ @@ -193,14 +165,7 @@ def create_document_by_text(self, name, text, extra_params: dict = None, **kwarg } :return: Response from the API """ - data = { - 'indexing_technique': 'high_quality', - 'process_rule': { - 'mode': 'automatic' - }, - 'name': name, - 'text': text - } + data = {"indexing_technique": "high_quality", "process_rule": {"mode": "automatic"}, "name": name, "text": text} if extra_params is not None and isinstance(extra_params, dict): data.update(extra_params) url = f"/datasets/{self._get_dataset_id()}/document/create_by_text" @@ -233,10 +198,7 @@ def update_document_by_text(self, document_id, name, text, extra_params: dict = } :return: Response from the API """ - data = { - 'name': name, - 'text': text - } + data = {"name": name, "text": text} if extra_params is not None and isinstance(extra_params, dict): data.update(extra_params) url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/update_by_text" @@ -269,16 +231,11 @@ def create_document_by_file(self, file_path, original_document_id=None, extra_pa :return: Response from the API """ files = {"file": open(file_path, "rb")} - data = { - 'process_rule': { - 'mode': 'automatic' - }, - 'indexing_technique': 'high_quality' - } + data = {"process_rule": {"mode": "automatic"}, "indexing_technique": "high_quality"} if extra_params is not None and isinstance(extra_params, dict): data.update(extra_params) if original_document_id is not None: - data['original_document_id'] = original_document_id + data["original_document_id"] = original_document_id url = f"/datasets/{self._get_dataset_id()}/document/create_by_file" return self._send_request_with_files("POST", url, {"data": json.dumps(data)}, files) @@ -352,11 +309,11 @@ def list_documents(self, page: int = None, page_size: int = None, keyword: str = """ params = {} if page is not None: - params['page'] = page + params["page"] = page if page_size is not None: - params['limit'] = page_size + params["limit"] = page_size if keyword is not None: - params['keyword'] = keyword + params["keyword"] = keyword url = f"/datasets/{self._get_dataset_id()}/documents" return self._send_request("GET", url, params=params, **kwargs) @@ -383,9 +340,9 @@ def query_segments(self, document_id, keyword: str = None, status: str = None, * url = f"/datasets/{self._get_dataset_id()}/documents/{document_id}/segments" params = {} if keyword is not None: - params['keyword'] = keyword + params["keyword"] = keyword if status is not None: - params['status'] = status + params["status"] = status if "params" in kwargs: params.update(kwargs["params"]) return self._send_request("GET", url, params=params, **kwargs) diff --git a/web/app/activate/page.tsx b/web/app/activate/page.tsx index 90874f50cefe0a..0f1854433552db 100644 --- a/web/app/activate/page.tsx +++ b/web/app/activate/page.tsx @@ -22,7 +22,7 @@ const Activate = () => {
- © {new Date().getFullYear()} Dify, Inc. All rights reserved. + © {new Date().getFullYear()} LangGenius, Inc. All rights reserved.
diff --git a/web/app/components/app/configuration/config-var/config-modal/index.tsx b/web/app/components/app/configuration/config-var/config-modal/index.tsx index 606280653eeec2..ac1d86c1a2c2da 100644 --- a/web/app/components/app/configuration/config-var/config-modal/index.tsx +++ b/web/app/components/app/configuration/config-var/config-modal/index.tsx @@ -1,6 +1,6 @@ 'use client' import type { FC } from 'react' -import React, { useCallback, useState } from 'react' +import React, { useCallback, useEffect, useRef, useState } from 'react' import { useTranslation } from 'react-i18next' import { useContext } from 'use-context-selector' import ModalFoot from '../modal-foot' @@ -40,6 +40,12 @@ const ConfigModal: FC = ({ const { t } = useTranslation() const [tempPayload, setTempPayload] = useState(payload || getNewVarInWorkflow('') as any) const { type, label, variable, options, max_length } = tempPayload + const modalRef = useRef(null) + useEffect(() => { + // To fix the first input element auto focus, then directly close modal will raise error + if (isShow) + modalRef.current?.focus() + }, [isShow]) const isStringInput = type === InputVarType.textInput || type === InputVarType.paragraph const checkVariableName = useCallback((value: string) => { @@ -135,7 +141,7 @@ const ConfigModal: FC = ({ isShow={isShow} onClose={onClose} > -
+
diff --git a/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx b/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx index 5faef46d988e0d..2de500a3a6211b 100644 --- a/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx +++ b/web/app/components/app/configuration/debug/debug-with-single-model/index.tsx @@ -22,6 +22,7 @@ import { import Avatar from '@/app/components/base/avatar' import { useAppContext } from '@/context/app-context' import { ModelFeatureEnum } from '@/app/components/header/account-setting/model-provider-page/declarations' +import { getLastAnswer } from '@/app/components/base/chat/utils' type DebugWithSingleModelProps = { checkCanSend?: () => boolean @@ -83,17 +84,11 @@ const DebugWithSingleModel = forwardRef { diff --git a/web/app/components/app/log/filter.tsx b/web/app/components/app/log/filter.tsx index 0552b44d16b3cb..b8d7ca5a36b783 100644 --- a/web/app/components/app/log/filter.tsx +++ b/web/app/components/app/log/filter.tsx @@ -55,6 +55,8 @@ const Filter: FC = ({ isChatMode, appId, queryParams, setQueryPara className='!w-[300px]' onSelect={ (item) => { + if (!item.value) + return setQueryParams({ ...queryParams, annotation_status: item.value as string }) } } diff --git a/web/app/components/app/workflow-log/filter.tsx b/web/app/components/app/workflow-log/filter.tsx index d239c39d2cf618..58b5252c07446b 100644 --- a/web/app/components/app/workflow-log/filter.tsx +++ b/web/app/components/app/workflow-log/filter.tsx @@ -23,6 +23,8 @@ const Filter: FC = ({ queryParams, setQueryParams }: IFilterProps) className='!min-w-[100px]' onSelect={ (item) => { + if (!item.value) + return setQueryParams({ ...queryParams, status: item.value as string }) } } diff --git a/web/app/components/base/app-icon-picker/index.tsx b/web/app/components/base/app-icon-picker/index.tsx index 825481547501a8..ba375abdd9c04a 100644 --- a/web/app/components/base/app-icon-picker/index.tsx +++ b/web/app/components/base/app-icon-picker/index.tsx @@ -88,7 +88,7 @@ const AppIconPicker: FC = ({ if (!imageCropInfo) return setUploading(true) - const blob = await getCroppedImg(imageCropInfo.tempUrl, imageCropInfo.croppedAreaPixels) + const blob = await getCroppedImg(imageCropInfo.tempUrl, imageCropInfo.croppedAreaPixels, imageCropInfo.fileName) const file = new File([blob], imageCropInfo.fileName, { type: blob.type }) handleLocalFileUpload(file) } diff --git a/web/app/components/base/app-icon-picker/utils.ts b/web/app/components/base/app-icon-picker/utils.ts index 0c90e96febda83..14c9ae3f289d7d 100644 --- a/web/app/components/base/app-icon-picker/utils.ts +++ b/web/app/components/base/app-icon-picker/utils.ts @@ -11,6 +11,23 @@ export function getRadianAngle(degreeValue: number) { return (degreeValue * Math.PI) / 180 } +export function getMimeType(fileName: string): string { + const extension = fileName.split('.').pop()?.toLowerCase() + switch (extension) { + case 'png': + return 'image/png' + case 'jpg': + case 'jpeg': + return 'image/jpeg' + case 'gif': + return 'image/gif' + case 'webp': + return 'image/webp' + default: + return 'image/jpeg' + } +} + /** * Returns the new bounding area of a rotated rectangle. */ @@ -31,12 +48,14 @@ export function rotateSize(width: number, height: number, rotation: number) { export default async function getCroppedImg( imageSrc: string, pixelCrop: { x: number; y: number; width: number; height: number }, + fileName: string, rotation = 0, flip = { horizontal: false, vertical: false }, ): Promise { const image = await createImage(imageSrc) const canvas = document.createElement('canvas') const ctx = canvas.getContext('2d') + const mimeType = getMimeType(fileName) if (!ctx) throw new Error('Could not create a canvas context') @@ -93,6 +112,6 @@ export default async function getCroppedImg( resolve(file) else reject(new Error('Could not create a blob')) - }, 'image/jpeg') + }, mimeType) }) } diff --git a/web/app/components/base/audio-btn/audio.ts b/web/app/components/base/audio-btn/audio.ts index a61fd085d44c45..baf675d0be92d2 100644 --- a/web/app/components/base/audio-btn/audio.ts +++ b/web/app/components/base/audio-btn/audio.ts @@ -12,7 +12,7 @@ export default class AudioPlayer { mediaSource: MediaSource | null audio: HTMLAudioElement audioContext: AudioContext - sourceBuffer?: SourceBuffer + sourceBuffer?: any cacheBuffers: ArrayBuffer[] = [] pauseTimer: number | null = null msgId: string | undefined @@ -33,7 +33,7 @@ export default class AudioPlayer { this.callback = callback // Compatible with iphone ios17 ManagedMediaSource - const MediaSource = window.MediaSource || window.ManagedMediaSource + const MediaSource = window.ManagedMediaSource || window.MediaSource if (!MediaSource) { Toast.notify({ message: 'Your browser does not support audio streaming, if you are using an iPhone, please update to iOS 17.1 or later.', @@ -43,6 +43,10 @@ export default class AudioPlayer { this.mediaSource = MediaSource ? new MediaSource() : null this.audio = new Audio() this.setCallback(callback) + if (!window.MediaSource) { // if use ManagedMediaSource + this.audio.disableRemotePlayback = true + this.audio.controls = true + } this.audio.src = this.mediaSource ? URL.createObjectURL(this.mediaSource) : '' this.audio.autoplay = true diff --git a/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx b/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx index 225bbac714084c..9d7b360f38d066 100644 --- a/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx +++ b/web/app/components/base/chat/chat-with-history/chat-wrapper.tsx @@ -6,6 +6,7 @@ import type { OnSend, } from '../types' import { useChat } from '../chat/hooks' +import { getLastAnswer } from '../utils' import { useChatWithHistoryContext } from './context' import Header from './header' import ConfigPanel from './config-panel' @@ -67,17 +68,11 @@ const ChatWrapper = () => { }, []) const doSend: OnSend = useCallback((message, files, last_answer) => { - const lastAnswer = chatListRef.current.at(-1) - const data: any = { query: message, inputs: currentConversationId ? currentConversationItem?.inputs : newConversationInputs, conversation_id: currentConversationId, - parent_message_id: last_answer?.id || (lastAnswer - ? lastAnswer.isOpeningStatement - ? null - : lastAnswer.id - : null), + parent_message_id: last_answer?.id || getLastAnswer(chatListRef.current)?.id || null, } if (appConfig?.file_upload?.image.enabled && files?.length) @@ -111,13 +106,13 @@ const ChatWrapper = () => { const prevMessages = chatList.slice(0, index) const question = prevMessages.pop() - const lastAnswer = prevMessages.at(-1) + const lastAnswer = getLastAnswer(prevMessages) if (!question) return handleUpdateChatList(prevMessages) - doSend(question.content, question.message_files, (!lastAnswer || lastAnswer.isOpeningStatement) ? undefined : lastAnswer) + doSend(question.content, question.message_files, lastAnswer) }, [chatList, handleUpdateChatList, doSend]) const chatNode = useMemo(() => { diff --git a/web/app/components/base/chat/chat/answer/index.tsx b/web/app/components/base/chat/chat/answer/index.tsx index 705cd73ddf18d9..8184967edc9820 100644 --- a/web/app/components/base/chat/chat/answer/index.tsx +++ b/web/app/components/base/chat/chat/answer/index.tsx @@ -85,6 +85,19 @@ const Answer: FC = ({ getContentWidth() }, [responding]) + // Recalculate contentWidth when content changes (e.g., SVG preview/source toggle) + useEffect(() => { + if (!containerRef.current) + return + const resizeObserver = new ResizeObserver(() => { + getContentWidth() + }) + resizeObserver.observe(containerRef.current) + return () => { + resizeObserver.disconnect() + } + }, []) + return (
diff --git a/web/app/components/base/chat/chat/hooks.ts b/web/app/components/base/chat/chat/hooks.ts index dfb5a1b6855d5f..64c238f9d1e56a 100644 --- a/web/app/components/base/chat/chat/hooks.ts +++ b/web/app/components/base/chat/chat/hooks.ts @@ -334,9 +334,9 @@ export const useChat = ( const newChatList = produce(chatListRef.current, (draft) => { const index = draft.findIndex(item => item.id === responseItem.id) if (index !== -1) { - const requestion = draft[index - 1] + const question = draft[index - 1] draft[index - 1] = { - ...requestion, + ...question, } draft[index] = { ...draft[index], diff --git a/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx b/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx index ed2f24274dcd7b..b97c940eece7d4 100644 --- a/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx +++ b/web/app/components/base/chat/embedded-chatbot/chat-wrapper.tsx @@ -6,6 +6,7 @@ import type { OnSend, } from '../types' import { useChat } from '../chat/hooks' +import { getLastAnswer } from '../utils' import { useEmbeddedChatbotContext } from './context' import ConfigPanel from './config-panel' import { isDify } from './utils' @@ -69,17 +70,11 @@ const ChatWrapper = () => { }, []) const doSend: OnSend = useCallback((message, files, last_answer) => { - const lastAnswer = chatListRef.current.at(-1) - const data: any = { query: message, inputs: currentConversationId ? currentConversationItem?.inputs : newConversationInputs, conversation_id: currentConversationId, - parent_message_id: last_answer?.id || (lastAnswer - ? lastAnswer.isOpeningStatement - ? null - : lastAnswer.id - : null), + parent_message_id: last_answer?.id || getLastAnswer(chatListRef.current)?.id || null, } if (appConfig?.file_upload?.image.enabled && files?.length) @@ -113,13 +108,13 @@ const ChatWrapper = () => { const prevMessages = chatList.slice(0, index) const question = prevMessages.pop() - const lastAnswer = prevMessages.at(-1) + const lastAnswer = getLastAnswer(prevMessages) if (!question) return handleUpdateChatList(prevMessages) - doSend(question.content, question.message_files, (!lastAnswer || lastAnswer.isOpeningStatement) ? undefined : lastAnswer) + doSend(question.content, question.message_files, lastAnswer) }, [chatList, handleUpdateChatList, doSend]) const chatNode = useMemo(() => { diff --git a/web/app/components/base/chat/types.ts b/web/app/components/base/chat/types.ts index 489dbb44cf67e7..0bc50518ebb814 100644 --- a/web/app/components/base/chat/types.ts +++ b/web/app/components/base/chat/types.ts @@ -63,7 +63,7 @@ export type ChatItem = IChatItem & { conversationId?: string } -export type OnSend = (message: string, files?: VisionFile[], last_answer?: ChatItem) => void +export type OnSend = (message: string, files?: VisionFile[], last_answer?: ChatItem | null) => void export type OnRegenerate = (chatItem: ChatItem) => void diff --git a/web/app/components/base/chat/utils.ts b/web/app/components/base/chat/utils.ts index e851c4c4633fb5..305df5995d128b 100644 --- a/web/app/components/base/chat/utils.ts +++ b/web/app/components/base/chat/utils.ts @@ -19,6 +19,15 @@ function getProcessedInputsFromUrlParams(): Record { return inputs } +function getLastAnswer(chatList: ChatItem[]) { + for (let i = chatList.length - 1; i >= 0; i--) { + const item = chatList[i] + if (item.isAnswer && !item.isOpeningStatement) + return item + } + return null +} + function appendQAToChatList(chatList: ChatItem[], item: any) { // we append answer first and then question since will reverse the whole chatList later chatList.push({ @@ -71,5 +80,6 @@ function getPrevChatList(fetchedMessages: any[]) { export { getProcessedInputsFromUrlParams, + getLastAnswer, getPrevChatList, } diff --git a/web/app/components/base/image-uploader/image-preview.tsx b/web/app/components/base/image-uploader/image-preview.tsx index e5bd4c1bbc0dc5..096facabfd2a85 100644 --- a/web/app/components/base/image-uploader/image-preview.tsx +++ b/web/app/components/base/image-uploader/image-preview.tsx @@ -88,7 +88,7 @@ const ImagePreview: FC = ({ }) } - const imageTobase64ToBlob = (base64: string, type = 'image/png'): Blob => { + const imageBase64ToBlob = (base64: string, type = 'image/png'): Blob => { const byteCharacters = atob(base64) const byteArrays = [] @@ -109,7 +109,7 @@ const ImagePreview: FC = ({ const shareImage = async () => { try { const base64Data = url.split(',')[1] - const blob = imageTobase64ToBlob(base64Data, 'image/png') + const blob = imageBase64ToBlob(base64Data, 'image/png') await navigator.clipboard.write([ new ClipboardItem({ diff --git a/web/app/components/base/markdown.tsx b/web/app/components/base/markdown.tsx index 443ee3410c4de4..39a399cc9f73c9 100644 --- a/web/app/components/base/markdown.tsx +++ b/web/app/components/base/markdown.tsx @@ -116,59 +116,80 @@ const CodeBlock: CodeComponent = memo(({ inline, className, children, ...props } const match = /language-(\w+)/.exec(className || '') const language = match?.[1] const languageShowName = getCorrectCapitalizationLanguageName(language || '') - let chartData = JSON.parse(String('{"title":{"text":"ECharts error - Wrong JSON format."}}').replace(/\n$/, '')) - if (language === 'echarts') { - try { - chartData = JSON.parse(String(children).replace(/\n$/, '')) - } - catch (error) { + const chartData = useMemo(() => { + if (language === 'echarts') { + try { + return JSON.parse(String(children).replace(/\n$/, '')) + } + catch (error) {} } - } + return JSON.parse('{"title":{"text":"ECharts error - Wrong JSON format."}}') + }, [language, children]) - // Use `useMemo` to ensure that `SyntaxHighlighter` only re-renders when necessary - return useMemo(() => { - return (!inline && match) - ? ( -
-
-
{languageShowName}
-
- {language === 'mermaid' && } - -
-
- {(language === 'mermaid' && isSVG) - ? () - : (language === 'echarts' - ? (
) - : (language === 'svg' - ? () - : ( - {String(children).replace(/\n$/, '')} - )))} + const renderCodeContent = useMemo(() => { + const content = String(children).replace(/\n$/, '') + if (language === 'mermaid' && isSVG) { + return + } + else if (language === 'echarts') { + return ( +
+ + +
) - : ({children}) - }, [chartData, children, className, inline, isSVG, language, languageShowName, match, props]) + } + else if (language === 'svg' && isSVG) { + return ( + + + + ) + } + else { + return ( + + {content} + + ) + } + }, [language, match, props, children, chartData, isSVG]) + + if (inline || !match) + return {children} + + return ( +
+
+
{languageShowName}
+
+ {(['mermaid', 'svg']).includes(language!) && } + +
+
+ {renderCodeContent} +
+ ) }) CodeBlock.displayName = 'CodeBlock' diff --git a/web/app/components/base/svg-gallery/index.tsx b/web/app/components/base/svg-gallery/index.tsx index 81e8e876550097..4368df00e9d38d 100644 --- a/web/app/components/base/svg-gallery/index.tsx +++ b/web/app/components/base/svg-gallery/index.tsx @@ -29,7 +29,7 @@ export const SVGRenderer = ({ content }: { content: string }) => { if (svgRef.current) { try { svgRef.current.innerHTML = '' - const draw = SVG().addTo(svgRef.current).size('100%', '100%') + const draw = SVG().addTo(svgRef.current) const parser = new DOMParser() const svgDoc = parser.parseFromString(content, 'image/svg+xml') @@ -40,13 +40,11 @@ export const SVGRenderer = ({ content }: { content: string }) => { const originalWidth = parseInt(svgElement.getAttribute('width') || '400', 10) const originalHeight = parseInt(svgElement.getAttribute('height') || '600', 10) - const scale = Math.min(windowSize.width / originalWidth, windowSize.height / originalHeight, 1) - const scaledWidth = originalWidth * scale - const scaledHeight = originalHeight * scale - draw.size(scaledWidth, scaledHeight) + draw.viewbox(0, 0, originalWidth, originalHeight) + + svgRef.current.style.width = `${Math.min(originalWidth, 298)}px` const rootElement = draw.svg(content) - rootElement.scale(scale) rootElement.click(() => { setImagePreview(svgToDataURL(svgElement as Element)) @@ -54,7 +52,7 @@ export const SVGRenderer = ({ content }: { content: string }) => { } catch (error) { if (svgRef.current) - svgRef.current.innerHTML = 'Error rendering SVG. Wait for the image content to complete.' + svgRef.current.innerHTML = 'Error rendering SVG. Wait for the image content to complete.' } } }, [content, windowSize]) @@ -62,14 +60,14 @@ export const SVGRenderer = ({ content }: { content: string }) => { return ( <>
{imagePreview && ( setImagePreview('')} />)} diff --git a/web/app/components/develop/template/template_workflow.en.mdx b/web/app/components/develop/template/template_workflow.en.mdx index 2bd0fe9daf436d..5c712c2c2979e5 100644 --- a/web/app/components/develop/template/template_workflow.en.mdx +++ b/web/app/components/develop/template/template_workflow.en.mdx @@ -424,7 +424,7 @@ Workflow applications offers non-session support and is ideal for translation, a /> - Returns worklfow logs, with the first page returning the latest `{limit}` messages, i.e., in reverse order. + Returns workflow logs, with the first page returning the latest `{limit}` messages, i.e., in reverse order. ### Query diff --git a/web/app/components/tools/workflow-tool/configure-button.tsx b/web/app/components/tools/workflow-tool/configure-button.tsx index d2c5142f53c10e..6521410daea017 100644 --- a/web/app/components/tools/workflow-tool/configure-button.tsx +++ b/web/app/components/tools/workflow-tool/configure-button.tsx @@ -65,7 +65,7 @@ const WorkflowToolConfigureButton = ({ else { if (item.type === 'paragraph' && param.type !== 'string') return true - if (param.type !== item.type && !(param.type === 'string' && item.type === 'paragraph')) + if (item.type === 'text-input' && param.type !== 'string') return true } } diff --git a/web/app/components/workflow/hooks/use-workflow-run.ts b/web/app/components/workflow/hooks/use-workflow-run.ts index e1da503f3825f1..68c3ff0a4b458d 100644 --- a/web/app/components/workflow/hooks/use-workflow-run.ts +++ b/web/app/components/workflow/hooks/use-workflow-run.ts @@ -185,7 +185,7 @@ export const useWorkflowRun = () => { draft.forEach((edge) => { edge.data = { ...edge.data, - _runned: false, + _run: false, } }) }) @@ -292,7 +292,7 @@ export const useWorkflowRun = () => { const newEdges = produce(edges, (draft) => { draft.forEach((edge) => { if (edge.target === data.node_id && incomeNodesId.includes(edge.source)) - edge.data = { ...edge.data, _runned: true } as any + edge.data = { ...edge.data, _run: true } as any }) }) setEdges(newEdges) @@ -416,7 +416,7 @@ export const useWorkflowRun = () => { const edge = draft.find(edge => edge.target === data.node_id && edge.source === prevNodeId) if (edge) - edge.data = { ...edge.data, _runned: true } as any + edge.data = { ...edge.data, _run: true } as any }) setEdges(newEdges) diff --git a/web/app/components/workflow/nodes/http/node.tsx b/web/app/components/workflow/nodes/http/node.tsx index 5bbb10fc3a5966..4b7dbea2571ae1 100644 --- a/web/app/components/workflow/nodes/http/node.tsx +++ b/web/app/components/workflow/nodes/http/node.tsx @@ -15,7 +15,7 @@ const Node: FC> = ({
{method}
-
+
{ top_k: multipleRetrievalConfig?.top_k || DATASET_DEFAULT.top_k, score_threshold: multipleRetrievalConfig?.score_threshold, reranking_model: multipleRetrievalConfig?.reranking_model, + reranking_mode: multipleRetrievalConfig?.reranking_mode, + weights: multipleRetrievalConfig?.weights, } }) setInputs(newInput) diff --git a/web/app/components/workflow/panel/chat-record/index.tsx b/web/app/components/workflow/panel/chat-record/index.tsx index 1bcfd6474d7c88..16d2c304a73c5e 100644 --- a/web/app/components/workflow/panel/chat-record/index.tsx +++ b/web/app/components/workflow/panel/chat-record/index.tsx @@ -90,7 +90,7 @@ const ChatRecord = () => { return (
{ supportCitationHitInfo: true, } as any} chatList={chatList} - chatContainerClassName='px-4' + chatContainerClassName='px-3' chatContainerInnerClassName='pt-6 w-full max-w-full mx-auto' chatFooterClassName='px-4 rounded-b-2xl' chatFooterInnerClassName='pb-4 w-full max-w-full mx-auto' @@ -129,6 +129,8 @@ const ChatRecord = () => { noChatInput allToolIcons={{}} showPromptLog + noSpacing + chatAnswerContainerInner='!pr-2' />
diff --git a/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx b/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx index 86519af60357d7..230b2d7fa0c65c 100644 --- a/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx +++ b/web/app/components/workflow/panel/debug-and-preview/chat-wrapper.tsx @@ -25,6 +25,7 @@ import { stopChatMessageResponding, } from '@/service/debug' import { useStore as useAppStore } from '@/app/components/app/store' +import { getLastAnswer } from '@/app/components/base/chat/utils' type ChatWrapperProps = { showConversationVariableModal: boolean @@ -76,19 +77,13 @@ const ChatWrapper = forwardRef(({ showConv ) const doSend = useCallback((query, files, last_answer) => { - const lastAnswer = chatListRef.current.at(-1) - handleSend( { query, files, inputs: workflowStore.getState().inputs, conversation_id: conversationId, - parent_message_id: last_answer?.id || (lastAnswer - ? lastAnswer.isOpeningStatement - ? null - : lastAnswer.id - : null), + parent_message_id: last_answer?.id || getLastAnswer(chatListRef.current)?.id || null, }, { onGetSuggestedQuestions: (messageId, getAbortController) => fetchSuggestedQuestions(appDetail!.id, messageId, getAbortController), @@ -103,13 +98,13 @@ const ChatWrapper = forwardRef(({ showConv const prevMessages = chatList.slice(0, index) const question = prevMessages.pop() - const lastAnswer = prevMessages.at(-1) + const lastAnswer = getLastAnswer(prevMessages) if (!question) return handleUpdateChatList(prevMessages) - doSend(question.content, question.message_files, (!lastAnswer || lastAnswer.isOpeningStatement) ? undefined : lastAnswer) + doSend(question.content, question.message_files, lastAnswer) }, [chatList, handleUpdateChatList, doSend]) useImperativeHandle(ref, () => { diff --git a/web/app/forgot-password/page.tsx b/web/app/forgot-password/page.tsx index fa44d1a20c689a..bb46011c06302b 100644 --- a/web/app/forgot-password/page.tsx +++ b/web/app/forgot-password/page.tsx @@ -28,7 +28,7 @@ const ForgotPassword = () => {
{token ? : }
- © {new Date().getFullYear()} Dify, Inc. All rights reserved. + © {new Date().getFullYear()} LangGenius, Inc. All rights reserved.
diff --git a/web/app/install/page.tsx b/web/app/install/page.tsx index 9fa38dd15e2a8c..395fae34ec1690 100644 --- a/web/app/install/page.tsx +++ b/web/app/install/page.tsx @@ -22,7 +22,7 @@ const Install = () => {
- © {new Date().getFullYear()} Dify, Inc. All rights reserved. + © {new Date().getFullYear()} LangGenius, Inc. All rights reserved.
diff --git a/web/i18n/es-ES/common.ts b/web/i18n/es-ES/common.ts index 2ba907361fe8c8..59a05f63d8a3e2 100644 --- a/web/i18n/es-ES/common.ts +++ b/web/i18n/es-ES/common.ts @@ -202,7 +202,7 @@ const translation = { invitationLink: 'Enlace de invitación', failedInvitationEmails: 'Los siguientes usuarios no fueron invitados exitosamente', ok: 'OK', - removeFromTeam: 'Eliminar del equipo', + removeFromTeam: 'Eliminar del espacio de trabajo', removeFromTeamTip: 'Se eliminará el acceso al equipo', setAdmin: 'Establecer como administrador', setMember: 'Establecer como miembro ordinario', diff --git a/web/i18n/zh-Hans/common.ts b/web/i18n/zh-Hans/common.ts index 52ab7d6f024168..7947d32f251f19 100644 --- a/web/i18n/zh-Hans/common.ts +++ b/web/i18n/zh-Hans/common.ts @@ -200,7 +200,7 @@ const translation = { invitationLink: '邀请链接', failedInvitationEmails: '邀请以下邮箱失败', ok: '好的', - removeFromTeam: '移除团队', + removeFromTeam: '移出团队', removeFromTeamTip: '将取消团队访问', setAdmin: '设为管理员', setMember: '设为普通成员', diff --git a/web/i18n/zh-Hant/common.ts b/web/i18n/zh-Hant/common.ts index c1f3ed2b2b53e6..8cd51b1991f1a2 100644 --- a/web/i18n/zh-Hant/common.ts +++ b/web/i18n/zh-Hant/common.ts @@ -194,7 +194,7 @@ const translation = { invitationLink: '邀請連結', failedInvitationEmails: '邀請以下郵箱失敗', ok: '好的', - removeFromTeam: '移除團隊', + removeFromTeam: '移出團隊', removeFromTeamTip: '將取消團隊訪問', setAdmin: '設為管理員', setMember: '設為普通成員', diff --git a/web/public/embed.js b/web/public/embed.js index 8ed7a67dc8a311..3c2735b6fc8d1f 100644 --- a/web/public/embed.js +++ b/web/public/embed.js @@ -69,38 +69,47 @@ iframe.id = iframeId; iframe.src = iframeUrl; iframe.style.cssText = ` - border: none; position: fixed; flex-direction: column; justify-content: space-between; + border: none; position: absolute; flex-direction: column; justify-content: space-between; box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px; - bottom: 5rem; right: 1rem; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem; + bottom: 55px; right: 0; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem; max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647; overflow: hidden; left: unset; background-color: #F3F4F6;user-select: none; `; - document.body.appendChild(iframe); + return iframe; } // Function to reset the iframe position function resetIframePosition() { + if (window.innerWidth <= 640) + return + const targetIframe = document.getElementById(iframeId); const targetButton = document.getElementById(buttonId); if (targetIframe && targetButton) { const buttonRect = targetButton.getBoundingClientRect(); - const buttonBottom = window.innerHeight - buttonRect.bottom; - const buttonRight = window.innerWidth - buttonRect.right; - const buttonLeft = buttonRect.left; - - // Adjust iframe position to stay within viewport - targetIframe.style.bottom = `${ - buttonBottom + buttonRect.height + 5 + targetIframe.clientHeight > window.innerHeight - ? buttonBottom - targetIframe.clientHeight - 5 - : buttonBottom + buttonRect.height + 5 - }px`; - - targetIframe.style.right = `${ - buttonRight + targetIframe.clientWidth > window.innerWidth - ? window.innerWidth - buttonLeft - targetIframe.clientWidth - : buttonRight - }px`; + + const buttonInBottom = buttonRect.top - 5 > targetIframe.clientHeight + + if (buttonInBottom) { + targetIframe.style.bottom = `${buttonRect.height + 5}px`; + targetIframe.style.top = 'unset'; + } + else { + targetIframe.style.bottom = 'unset'; + targetIframe.style.top = `${buttonRect.height + 5}px`; + } + + const buttonInRight = buttonRect.right > targetIframe.clientWidth; + + if (buttonInRight) { + targetIframe.style.right = '0'; + targetIframe.style.left = 'unset'; + } + else { + targetIframe.style.right = 'unset'; + targetIframe.style.left = 0; + } } } @@ -146,12 +155,6 @@ box-shadow: var(--${containerDiv.id}-box-shadow, rgba(0, 0, 0, 0.2) 0px 4px 8px 0px); cursor: pointer; z-index: 2147483647; - transition: all 0.2s ease-in-out 0s; - } - `); - styleSheet.sheet.insertRule(` - #${containerDiv.id}:hover { - transform: var(--${containerDiv.id}-hover-transform, scale(1.1)); } `); @@ -167,7 +170,7 @@ containerDiv.addEventListener("click", function () { const targetIframe = document.getElementById(iframeId); if (!targetIframe) { - createIframe(); + containerDiv.appendChild(createIframe()); resetIframePosition(); this.title = "Exit (ESC)"; displayDiv.innerHTML = svgIcons.close; @@ -255,9 +258,6 @@ if (!document.getElementById(buttonId)) { createButton(); } - - createIframe(); - document.getElementById(iframeId).style.display = 'none'; } // Add esc Exit keyboard event triggered @@ -279,4 +279,4 @@ } else { document.body.onload = embedChatbot; } -})(); +})(); \ No newline at end of file diff --git a/web/public/embed.min.js b/web/public/embed.min.js index 0e023cb5d19704..eb2085814883b9 100644 --- a/web/public/embed.min.js +++ b/web/public/embed.min.js @@ -1,31 +1,26 @@ -(()=>{let t="difyChatbotConfig",a="dify-chatbot-bubble-button",c="dify-chatbot-bubble-window",h=window[t],p={open:` - - `,close:` - - `};async function e(){if(h&&h.token){var e=new URLSearchParams(await(async()=>{var e=h?.inputs||{};let n={};return await Promise.all(Object.entries(e).map(async([e,t])=>{n[e]=(e=t,e=(new TextEncoder).encode(e),e=new Response(new Blob([e]).stream().pipeThrough(new CompressionStream("gzip"))).arrayBuffer(),e=new Uint8Array(await e),await btoa(String.fromCharCode(...e)))})),n})());let t=`${h.baseUrl||`https://${h.isDev?"dev.":""}udify.app`}/chatbot/${h.token}?`+e;function o(){var e=document.createElement("iframe");e.allow="fullscreen;microphone",e.title="dify chatbot bubble window",e.id=c,e.src=t,e.style.cssText=` - border: none; position: fixed; flex-direction: column; justify-content: space-between; - box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px; - bottom: 5rem; right: 1rem; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem; - max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647; - overflow: hidden; left: unset; background-color: #F3F4F6;user-select: none; - `,document.body.appendChild(e)}function i(){var e,t,n,o=document.getElementById(c),i=document.getElementById(a);o&&i&&(i=i.getBoundingClientRect(),e=window.innerHeight-i.bottom,t=window.innerWidth-i.right,n=i.left,o.style.bottom=`${e+i.height+5+o.clientHeight>window.innerHeight?e-o.clientHeight-5:e+i.height+5}px`,o.style.right=`${t+o.clientWidth>window.innerWidth?window.innerWidth-n-o.clientWidth:t}px`)}function n(){let n=document.createElement("div");Object.entries(h.containerProps||{}).forEach(([e,t])=>{"className"===e?n.classList.add(...t.split(" ")):"style"===e?"object"==typeof t?Object.assign(n.style,t):n.style.cssText=t:"function"==typeof t?n.addEventListener(e.replace(/^on/,"").toLowerCase(),t):n[e]=t}),n.id=a;var e=document.createElement("style");document.head.appendChild(e),e.sheet.insertRule(` - #${n.id} { - position: fixed; - bottom: var(--${n.id}-bottom, 1rem); - right: var(--${n.id}-right, 1rem); - left: var(--${n.id}-left, unset); - top: var(--${n.id}-top, unset); - width: var(--${n.id}-width, 50px); - height: var(--${n.id}-height, 50px); - border-radius: var(--${n.id}-border-radius, 25px); - background-color: var(--${n.id}-bg-color, #155EEF); - box-shadow: var(--${n.id}-box-shadow, rgba(0, 0, 0, 0.2) 0px 4px 8px 0px); - cursor: pointer; - z-index: 2147483647; - transition: all 0.2s ease-in-out 0s; - } - `),e.sheet.insertRule(` - #${n.id}:hover { - transform: var(--${n.id}-hover-transform, scale(1.1)); - } - `);let t=document.createElement("div");if(t.style.cssText="display: flex; align-items: center; justify-content: center; width: 100%; height: 100%; z-index: 2147483647;",t.innerHTML=p.open,n.appendChild(t),document.body.appendChild(n),n.addEventListener("click",function(){var e=document.getElementById(c);e?(e.style.display="none"===e.style.display?"block":"none",t.innerHTML="none"===e.style.display?p.open:p.close,"none"===e.style.display?document.removeEventListener("keydown",d):document.addEventListener("keydown",d),i()):(o(),i(),this.title="Exit (ESC)",t.innerHTML=p.close,document.addEventListener("keydown",d))}),h.draggable){var s=n;var l=h.dragAxis||"both";let i=!1,d,r;s.addEventListener("mousedown",function(e){i=!0,d=e.clientX-s.offsetLeft,r=e.clientY-s.offsetTop}),document.addEventListener("mousemove",function(e){var t,n,o;i&&(s.style.transition="none",s.style.cursor="grabbing",(t=document.getElementById(c))&&(t.style.display="none",s.querySelector("div").innerHTML=p.open),t=e.clientX-d,e=window.innerHeight-e.clientY-r,o=s.getBoundingClientRect(),n=window.innerWidth-o.width,o=window.innerHeight-o.height,"x"!==l&&"both"!==l||s.style.setProperty(`--${a}-left`,Math.max(0,Math.min(t,n))+"px"),"y"!==l&&"both"!==l||s.style.setProperty(`--${a}-bottom`,Math.max(0,Math.min(e,o))+"px"))}),document.addEventListener("mouseup",function(){i=!1,s.style.transition="",s.style.cursor="pointer"})}}2048 + + `,close:` + + `};async function e(){if(p&&p.token){var e=new URLSearchParams(await async function(){var e=p?.inputs||{};const n={};return await Promise.all(Object.entries(e).map(async([e,t])=>{n[e]=(e=t,e=(new TextEncoder).encode(e),e=new Response(new Blob([e]).stream().pipeThrough(new CompressionStream("gzip"))).arrayBuffer(),e=new Uint8Array(await e),await btoa(String.fromCharCode(...e)))})),n}());const i=`${p.baseUrl||`https://${p.isDev?"dev.":""}udify.app`}/chatbot/${p.token}?`+e;function o(){var e,t;window.innerWidth<=640||(e=document.getElementById(c),t=document.getElementById(a),e&&t&&((t=t.getBoundingClientRect()).top-5>e.clientHeight?(e.style.bottom=t.height+5+"px",e.style.top="unset"):(e.style.bottom="unset",e.style.top=t.height+5+"px"),t.right>e.clientWidth?(e.style.right="0",e.style.left="unset"):(e.style.right="unset",e.style.left=0)))}function t(){const n=document.createElement("div");Object.entries(p.containerProps||{}).forEach(([e,t])=>{"className"===e?n.classList.add(...t.split(" ")):"style"===e?"object"==typeof t?Object.assign(n.style,t):n.style.cssText=t:"function"==typeof t?n.addEventListener(e.replace(/^on/,"").toLowerCase(),t):n[e]=t}),n.id=a;var e=document.createElement("style");document.head.appendChild(e),e.sheet.insertRule(` + #${n.id} { + position: fixed; + bottom: var(--${n.id}-bottom, 1rem); + right: var(--${n.id}-right, 1rem); + left: var(--${n.id}-left, unset); + top: var(--${n.id}-top, unset); + width: var(--${n.id}-width, 50px); + height: var(--${n.id}-height, 50px); + border-radius: var(--${n.id}-border-radius, 25px); + background-color: var(--${n.id}-bg-color, #155EEF); + box-shadow: var(--${n.id}-box-shadow, rgba(0, 0, 0, 0.2) 0px 4px 8px 0px); + cursor: pointer; + z-index: 2147483647; + } + `);const t=document.createElement("div");if(t.style.cssText="display: flex; align-items: center; justify-content: center; width: 100%; height: 100%; z-index: 2147483647;",t.innerHTML=h.open,n.appendChild(t),document.body.appendChild(n),n.addEventListener("click",function(){var e=document.getElementById(c);e?(e.style.display="none"===e.style.display?"block":"none",t.innerHTML="none"===e.style.display?h.open:h.close,"none"===e.style.display?document.removeEventListener("keydown",d):document.addEventListener("keydown",d),o()):(n.appendChild(((e=document.createElement("iframe")).allow="fullscreen;microphone",e.title="dify chatbot bubble window",e.id=c,e.src=i,e.style.cssText=` + border: none; position: absolute; flex-direction: column; justify-content: space-between; + box-shadow: rgba(150, 150, 150, 0.2) 0px 10px 30px 0px, rgba(150, 150, 150, 0.2) 0px 0px 0px 1px; + bottom: 55px; right: 0; width: 24rem; max-width: calc(100vw - 2rem); height: 40rem; + max-height: calc(100vh - 6rem); border-radius: 0.75rem; display: flex; z-index: 2147483647; + overflow: hidden; left: unset; background-color: #F3F4F6;user-select: none; + `,e)),o(),this.title="Exit (ESC)",t.innerHTML=h.close,document.addEventListener("keydown",d))}),p.draggable){var s=n;var l=p.dragAxis||"both";let i=!1,d,r;s.addEventListener("mousedown",function(e){i=!0,d=e.clientX-s.offsetLeft,r=e.clientY-s.offsetTop}),document.addEventListener("mousemove",function(e){var t,n,o;i&&(s.style.transition="none",s.style.cursor="grabbing",(t=document.getElementById(c))&&(t.style.display="none",s.querySelector("div").innerHTML=h.open),t=e.clientX-d,e=window.innerHeight-e.clientY-r,o=s.getBoundingClientRect(),n=window.innerWidth-o.width,o=window.innerHeight-o.height,"x"!==l&&"both"!==l||s.style.setProperty(`--${a}-left`,Math.max(0,Math.min(t,n))+"px"),"y"!==l&&"both"!==l||s.style.setProperty(`--${a}-bottom`,Math.max(0,Math.min(e,o))+"px"))}),document.addEventListener("mouseup",function(){i=!1,s.style.transition="",s.style.cursor="pointer"})}}2048