diff --git a/examples/examples/advanced/openai/structured-outputs.py b/examples/examples/advanced/openai/structured-outputs.py new file mode 100644 index 00000000..13e82730 --- /dev/null +++ b/examples/examples/advanced/openai/structured-outputs.py @@ -0,0 +1,59 @@ +import os + +import gentrace +from dotenv import load_dotenv +from pydantic import BaseModel + +load_dotenv() + +gentrace.init( + api_key=os.getenv("GENTRACE_API_KEY"), + host="http://localhost:3000/api", +) + +pipeline = gentrace.Pipeline( + "math-reasoning-pipeline", + openai_config={ + "api_key": os.getenv("OPENAI_KEY"), + }, +) + +pipeline.setup() + +runner = pipeline.start() + +openai = runner.get_openai() + +class Step(BaseModel): + explanation: str + output: str + +class MathReasoning(BaseModel): + steps: list[Step] + final_answer: str + +completion = openai.beta.chat.completions.parse( + model="gpt-4o-2024-08-06", + messages=[ + { + "role": "system", + "content": "You are a helpful math tutor. " \ + "Guide the user through the solution step by step.", + }, + {"role": "user", "content": "how can I solve 8x + 7 = -23"}, + ], + response_format=MathReasoning, + gentrace={ + "metadata": {"problem_type": {"type": "string", "value": "algebra"}}, + }, +) + +math_reasoning = completion.choices[0].message.parsed + +print(math_reasoning) + +info = runner.submit() + +print("Response: ", info["pipelineRunId"]) + +gentrace.flush() diff --git a/examples/poetry.lock b/examples/poetry.lock index 19adc39d..8bbcbae4 100644 --- a/examples/poetry.lock +++ b/examples/poetry.lock @@ -15,14 +15,14 @@ files = [ [[package]] name = "annotated-types" -version = "0.6.0" +version = "0.7.0" description = "Reusable constraint types to use with typing.Annotated" category = "main" optional = false python-versions = ">=3.8" files = [ - {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, - {file = "annotated_types-0.6.0.tar.gz", 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"sha256:c22b14cc4763c5a5b04134207736c107db42e9d3ef2d9779d465f5f1bcba572b"}, ] [package.extras] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.8.1" -content-hash = "8017f024ee27a5a16ed0f4427d60df859d1d7a8596ebd114fd3e8ed201a8a925" +content-hash = "3f9b45984ac1838ba3fe37fd65a924d18cc60135f67f881e45d762581277ce92" diff --git a/examples/pyproject.toml b/examples/pyproject.toml index 789e63a7..82fda28e 100644 --- a/examples/pyproject.toml +++ b/examples/pyproject.toml @@ -7,11 +7,11 @@ repository = "https://github.com/gentrace/gentrace-python" version = "0.0.1" [tool.poetry.dependencies] -openai = "1.13.3" +openai = "1.42.0" pinecone-client = "^2.2.1" python = "^3.8.1" python-dotenv = "^1.0.0" -gentrace-py = {path = "../package/dist/gentrace_py-0.32.0.tar.gz", develop = true} +gentrace-py = {path = "../package/dist/gentrace_py-0.32.1.tar.gz", develop = true} [tool.poetry.group.lint.dependencies] black = "^23.3.0" diff --git a/package/gentrace/providers/llms/openai_v1.py b/package/gentrace/providers/llms/openai_v1.py index 21789405..98167578 100644 --- a/package/gentrace/providers/llms/openai_v1.py +++ b/package/gentrace/providers/llms/openai_v1.py @@ -5,7 +5,7 @@ from typing import Any, Dict, List, Optional import pystache -from openai import AsyncOpenAI, OpenAI, chat, completions, embeddings +from openai import AsyncOpenAI, OpenAI, beta, chat, completions, embeddings from gentrace.providers.pipeline import Pipeline from gentrace.providers.pipeline_run import PipelineRun @@ -23,6 +23,11 @@ ExtractedChatCompletions = chat.completions.__class__ ExtractedEmbeddings = embeddings.__class__ +# New extractions +ExtractedBeta = beta.__class__ +ExtractedBetaChat = beta.chat.__class__ +ExtractedBetaChatCompletions = beta.chat.completions.__class__ + if prior_value is not None: os.environ["OPENAI_API_KEY"] = prior_value else: @@ -36,6 +41,11 @@ ExtractedAsyncChatCompletions = dummy_client.chat.completions.__class__ ExtractedAsyncEmbeddings = dummy_client.embeddings.__class__ +# New async extractions +ExtractedAsyncBeta = dummy_client.beta.__class__ +ExtractedAsyncBetaChat = dummy_client.beta.chat.__class__ +ExtractedAsyncBetaChatCompletions = dummy_client.beta.chat.completions.__class__ + class GentraceAsyncOpenAI(AsyncOpenAI): pipeline_run: Optional[PipelineRun] = None @@ -58,6 +68,11 @@ def __init__(self, *args, **kwargs): self.embeddings = GentraceAsyncEmbeddings(client=self, gentrace_config=self.config, pipeline=self.pipeline, pipeline_run=self.pipeline_run) + self.beta = ExtractedAsyncBeta(self) + self.beta.chat = ExtractedAsyncBetaChat(self) + self.beta.chat.completions = GentraceAsyncBetaChatCompletions(client=self, gentrace_config=self.config, + pipeline=self.pipeline, + pipeline_run=self.pipeline_run) class GentraceAsyncEmbeddings(ExtractedAsyncEmbeddings): @@ -96,6 +111,14 @@ async def create(self, *args, **kwargs): pipeline_run = PipelineRun(pipeline=pipeline, context=context) if pipeline_run: + print("Adding step run for GentraceAsyncEmbeddings.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Input params:", input_params) + print("Model:", {"model": model}) + print("Completion:", completion.dict()) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateEmbeddingStepRun( elapsed_time, @@ -174,6 +197,17 @@ async def profiled_completion(): full_response = create_completion_stream_response(modified_response) + print("Adding step run for GentraceAsyncCompletions.create (stream)") + print("Elapsed time:", int((end_time - start_time) * 1000)) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Base completion options:", base_completion_options) + print("Prompt template:", prompt_template) + print("Prompt inputs:", prompt_inputs) + print("Full response:", full_response) + print("Pipeline run ID:", pipeline_run_id if is_self_contained else None) + print("Stream:", stream) + print("Context:", context) create_completion_step_run( self.pipeline_run, pipeline_slug, @@ -209,6 +243,16 @@ async def profiled_completion(): completion = await super().create(**new_completion_options) end_time = time.time() + print("Adding step run for GentraceAsyncCompletions.create (non-stream)") + print("Elapsed time:", int((end_time - start_time) * 1000)) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Base completion options:", base_completion_options) + print("Prompt template:", prompt_template) + print("Prompt inputs:", prompt_inputs) + print("Completion:", completion.dict()) + print("Stream:", stream) + print("Context:", context) create_completion_step_run( self.pipeline_run, pipeline_slug, @@ -300,6 +344,17 @@ async def profiled_completion(): ) if pipeline_run: + print("Adding step run for GentraceAsyncChatCompletions.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Completion:", full_response) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateChatCompletionStepRun( elapsed_time, @@ -344,6 +399,17 @@ async def profiled_completion(): ) if pipeline_run: + print("Adding step run for GentraceAsyncChatCompletions.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Completion:", completion.dict()) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateChatCompletionStepRun( elapsed_time, @@ -371,6 +437,177 @@ async def profiled_completion(): return completion +class GentraceAsyncBetaChatCompletions(ExtractedAsyncBetaChatCompletions): + def __init__(self, *, gentrace_config: Dict[str, Any], client: AsyncOpenAI, pipeline: Optional[Pipeline] = None, + pipeline_run: Optional[PipelineRun] = None): + super().__init__(client) + + self.pipeline = pipeline + self.pipeline_run = pipeline_run + self.gentrace_config = gentrace_config + + async def parse(self, *args, **kwargs): + pipeline_slug = kwargs.pop("pipeline_slug", None) + context = kwargs.pop("gentrace", {}) + extra_headers = kwargs.pop("extra_headers", {}) or {} + extra_headers["X-Gentrace-Helper-Method"] = "true" + + # Render chat messages before calling super().parse() + messages = kwargs.get("messages", []) + rendered_messages = create_rendered_chat_messages(messages) + kwargs["messages"] = rendered_messages + + start_time = time.time() + result = await super().parse(*args, **kwargs, extra_headers=extra_headers) + end_time = time.time() + elapsed_time = int((end_time - start_time) * 1000) + + pipeline_run = self.pipeline_run + is_self_contained = not pipeline_run and pipeline_slug + + if is_self_contained: + pipeline = Pipeline( + slug=pipeline_slug, + **self.gentrace_config + ) + + pipeline_run = PipelineRun( + pipeline=pipeline, + context=context, + ) + + if pipeline_run: + # Extract necessary data from args and kwargs + user = kwargs.get("user") + content_inputs_array = [ + message.get("contentInputs") for message in messages if "contentInputs" in message + ] + content_templates_array = [ + message.get("contentTemplate") for message in messages if "contentTemplate" in message + ] + model_params = {k: v for k, v in kwargs.items() if k not in ["messages", "user"]} + + print("Adding step run for GentraceAsyncBetaChatCompletions.parse") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", rendered_messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Result:", result.dict()) + print("Context:", context) + pipeline_run.add_step_run( + OpenAICreateChatCompletionStepRun( + elapsed_time, + to_date_string(start_time), + to_date_string(end_time), + { + "messages": rendered_messages, + "user": user, + "contentInputs": content_inputs_array, + }, + {**model_params, "contentTemplates": content_templates_array}, + result.dict(), + context, + ) + ) + + if is_self_contained: + submit_result = pipeline_run.submit() + setattr(result, "pipelineRunId", + submit_result["pipelineRunId"] if "pipelineRunId" in submit_result else None) + + return result + + +class GentraceSyncBetaChatCompletions(ExtractedBetaChatCompletions): + def __init__(self, *, gentrace_config: Dict[str, Any], client: OpenAI, pipeline: Optional[Pipeline] = None, + pipeline_run: Optional[PipelineRun] = None): + super().__init__(client) + + self.pipeline = pipeline + self.pipeline_run = pipeline_run + self.gentrace_config = gentrace_config + + def parse(self, *args, **kwargs): + pipeline_slug = kwargs.pop("pipeline_slug", None) + context = kwargs.pop("gentrace", {}) + extra_headers = kwargs.pop("extra_headers", {}) or {} + extra_headers["X-Gentrace-Helper-Method"] = "true" + + # Render chat messages before calling super().parse() + messages = kwargs.get("messages", []) + rendered_messages = create_rendered_chat_messages(messages) + kwargs["messages"] = rendered_messages + + start_time = time.time() + result = super().parse(*args, **kwargs, extra_headers=extra_headers) + end_time = time.time() + elapsed_time = int((end_time - start_time) * 1000) + + pipeline_run = self.pipeline_run + is_self_contained = not pipeline_run and pipeline_slug + + if is_self_contained: + pipeline = Pipeline( + slug=pipeline_slug, + **self.gentrace_config + ) + + pipeline_run = PipelineRun( + pipeline=pipeline, + context=context, + ) + + if pipeline_run: + # Extract necessary data from args and kwargs + user = kwargs.get("user") + content_inputs_array = [ + message.get("contentInputs") for message in messages if "contentInputs" in message + ] + content_templates_array = [ + message.get("contentTemplate") for message in messages if "contentTemplate" in message + ] + model_params = {k: v for k, v in kwargs.items() if k not in ["messages", "user"]} + + print("Adding step run for GentraceSyncBetaChatCompletions.parse") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", rendered_messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Result:", result.dict()) + print("Context:", context) + pipeline_run.add_step_run( + OpenAICreateChatCompletionStepRun( + elapsed_time, + to_date_string(start_time), + to_date_string(end_time), + { + "messages": rendered_messages, + "user": user, + "contentInputs": content_inputs_array, + }, + {**model_params, "contentTemplates": content_templates_array}, + result.dict(), + context, + ) + ) + + if is_self_contained: + submit_result = pipeline_run.submit() + setattr(result, "pipelineRunId", + submit_result["pipelineRunId"] if "pipelineRunId" in submit_result else None) + + return result + + + class GentraceSyncOpenAI(OpenAI): pipeline_run: Optional[PipelineRun] = None @@ -389,9 +626,15 @@ def __init__(self, *args, **kwargs): self.chat.completions = GentraceSyncChatCompletions(client=self, gentrace_config=self.config, pipeline=self.pipeline, pipeline_run=self.pipeline_run) + self.embeddings = GentraceSyncEmbeddings(client=self, gentrace_config=self.config, pipeline=self.pipeline, pipeline_run=self.pipeline_run) + self.beta = ExtractedBeta(self) + self.beta.chat = ExtractedBetaChat(self) + self.beta.chat.completions = GentraceSyncBetaChatCompletions(client=self, gentrace_config=self.config, + pipeline=self.pipeline, + pipeline_run=self.pipeline_run) class GentraceSyncEmbeddings(ExtractedEmbeddings): @@ -432,6 +675,14 @@ def create(self, *args, **kwargs): ) if pipeline_run: + print("Adding step run for GentraceSyncEmbeddings.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Input params:", input_params) + print("Model:", {"model": model}) + print("Completion:", completion.dict()) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateEmbeddingStepRun( elapsed_time, @@ -523,6 +774,10 @@ def __init__(self, *, gentrace_config: Dict[str, Any], client: OpenAI, pipeline: self.gentrace_config = gentrace_config def create(self, *args, **kwargs): + extra_headers = kwargs.get("extra_headers", {}) + if extra_headers.get("X-Gentrace-Helper-Method") == "true": + return super().create(*args, **kwargs) + messages = kwargs.get("messages") user = kwargs.get("user") pipeline_slug = kwargs.pop("pipeline_slug", None) @@ -583,6 +838,17 @@ def profiled_completion(): ) if pipeline_run: + print("Adding step run for GentraceSyncChatCompletions.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Completion:", full_response) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateChatCompletionStepRun( elapsed_time, @@ -632,6 +898,17 @@ def profiled_completion(): ) if pipeline_run: + print("Adding step run for GentraceSyncChatCompletions.create") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Messages:", messages) + print("User:", user) + print("Content inputs:", content_inputs_array) + print("Model params:", model_params) + print("Content templates:", content_templates_array) + print("Completion:", completion.dict()) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateChatCompletionStepRun( elapsed_time, @@ -734,6 +1011,17 @@ def profiled_completion(): full_response = create_completion_stream_response(modified_response) + print("Adding step run for GentraceSyncCompletions.create (stream)") + print("Elapsed time:", int((end_time - start_time) * 1000)) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Base completion options:", base_completion_options) + print("Prompt template:", prompt_template) + print("Prompt inputs:", prompt_inputs) + print("Full response:", full_response) + print("Pipeline run ID:", pipeline_run_id if is_self_contained else None) + print("Stream:", stream) + print("Context:", context) create_completion_step_run( self.pipeline_run, pipeline_slug, @@ -770,6 +1058,16 @@ def profiled_completion(): end_time = time.time() + print("Adding step run for GentraceSyncCompletions.create") + print("Elapsed time:", int((end_time - start_time) * 1000)) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Base completion options:", base_completion_options) + print("Prompt template:", prompt_template) + print("Prompt inputs:", prompt_inputs) + print("Completion:", completion.dict()) + print("Stream:", stream) + print("Context:", context) create_completion_step_run( self.pipeline_run, pipeline_slug, @@ -832,6 +1130,14 @@ def create_completion_step_run( ) if pipeline_run: + print("Adding step run in create_completion_step_run") + print("Elapsed time:", elapsed_time) + print("Start time:", to_date_string(start_time)) + print("End time:", to_date_string(end_time)) + print("Inputs:", inputs_dict) + print("Model params:", {**partial_model_params, "promptTemplate": prompt_template}) + print("Completion:", completion_dict) + print("Context:", context) pipeline_run.add_step_run( OpenAICreateCompletionStepRun( elapsed_time, diff --git a/package/gentrace/providers/pipeline_run.py b/package/gentrace/providers/pipeline_run.py index ab16f4b6..41b9160d 100644 --- a/package/gentrace/providers/pipeline_run.py +++ b/package/gentrace/providers/pipeline_run.py @@ -417,6 +417,19 @@ def submit(self, wait_for_server=False) -> Dict: if len(step_runs_data) == 0: return {"pipelineRunId": None} + + print("Step Runs:") + for idx, step_run in enumerate(step_runs_data, 1): + print(f"Step {idx}:") + print(f" Provider: {step_run.get('provider', 'N/A')}") + print(f" Invocation: {step_run.get('invocation', 'N/A')}") + print(f" Elapsed Time: {step_run.get('elapsedTime', 'N/A')} ms") + print(f" Start Time: {step_run.get('startTime', 'N/A')}") + print(f" End Time: {step_run.get('endTime', 'N/A')}") + print(" Input Params:", step_run.get('inputParams', {})) + print(" Model Params:", step_run.get('modelParams', {})) + print(" Outputs:", step_run.get('outputs', {})) + print() # Add a blank line between steps for readability if not wait_for_server: fire_and_forget( diff --git a/package/gentrace/providers/step_run.py b/package/gentrace/providers/step_run.py index bb903c17..0fa30760 100644 --- a/package/gentrace/providers/step_run.py +++ b/package/gentrace/providers/step_run.py @@ -24,9 +24,12 @@ def __init__( self.end_time = end_time self.inputs = self._convert_to_dict(inputs) self.model_params = model_params + print("Outputs before conversion:", outputs) self.outputs = self._convert_to_dict(outputs) + print("Outputs after conversion:", self.outputs) self.context = self._convert_to_dict(context or {}) + def _convert_to_dict(self, obj: Any) -> Any: if hasattr(obj, '__dict__'): # Check for DynamicSchema Gentrace custom types