From 6d6e0e4bfc2a973261f09434714607f0a5b75f63 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sat, 12 Oct 2024 18:06:36 -0400 Subject: [PATCH 01/58] feat: add reduce operation lineage --- docetl/operations/reduce.py | 25 ++++++++++++++++++++++++ tests/basic/test_basic_reduce_resolve.py | 21 ++++++++++++++++++++ 2 files changed, 46 insertions(+) diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index 682b5d70..cd3cee78 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -55,6 +55,7 @@ def __init__(self, *args, **kwargs): else self.config["reduce_key"] ) self.intermediates = {} + self.lineage_keys = self.config.get("output", {}).get("lineage", []) def syntax_check(self) -> None: """ @@ -258,6 +259,19 @@ def syntax_check(self) -> None: f"'embedding_keys' is required when using embedding-based sampling in {self.config['name']}" ) + # Check if lineage is a list of strings + if "lineage" in self.config.get("output", {}): + if not isinstance(self.config["output"]["lineage"], list): + raise TypeError( + f"'lineage' in {self.config['name']} 'output' configuration must be a list" + ) + if not all( + isinstance(key, str) for key in self.config["output"]["lineage"] + ): + raise TypeError( + f"All elements in 'lineage' list in {self.config['name']} 'output' configuration must be strings" + ) + self.gleaning_check() def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: @@ -363,6 +377,17 @@ def process_group( if k not in self.config["output"]["schema"] and k not in result: result[k] = v + # Add lineage information + if result is not None and self.lineage_keys: + lineage = [] + for item in group_elems: + lineage_item = { + k: item.get(k) for k in self.lineage_keys if k in item + } + if lineage_item: + lineage.append(lineage_item) + result[f"{self.config['name']}_lineage"] = lineage + return result, total_cost with ThreadPoolExecutor(max_workers=self.max_threads) as executor: diff --git a/tests/basic/test_basic_reduce_resolve.py b/tests/basic/test_basic_reduce_resolve.py index a0a93c2f..5326b5ab 100644 --- a/tests/basic/test_basic_reduce_resolve.py +++ b/tests/basic/test_basic_reduce_resolve.py @@ -143,3 +143,24 @@ def test_resolve_operation_empty_input(resolve_config, max_threads, api_wrapper) assert len(results) == 0 assert cost == 0 + + +def test_reduce_operation_with_lineage( + reduce_config, max_threads, reduce_sample_data, api_wrapper +): + # Add lineage configuration to reduce_config + reduce_config["output"]["lineage"] = ["name", "email"] + + operation = ReduceOperation( + api_wrapper, reduce_config, "text-embedding-3-small", max_threads + ) + results, cost = operation.execute(reduce_sample_data) + + # Check if lineage information is present in the results + assert all(f"{reduce_config['name']}_lineage" in result for result in results) + + # Check if lineage contains the specified keys + for result in results: + lineage = result[f"{reduce_config['name']}_lineage"] + assert all(isinstance(item, dict) for item in lineage) + assert all("name" in item and "email" in item for item in lineage) From 0aeb643c3e00d078d400b046da8f7d900c86543b Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sat, 12 Oct 2024 18:08:46 -0400 Subject: [PATCH 02/58] feat: add reduce operation lineage --- docs/operators/reduce.md | 24 +++++++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/docs/operators/reduce.md b/docs/operators/reduce.md index fd8d0b80..3c01fd82 100644 --- a/docs/operators/reduce.md +++ b/docs/operators/reduce.md @@ -51,7 +51,7 @@ This Reduce operation processes customer feedback grouped by department: | Parameter | Description | Default | | ------------------------- | ------------------------------------------------------------------------------------------------------ | --------------------------- | -| `sample` | Number of samples to use for the operation | None | +| `sample` | Number of samples to use for the operation | None | | `synthesize_resolve` | If false, won't synthesize a resolve operation between map and reduce | true | | `model` | The language model to use | Falls back to default_model | | `input` | Specifies the schema or keys to subselect from each item | All keys from input items | @@ -196,6 +196,28 @@ For semantic similarity sampling, you can use a query to select the most relevan In this example, the Reduce operation will use semantic similarity to select the 30 reviews most relevant to battery life and performance for each product_id. This allows you to focus the summarization on specific aspects of the product reviews. +### Lineage + +The Reduce operation supports lineage, which allows you to track the original input data for each output. This can be useful for debugging and auditing. To enable lineage, add a `lineage` configuration to your reduce operation, specifying the keys to include in the lineage. For example: + +```yaml +- name: summarize_reviews_by_category + type: reduce + reduce_key: category + prompt: | + Summarize the reviews for category {{ inputs[0].category }}: + {% for item in inputs %} + Review {{ loop.index }}: {{ item.review }} + {% endfor %} + output: + schema: + summary: string + lineage: + - product_id +``` + +This output will include a list of all product_ids for each category in the lineage, saved under the key `summarize_reviews_by_category_lineage`. + ## Best Practices 1. **Choose Appropriate Keys**: Select `reduce_key`(s) that logically group your data for the desired aggregation. From 652efad63ee32ac5d4ca8e2d704a65cd86760dba Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 13 Oct 2024 00:43:26 -0400 Subject: [PATCH 03/58] fix: change gleaning prompt to validation_prompt --- docetl/operations/utils.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/docetl/operations/utils.py b/docetl/operations/utils.py index f7313690..bd9899dc 100644 --- a/docetl/operations/utils.py +++ b/docetl/operations/utils.py @@ -470,7 +470,9 @@ def _cached_call_llm( if gleaning_config: # Retry gleaning prompt + regular LLM num_gleaning_rounds = gleaning_config.get("num_rounds", 2) - validator_prompt_template = Template(gleaning_config["prompt"]) + validator_prompt_template = Template( + gleaning_config["validation_prompt"] + ) parsed_output = self.parse_llm_response( response, output_schema, tools From 7635afa8e703b74dc73be60670e260fa8a7b66bb Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 13 Oct 2024 13:53:17 -0400 Subject: [PATCH 04/58] fix: change gleaning prompt to validation_prompt --- docetl/operations/utils.py | 11 +++++------ tests/basic/test_basic_map.py | 3 ++- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docetl/operations/utils.py b/docetl/operations/utils.py index bd9899dc..a5807168 100644 --- a/docetl/operations/utils.py +++ b/docetl/operations/utils.py @@ -486,9 +486,7 @@ def _cached_call_llm( } ] + messages - + [ - {"role": "assistant", "content": json.dumps(parsed_output)}, - ] + + [{"role": "assistant", "content": json.dumps(parsed_output)}] ) for rnd in range(num_gleaning_rounds): @@ -553,9 +551,10 @@ def _cached_call_llm( parsed_output = self.parse_llm_response( response, output_schema, tools )[0] - validator_messages[-1] = [ - {"role": "assistant", "content": json.dumps(parsed_output)}, - ] + validator_messages[-1] = { + "role": "assistant", + "content": json.dumps(parsed_output), + } total_cost += completion_cost(response) diff --git a/tests/basic/test_basic_map.py b/tests/basic/test_basic_map.py index 21a21d95..357a90b0 100644 --- a/tests/basic/test_basic_map.py +++ b/tests/basic/test_basic_map.py @@ -191,9 +191,10 @@ def test_map_operation_with_gleaning(simple_map_config, map_sample_data, api_wra map_config_with_gleaning = { **simple_map_config, "gleaning": { - "num_rounds": 1, + "num_rounds": 2, "validation_prompt": "Review the sentiment analysis. Is it accurate? If not, suggest improvements.", }, + "bypass_cache": True, } operation = MapOperation(api_wrapper, map_config_with_gleaning, "gpt-4o-mini", 4) From 7b1e04d04a20b93676feb8ed6218fa670860ef5f Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 13 Oct 2024 14:36:56 -0400 Subject: [PATCH 05/58] fix: change gleaning prompt to validation_prompt --- docetl/dataset.py | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/docetl/dataset.py b/docetl/dataset.py index 9d53753c..c90fda4e 100644 --- a/docetl/dataset.py +++ b/docetl/dataset.py @@ -146,17 +146,12 @@ def _validate_parsing( return [] for tool in parsing_tools: - if ( - not isinstance(tool, dict) - or "function" not in tool - ): + if not isinstance(tool, dict) or "function" not in tool: raise ValueError( "Each parsing tool must be a dictionary with a 'function' key and any arguments required by that function" ) if not isinstance(tool["function"], str): - raise ValueError( - "'function' in parsing tools must be a string" - ) + raise ValueError("'function' in parsing tools must be a string") if "function_kwargs" in tool and not isinstance( tool["function_kwargs"], dict ): @@ -212,7 +207,7 @@ def _process_item( ): result = func(item, **function_kwargs) return [item.copy() | res for res in result] - + def _apply_parsing_tools(self, data: List[Dict]) -> List[Dict]: """ Apply parsing tools to the data. @@ -233,7 +228,7 @@ def _apply_parsing_tools(self, data: List[Dict]) -> List[Dict]: # with the existing yaml format... if "function_kwargs" in function_kwargs: function_kwargs.update(function_kwargs.pop("function_kwargs")) - + try: func = get_parser(tool["function"]) except KeyError: @@ -243,7 +238,8 @@ def _apply_parsing_tools(self, data: List[Dict]) -> List[Dict]: ): # Define the custom function in the current scope exec( - self.user_defined_parsing_tool_map[ + "from typing import List, Dict\n" + + self.user_defined_parsing_tool_map[ tool["function"] ].function_code ) From d939a8622020be016a85483c3ec6c21b655e6469 Mon Sep 17 00:00:00 2001 From: Anurag Date: Mon, 14 Oct 2024 00:28:14 +0530 Subject: [PATCH 06/58] refactor: md changes documents -> items --- docs/concepts/pipelines.md | 4 ++-- docs/examples/custom-parsing.md | 4 ++-- docs/examples/mining-product-reviews.md | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/concepts/pipelines.md b/docs/concepts/pipelines.md index ed0e4cae..bc1d1b3b 100644 --- a/docs/concepts/pipelines.md +++ b/docs/concepts/pipelines.md @@ -1,6 +1,6 @@ # Pipelines -Pipelines in DocETL are the core structures that define the flow of data processing. They orchestrate the application of operators to datasets, creating a seamless workflow for complex document processing tasks. +Pipelines in DocETL are the core structures that define the flow of data processing. They orchestrate the application of operators to datasets, creating a seamless workflow for complex chunk processing tasks. ## Components of a Pipeline @@ -21,7 +21,7 @@ default_model: gpt-4o-mini ### Datasets -Datasets define the input data for your pipeline. They are collections of documents, where each document is an object in a JSON list (or row in a CSV file). Datasets are typically specified in the YAML configuration file, indicating the type and path of the data source. For example: +Datasets define the input data for your pipeline. They are collections of items/chunks, where each item/chunk is an object in a JSON list (or row in a CSV file). Datasets are typically specified in the YAML configuration file, indicating the type and path of the data source. For example: ```yaml datasets: diff --git a/docs/examples/custom-parsing.md b/docs/examples/custom-parsing.md index a8959f97..9b4d1b3a 100644 --- a/docs/examples/custom-parsing.md +++ b/docs/examples/custom-parsing.md @@ -283,7 +283,7 @@ While DocETL provides several built-in parsing tools, the community can always b If the built-in tools don't meet your needs, you can create your own custom parsing tools. Here's how: 1. Define your parsing function in the `parsing_tools` section of your configuration. -2. Ensure your function takes a document (dict) as input and returns a list of documents (dicts). +2. Ensure your function takes a item (dict) as input and returns a list of items (dicts). 3. Use your custom parser in the `parsing` section of your dataset configuration. For example: @@ -292,7 +292,7 @@ For example: parsing_tools: - name: my_custom_parser function_code: | - def my_custom_parser(document: Dict) -> List[Dict]: + def my_custom_parser(item: Dict) -> List[Dict]: # Your custom parsing logic here return [processed_data] diff --git a/docs/examples/mining-product-reviews.md b/docs/examples/mining-product-reviews.md index 9e120d85..c6edf3ec 100644 --- a/docs/examples/mining-product-reviews.md +++ b/docs/examples/mining-product-reviews.md @@ -16,7 +16,7 @@ Our goal is to create a pipeline that will: 2. Resolve similar themes across different games 3. Generate reports of polarizing themes common across games, supported by quotes from different game reviews -We'll be using a subset of the [STEAM review dataset](https://www.kaggle.com/datasets/andrewmvd/steam-reviews). We've created a subset that contains reviews for 500 of the most popular games, with approximately 400 reviews per game, balanced between positive and negative ratings. For each game, we concatenate all reviews into a single text for analysis---so we'll have 500 input documents, each representing a game. You can get the dataset sample [here](https://drive.google.com/file/d/1hroljsvn8m23iVsNpET8Ma7sfb1OUu_u/view?usp=drive_link). +We'll be using a subset of the [STEAM review dataset](https://www.kaggle.com/datasets/andrewmvd/steam-reviews). We've created a subset that contains reviews for 500 of the most popular games, with approximately 400 reviews per game, balanced between positive and negative ratings. For each game, we concatenate all reviews into a single text for analysis---so we'll have 500 input items/reviews, each representing a game. You can get the dataset sample [here](https://drive.google.com/file/d/1hroljsvn8m23iVsNpET8Ma7sfb1OUu_u/view?usp=drive_link). ## Pipeline Structure @@ -284,7 +284,7 @@ This command, with `optimize: true` set for the map and resolve operations, prov 2. Blocking statements and thresholds for the resolve operation: This optimizes the theme resolution process, making it more efficient when dealing with a large number of themes across multiple games. The optimizer provided us with blocking keys of `summary` and `theme`, and a threshold of 0.596 for similarity (to get 95% recall of duplicates). -These optimizations are crucial for handling the scale of our dataset, which includes 500 games with an _average_ of 66,000 tokens per game, and 12% of the documents exceeding the context length limits of the OpenAI LLMs (128k tokens). +These optimizations are crucial for handling the scale of our dataset, which includes 500 games with an _average_ of 66,000 tokens per game, and 12% of the items/reviews exceeding the context length limits of the OpenAI LLMs (128k tokens). ??? info "Optimized Pipeline" From bcd4e8cb9f67aa7f5861be69a459b68385e347c9 Mon Sep 17 00:00:00 2001 From: Anurag Date: Mon, 14 Oct 2024 00:28:21 +0530 Subject: [PATCH 07/58] refactor: code changes documents -> items --- docetl/operations/clustering_utils.py | 12 ++++++------ docetl/operations/reduce.py | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docetl/operations/clustering_utils.py b/docetl/operations/clustering_utils.py index 7663b892..05fed867 100644 --- a/docetl/operations/clustering_utils.py +++ b/docetl/operations/clustering_utils.py @@ -60,8 +60,8 @@ def get_embeddings_for_clustering_with_st( return embeddings, 0 -def cluster_documents( - documents: List[Dict], +def cluster_items( + items: List[Dict], sampling_config: Dict, sample_size: int, api_wrapper: APIWrapper, @@ -70,7 +70,7 @@ def cluster_documents( Cluster documents using KMeans clustering algorithm. Args: - documents (List[Dict]): The list of documents to cluster. + items (List[Dict]): The list of documents to cluster. sampling_config (Dict): The sampling configuration. Must contain embedding_model. If embedding_keys is not specified, it will use all keys in the document. If embedding_model is not specified, it will use text-embedding-3-small. If embedding_model is sentence-transformer, it will use all-MiniLM-L6-v2. sample_size (int): The number of clusters to create. api_wrapper (APIWrapper): The API wrapper to use for embedding. @@ -78,17 +78,17 @@ def cluster_documents( Dict[int, List[Dict]]: A dictionary of clusters, where each cluster is a list of documents. """ embeddings, cost = get_embeddings_for_clustering( - documents, sampling_config, api_wrapper + items, sampling_config, api_wrapper ) from sklearn.cluster import KMeans - num_clusters = min(sample_size, len(documents)) + num_clusters = min(sample_size, len(items)) kmeans = KMeans(n_clusters=num_clusters, random_state=42) cluster_labels = kmeans.fit_predict(embeddings) clusters = {i: [] for i in range(num_clusters)} for idx, label in enumerate(cluster_labels): - clusters[label].append(documents[idx]) + clusters[label].append(items[idx]) return clusters, cost diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index cd3cee78..5d7f5012 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -20,7 +20,7 @@ from docetl.operations.base import BaseOperation from docetl.operations.clustering_utils import ( - cluster_documents, + cluster_items, get_embeddings_for_clustering, ) from docetl.operations.utils import rich_as_completed @@ -428,7 +428,7 @@ def _cluster_based_sampling( if sample_size >= len(group_list): return group_list, 0 - clusters, cost = cluster_documents( + clusters, cost = cluster_items( group_list, value_sampling, sample_size, self.runner.api ) From 82b2654c0827b57b40f9bfb8e6f16f5dffee9c33 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 13 Oct 2024 21:44:53 -0400 Subject: [PATCH 08/58] fix: tests check for cost --- tests/basic/test_pipeline_with_parsing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/basic/test_pipeline_with_parsing.py b/tests/basic/test_pipeline_with_parsing.py index 03ad15e4..e10de629 100644 --- a/tests/basic/test_pipeline_with_parsing.py +++ b/tests/basic/test_pipeline_with_parsing.py @@ -21,6 +21,7 @@ operations: - name: map_operation type: map + bypass_cache: true prompt: | Summarize the following text in one sentence: "{{ input.content }}" output: @@ -200,7 +201,6 @@ def test_pipeline_with_custom_parsing(): os.unlink(tmp_input.name) os.unlink(tmp_output.name) - assert cost > 0, "Total cost was not calculated or is 0" print( f"Pipeline with custom parsing executed successfully. Total cost: ${cost:.2f}" ) From b0ef0c1e9c92a12e058ddbbe6a7e420b21265c64 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 13 Oct 2024 22:00:47 -0400 Subject: [PATCH 09/58] bump up version --- docetl/__init__.py | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docetl/__init__.py b/docetl/__init__.py index 419d37d4..4fb6fcf3 100644 --- a/docetl/__init__.py +++ b/docetl/__init__.py @@ -1,4 +1,4 @@ -__version__ = "0.1.6" +__version__ = "0.1.7" from docetl.runner import DSLRunner from docetl.builder import Optimizer diff --git a/pyproject.toml b/pyproject.toml index e0cf9bb2..e909ca87 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "docetl" -version = "0.1.6" +version = "0.1.7" description = "ETL with LLM operations." authors = ["Shreya Shankar "] license = "MIT" From 283f79b59f37e75ecf894e9d9bc617e34345f285 Mon Sep 17 00:00:00 2001 From: Anurag Date: Mon, 14 Oct 2024 13:23:26 +0530 Subject: [PATCH 10/58] refactor: operators.md documents -> items --- docs/concepts/operators.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/concepts/operators.md b/docs/concepts/operators.md index fc9070ef..617e4499 100644 --- a/docs/concepts/operators.md +++ b/docs/concepts/operators.md @@ -4,7 +4,7 @@ Operators in DocETL are designed for semantically processing unstructured data. ## Overview -- Datasets contain documents, where a document is an object in the JSON list, with fields and values. +- Datasets contain items, where a item is an object in the JSON list, with fields and values. An item here could be simple text chunk or a document reference. - DocETL provides several operators, each tailored for specific unstructured data processing tasks. - By default, operations are parallelized on your data using multithreading for improved performance. From 6e0f2bd3ea50322e02f3758cd66573d1452a192b Mon Sep 17 00:00:00 2001 From: Anurag Date: Mon, 14 Oct 2024 13:28:52 +0530 Subject: [PATCH 11/58] Revert "refactor: code changes documents -> items" This reverts commit bcd4e8cb9f67aa7f5861be69a459b68385e347c9. --- docetl/operations/clustering_utils.py | 12 ++++++------ docetl/operations/reduce.py | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docetl/operations/clustering_utils.py b/docetl/operations/clustering_utils.py index 05fed867..7663b892 100644 --- a/docetl/operations/clustering_utils.py +++ b/docetl/operations/clustering_utils.py @@ -60,8 +60,8 @@ def get_embeddings_for_clustering_with_st( return embeddings, 0 -def cluster_items( - items: List[Dict], +def cluster_documents( + documents: List[Dict], sampling_config: Dict, sample_size: int, api_wrapper: APIWrapper, @@ -70,7 +70,7 @@ def cluster_items( Cluster documents using KMeans clustering algorithm. Args: - items (List[Dict]): The list of documents to cluster. + documents (List[Dict]): The list of documents to cluster. sampling_config (Dict): The sampling configuration. Must contain embedding_model. If embedding_keys is not specified, it will use all keys in the document. If embedding_model is not specified, it will use text-embedding-3-small. If embedding_model is sentence-transformer, it will use all-MiniLM-L6-v2. sample_size (int): The number of clusters to create. api_wrapper (APIWrapper): The API wrapper to use for embedding. @@ -78,17 +78,17 @@ def cluster_items( Dict[int, List[Dict]]: A dictionary of clusters, where each cluster is a list of documents. """ embeddings, cost = get_embeddings_for_clustering( - items, sampling_config, api_wrapper + documents, sampling_config, api_wrapper ) from sklearn.cluster import KMeans - num_clusters = min(sample_size, len(items)) + num_clusters = min(sample_size, len(documents)) kmeans = KMeans(n_clusters=num_clusters, random_state=42) cluster_labels = kmeans.fit_predict(embeddings) clusters = {i: [] for i in range(num_clusters)} for idx, label in enumerate(cluster_labels): - clusters[label].append(items[idx]) + clusters[label].append(documents[idx]) return clusters, cost diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index 5d7f5012..cd3cee78 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -20,7 +20,7 @@ from docetl.operations.base import BaseOperation from docetl.operations.clustering_utils import ( - cluster_items, + cluster_documents, get_embeddings_for_clustering, ) from docetl.operations.utils import rich_as_completed @@ -428,7 +428,7 @@ def _cluster_based_sampling( if sample_size >= len(group_list): return group_list, 0 - clusters, cost = cluster_items( + clusters, cost = cluster_documents( group_list, value_sampling, sample_size, self.runner.api ) From 8cab6b060b6f4d57335bf01001a11058792983e7 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Mon, 14 Oct 2024 10:14:32 -0400 Subject: [PATCH 12/58] chore: update dependency versions --- poetry.lock | 1625 +++++++++++++++++++++++++++------------------------ 1 file changed, 862 insertions(+), 763 deletions(-) diff --git a/poetry.lock b/poetry.lock index a5ae584d..9926319a 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,113 +2,113 @@ [[package]] name = "aiohappyeyeballs" -version = "2.4.2" +version = "2.4.3" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiohappyeyeballs-2.4.2-py3-none-any.whl", hash = "sha256:8522691d9a154ba1145b157d6d5c15e5c692527ce6a53c5e5f9876977f6dab2f"}, - {file = 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for entry in inputs %}\n- {{ entry.medication }}\n{% endfor %}" ), From df728f831823f5a518dcc0f0cf0cb0ed7504e60e Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Tue, 15 Oct 2024 23:57:50 -0400 Subject: [PATCH 14/58] fix: edit agent for synth resolve task --- docetl/optimizers/join_optimizer.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docetl/optimizers/join_optimizer.py b/docetl/optimizers/join_optimizer.py index ddb37100..02a7cc10 100644 --- a/docetl/optimizers/join_optimizer.py +++ b/docetl/optimizers/join_optimizer.py @@ -258,7 +258,7 @@ def synthesize_compare_prompt( Are these [entities] likely referring to the same [entity type]? Consider [list relevant attributes or characteristics to compare]. Respond with "True" if they are likely the same [entity type], or "False" if they are likely different [entity types]. ``` - Please generate the comparison prompt: + Please generate the comparison prompt, which should be a Jinja2 template: """, } ] @@ -1078,12 +1078,13 @@ def _perform_comparisons_resolve( ) -> Tuple[List[Tuple[int, int, bool]], float]: comparisons, total_cost = [], 0 op = ResolveOperation( - self, + self.runner, self.op_config, self.runner.default_model, self.max_threads, self.console, - self.status) + self.status, + ) with ThreadPoolExecutor(max_workers=self.max_threads) as executor: futures = [ executor.submit( @@ -1115,12 +1116,13 @@ def _perform_comparisons_equijoin( ) -> Tuple[List[Tuple[int, int, bool]], float]: comparisons, total_cost = [], 0 op = EquijoinOperation( - self, + self.runner, self.op_config, self.runner.default_model, self.max_threads, self.console, - self.status) + self.status, + ) with ThreadPoolExecutor(max_workers=self.max_threads) as executor: futures = [ executor.submit( From e1685ebe088a3320eeb4e15e8ede320e997fa23c Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 16 Oct 2024 10:35:22 -0400 Subject: [PATCH 15/58] feat: add links to external projects --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 9ac1319d..ac3e2c36 100644 --- a/README.md +++ b/README.md @@ -16,6 +16,11 @@ DocETL is the ideal choice when you're looking to maximize correctness and outpu - You're working with long documents that don't fit into a single prompt or are too lengthy for effective LLM reasoning - You have validation criteria and want tasks to automatically retry when the validation fails +## Cool Things People Are Doing with DocETL + +- [Conversation Generator](https://github.com/redhog/docetl-conversation) +- [YouTube Transcript Topic Extraction](https://github.com/rajib76/docetl_examples) + ## Installation See the documentation for installing from PyPI. From 2e14973aebfda9c91d882499a310cee3a77cfc3b Mon Sep 17 00:00:00 2001 From: Egil Date: Thu, 17 Oct 2024 18:14:32 +0200 Subject: [PATCH 16/58] New api --- docetl/cli.py | 2 +- docetl/runner.py | 91 +++++++++++++++++++++++++++++++----------------- 2 files changed, 60 insertions(+), 33 deletions(-) diff --git a/docetl/cli.py b/docetl/cli.py index 2594c862..7bca3006 100644 --- a/docetl/cli.py +++ b/docetl/cli.py @@ -83,7 +83,7 @@ def run( load_dotenv(env_file) runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads) - runner.run() + runner.load_run_save() @app.command() diff --git a/docetl/runner.py b/docetl/runner.py index 6be5da4d..97d6fcdc 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -51,21 +51,6 @@ def __init__(self, config: Dict, max_threads: int = None): self.config.get("parsing_tools", None) ) - # Check if output path is correctly formatted as JSON - output_path = self.config.get("pipeline", {}).get("output", {}).get("path") - if output_path: - if not ( - output_path.lower().endswith(".json") - or output_path.lower().endswith(".csv") - ): - raise ValueError( - f"Output path '{output_path}' is not a JSON or CSV file. Please provide a path ending with '.json' or '.csv'." - ) - else: - raise ValueError( - "No output path specified in the configuration. Please provide an output path ending with '.json' or '.csv' in the configuration." - ) - self.syntax_check() op_map = {op["name"]: op for op in self.config["operations"]} @@ -90,6 +75,23 @@ def __init__(self, config: Dict, max_threads: int = None): all_ops_str.encode() ).hexdigest() + def get_output_path(self, require = False): + output_path = self.config.get("pipeline", {}).get("output", {}).get("path") + if output_path: + if not ( + output_path.lower().endswith(".json") + or output_path.lower().endswith(".csv") + ): + raise ValueError( + f"Output path '{output_path}' is not a JSON or CSV file. Please provide a path ending with '.json' or '.csv'." + ) + elif require: + raise ValueError( + "No output path specified in the configuration. Please provide an output path ending with '.json' or '.csv' in the configuration to use the save() method." + ) + + return output_path + def syntax_check(self): """ Perform a syntax check on all operations defined in the configuration. @@ -105,6 +107,9 @@ def syntax_check(self): "[yellow]Performing syntax check on all operations...[/yellow]" ) + # Just validate that it's a json file if specified + self.get_output_path() + for operation_config in self.config["operations"]: operation = operation_config["name"] operation_type = operation_config["type"] @@ -131,7 +136,7 @@ def find_operation(self, op_name: str) -> Dict: return operation_config raise ValueError(f"Operation '{op_name}' not found in configuration.") - def run(self) -> float: + def load_run_save(self) -> float: """ Execute the entire pipeline defined in the configuration. @@ -141,9 +146,37 @@ def run(self) -> float: Returns: float: The total cost of executing the pipeline. """ + + # Fail early if we can't save the output... + self.get_output_path(require=True) + self.console.rule("[bold blue]Pipeline Execution[/bold blue]") start_time = time.time() - self.load_datasets() + + output, total_cost = self.run(self.load()) + self.save(output) + + self.console.rule("[bold green]Execution Summary[/bold green]") + self.console.print(f"[bold green]Total cost: [green]${total_cost:.2f}[/green]") + self.console.print( + f"[bold green]Total time: [green]{time.time() - start_time:.2f} seconds[/green]" + ) + + return total_cost + + def run(self, datasets) -> float: + """ + Execute the entire pipeline defined in the configuration on some data. + + Args: + datasets (dict[str, Dataset | List[Dict]]): input datasets to transform + + Returns: + (List[Dict], float): The transformed data and the total cost of execution. + """ + self.datasets = {name: (dataset if isinstance(dataset, Dataset) + else Dataset(self, "memory", dataset)) + for name, dataset in datasets.items()} total_cost = 0 for step in self.config["pipeline"]["steps"]: step_name = step["name"] @@ -158,10 +191,6 @@ def run(self) -> float: f"Step [cyan]{step_name}[/cyan] completed. Cost: [green]${step_cost:.2f}[/green]" ) - self.save_output( - self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load() - ) - # Save the self.step_op_hashes to a file if self.intermediate_dir exists if self.intermediate_dir: with open( @@ -170,15 +199,9 @@ def run(self) -> float: ) as f: json.dump(self.step_op_hashes, f) - self.console.rule("[bold green]Execution Summary[/bold green]") - self.console.print(f"[bold green]Total cost: [green]${total_cost:.2f}[/green]") - self.console.print( - f"[bold green]Total time: [green]{time.time() - start_time:.2f} seconds[/green]" - ) - - return total_cost + return self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load(), total_cost - def load_datasets(self): + def load(self): """ Load all datasets defined in the configuration. @@ -188,9 +211,10 @@ def load_datasets(self): ValueError: If an unsupported dataset type is encountered. """ self.console.rule("[cyan]Loading Datasets[/cyan]") + datasets = {} for name, dataset_config in self.config["datasets"].items(): if dataset_config["type"] == "file": - self.datasets[name] = Dataset( + datasets[name] = Dataset( self, "file", dataset_config["path"], @@ -201,8 +225,9 @@ def load_datasets(self): self.console.print(f"Loaded dataset: [bold]{name}[/bold]") else: raise ValueError(f"Unsupported dataset type: {dataset_config['type']}") - - def save_output(self, data: List[Dict]): + return datasets + + def save(self, data: List[Dict]): """ Save the final output of the pipeline. @@ -212,6 +237,8 @@ def save_output(self, data: List[Dict]): Raises: ValueError: If an unsupported output type is specified in the configuration. """ + self.get_output_path(require=True) + self.console.rule("[cyan]Saving Output[/cyan]") output_config = self.config["pipeline"]["output"] if output_config["type"] == "file": From cca416e8b747737237429731ed5292e9109599b2 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Thu, 17 Oct 2024 11:49:59 -0700 Subject: [PATCH 17/58] feat: add paper links --- website/src/app/page.tsx | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/website/src/app/page.tsx b/website/src/app/page.tsx index 95366b9b..44ec7ef4 100644 --- a/website/src/app/page.tsx +++ b/website/src/app/page.tsx @@ -61,8 +61,10 @@ export default function Home() { Docs - From 784f26511bb56f79ae3658f02114ebedb440430b Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Thu, 17 Oct 2024 13:16:17 -0700 Subject: [PATCH 18/58] fix: update python api --- README.md | 2 +- docetl/api.py | 16 ++++++++++------ docetl/schemas.py | 20 +++++++++++++++++++- docs/operators/cluster.md | 2 -- 4 files changed, 30 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index ac3e2c36..e71189aa 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # DocETL: Powering Complex Document Processing Pipelines -[Website (Includes Demo)](https://docetl.com) | [Documentation](https://ucbepic.github.io/docetl) | [Discord](https://discord.gg/fHp7B2X3xx) | [NotebookLM Podcast](https://notebooklm.google.com/notebook/ef73248b-5a43-49cd-9976-432d20f9fa4f/audio?pli=1) (thanks Shabie from our Discord community!) | Paper (coming soon!) +[Website (Includes Demo)](https://docetl.org) | [Documentation](https://ucbepic.github.io/docetl) | [Discord](https://discord.gg/fHp7B2X3xx) | [Paper](https://arxiv.org/abs/2410.12189) ![DocETL Figure](docs/assets/readmefig.png) diff --git a/docetl/api.py b/docetl/api.py index 893a4faf..f7f7f6de 100644 --- a/docetl/api.py +++ b/docetl/api.py @@ -64,17 +64,17 @@ FilterOp, GatherOp, MapOp, + ReduceOp, + ResolveOp, + SplitOp, + UnnestOp, + ClusterOp, + SampleOp, OpType, ParallelMapOp, ParsingTool, -) -from docetl.schemas import ( PipelineOutput, PipelineStep, - ReduceOp, - ResolveOp, - SplitOp, - UnnestOp, ) @@ -322,6 +322,10 @@ def _update_from_dict(self, config: Dict[str, Any]): self.operations.append(GatherOp(**op, type=op_type)) elif op_type == "unnest": self.operations.append(UnnestOp(**op, type=op_type)) + elif op_type == "cluster": + self.operations.append(ClusterOp(**op, type=op_type)) + elif op_type == "sample": + self.operations.append(SampleOp(**op, type=op_type)) self.steps = [PipelineStep(**step) for step in config["pipeline"]["steps"]] self.output = PipelineOutput(**config["pipeline"]["output"]) self.default_model = config.get("default_model") diff --git a/docetl/schemas.py b/docetl/schemas.py index ce89473e..90382fe0 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -78,7 +78,7 @@ class Dataset(BaseModel): parsing: Optional[List[Dict[str, str]]] = None -class BaseOp(BaseModel): +class BaseOp(BaseModel, extra="allow"): name: str type: str @@ -222,6 +222,22 @@ class UnnestOp(BaseOp): depth: Optional[int] = None +class ClusterOp(BaseOp): + type: str = "cluster" + embedding_keys: List[str] + summary_prompt: str + summary_schema: Dict[str, Any] + output_key: Optional[str] = "clusters" + + +class SampleOp(BaseOp): + type: str = "sample" + method: str + samples: Union[int, float, List[Dict[str, Any]]] + method_kwargs: Optional[Dict[str, Any]] = None + random_state: Optional[int] = None + + OpType = Union[ MapOp, ResolveOp, @@ -232,6 +248,8 @@ class UnnestOp(BaseOp): SplitOp, GatherOp, UnnestOp, + ClusterOp, + SampleOp, ] diff --git a/docs/operators/cluster.md b/docs/operators/cluster.md index 51ef82a1..e7abb65a 100644 --- a/docs/operators/cluster.md +++ b/docs/operators/cluster.md @@ -181,8 +181,6 @@ and a description, and groups them into a tree of categories. | `output_key` | The name of the output key where the cluster path will be inserted in the items. | "clusters" | | `model` | The language model to use | Falls back to `default_model` | | `embedding_model` | The embedding model to use | "text-embedding-3-small" | -| `tools` | List of tool definitions for LLM use | None | | `timeout` | Timeout for each LLM call in seconds | 120 | | `max_retries_per_timeout` | Maximum number of retries per timeout | 2 | -| `validate` | List of Python expressions to validate the output | None | | `sample` | Number of items to sample for this operation | None | From ead12e8e282361937ae0a525becfa318b47b3ba0 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Thu, 17 Oct 2024 13:21:17 -0700 Subject: [PATCH 19/58] add flaky test --- tests/test_runner_caching.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/test_runner_caching.py b/tests/test_runner_caching.py index fbd973fa..42f02f2f 100644 --- a/tests/test_runner_caching.py +++ b/tests/test_runner_caching.py @@ -58,6 +58,7 @@ def create_pipeline(input_file, output_file, intermediate_dir, operation_prompt) ) +@pytest.mark.flaky(reruns=3) def test_pipeline_rerun_on_operation_change( temp_input_file, temp_output_file, temp_intermediate_dir ): From f7767a6a4fd8ede198465e5f3cc1191ead9a620b Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Thu, 17 Oct 2024 20:28:11 -0700 Subject: [PATCH 20/58] docs: add sample and cluster to docs --- docs/api-reference/python.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/docs/api-reference/python.md b/docs/api-reference/python.md index 9ec82bd0..0291a6ea 100644 --- a/docs/api-reference/python.md +++ b/docs/api-reference/python.md @@ -92,6 +92,24 @@ ignore_init_summary: false trim_doctest_flags: true +::: docetl.schemas.SampleOp + options: + show_root_heading: true + heading_level: 3 + show_if_no_docstring: false + docstring_options: + ignore_init_summary: false + trim_doctest_flags: true + +::: docetl.schemas.ClusterOp + options: + show_root_heading: true + heading_level: 3 + show_if_no_docstring: false + docstring_options: + ignore_init_summary: false + trim_doctest_flags: true + ## Dataset and Pipeline ::: docetl.schemas.Dataset From 393f6409d5f09b4ce360508e7a50c20bcc66b20b Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 20:41:14 +0200 Subject: [PATCH 21/58] Moved all schemas to be part of each operation --- docetl/base_schemas.py | 158 ++++++++++++++++ docetl/operations/base.py | 20 ++- docetl/operations/equijoin.py | 20 ++- docetl/operations/filter.py | 15 ++ docetl/operations/gather.py | 10 +- docetl/operations/map.py | 34 +++- docetl/operations/reduce.py | 19 +- docetl/operations/resolve.py | 20 ++- docetl/operations/split.py | 8 +- docetl/operations/unnest.py | 10 +- docetl/schemas.py | 326 +++------------------------------- 11 files changed, 325 insertions(+), 315 deletions(-) create mode 100644 docetl/base_schemas.py diff --git a/docetl/base_schemas.py b/docetl/base_schemas.py new file mode 100644 index 00000000..f5dcdb4a --- /dev/null +++ b/docetl/base_schemas.py @@ -0,0 +1,158 @@ +from typing import Any, Dict, List, Optional, Union + +from pydantic import BaseModel, Field, field_validator + +# from ..operations import map +# MapOp = map.MapOperation.schema + +class ToolFunction(BaseModel): + name: str + description: str + parameters: Dict[str, Any] + + +class Tool(BaseModel): + code: str + function: ToolFunction + + +class ParsingTool(BaseModel): + """ + Represents a parsing tool used for custom data parsing in the pipeline. + + Attributes: + name (str): The name of the parsing tool. This should be unique within the pipeline configuration. + function_code (str): The Python code defining the parsing function. This code will be executed + to parse the input data according to the specified logic. It should return a list of strings, where each string is its own document. + + Example: + ```yaml + parsing_tools: + - name: ocr_parser + function_code: | + import pytesseract + from pdf2image import convert_from_path + def ocr_parser(filename: str) -> List[str]: + images = convert_from_path(filename) + text = "" + for image in images: + text += pytesseract.image_to_string(image) + return [text] + ``` + """ + + name: str + function_code: str + + +class Dataset(BaseModel): + """ + Represents a dataset configuration in the pipeline. + + Attributes: + type (str): The type of the dataset. Must be either 'file' or 'memory'. + path (str): The path to the dataset file or the in-memory data, depending on the type. + source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. + parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool + is represented by a dictionary with 'input_key', 'function', and + 'output_key' keys. Defaults to None. + + Example: + ```yaml + datasets: + my_dataset: + type: file + path: input.json + parsing: + - input_key: file_path + function: txt_to_string + output_key: content + ``` + + Note: + The parsing tools are applied in the order they are listed. Each parsing tool takes the output + of the previous tool as its input, allowing for chained processing of the data. + """ + + type: str + path: str + source: str = "local" + parsing: Optional[List[Dict[str, str]]] = None + +class PipelineStep(BaseModel): + """ + Represents a step in the pipeline. + + Attributes: + name (str): The name of the step. + operations (List[Union[Dict[str, Any], str]]): A list of operations to be applied in this step. + Each operation can be either a string (the name of the operation) or a dictionary + (for more complex configurations). + input (Optional[str]): The input for this step. It can be either the name of a dataset + or the name of a previous step. If not provided, the step will use the output + of the previous step as its input. + + Example: + ```python + # Simple step with a single operation + process_step = PipelineStep( + name="process_step", + input="my_dataset", + operations=["process"] + ) + + # Step with multiple operations + summarize_step = PipelineStep( + name="summarize_step", + input="process_step", + operations=["summarize"] + ) + + # Step with a more complex operation configuration + custom_step = PipelineStep( + name="custom_step", + input="previous_step", + operations=[ + { + "custom_operation": { + "model": "gpt-4", + "prompt": "Perform a custom analysis on the following text:" + } + } + ] + ) + ``` + + These examples show different ways to configure pipeline steps, from simple + single-operation steps to more complex configurations with custom parameters. + """ + + name: str + operations: List[Union[Dict[str, Any], str]] + input: Optional[str] = None + + +class PipelineOutput(BaseModel): + """ + Represents the output configuration for a pipeline. + + Attributes: + type (str): The type of output. This could be 'file', 'database', etc. + path (str): The path where the output will be stored. This could be a file path, + database connection string, etc., depending on the type. + intermediate_dir (Optional[str]): The directory to store intermediate results, + if applicable. Defaults to None. + + Example: + ```python + output = PipelineOutput( + type="file", + path="/path/to/output.json", + intermediate_dir="/path/to/intermediate/results" + ) + ``` + """ + + type: str + path: str + intermediate_dir: Optional[str] = None diff --git a/docetl/operations/base.py b/docetl/operations/base.py index a41f5ddc..44a9c564 100644 --- a/docetl/operations/base.py +++ b/docetl/operations/base.py @@ -8,7 +8,15 @@ from docetl.operations.utils import APIWrapper from rich.console import Console from rich.status import Status +import jsonschema +from pydantic import BaseModel +# FIXME: This should probably live in some utils module? +class classproperty(object): + def __init__(self, f): + self.f = f + def __get__(self, obj, owner): + return self.f(owner) class BaseOperation(ABC): def __init__( @@ -45,6 +53,16 @@ def __init__( ) self.syntax_check() + # This must be overridden in a subclass + class schema(BaseModel): + name: str + type: str + + @classproperty + def json_schema(cls): + assert hasattr(cls.schema, "model_json_schema"), "Programming error: %s.schema must be a pydantic object but is a %s" % (cls, type(cls.schema)) + return cls.schema.model_json_schema() + @abstractmethod def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: """ @@ -73,7 +91,7 @@ def syntax_check(self) -> None: Raises: ValueError: If the configuration is invalid. """ - pass + jsonschema.validate(instance = self.config, schema = self.json_schema) def gleaning_check(self) -> None: """ diff --git a/docetl/operations/equijoin.py b/docetl/operations/equijoin.py index d6cf6469..3de669bc 100644 --- a/docetl/operations/equijoin.py +++ b/docetl/operations/equijoin.py @@ -7,7 +7,7 @@ from collections import defaultdict from concurrent.futures import ThreadPoolExecutor from multiprocessing import Pool, cpu_count -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional import numpy as np from jinja2 import Template @@ -49,6 +49,24 @@ def process_left_item( class EquijoinOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "equijoin" + left: str + right: str + comparison_prompt: str + output: Optional[Dict[str, Any]] = None + blocking_threshold: Optional[float] = None + blocking_conditions: Optional[Dict[str, List[str]]] = None + limits: Optional[Dict[str, int]] = None + comparison_model: Optional[str] = None + optimize: Optional[bool] = None + embedding_model: Optional[str] = None + embedding_batch_size: Optional[int] = None + compare_batch_size: Optional[int] = None + limit_comparisons: Optional[int] = None + blocking_keys: Optional[Dict[str, List[str]]] = None + timeout: Optional[int] = None + def compare_pair( self, comparison_prompt: str, diff --git a/docetl/operations/filter.py b/docetl/operations/filter.py index 48037eef..08ffba15 100644 --- a/docetl/operations/filter.py +++ b/docetl/operations/filter.py @@ -2,9 +2,11 @@ from concurrent.futures import ThreadPoolExecutor from typing import Any, Dict, List, Optional, Tuple +from pydantic import Field from jinja2 import Template +from docetl.operations.base import BaseOperation from docetl.operations.map import MapOperation from docetl.operations.utils import ( RichLoopBar, @@ -12,6 +14,19 @@ class FilterOperation(MapOperation): + # FIXME: What happens if we actually inherit from MapOperation.schema? + class schema(BaseOperation.schema): + type: str = "filter" + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + model: Optional[str] = None + optimize: Optional[bool] = None + recursively_optimize: Optional[bool] = None + sample_size: Optional[int] = None + validation_rules: Optional[List[str]] = Field(None, alias="validate") + num_retries_on_validate_failure: Optional[int] = None + timeout: Optional[int] = None + def syntax_check(self) -> None: """ Checks the configuration of the FilterOperation for required keys and valid structure. diff --git a/docetl/operations/gather.py b/docetl/operations/gather.py index a2e81879..6d97020c 100644 --- a/docetl/operations/gather.py +++ b/docetl/operations/gather.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional from docetl.operations.base import BaseOperation @@ -15,6 +15,14 @@ class GatherOperation(BaseOperation): 5. Return results containing the rendered chunks with added context, including information about skipped characters and headers. """ + class schema(BaseOperation.schema): + type: str = "gather" + content_key: str + doc_id_key: str + order_key: str + peripheral_chunks: Dict[str, Any] + doc_header_key: Optional[str] = None + def __init__(self, *args: Any, **kwargs: Any) -> None: """ Initialize the GatherOperation. diff --git a/docetl/operations/map.py b/docetl/operations/map.py index 300e8419..36322ab0 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -10,9 +10,9 @@ from docetl.operations.base import BaseOperation from docetl.operations.utils import RichLoopBar -from docetl.schemas import MapOp, Tool, ToolFunction +from docetl.base_schemas import Tool, ToolFunction from docetl.utils import completion_cost - +from pydantic import Field, field_validator def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: """ @@ -25,9 +25,31 @@ def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: env = Environment(autoescape=True) template = env.from_string(template_string) return template.render(input=data) - - + class MapOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "map" + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + model: Optional[str] = None + optimize: Optional[bool] = None + recursively_optimize: Optional[bool] = None + sample_size: Optional[int] = None + tools: Optional[List[Dict[str, Any]]] = None # FIXME: Why isn't this using the Tool data class so validation works automatically? + validation_rules: Optional[List[str]] = Field(None, alias="validate") + num_retries_on_validate_failure: Optional[int] = None + gleaning: Optional[Dict[str, Any]] = None + drop_keys: Optional[List[str]] = None + timeout: Optional[int] = None + batch_size: Optional[int] = None + clustering_method: Optional[str] = None + + @field_validator("drop_keys") + def validate_drop_keys(cls, v): + if isinstance(v, str): + return [v] + return v + def __init__( self, *args, @@ -38,7 +60,7 @@ def __init__( "max_batch_size", kwargs.get("max_batch_size", float("inf")) ) self.clustering_method = "random" - + def syntax_check(self) -> None: """ Checks the configuration of the MapOperation for required keys and valid structure. @@ -47,7 +69,7 @@ def syntax_check(self) -> None: ValueError: If required keys are missing or invalid in the configuration. TypeError: If configuration values have incorrect types. """ - config = MapOp(**self.config) + config = self.schema(**self.config) if config.drop_keys: if any(not isinstance(key, str) for key in config.drop_keys): diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index cd3cee78..de535b6c 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -12,7 +12,7 @@ from collections import deque from concurrent.futures import ThreadPoolExecutor, as_completed from threading import Lock -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple, Union import jinja2 import numpy as np @@ -35,6 +35,23 @@ class ReduceOperation(BaseOperation): using various strategies including batch reduce, incremental reduce, and parallel fold and merge. """ + class schema(BaseOperation.schema): + type: str = "reduce" + reduce_key: Union[str, List[str]] + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + optimize: Optional[bool] = None + synthesize_resolve: Optional[bool] = None + model: Optional[str] = None + input: Optional[Dict[str, Any]] = None + pass_through: Optional[bool] = None + associative: Optional[bool] = None + fold_prompt: Optional[str] = None + fold_batch_size: Optional[int] = None + value_sampling: Optional[Dict[str, Any]] = None + verbose: Optional[bool] = None + timeout: Optional[int] = None + def __init__(self, *args, **kwargs): """ Initialize the ReduceOperation. diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index 0184f8b2..ac8bae44 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -4,7 +4,7 @@ import random from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional import jinja2 from jinja2 import Template @@ -16,6 +16,24 @@ class ResolveOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "resolve" + comparison_prompt: str + resolution_prompt: str + output: Optional[Dict[str, Any]] = None + embedding_model: Optional[str] = None + resolution_model: Optional[str] = None + comparison_model: Optional[str] = None + blocking_keys: Optional[List[str]] = None + blocking_threshold: Optional[float] = None + blocking_conditions: Optional[List[str]] = None + input: Optional[Dict[str, Any]] = None + embedding_batch_size: Optional[int] = None + compare_batch_size: Optional[int] = None + limit_comparisons: Optional[int] = None + optimize: Optional[bool] = None + timeout: Optional[int] = None + def compare_pair( self, comparison_prompt: str, diff --git a/docetl/operations/split.py b/docetl/operations/split.py index 8b9f26a3..c32407ea 100644 --- a/docetl/operations/split.py +++ b/docetl/operations/split.py @@ -1,5 +1,5 @@ import uuid -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Any, Optional import tiktoken @@ -18,6 +18,12 @@ class SplitOperation(BaseOperation): - {name}_id: A unique identifier for each original document. - {name}_chunk_num: The sequential number of the chunk within its original document. """ + class schema(BaseOperation.schema): + type: str = "split" + split_key: str + method: str + method_kwargs: Dict[str, Any] + model: Optional[str] = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) diff --git a/docetl/operations/unnest.py b/docetl/operations/unnest.py index 8931407e..a8bcbb38 100644 --- a/docetl/operations/unnest.py +++ b/docetl/operations/unnest.py @@ -1,5 +1,5 @@ import copy -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Optional from docetl.operations.base import BaseOperation @@ -56,6 +56,14 @@ class UnnestOperation(BaseOperation): ``` """ + class schema(BaseOperation.schema): + type: str = "unnest" + unnest_key: str + keep_empty: Optional[bool] = None + expand_fields: Optional[List[str]] = None + recursive: Optional[bool] = None + depth: Optional[int] = None + def syntax_check(self) -> None: """ Checks if the required configuration key is present in the operation's config. diff --git a/docetl/schemas.py b/docetl/schemas.py index ce89473e..dd44a601 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -1,226 +1,27 @@ -from typing import Any, Dict, List, Optional, Union - -from pydantic import BaseModel, Field, field_validator - - -class ToolFunction(BaseModel): - name: str - description: str - parameters: Dict[str, Any] - - -class Tool(BaseModel): - code: str - function: ToolFunction - - -class ParsingTool(BaseModel): - """ - Represents a parsing tool used for custom data parsing in the pipeline. - - Attributes: - name (str): The name of the parsing tool. This should be unique within the pipeline configuration. - function_code (str): The Python code defining the parsing function. This code will be executed - to parse the input data according to the specified logic. It should return a list of strings, where each string is its own document. - - Example: - ```yaml - parsing_tools: - - name: ocr_parser - function_code: | - import pytesseract - from pdf2image import convert_from_path - def ocr_parser(filename: str) -> List[str]: - images = convert_from_path(filename) - text = "" - for image in images: - text += pytesseract.image_to_string(image) - return [text] - ``` - """ - - name: str - function_code: str - - -class Dataset(BaseModel): - """ - Represents a dataset configuration in the pipeline. - - Attributes: - type (str): The type of the dataset. Must be either 'file' or 'memory'. - path (str): The path to the dataset file or the in-memory data, depending on the type. - source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. - parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool - is represented by a dictionary with 'input_key', 'function', and - 'output_key' keys. Defaults to None. - - Example: - ```yaml - datasets: - my_dataset: - type: file - path: input.json - parsing: - - input_key: file_path - function: txt_to_string - output_key: content - ``` - - Note: - The parsing tools are applied in the order they are listed. Each parsing tool takes the output - of the previous tool as its input, allowing for chained processing of the data. - """ - - type: str - path: str - source: str = "local" - parsing: Optional[List[Dict[str, str]]] = None - - -class BaseOp(BaseModel): - name: str - type: str - - -class MapOp(BaseOp): - type: str = "map" - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - tools: Optional[List[Dict[str, Any]]] = None - validation_rules: Optional[List[str]] = Field(None, alias="validate") - num_retries_on_validate_failure: Optional[int] = None - gleaning: Optional[Dict[str, Any]] = None - drop_keys: Optional[List[str]] = None - timeout: Optional[int] = None - batch_size: Optional[int] = None - clustering_method: Optional[str] = None - - @field_validator("drop_keys") - def validate_drop_keys(cls, v): - if isinstance(v, str): - return [v] - return v - - -class ResolveOp(BaseOp): - type: str = "resolve" - comparison_prompt: str - resolution_prompt: str - output: Optional[Dict[str, Any]] = None - embedding_model: Optional[str] = None - resolution_model: Optional[str] = None - comparison_model: Optional[str] = None - blocking_keys: Optional[List[str]] = None - blocking_threshold: Optional[float] = None - blocking_conditions: Optional[List[str]] = None - input: Optional[Dict[str, Any]] = None - embedding_batch_size: Optional[int] = None - compare_batch_size: Optional[int] = None - limit_comparisons: Optional[int] = None - optimize: Optional[bool] = None - timeout: Optional[int] = None - - -class ReduceOp(BaseOp): - type: str = "reduce" - reduce_key: Union[str, List[str]] - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - optimize: Optional[bool] = None - synthesize_resolve: Optional[bool] = None - model: Optional[str] = None - input: Optional[Dict[str, Any]] = None - pass_through: Optional[bool] = None - associative: Optional[bool] = None - fold_prompt: Optional[str] = None - fold_batch_size: Optional[int] = None - value_sampling: Optional[Dict[str, Any]] = None - verbose: Optional[bool] = None - timeout: Optional[int] = None - - -class ParallelMapOp(BaseOp): - type: str = "parallel_map" - prompts: List[Dict[str, Any]] - output: Optional[Dict[str, Any]] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - drop_keys: Optional[List[str]] = None - timeout: Optional[int] = None - batch_size: Optional[int] = None - clustering_method: Optional[str] = None - - @field_validator("drop_keys") - def validate_drop_keys(cls, v): - if isinstance(v, str): - return [v] - return v - - -class FilterOp(BaseOp): - type: str = "filter" - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - validation_rules: Optional[List[str]] = Field(None, alias="validate") - num_retries_on_validate_failure: Optional[int] = None - timeout: Optional[int] = None - - -class EquijoinOp(BaseOp): - type: str = "equijoin" - left: str - right: str - comparison_prompt: str - output: Optional[Dict[str, Any]] = None - blocking_threshold: Optional[float] = None - blocking_conditions: Optional[Dict[str, List[str]]] = None - limits: Optional[Dict[str, int]] = None - comparison_model: Optional[str] = None - optimize: Optional[bool] = None - embedding_model: Optional[str] = None - embedding_batch_size: Optional[int] = None - compare_batch_size: Optional[int] = None - limit_comparisons: Optional[int] = None - blocking_keys: Optional[Dict[str, List[str]]] = None - timeout: Optional[int] = None - - -class SplitOp(BaseOp): - type: str = "split" - split_key: str - method: str - method_kwargs: Dict[str, Any] - model: Optional[str] = None - - -class GatherOp(BaseOp): - type: str = "gather" - content_key: str - doc_id_key: str - order_key: str - peripheral_chunks: Dict[str, Any] - doc_header_key: Optional[str] = None - - -class UnnestOp(BaseOp): - type: str = "unnest" - unnest_key: str - keep_empty: Optional[bool] = None - expand_fields: Optional[List[str]] = None - recursive: Optional[bool] = None - depth: Optional[int] = None - +from .base_schemas import * + +from .operations import cluster +from .operations import equijoin +from .operations import filter +from .operations import gather +from .operations import map +from .operations import reduce +from .operations import resolve +from .operations import sample +from .operations import split +from .operations import unnest + +MapOp = map.MapOperation.schema + +MapOp = map.MapOperation.schema +ResolveOp = resolve.ResolveOperation.schema +ReduceOp = reduce.ReduceOperation.schema +ParallelMapOp = map.ParallelMapOperation.schema +FilterOp = filter.FilterOperation.schema +EquijoinOp = equijoin.EquijoinOperation.schema +SplitOp = split.SplitOperation.schema +GatherOp = gather.GatherOperation.schema +UnnestOp = unnest.UnnestOperation.schema OpType = Union[ MapOp, @@ -233,82 +34,3 @@ class UnnestOp(BaseOp): GatherOp, UnnestOp, ] - - -class PipelineStep(BaseModel): - """ - Represents a step in the pipeline. - - Attributes: - name (str): The name of the step. - operations (List[Union[Dict[str, Any], str]]): A list of operations to be applied in this step. - Each operation can be either a string (the name of the operation) or a dictionary - (for more complex configurations). - input (Optional[str]): The input for this step. It can be either the name of a dataset - or the name of a previous step. If not provided, the step will use the output - of the previous step as its input. - - Example: - ```python - # Simple step with a single operation - process_step = PipelineStep( - name="process_step", - input="my_dataset", - operations=["process"] - ) - - # Step with multiple operations - summarize_step = PipelineStep( - name="summarize_step", - input="process_step", - operations=["summarize"] - ) - - # Step with a more complex operation configuration - custom_step = PipelineStep( - name="custom_step", - input="previous_step", - operations=[ - { - "custom_operation": { - "model": "gpt-4", - "prompt": "Perform a custom analysis on the following text:" - } - } - ] - ) - ``` - - These examples show different ways to configure pipeline steps, from simple - single-operation steps to more complex configurations with custom parameters. - """ - - name: str - operations: List[Union[Dict[str, Any], str]] - input: Optional[str] = None - - -class PipelineOutput(BaseModel): - """ - Represents the output configuration for a pipeline. - - Attributes: - type (str): The type of output. This could be 'file', 'database', etc. - path (str): The path where the output will be stored. This could be a file path, - database connection string, etc., depending on the type. - intermediate_dir (Optional[str]): The directory to store intermediate results, - if applicable. Defaults to None. - - Example: - ```python - output = PipelineOutput( - type="file", - path="/path/to/output.json", - intermediate_dir="/path/to/intermediate/results" - ) - ``` - """ - - type: str - path: str - intermediate_dir: Optional[str] = None From f2fcd3eba1fa6f9aefeea5ae88b0b370f48e0554 Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 20:46:16 +0200 Subject: [PATCH 22/58] Bugfixes --- pyproject.toml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/pyproject.toml b/pyproject.toml index 54640b3c..e8791a2b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -34,6 +34,8 @@ azure-ai-documentintelligence = { version = "^1.0.0b4", optional = true } paddlepaddle = { version = "^2.6.2", optional = true } paddleocr = { version = "^2.8.1", optional = true } pymupdf = { version = "^1.24.10", optional = true } +jsonschema = "^4.23.0" +rapidfuzz = "^3.10.0" [tool.poetry.extras] parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "paddleocr", "pymupdf"] From 18503308a442bac1cdf5cc202d7e7cd14b6cbabf Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 21:13:33 +0200 Subject: [PATCH 23/58] Expose a schema on the runner --- docetl/base_schemas.py | 8 ++++++-- docetl/operations/__init__.py | 7 +++++++ docetl/runner.py | 12 ++++++++++++ docetl/utils.py | 8 ++++++++ 4 files changed, 33 insertions(+), 2 deletions(-) diff --git a/docetl/base_schemas.py b/docetl/base_schemas.py index f5dcdb4a..7b9bc8d8 100644 --- a/docetl/base_schemas.py +++ b/docetl/base_schemas.py @@ -130,8 +130,7 @@ class PipelineStep(BaseModel): name: str operations: List[Union[Dict[str, Any], str]] input: Optional[str] = None - - + class PipelineOutput(BaseModel): """ Represents the output configuration for a pipeline. @@ -156,3 +155,8 @@ class PipelineOutput(BaseModel): type: str path: str intermediate_dir: Optional[str] = None + + +class PipelineSpec(BaseModel): + steps: list[PipelineStep] + output: PipelineOutput diff --git a/docetl/operations/__init__.py b/docetl/operations/__init__.py index 99d4fab5..b5fe98e7 100644 --- a/docetl/operations/__init__.py +++ b/docetl/operations/__init__.py @@ -2,6 +2,7 @@ def get_operation(operation_type: str): + """Loads a single operation by name""" try: entrypoint = importlib.metadata.entry_points(group="docetl.operation")[ operation_type @@ -9,3 +10,9 @@ def get_operation(operation_type: str): except KeyError as e: raise KeyError(f"Unrecognized operation {operation_type}") return entrypoint.load() + +def get_operations(): + """Load all available operations and return them as a dictionary""" + return { + name: op.load() + for name, op in importlib.metadata.entry_points(group="docetl.operation").items()} diff --git a/docetl/runner.py b/docetl/runner.py index 97d6fcdc..fd292dd8 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -3,6 +3,7 @@ import os import time from typing import Dict, List, Optional, Tuple +from pydantic import BaseModel from dotenv import load_dotenv import hashlib @@ -12,6 +13,8 @@ from docetl.operations import get_operation from docetl.operations.utils import flush_cache from docetl.config_wrapper import ConfigWrapper +from . import schemas +from .utils import classproperty load_dotenv() @@ -32,6 +35,15 @@ class DSLRunner(ConfigWrapper): datasets (Dict): Storage for loaded datasets. """ + class schema(BaseModel): + datasets: dict[str, schemas.Dataset] + operations: list[schemas.OpType] + pipeline: schemas.PipelineSpec + + @classproperty + def json_schema(cls): + return cls.schema.model_json_schema() + def __init__(self, config: Dict, max_threads: int = None): """ Initialize the DSLRunner with a YAML configuration file. diff --git a/docetl/utils.py b/docetl/utils.py index 61f95379..e1635b1b 100644 --- a/docetl/utils.py +++ b/docetl/utils.py @@ -152,3 +152,11 @@ def truncate_sample_data( return truncated_data return truncated_data + + + +class classproperty(object): + def __init__(self, f): + self.f = f + def __get__(self, obj, owner): + return self.f(owner) From a6a4d2e5aca8e8c318fba176a33d8343a83bc3e3 Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 21:25:01 +0200 Subject: [PATCH 24/58] Dynamic schema for DSLRunner --- docetl/operations/__init__.py | 4 ++-- docetl/runner.py | 24 +++++++++++++++++------- docetl/schemas.py | 2 -- 3 files changed, 19 insertions(+), 11 deletions(-) diff --git a/docetl/operations/__init__.py b/docetl/operations/__init__.py index b5fe98e7..66f91032 100644 --- a/docetl/operations/__init__.py +++ b/docetl/operations/__init__.py @@ -14,5 +14,5 @@ def get_operation(operation_type: str): def get_operations(): """Load all available operations and return them as a dictionary""" return { - name: op.load() - for name, op in importlib.metadata.entry_points(group="docetl.operation").items()} + op.name: op.load() + for op in importlib.metadata.entry_points(group="docetl.operation")} diff --git a/docetl/runner.py b/docetl/runner.py index fd292dd8..74b4a0bf 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -2,7 +2,7 @@ import json import os import time -from typing import Dict, List, Optional, Tuple +from typing import Dict, List, Optional, Tuple, Union from pydantic import BaseModel from dotenv import load_dotenv @@ -10,7 +10,7 @@ from rich.console import Console from docetl.dataset import Dataset, create_parsing_tool_map -from docetl.operations import get_operation +from docetl.operations import get_operation, get_operations from docetl.operations.utils import flush_cache from docetl.config_wrapper import ConfigWrapper from . import schemas @@ -35,11 +35,21 @@ class DSLRunner(ConfigWrapper): datasets (Dict): Storage for loaded datasets. """ - class schema(BaseModel): - datasets: dict[str, schemas.Dataset] - operations: list[schemas.OpType] - pipeline: schemas.PipelineSpec - + @classproperty + def schema(cls): + # Accessing the schema loads all operations, so only do this + # when we actually need it... + # Yes, this means DSLRunner.schema isn't really accessible to + # static type checkers. But it /is/ available for dynamic + # checking, and for generating json schema. + OpType = Union[*[op.schema for op in get_operations().values()]] + + class schema(BaseModel): + datasets: dict[str, schemas.Dataset] + operations: list[OpType] + pipeline: schemas.PipelineSpec + return schema + @classproperty def json_schema(cls): return cls.schema.model_json_schema() diff --git a/docetl/schemas.py b/docetl/schemas.py index dd44a601..ea37fa69 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -11,8 +11,6 @@ from .operations import split from .operations import unnest -MapOp = map.MapOperation.schema - MapOp = map.MapOperation.schema ResolveOp = resolve.ResolveOperation.schema ReduceOp = reduce.ReduceOperation.schema From 39b7f75abc89d280ee8e4f2655517d9ff9a90d36 Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 22:35:52 +0200 Subject: [PATCH 25/58] Bugfix for schema titles --- docetl/operations/base.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/docetl/operations/base.py b/docetl/operations/base.py index 44a9c564..127c5b95 100644 --- a/docetl/operations/base.py +++ b/docetl/operations/base.py @@ -2,7 +2,7 @@ The BaseOperation class is an abstract base class for all operations in the docetl framework. It provides a common structure and interface for various data processing operations. """ -from abc import ABC, abstractmethod +from abc import ABC, ABCMeta, abstractmethod from typing import Dict, List, Optional, Tuple from docetl.operations.utils import APIWrapper @@ -18,7 +18,13 @@ def __init__(self, f): def __get__(self, obj, owner): return self.f(owner) -class BaseOperation(ABC): +class BaseOperationMeta(ABCMeta): + def __new__(cls, *arg, **kw): + self = ABCMeta.__new__(cls, *arg, **kw) + self.schema.__name__ = self.__name__ + return self + +class BaseOperation(ABC, metaclass=BaseOperationMeta): def __init__( self, runner: "ConfigWrapper", @@ -57,7 +63,7 @@ def __init__( class schema(BaseModel): name: str type: str - + @classproperty def json_schema(cls): assert hasattr(cls.schema, "model_json_schema"), "Programming error: %s.schema must be a pydantic object but is a %s" % (cls, type(cls.schema)) From e75f82a06ae9d8b0b3a667d10a58f7815c06cd61 Mon Sep 17 00:00:00 2001 From: Egil Date: Fri, 18 Oct 2024 22:55:53 +0200 Subject: [PATCH 26/58] Moved dataset type to Dataset.schema --- docetl/base_schemas.py | 34 ---------------------------------- docetl/dataset.py | 39 +++++++++++++++++++++++++++++++++++++-- docetl/schemas.py | 4 ++++ 3 files changed, 41 insertions(+), 36 deletions(-) diff --git a/docetl/base_schemas.py b/docetl/base_schemas.py index 7b9bc8d8..88547a3c 100644 --- a/docetl/base_schemas.py +++ b/docetl/base_schemas.py @@ -45,40 +45,6 @@ def ocr_parser(filename: str) -> List[str]: function_code: str -class Dataset(BaseModel): - """ - Represents a dataset configuration in the pipeline. - - Attributes: - type (str): The type of the dataset. Must be either 'file' or 'memory'. - path (str): The path to the dataset file or the in-memory data, depending on the type. - source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. - parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool - is represented by a dictionary with 'input_key', 'function', and - 'output_key' keys. Defaults to None. - - Example: - ```yaml - datasets: - my_dataset: - type: file - path: input.json - parsing: - - input_key: file_path - function: txt_to_string - output_key: content - ``` - - Note: - The parsing tools are applied in the order they are listed. Each parsing tool takes the output - of the previous tool as its input, allowing for chained processing of the data. - """ - - type: str - path: str - source: str = "local" - parsing: Optional[List[Dict[str, str]]] = None - class PipelineStep(BaseModel): """ Represents a step in the pipeline. diff --git a/docetl/dataset.py b/docetl/dataset.py index c90fda4e..b72398c0 100644 --- a/docetl/dataset.py +++ b/docetl/dataset.py @@ -1,9 +1,10 @@ import os from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Union, Literal +from pydantic import BaseModel from docetl.parsing_tools import get_parser, get_parsing_tools -from docetl.schemas import ParsingTool +from docetl.base_schemas import ParsingTool def create_parsing_tool_map( @@ -39,6 +40,40 @@ class Dataset: user_defined_parsing_tool_map (Dict[str, ParsingTool]): A map of user-defined parsing tools. """ + class schema(BaseModel): + """ + Represents a dataset configuration in the pipeline. + + Attributes: + type (str): The type of the dataset. Must be either 'file' or 'memory'. + path (str): The path to the dataset file or the in-memory data, depending on the type. + source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. + parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool + is represented by a dictionary with 'input_key', 'function', and + 'output_key' keys. Defaults to None. + + Example: + ```yaml + datasets: + my_dataset: + type: file + path: input.json + parsing: + - input_key: file_path + function: txt_to_string + output_key: content + ``` + + Note: + The parsing tools are applied in the order they are listed. Each parsing tool takes the output + of the previous tool as its input, allowing for chained processing of the data. + """ + + type: Literal["file", "memory"] + path: str + source: str = "local" + parsing: Optional[List[Dict[str, str]]] = None + def __init__( self, runner, diff --git a/docetl/schemas.py b/docetl/schemas.py index ea37fa69..17dd912a 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -11,6 +11,8 @@ from .operations import split from .operations import unnest +from . import dataset + MapOp = map.MapOperation.schema ResolveOp = resolve.ResolveOperation.schema ReduceOp = reduce.ReduceOperation.schema @@ -32,3 +34,5 @@ GatherOp, UnnestOp, ] + +Dataset = dataset.Dataset.schema From f0fe599a91c13ce0dd2a7bd28afdacba6921ae5c Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Fri, 18 Oct 2024 20:24:50 -0700 Subject: [PATCH 27/58] refactor: include an optimize method in the DSLRunner --- docetl/builder.py | 26 ++++----------- docetl/cli.py | 12 ++----- docetl/config_wrapper.py | 24 ++++++++++++-- docetl/runner.py | 70 +++++++++++++++++++++++++++------------- 4 files changed, 77 insertions(+), 55 deletions(-) diff --git a/docetl/builder.py b/docetl/builder.py index d2ace213..9a3b0132 100644 --- a/docetl/builder.py +++ b/docetl/builder.py @@ -78,25 +78,12 @@ def items(self): class Optimizer(ConfigWrapper): - @classmethod - def from_yaml(cls, yaml_file: str, **kwargs): - # check that file ends with .yaml or .yml - if not yaml_file.endswith(".yaml") and not yaml_file.endswith(".yml"): - raise ValueError( - "Invalid file type. Please provide a YAML file ending with '.yaml' or '.yml'." - ) - - base_name = yaml_file.rsplit(".", 1)[0] - suffix = yaml_file.split("/")[-1].split(".")[0] - return super(Optimizer, cls).from_yaml( - yaml_file, base_name=base_name, yaml_file_suffix=suffix, **kwargs - ) def __init__( self, config: Dict, - base_name: str, - yaml_file_suffix: str, + pipeline_name: str, + console: Console, max_threads: Optional[int] = None, model: str = "gpt-4o", resume: bool = False, @@ -136,7 +123,7 @@ def __init__( The method also calls print_optimizer_config() to display the initial configuration. """ - ConfigWrapper.__init__(self, config, max_threads) + ConfigWrapper.__init__(self, config, console=console, max_threads=max_threads) self.optimized_config = copy.deepcopy(self.config) self.llm_client = LLMClient(model) self.operations_cost = 0 @@ -151,11 +138,10 @@ def __init__( ) home_dir = os.path.expanduser("~") - cache_dir = os.path.join(home_dir, f".docetl/cache/{yaml_file_suffix}") + cache_dir = os.path.join(home_dir, f".docetl/cache/{pipeline_name}") os.makedirs(cache_dir, exist_ok=True) self.datasets = DatasetOnDisk(dir=cache_dir, console=self.console) self.optimized_ops_path = f"{cache_dir}/optimized_ops" - self.optimized_config_path = f"{base_name}_opt.yaml" # Update sample size map self.sample_size_map = SAMPLE_SIZE_MAP @@ -1418,7 +1404,7 @@ def clean_optimized_config(self): return resolved_config - def save_optimized_config(self): + def save_optimized_config(self, optimized_config_path: str): """ Save the optimized configuration to a YAML file. @@ -1427,7 +1413,7 @@ def save_optimized_config(self): """ resolved_config = self.clean_optimized_config() - with open(self.optimized_config_path, "w") as f: + with open(optimized_config_path, "w") as f: yaml.safe_dump(resolved_config, f, default_flow_style=False, width=80) self.console.log( f"[green italic]💾 Optimized config saved to {self.optimized_config_path}[/green italic]" diff --git a/docetl/cli.py b/docetl/cli.py index 7bca3006..a596fe3b 100644 --- a/docetl/cli.py +++ b/docetl/cli.py @@ -4,7 +4,6 @@ import os import typer -from docetl.builder import Optimizer from docetl.operations.utils import clear_cache as cc from docetl.runner import DSLRunner @@ -47,15 +46,8 @@ def build( if os.path.exists(env_file): load_dotenv(env_file) - optimizer = Optimizer.from_yaml( - str(yaml_file), - max_threads=max_threads, - model=model, - timeout=timeout, - resume=resume, - ) - optimizer.optimize() - optimizer.save_optimized_config() + runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads) + runner.optimize(save=True, model=model, resume=resume, timeout=timeout) @app.command() diff --git a/docetl/config_wrapper.py b/docetl/config_wrapper.py index 4e4a5f8d..06fadc15 100644 --- a/docetl/config_wrapper.py +++ b/docetl/config_wrapper.py @@ -31,15 +31,33 @@ def get(self, item: pyrate_limiter.RateItem) -> pyrate_limiter.AbstractBucket: class ConfigWrapper(object): + @classmethod def from_yaml(cls, yaml_file: str, **kwargs): + # check that file ends with .yaml or .yml + if not yaml_file.endswith(".yaml") and not yaml_file.endswith(".yml"): + raise ValueError( + "Invalid file type. Please provide a YAML file ending with '.yaml' or '.yml'." + ) + + base_name = yaml_file.rsplit(".", 1)[0] + suffix = yaml_file.split("/")[-1].split(".")[0] config = load_config(yaml_file) - return cls(config, **kwargs) + return cls(config, base_name=base_name, yaml_file_suffix=suffix, **kwargs) - def __init__(self, config: Dict, max_threads: int = None): + def __init__( + self, + config: Dict, + base_name: str, + yaml_file_suffix: str, + max_threads: int = None, + console: Console = Console(), + ): self.config = config + self.base_name = base_name + self.yaml_file_suffix = yaml_file_suffix self.default_model = self.config.get("default_model", "gpt-4o-mini") - self.console = Console() + self.console = console self.max_threads = max_threads or (os.cpu_count() or 1) * 4 self.status = None diff --git a/docetl/runner.py b/docetl/runner.py index 74b4a0bf..77f6df4b 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -3,6 +3,7 @@ import os import time from typing import Dict, List, Optional, Tuple, Union +from docetl.builder import Optimizer from pydantic import BaseModel from dotenv import load_dotenv @@ -21,10 +22,7 @@ class DSLRunner(ConfigWrapper): """ - A class for executing Domain-Specific Language (DSL) configurations. - - This class is responsible for loading, validating, and executing DSL configurations - defined in YAML files. It manages datasets, executes pipeline steps, and tracks + This class is responsible for running DocETL pipelines. It manages datasets, executes pipeline steps, and tracks the cost of operations. Attributes: @@ -41,19 +39,20 @@ def schema(cls): # when we actually need it... # Yes, this means DSLRunner.schema isn't really accessible to # static type checkers. But it /is/ available for dynamic - # checking, and for generating json schema. + # checking, and for generating json schema. OpType = Union[*[op.schema for op in get_operations().values()]] - + class schema(BaseModel): - datasets: dict[str, schemas.Dataset] - operations: list[OpType] + datasets: Dict[str, schemas.Dataset] + operations: List[OpType] pipeline: schemas.PipelineSpec + return schema - + @classproperty def json_schema(cls): return cls.schema.model_json_schema() - + def __init__(self, config: Dict, max_threads: int = None): """ Initialize the DSLRunner with a YAML configuration file. @@ -97,7 +96,7 @@ def __init__(self, config: Dict, max_threads: int = None): all_ops_str.encode() ).hexdigest() - def get_output_path(self, require = False): + def get_output_path(self, require=False): output_path = self.config.get("pipeline", {}).get("output", {}).get("path") if output_path: if not ( @@ -113,7 +112,7 @@ def get_output_path(self, require = False): ) return output_path - + def syntax_check(self): """ Perform a syntax check on all operations defined in the configuration. @@ -168,16 +167,16 @@ def load_run_save(self) -> float: Returns: float: The total cost of executing the pipeline. """ - + # Fail early if we can't save the output... self.get_output_path(require=True) - + self.console.rule("[bold blue]Pipeline Execution[/bold blue]") start_time = time.time() output, total_cost = self.run(self.load()) self.save(output) - + self.console.rule("[bold green]Execution Summary[/bold green]") self.console.print(f"[bold green]Total cost: [green]${total_cost:.2f}[/green]") self.console.print( @@ -192,13 +191,18 @@ def run(self, datasets) -> float: Args: datasets (dict[str, Dataset | List[Dict]]): input datasets to transform - + Returns: (List[Dict], float): The transformed data and the total cost of execution. """ - self.datasets = {name: (dataset if isinstance(dataset, Dataset) - else Dataset(self, "memory", dataset)) - for name, dataset in datasets.items()} + self.datasets = { + name: ( + dataset + if isinstance(dataset, Dataset) + else Dataset(self, "memory", dataset) + ) + for name, dataset in datasets.items() + } total_cost = 0 for step in self.config["pipeline"]["steps"]: step_name = step["name"] @@ -221,7 +225,10 @@ def run(self, datasets) -> float: ) as f: json.dump(self.step_op_hashes, f) - return self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load(), total_cost + return ( + self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load(), + total_cost, + ) def load(self): """ @@ -248,7 +255,7 @@ def load(self): else: raise ValueError(f"Unsupported dataset type: {dataset_config['type']}") return datasets - + def save(self, data: List[Dict]): """ Save the final output of the pipeline. @@ -260,7 +267,7 @@ def save(self, data: List[Dict]): ValueError: If an unsupported output type is specified in the configuration. """ self.get_output_path(require=True) - + self.console.rule("[cyan]Saving Output[/cyan]") output_config = self.config["pipeline"]["output"] if output_config["type"] == "file": @@ -417,6 +424,25 @@ def _save_checkpoint(self, step_name: str, operation_name: str, data: List[Dict] f"[green]✓ [italic]Intermediate saved for operation '{operation_name}' in step '{step_name}' at {checkpoint_path}[/italic][/green]" ) + def optimize( + self, save: bool = False, return_pipeline: bool = False, **kwargs + ) -> Union[None, "DSLRunner"]: + builder = Optimizer( + self.config, + self.yaml_file_suffix, + console=self.console, + max_threads=self.max_threads, + **kwargs, + ) + builder.optimize() + if save: + builder.save_optimized_config(f"{self.base_name}_opt.yaml") + + if return_pipeline: + return DSLRunner(builder.clean_optimized_config(), self.max_threads) + + return None + if __name__ == "__main__": runner = DSLRunner("workloads/medical/map_opt.yaml") From f0a1fd18426e51b380ca1f33347e2d27de4b3d91 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Fri, 18 Oct 2024 20:26:20 -0700 Subject: [PATCH 28/58] fix: poetry lockfile --- poetry.lock | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/poetry.lock b/poetry.lock index 9926319a..c765dd0e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -3368,7 +3368,7 @@ pyyaml = "*" name = "rapidfuzz" version = "3.10.0" description = "rapid fuzzy string matching" -optional = true +optional = false python-versions = ">=3.9" files = [ {file = "rapidfuzz-3.10.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:884453860de029380dded8f3c1918af2d8eb5adf8010261645c7e5c88c2b5428"}, @@ -4518,4 +4518,4 @@ parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepadd [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "d31c822902e5bba42608323eb0ec787636268bfa35dae5d12daa2d70f2ef55fe" +content-hash = "6cfc3498d69d4df1672e110994e4791b4d163615f146f36f041a90dced5846ea" From 91bae13399e3b6e674601612333b35f3a19f6e41 Mon Sep 17 00:00:00 2001 From: Egil Date: Sat, 19 Oct 2024 10:40:21 +0200 Subject: [PATCH 29/58] Bugfix for older python --- docetl/runner.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/docetl/runner.py b/docetl/runner.py index 74b4a0bf..5ef9ee70 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -2,7 +2,8 @@ import json import os import time -from typing import Dict, List, Optional, Tuple, Union +import functools +from typing import Dict, List, Optional, Tuple, Union, Any from pydantic import BaseModel from dotenv import load_dotenv @@ -41,14 +42,19 @@ def schema(cls): # when we actually need it... # Yes, this means DSLRunner.schema isn't really accessible to # static type checkers. But it /is/ available for dynamic - # checking, and for generating json schema. - OpType = Union[*[op.schema for op in get_operations().values()]] + # checking, and for generating json schema. + + OpType = functools.reduce(lambda a, b: a | b, [op.schema for op in get_operations().values()]) + # More pythonic implementation of the above, but only works in python 3.11: + # OpType = Union[*[op.schema for op in get_operations().values()]] - class schema(BaseModel): + class Pipeline(BaseModel): + config: Optional[dict[str, Any]] + parsing_tools: Optional[list[schemas.ParsingTool]] datasets: dict[str, schemas.Dataset] operations: list[OpType] pipeline: schemas.PipelineSpec - return schema + return Pipeline @classproperty def json_schema(cls): From a6f72510cd1fd1bfd934626b04074b4bd564f005 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sat, 19 Oct 2024 13:25:00 -0700 Subject: [PATCH 30/58] refactor: remove optimizer from configwrapper --- docetl/api.py | 17 +++++----- docetl/builder.py | 38 ++++++++++++----------- docetl/cli.py | 4 ++- docetl/config_wrapper.py | 9 ++++-- docetl/runner.py | 30 +++++++++++------- docetl/schemas.py | 4 ++- tests/basic/test_basic_map.py | 7 +++-- tests/basic/test_pipeline_with_parsing.py | 2 +- tests/test_synth_resolve.py | 15 ++++----- 9 files changed, 72 insertions(+), 54 deletions(-) diff --git a/docetl/api.py b/docetl/api.py index f7f7f6de..192222f7 100644 --- a/docetl/api.py +++ b/docetl/api.py @@ -197,17 +197,13 @@ def optimize( Pipeline: An optimized version of the pipeline. """ config = self._to_dict() - optimizer = Optimizer( + runner = DSLRunner( config, base_name=os.path.join(os.getcwd(), self.name), yaml_file_suffix=self.name, max_threads=max_threads, - model=model, - timeout=timeout, - resume=resume, ) - optimizer.optimize() - optimized_config = optimizer.clean_optimized_config() + optimized_config = runner.optimize(return_pipeline=False) updated_pipeline = Pipeline( name=self.name, @@ -232,8 +228,13 @@ def run(self, max_threads: Optional[int] = None) -> float: float: The total cost of running the pipeline. """ config = self._to_dict() - runner = DSLRunner(config, max_threads=max_threads) - result = runner.run() + runner = DSLRunner( + config, + base_name=os.path.join(os.getcwd(), self.name), + yaml_file_suffix=self.name, + max_threads=max_threads, + ) + result = runner.load_run_save() return result def to_yaml(self, path: str) -> None: diff --git a/docetl/builder.py b/docetl/builder.py index 9a3b0132..840258e8 100644 --- a/docetl/builder.py +++ b/docetl/builder.py @@ -77,13 +77,11 @@ def items(self): return [(key, self[key]) for key in self.keys()] -class Optimizer(ConfigWrapper): +class Optimizer: def __init__( self, - config: Dict, - pipeline_name: str, - console: Console, + runner: "DSLRunner", max_threads: Optional[int] = None, model: str = "gpt-4o", resume: bool = False, @@ -123,7 +121,16 @@ def __init__( The method also calls print_optimizer_config() to display the initial configuration. """ - ConfigWrapper.__init__(self, config, console=console, max_threads=max_threads) + self.config = runner.config + self.console = runner.console + self.max_threads = runner.max_threads + + self.base_name = runner.base_name + self.yaml_file_suffix = runner.yaml_file_suffix + self.config = runner.config + self.runner = runner + self.status = runner.status + self.optimized_config = copy.deepcopy(self.config) self.llm_client = LLMClient(model) self.operations_cost = 0 @@ -132,13 +139,8 @@ def __init__( self.samples_taken = defaultdict(dict) self.resume = resume - # create parsing tool map - self.parsing_tool_map = create_parsing_tool_map( - self.config.get("parsing_tools", None) - ) - home_dir = os.path.expanduser("~") - cache_dir = os.path.join(home_dir, f".docetl/cache/{pipeline_name}") + cache_dir = os.path.join(home_dir, f".docetl/cache/{runner.yaml_file_suffix}") os.makedirs(cache_dir, exist_ok=True) self.datasets = DatasetOnDisk(dir=cache_dir, console=self.console) self.optimized_ops_path = f"{cache_dir}/optimized_ops" @@ -177,7 +179,7 @@ def syntax_check(self): try: operation_class = get_operation(operation_type) operation_class( - self, + self.runner, operation_config, self.config.get("default_model", "gpt-4o-mini"), self.max_threads, @@ -962,7 +964,7 @@ def _get_sample_data( type=dataset_config["type"], path_or_data=dataset_config["path"], parsing=dataset_config.get("parsing", []), - user_defined_parsing_tool_map=self.parsing_tool_map, + user_defined_parsing_tool_map=self.runner.parsing_tool_map, ) data = dataset.load() @@ -1101,7 +1103,7 @@ def _optimize_reduce( List[Dict[str, Any]]: The optimized operation configuration. """ reduce_optimizer = ReduceOptimizer( - self, + self.runner, self.config, self.console, self.llm_client, @@ -1144,7 +1146,7 @@ def _optimize_equijoin( new_right_name = right_name for _ in range(max_iterations): join_optimizer = JoinOptimizer( - self, + self.runner, self.config, op_config, self.console, @@ -1296,7 +1298,7 @@ def _optimize_resolve( List[Dict[str, Any]]: The optimized operation configuration. """ optimized_config, cost = JoinOptimizer( - self, + self.runner, self.config, op_config, self.console, @@ -1344,7 +1346,7 @@ def _run_operation( operation_class = get_operation(op_config["type"]) oc_kwargs = { - "runner": self, + "runner": self.runner, "config": op_config, "default_model": self.config["default_model"], "max_threads": self.max_threads, @@ -1416,7 +1418,7 @@ def save_optimized_config(self, optimized_config_path: str): with open(optimized_config_path, "w") as f: yaml.safe_dump(resolved_config, f, default_flow_style=False, width=80) self.console.log( - f"[green italic]💾 Optimized config saved to {self.optimized_config_path}[/green italic]" + f"[green italic]💾 Optimized config saved to {optimized_config_path}[/green italic]" ) diff --git a/docetl/cli.py b/docetl/cli.py index a596fe3b..dbbe09fe 100644 --- a/docetl/cli.py +++ b/docetl/cli.py @@ -47,7 +47,9 @@ def build( load_dotenv(env_file) runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads) - runner.optimize(save=True, model=model, resume=resume, timeout=timeout) + runner.optimize( + save=True, return_pipeline=False, model=model, resume=resume, timeout=timeout + ) @app.command() diff --git a/docetl/config_wrapper.py b/docetl/config_wrapper.py index 06fadc15..d1f966e8 100644 --- a/docetl/config_wrapper.py +++ b/docetl/config_wrapper.py @@ -1,3 +1,4 @@ +import datetime import os from docetl.utils import load_config from typing import Any, Dict, List, Optional, Tuple, Union @@ -48,14 +49,16 @@ def from_yaml(cls, yaml_file: str, **kwargs): def __init__( self, config: Dict, - base_name: str, - yaml_file_suffix: str, + base_name: Optional[str] = None, + yaml_file_suffix: Optional[str] = None, max_threads: int = None, console: Console = Console(), ): self.config = config self.base_name = base_name - self.yaml_file_suffix = yaml_file_suffix + self.yaml_file_suffix = yaml_file_suffix or datetime.datetime.now().strftime( + "%Y%m%d_%H%M%S" + ) self.default_model = self.config.get("default_model", "gpt-4o-mini") self.console = console self.max_threads = max_threads or (os.cpu_count() or 1) * 4 diff --git a/docetl/runner.py b/docetl/runner.py index 9074eba6..6c46bdb1 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -3,7 +3,7 @@ import os import time import functools -from typing import Dict, List, Optional, Tuple, Union +from typing import Any, Dict, List, Optional, Tuple, Union from docetl.builder import Optimizer from pydantic import BaseModel @@ -42,7 +42,9 @@ def schema(cls): # static type checkers. But it /is/ available for dynamic # checking, and for generating json schema. - OpType = functools.reduce(lambda a, b: a | b, [op.schema for op in get_operations().values()]) + OpType = functools.reduce( + lambda a, b: a | b, [op.schema for op in get_operations().values()] + ) # More pythonic implementation of the above, but only works in python 3.11: # OpType = Union[*[op.schema for op in get_operations().values()]] @@ -52,20 +54,27 @@ class Pipeline(BaseModel): datasets: Dict[str, schemas.Dataset] operations: list[OpType] pipeline: schemas.PipelineSpec + return Pipeline - + @classproperty def json_schema(cls): return cls.schema.model_json_schema() - def __init__(self, config: Dict, max_threads: int = None): + def __init__(self, config: Dict, max_threads: int = None, **kwargs): """ Initialize the DSLRunner with a YAML configuration file. Args: max_threads (int, optional): Maximum number of threads to use. Defaults to None. """ - ConfigWrapper.__init__(self, config, max_threads) + super().__init__( + config, + base_name=kwargs.pop("base_name", None), + yaml_file_suffix=kwargs.pop("yaml_file_suffix", None), + max_threads=max_threads, + **kwargs, + ) self.datasets = {} self.intermediate_dir = ( @@ -430,23 +439,22 @@ def _save_checkpoint(self, step_name: str, operation_name: str, data: List[Dict] ) def optimize( - self, save: bool = False, return_pipeline: bool = False, **kwargs - ) -> Union[None, "DSLRunner"]: + self, save: bool = False, return_pipeline: bool = True, **kwargs + ) -> Union[Dict, "DSLRunner"]: builder = Optimizer( - self.config, - self.yaml_file_suffix, - console=self.console, + self, max_threads=self.max_threads, **kwargs, ) builder.optimize() if save: builder.save_optimized_config(f"{self.base_name}_opt.yaml") + self.optimized_config_path = f"{self.base_name}_opt.yaml" if return_pipeline: return DSLRunner(builder.clean_optimized_config(), self.max_threads) - return None + return builder.clean_optimized_config() if __name__ == "__main__": diff --git a/docetl/schemas.py b/docetl/schemas.py index e02c8fe9..b54f3438 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -22,6 +22,8 @@ SplitOp = split.SplitOperation.schema GatherOp = gather.GatherOperation.schema UnnestOp = unnest.UnnestOperation.schema +ClusterOp = cluster.ClusterOperation.schema +SampleOp = sample.SampleOperation.schema OpType = Union[ MapOp, @@ -35,4 +37,4 @@ UnnestOp, ] -Dataset = dataset.Dataset.schema \ No newline at end of file +Dataset = dataset.Dataset.schema diff --git a/tests/basic/test_basic_map.py b/tests/basic/test_basic_map.py index 357a90b0..bd410634 100644 --- a/tests/basic/test_basic_map.py +++ b/tests/basic/test_basic_map.py @@ -26,8 +26,9 @@ def test_map_operation( assert len(results) == len(map_sample_data) assert all("sentiment" in result for result in results) + valid_sentiments = ["positive", "negative", "neutral"] assert all( - result["sentiment"] in ["positive", "negative", "neutral"] for result in results + any(vs in result["sentiment"] for vs in valid_sentiments) for result in results ) @@ -96,8 +97,9 @@ def test_map_operation_with_batching( assert len(results) == len(map_sample_data) assert all("sentiment" in result for result in results) + valid_sentiments = ["positive", "negative", "neutral"] assert all( - result["sentiment"] in ["positive", "negative", "neutral"] for result in results + any(vs in result["sentiment"] for vs in valid_sentiments) for result in results ) @@ -177,6 +179,7 @@ def test_map_operation_with_timeout(simple_map_config, simple_sample_data, api_w **simple_map_config, "timeout": 1, "max_retries_per_timeout": 0, + "bypass_cache": True, } operation = MapOperation(api_wrapper, map_config_with_timeout, "gpt-4o-mini", 4) diff --git a/tests/basic/test_pipeline_with_parsing.py b/tests/basic/test_pipeline_with_parsing.py index e10de629..3487a5de 100644 --- a/tests/basic/test_pipeline_with_parsing.py +++ b/tests/basic/test_pipeline_with_parsing.py @@ -97,7 +97,7 @@ def test_pipeline_with_parsing(config_file): # Create and run the DSLRunner runner = DSLRunner.from_yaml(config_file) - total_cost = runner.run() + total_cost = runner.load_run_save() # Check if the output file was created assert os.path.exists(output_file.name), "Output file was not created" diff --git a/tests/test_synth_resolve.py b/tests/test_synth_resolve.py index ecdd4321..f2629af3 100644 --- a/tests/test_synth_resolve.py +++ b/tests/test_synth_resolve.py @@ -1,3 +1,4 @@ +from docetl.runner import DSLRunner import pytest import json import tempfile @@ -76,21 +77,20 @@ def config_yaml(sample_data): def test_synth_resolve(config_yaml): # Initialize the optimizer - optimizer = Optimizer.from_yaml(config_yaml) + runner = DSLRunner.from_yaml(config_yaml) # Run the optimization - optimizer.optimize() - optimizer.save_optimized_config() + optimized_config = runner.optimize(save=True, return_pipeline=False) # Check if a resolve operation was synthesized synthesized_resolve_found = False - for step in optimizer.optimized_config["pipeline"]["steps"]: + for step in optimized_config["pipeline"]["steps"]: for op in step["operations"]: if op.startswith("synthesized_resolve_"): synthesized_resolve_found = True synthesized_op = [ operation - for operation in optimizer.optimized_config["operations"] + for operation in optimized_config["operations"] if operation["name"] == op ][0] @@ -99,9 +99,6 @@ def test_synth_resolve(config_yaml): assert "embedding_model" in synthesized_op assert "resolution_model" in synthesized_op assert "comparison_model" in synthesized_op - assert "_intermediates" in synthesized_op - assert "map_prompt" in synthesized_op["_intermediates"] - assert "reduce_key" in synthesized_op["_intermediates"] assert "comparison_prompt" in synthesized_op assert "resolution_prompt" in synthesized_op assert "blocking_threshold" in synthesized_op @@ -116,7 +113,7 @@ def test_synth_resolve(config_yaml): # Clean up temporary files os.remove(config_yaml) - os.remove(optimizer.optimized_config_path) + os.remove(runner.optimized_config_path) os.remove("patient_records.json") From 6b5afb7021740be98fcaa7587d514b8b59c08554 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sat, 19 Oct 2024 13:37:55 -0700 Subject: [PATCH 31/58] pass tests --- docetl/operations/base.py | 21 +++++++++++++++------ docetl/operations/map.py | 15 ++++++++++++--- 2 files changed, 27 insertions(+), 9 deletions(-) diff --git a/docetl/operations/base.py b/docetl/operations/base.py index 127c5b95..c9272d75 100644 --- a/docetl/operations/base.py +++ b/docetl/operations/base.py @@ -11,19 +11,23 @@ import jsonschema from pydantic import BaseModel + # FIXME: This should probably live in some utils module? class classproperty(object): def __init__(self, f): self.f = f + def __get__(self, obj, owner): return self.f(owner) + class BaseOperationMeta(ABCMeta): def __new__(cls, *arg, **kw): self = ABCMeta.__new__(cls, *arg, **kw) self.schema.__name__ = self.__name__ return self - + + class BaseOperation(ABC, metaclass=BaseOperationMeta): def __init__( self, @@ -60,15 +64,20 @@ def __init__( self.syntax_check() # This must be overridden in a subclass - class schema(BaseModel): + class schema(BaseModel, extra="allow"): name: str type: str - + @classproperty def json_schema(cls): - assert hasattr(cls.schema, "model_json_schema"), "Programming error: %s.schema must be a pydantic object but is a %s" % (cls, type(cls.schema)) + assert hasattr( + cls.schema, "model_json_schema" + ), "Programming error: %s.schema must be a pydantic object but is a %s" % ( + cls, + type(cls.schema), + ) return cls.schema.model_json_schema() - + @abstractmethod def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: """ @@ -97,7 +106,7 @@ def syntax_check(self) -> None: Raises: ValueError: If the configuration is invalid. """ - jsonschema.validate(instance = self.config, schema = self.json_schema) + jsonschema.validate(instance=self.config, schema=self.json_schema) def gleaning_check(self) -> None: """ diff --git a/docetl/operations/map.py b/docetl/operations/map.py index 36322ab0..7073ff3e 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -14,6 +14,7 @@ from docetl.utils import completion_cost from pydantic import Field, field_validator + def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: """ Render a Jinja2 template with the given data, ensuring protection against template injection vulnerabilities. @@ -25,7 +26,8 @@ def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: env = Environment(autoescape=True) template = env.from_string(template_string) return template.render(input=data) - + + class MapOperation(BaseOperation): class schema(BaseOperation.schema): type: str = "map" @@ -35,7 +37,9 @@ class schema(BaseOperation.schema): optimize: Optional[bool] = None recursively_optimize: Optional[bool] = None sample_size: Optional[int] = None - tools: Optional[List[Dict[str, Any]]] = None # FIXME: Why isn't this using the Tool data class so validation works automatically? + tools: Optional[List[Dict[str, Any]]] = ( + None # FIXME: Why isn't this using the Tool data class so validation works automatically? + ) validation_rules: Optional[List[str]] = Field(None, alias="validate") num_retries_on_validate_failure: Optional[int] = None gleaning: Optional[Dict[str, Any]] = None @@ -60,7 +64,7 @@ def __init__( "max_batch_size", kwargs.get("max_batch_size", float("inf")) ) self.clustering_method = "random" - + def syntax_check(self) -> None: """ Checks the configuration of the MapOperation for required keys and valid structure. @@ -241,6 +245,11 @@ def validation_fn(response: Dict[str, Any]): class ParallelMapOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "parallel_map" + prompts: List[Dict[str, Any]] + output: Dict[str, Any] + def __init__( self, *args, From cf3028b553bc7fb5bdf03e46138aabbaf31d8cfa Mon Sep 17 00:00:00 2001 From: Egil Date: Sat, 19 Oct 2024 22:56:42 +0200 Subject: [PATCH 32/58] LinkResolveOperation drycoded --- docetl/operations/link_resolve.py | 179 ++++++++++++++++++++++++++++++ pyproject.toml | 1 + 2 files changed, 180 insertions(+) create mode 100644 docetl/operations/link_resolve.py diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py new file mode 100644 index 00000000..a00b50fa --- /dev/null +++ b/docetl/operations/link_resolve.py @@ -0,0 +1,179 @@ +import random +from concurrent.futures import ThreadPoolExecutor, as_completed +from typing import Any, Dict, List, Tuple + +import jinja2 +from jinja2 import Template +from rich.prompt import Confirm + +from docetl.operations.base import BaseOperation +from docetl.operations.utils import RichLoopBar, rich_as_completed +from docetl.utils import completion_cost, extract_jinja_variables +from .clustering_utils import get_embeddings_for_clustering +from sklearn.metrics.pairwise import cosine_similarity + +class LinkResolveOperation(BaseOperation): + def syntax_check(self) -> None: + pass + + def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: + """ + Executes the resolve links operation on the provided dataset. + + Args: + input_data (List[Dict]): The dataset to resolve. + + Returns: + Tuple[List[Dict], float]: A tuple containing the resolved results and the total cost of the operation. + + """ + if len(input_data) == 0: + return [], 0 + + self.prompt_template = Template(self.config["comparison_prompt"]) + + id_key = self.config.get("id_key", "title") + link_key = self.config.get("link_key", "related_to") + blocking_threshold = self.config.get("blocking_threshold") + blocking_conditions = self.config.get("blocking_conditions", []) + + embedding_model = self.config.get("embedding_model", "text-embedding-3-small") + + item_by_id = {item[id_key]: item + for item in input_data} + + id_values = set([item[id_key] for item in input_data]) + + link_values = set() + for item in input_data: + link_values.update(item[link_key]) + + to_resolve = list(link_values - id_values) + id_values = list(id_values) + + if not blocking_threshold and not blocking_conditions: + # Prompt the user for confirmation + if not Confirm.ask( + f"[yellow]Warning: No blocking keys or conditions specified. " + f"This may result in a large number of comparisons. " + f"We recommend specifying at least one blocking key or condition, or using the optimizer to automatically come up with these. " + f"Do you want to continue without blocking?[/yellow]", + ): + raise ValueError("Operation cancelled by user.") + + + id_embeddings, id_embedding_cost = get_embeddings_for_clustering( + [{"key": value} for value in id_values],, + { + "embedding_model": embedding_model + "embedding_keys": ["key"] + }, + self.runner.api + ) + link_embeddings, link_embedding_cost = get_embeddings_for_clustering( + [{"key": value} for value in to_resolve], + { + "embedding_model": embedding_model + "embedding_keys": ["key"] + }, + self.runner.api + ) + + similarity_matrix = cosine_similarity(link_embeddings, id_embeddings) + + closest = np.argmin(similarity_matrix, axis=1) + acceptable = similarity_matrix.min(axis=1) < blocking_threshold + + acceptable_idxs = np.nonzero(acceptable) + close_enough = np.zeros(len(acceptable_idxs)) + + self.replacements = {} + + with ThreadPoolExecutor(max_workers=self.max_batch_size) as executor: + + futures = [] + for link_idx in np.nonzero(acceptable): + id_idx = closest[link_idx] + link_value = to_resolve[id_idx] + id_value = id_values[id_idx] + item = item_by_id[id_value] + + futures.append( + executor.submit( + self.compare, + link_idx = link_idx, + id_idx = id_idx, + link_value = link_value, + id_value = id_value, + item = item)) + + total_cost = 0 + pbar = RichLoopBar( + range(len(futures)), + desc=f"Processing {self.config['name']} (map) on all documents", + console=self.console, + ) + for i in pbar: + total_cost += futures[i].result() + pbar.update(i) + + for item in input_data: + item[link_key] = [self.replacements.get(value, value) + for value in item[link_key]] + + return input_data, total_cost + + def compare(self, link_idx, id_idx, link_value, id_value, item): + prompt = self.prompt_template.render( + link_value = link_value, + id_value = id_value, + item = item + ) + + schema = {"is_same": "bool"} + + def validation_fn(response: Dict[str, Any]): + output = self.runner.api.parse_llm_response( + response, + schema=schema, + manually_fix_errors=self.manually_fix_errors, + )[0] + if self.runner.api.validate_output(self.config, output, self.console): + return output, True + return output, False + + response = self.runner.api.call_llm( + model=self.config.get("model", self.default_model), + op_type="cluster", + messages=[{"role": "user", "content": prompt}], + output_schema=schema, + timeout_seconds=self.config.get("timeout", 120), + bypass_cache=self.config.get("bypass_cache", False), + max_retries_per_timeout=self.config.get("max_retries_per_timeout", 2), + validation_config=( + { + "num_retries": self.num_retries_on_validate_failure, + "val_rule": self.config.get("validate", []), + "validation_fn": validation_fn, + } + if self.config.get("validate", None) + else None + ), + verbose=self.config.get("verbose", False), + ) + total_cost += response.total_cost + if response.validated: + output = self.runner.api.parse_llm_response( + response.response, + schema=schema, + manually_fix_errors=self.manually_fix_errors, + )[0] + if output["is_same"]: + self.replacements[link_value] = id_value + + return total_cost + + + + + diff --git a/pyproject.toml b/pyproject.toml index 54640b3c..0459bf35 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -89,6 +89,7 @@ resolve = "docetl.operations.resolve:ResolveOperation" gather = "docetl.operations.gather:GatherOperation" cluster = "docetl.operations.cluster:ClusterOperation" sample = "docetl.operations.sample:SampleOperation" +link_resolve = "docetl.operations.link_resolve:LinkResolveOperation" [tool.poetry.plugins."docetl.parser"] llama_index_simple_directory_reader = "docetl.parsing_tools:llama_index_simple_directory_reader" From 150f075ce4d8668f6d2dc0438b93d7efa7379477 Mon Sep 17 00:00:00 2001 From: Egil Date: Sat, 19 Oct 2024 23:05:21 +0200 Subject: [PATCH 33/58] Bugfixes --- docetl/operations/link_resolve.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index a00b50fa..e1334f52 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -63,9 +63,9 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: id_embeddings, id_embedding_cost = get_embeddings_for_clustering( - [{"key": value} for value in id_values],, + [{"key": value} for value in id_values], { - "embedding_model": embedding_model + "embedding_model": embedding_model, "embedding_keys": ["key"] }, self.runner.api @@ -73,7 +73,7 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: link_embeddings, link_embedding_cost = get_embeddings_for_clustering( [{"key": value} for value in to_resolve], { - "embedding_model": embedding_model + "embedding_model": embedding_model, "embedding_keys": ["key"] }, self.runner.api From 612c8fcea54ec00df6ce384884ecd4bef3e25d3a Mon Sep 17 00:00:00 2001 From: Egil Date: Sat, 19 Oct 2024 23:09:51 +0200 Subject: [PATCH 34/58] Log comparisons saved --- docetl/operations/link_resolve.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index e1334f52..43012696 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -87,6 +87,15 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: acceptable_idxs = np.nonzero(acceptable) close_enough = np.zeros(len(acceptable_idxs)) + + total_possible_comparisons = len(acceptable) + comparisons_saved = total_possible_comparisons - acceptable.sum() + + self.console.log( + f"[green]Comparisons saved by blocking: {comparisons_saved} " + f"({(comparisons_saved / total_possible_comparisons) * 100:.2f}%)[/green]" + ) + self.replacements = {} with ThreadPoolExecutor(max_workers=self.max_batch_size) as executor: From aa70567c42d473aed62b9c0e7c68ebb075e2ef53 Mon Sep 17 00:00:00 2001 From: Egil Date: Sat, 19 Oct 2024 23:16:36 +0200 Subject: [PATCH 35/58] Log replacements made --- docetl/operations/link_resolve.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index 43012696..a3e3d4f3 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -126,6 +126,11 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: total_cost += futures[i].result() pbar.update(i) + + self.console.log( + f"[green]Number of replacements found: {len(self.replacements)}[/green]" + ) + for item in input_data: item[link_key] = [self.replacements.get(value, value) for value in item[link_key]] From ebeda157d65f56ec9f085353b5474009560954d8 Mon Sep 17 00:00:00 2001 From: Egil Date: Sun, 20 Oct 2024 00:13:44 +0200 Subject: [PATCH 36/58] Bugfixes --- docetl/operations/link_resolve.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index a3e3d4f3..af8b931d 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -11,6 +11,7 @@ from docetl.utils import completion_cost, extract_jinja_variables from .clustering_utils import get_embeddings_for_clustering from sklearn.metrics.pairwise import cosine_similarity +import numpy as np class LinkResolveOperation(BaseOperation): def syntax_check(self) -> None: @@ -82,11 +83,9 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: similarity_matrix = cosine_similarity(link_embeddings, id_embeddings) closest = np.argmin(similarity_matrix, axis=1) - acceptable = similarity_matrix.min(axis=1) < blocking_threshold - - acceptable_idxs = np.nonzero(acceptable) - close_enough = np.zeros(len(acceptable_idxs)) + acceptable = similarity_matrix.min(axis=1) >= blocking_threshold + acceptable_idxs = np.nonzero(acceptable)[0] total_possible_comparisons = len(acceptable) comparisons_saved = total_possible_comparisons - acceptable.sum() @@ -97,14 +96,16 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: ) self.replacements = {} - - with ThreadPoolExecutor(max_workers=self.max_batch_size) as executor: + + + batch_size = self.config.get("compare_batch_size", 100) + with ThreadPoolExecutor(max_workers=batch_size) as executor: futures = [] - for link_idx in np.nonzero(acceptable): + for link_idx in acceptable_idxs: id_idx = closest[link_idx] - link_value = to_resolve[id_idx] id_value = id_values[id_idx] + link_value = to_resolve[link_idx] item = item_by_id[id_value] futures.append( @@ -157,7 +158,7 @@ def validation_fn(response: Dict[str, Any]): return output, False response = self.runner.api.call_llm( - model=self.config.get("model", self.default_model), + model=self.config.get("comparison_model", self.default_model), op_type="cluster", messages=[{"role": "user", "content": prompt}], output_schema=schema, From 34c4be7a8439801928d6e6215c5a32cd7c1a3a34 Mon Sep 17 00:00:00 2001 From: Egil Date: Sun, 20 Oct 2024 00:23:40 +0200 Subject: [PATCH 37/58] Use a better default model --- docetl/operations/link_resolve.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index af8b931d..4c33bd5e 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -38,7 +38,8 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: blocking_threshold = self.config.get("blocking_threshold") blocking_conditions = self.config.get("blocking_conditions", []) - embedding_model = self.config.get("embedding_model", "text-embedding-3-small") + # Note: We don't want to use text-embedding-3-small as it has bad performance on short texts... + embedding_model = self.config.get("embedding_model", "text-embedding-ada-002") item_by_id = {item[id_key]: item for item in input_data} From 33717293f4c008e6e728c6ff29d403bfff913c0a Mon Sep 17 00:00:00 2001 From: Egil Date: Sun, 20 Oct 2024 00:31:21 +0200 Subject: [PATCH 38/58] Bugfix --- docetl/operations/link_resolve.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index 4c33bd5e..22e98608 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -177,7 +177,6 @@ def validation_fn(response: Dict[str, Any]): ), verbose=self.config.get("verbose", False), ) - total_cost += response.total_cost if response.validated: output = self.runner.api.parse_llm_response( response.response, @@ -187,7 +186,7 @@ def validation_fn(response: Dict[str, Any]): if output["is_same"]: self.replacements[link_value] = id_value - return total_cost + return response.total_cost From 9ca4e6115f89c2e0500a14389d8bd7fc271f28af Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 20 Oct 2024 20:00:25 -0700 Subject: [PATCH 39/58] fix: make tests pass --- docetl/parsing_tools.py | 22 ++++++++++++++-------- tests/test_azure_rl.py | 17 +++++++++++++---- tests/test_config.py | 2 +- tests/test_parsing_tools.py | 30 +++++++++++++++++------------- tests/test_split.py | 1 - 5 files changed, 45 insertions(+), 27 deletions(-) diff --git a/docetl/parsing_tools.py b/docetl/parsing_tools.py index 86014abd..a85290ab 100644 --- a/docetl/parsing_tools.py +++ b/docetl/parsing_tools.py @@ -2,12 +2,16 @@ import io import os from typing import Dict, List, Optional, Any +from functools import wraps + def with_input_output_key(fn): """Decorator that wraps a parser function that takes a single string parameter and return list of strings and makes it a full parser function that takes an item as a dictionary and return a list of dictionaries.""" + + @wraps(fn) def wrapper(item, input_key="text", output_key="text", **kw): if input_key not in item: raise ValueError(f"Input key {input_key} not found in item: {item}") @@ -15,18 +19,22 @@ def wrapper(item, input_key="text", output_key="text", **kw): if not isinstance(result, list): result = [result] return [{output_key: res} for res in result] + return wrapper -def llama_index_simple_directory_reader(item: dict[str, Any], input_key: str ="path") -> List[dict[str, Any]]: + +def llama_index_simple_directory_reader( + item: dict[str, Any], input_key: str = "path" +) -> List[dict[str, Any]]: from llama_index.core import SimpleDirectoryReader documents = SimpleDirectoryReader(item[input_key]).load_data() - return [{"text": doc.text, - "metadata": doc.metadata} - for doc in documents] + return [{"text": doc.text, "metadata": doc.metadata} for doc in documents] -def llama_index_wikipedia_reader(item: dict[str, Any], input_key: str = "pages") -> List[dict[str, Any]]: +def llama_index_wikipedia_reader( + item: dict[str, Any], input_key: str = "pages" +) -> List[dict[str, Any]]: from llama_index.readers.wikipedia import WikipediaReader loader = WikipediaReader() @@ -38,9 +46,7 @@ def llama_index_wikipedia_reader(item: dict[str, Any], input_key: str = "pages") for name, doc in zip(pages, documents): doc.metadata["source"] = "https://en.wikipedia.org/wiki/" + name - return [{"text": doc.text, - "metadata": doc.metadata} - for doc in documents] + return [{"text": doc.text, "metadata": doc.metadata} for doc in documents] @with_input_output_key diff --git a/tests/test_azure_rl.py b/tests/test_azure_rl.py index be23c387..3f051ff7 100644 --- a/tests/test_azure_rl.py +++ b/tests/test_azure_rl.py @@ -1,3 +1,4 @@ +from docetl.runner import DSLRunner import pytest from docetl.operations.map import MapOperation import random @@ -41,12 +42,20 @@ def test_map_operation_over_15_documents(simple_map_config, sample_documents): os.environ["AZURE_API_VERSION"] = os.getenv("LOW_RES_AZURE_API_VERSION") os.environ["AZURE_API_KEY"] = os.getenv("LOW_RES_AZURE_API_KEY") - operation = MapOperation(api_wrapper, simple_map_config, "azure/gpt-4o", 4) - results, cost = operation.execute(sample_documents) + runner = DSLRunner( + { + "default_model": "gpt-4o-mini", + "operations": [], + "pipeline": {"steps": [], "output": {"path": "/tmp/testingdocetl.json"}}, + }, + max_threads=64, + ) + + operation = MapOperation(runner, simple_map_config, "azure/gpt-4o", 4) + results, cost = operation.execute(sample_documents + sample_documents) - assert len(results) == 15 + assert len(results) == 16 assert all("sentiment" in result for result in results) assert all( result["sentiment"] in ["positive", "negative", "neutral"] for result in results ) - assert cost > 0 diff --git a/tests/test_config.py b/tests/test_config.py index 65102948..afceaed1 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -87,7 +87,7 @@ def test_end_to_end_pipeline(config_file, sample_data_file, tmp_path): # Create and run the DSLRunner runner = DSLRunner.from_yaml(str(config_file)) - total_cost = runner.run() + total_cost = runner.load_run_save() # Check if the output file was created output_path = tmp_path / "output.json" diff --git a/tests/test_parsing_tools.py b/tests/test_parsing_tools.py index 9118156b..5d482d28 100644 --- a/tests/test_parsing_tools.py +++ b/tests/test_parsing_tools.py @@ -74,7 +74,7 @@ def temp_pptx_file(): def test_whisper_speech_to_text(temp_audio_file): - result = parsing_tools.whisper_speech_to_text(temp_audio_file) + result = parsing_tools.whisper_speech_to_text.__wrapped__(temp_audio_file) assert isinstance(result, list) assert len(result) == 1 @@ -83,7 +83,7 @@ def test_whisper_speech_to_text(temp_audio_file): def test_xlsx_to_string(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file) + result = parsing_tools.xlsx_to_string.__wrapped__(temp_xlsx_file) assert isinstance(result, list) assert len(result) == 1 @@ -93,7 +93,7 @@ def test_xlsx_to_string(temp_xlsx_file): def test_xlsx_to_string_row_orientation(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file, orientation="row") + result = parsing_tools.xlsx_to_string.__wrapped__(temp_xlsx_file, orientation="row") assert isinstance(result, list) assert len(result) == 1 @@ -102,7 +102,9 @@ def test_xlsx_to_string_row_orientation(temp_xlsx_file): def test_xlsx_to_string_doc_per_sheet(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file, doc_per_sheet=True) + result = parsing_tools.xlsx_to_string.__wrapped__( + temp_xlsx_file, doc_per_sheet=True + ) assert isinstance(result, list) assert len(result) == 1 # Only one sheet in our test file @@ -112,7 +114,7 @@ def test_xlsx_to_string_doc_per_sheet(temp_xlsx_file): def test_txt_to_string(temp_txt_file): - result = parsing_tools.txt_to_string(temp_txt_file) + result = parsing_tools.txt_to_string.__wrapped__(temp_txt_file) assert isinstance(result, list) assert len(result) == 1 @@ -120,7 +122,7 @@ def test_txt_to_string(temp_txt_file): def test_docx_to_string(temp_docx_file): - result = parsing_tools.docx_to_string(temp_docx_file) + result = parsing_tools.docx_to_string.__wrapped__(temp_docx_file) assert isinstance(result, list) assert len(result) == 1 @@ -129,7 +131,7 @@ def test_docx_to_string(temp_docx_file): def test_pptx_to_string(temp_pptx_file): - result = parsing_tools.pptx_to_string(temp_pptx_file) + result = parsing_tools.pptx_to_string.__wrapped__(temp_pptx_file) assert isinstance(result, list) assert len(result) == 1 @@ -140,7 +142,9 @@ def test_pptx_to_string(temp_pptx_file): def test_pptx_to_string_doc_per_slide(temp_pptx_file): - result = parsing_tools.pptx_to_string(temp_pptx_file, doc_per_slide=True) + result = parsing_tools.pptx_to_string.__wrapped__( + temp_pptx_file, doc_per_slide=True + ) assert isinstance(result, list) assert len(result) == 2 @@ -157,7 +161,7 @@ def pdf_url(): def test_azure_di_read(pdf_url): # Test with default parameters - result = parsing_tools.azure_di_read(pdf_url, use_url=True) + result = parsing_tools.azure_di_read.__wrapped__(pdf_url, use_url=True) assert isinstance(result, list) assert len(result) == 1 @@ -169,7 +173,7 @@ def test_azure_di_read(pdf_url): assert "Enhancing the patient" in content # Test with include_line_numbers=True - result_line_numbers = parsing_tools.azure_di_read( + result_line_numbers = parsing_tools.azure_di_read.__wrapped__( pdf_url, use_url=True, include_line_numbers=True ) assert isinstance(result_line_numbers, list) @@ -179,7 +183,7 @@ def test_azure_di_read(pdf_url): def test_azure_di_read_invoice(): invoice_url = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf" - result = parsing_tools.azure_di_read(invoice_url, use_url=True) + result = parsing_tools.azure_di_read.__wrapped__(invoice_url, use_url=True) assert isinstance(result, list) assert len(result) == 1 @@ -207,7 +211,7 @@ def pytest_sessionfinish(session, exitstatus): def test_paddleocr_pdf_to_string(): pdf_path = "tests/data/PublicWaterMassMailing.pdf" - result = parsing_tools.paddleocr_pdf_to_string(pdf_path, lang="en") + result = parsing_tools.paddleocr_pdf_to_string.__wrapped__(pdf_path, lang="en") assert isinstance(result, list) assert len(result) == 1 @@ -221,7 +225,7 @@ def test_paddleocr_pdf_to_string(): def test_gptpdf_to_string(): input_pdf = "tests/data/PublicWaterMassMailing.pdf" - result = parsing_tools.gptpdf_to_string( + result = parsing_tools.gptpdf_to_string.__wrapped__( input_path=input_pdf, gpt_model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], diff --git a/tests/test_split.py b/tests/test_split.py index 939563dc..71a89b86 100644 --- a/tests/test_split.py +++ b/tests/test_split.py @@ -137,7 +137,6 @@ def test_split_map_gather_operations( assert len(map_results) == len( split_results ), "Map operation should produce same number of results as split operation" - assert map_cost > 0, "Map operation cost should be greater than zero" for result in map_results: assert "summary" in result, "Each result should have a summary" From 75822dbee928f34dd4051463b79176d51c2146b4 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 20 Oct 2024 20:06:48 -0700 Subject: [PATCH 40/58] fix: make docs work --- docs/api-reference/python.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/api-reference/python.md b/docs/api-reference/python.md index 0291a6ea..f91f11bc 100644 --- a/docs/api-reference/python.md +++ b/docs/api-reference/python.md @@ -2,15 +2,6 @@ ## Operations -::: docetl.schemas.BaseOp - options: - show_root_heading: true - heading_level: 3 - show_if_no_docstring: false - docstring_options: - ignore_init_summary: false - trim_doctest_flags: true - ::: docetl.schemas.MapOp options: show_root_heading: true From b793fbd51ccf1f0e09cc67a3165993b396400f94 Mon Sep 17 00:00:00 2001 From: Egil Date: Mon, 21 Oct 2024 13:17:57 +0200 Subject: [PATCH 41/58] Consider all pairs not blocked --- docetl/operations/link_resolve.py | 42 +++++++++++++++---------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index 22e98608..5be3232d 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -83,13 +83,10 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: similarity_matrix = cosine_similarity(link_embeddings, id_embeddings) - closest = np.argmin(similarity_matrix, axis=1) - acceptable = similarity_matrix.min(axis=1) >= blocking_threshold + acceptable = similarity_matrix >= blocking_threshold - acceptable_idxs = np.nonzero(acceptable)[0] - - total_possible_comparisons = len(acceptable) - comparisons_saved = total_possible_comparisons - acceptable.sum() + total_possible_comparisons = acceptable.shape[0] * acceptable.shape[1] + comparisons_saved = total_possible_comparisons - acceptable.sum().sum() self.console.log( f"[green]Comparisons saved by blocking: {comparisons_saved} " @@ -98,25 +95,26 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: self.replacements = {} - batch_size = self.config.get("compare_batch_size", 100) with ThreadPoolExecutor(max_workers=batch_size) as executor: futures = [] - for link_idx in acceptable_idxs: - id_idx = closest[link_idx] - id_value = id_values[id_idx] - link_value = to_resolve[link_idx] - item = item_by_id[id_value] - - futures.append( - executor.submit( - self.compare, - link_idx = link_idx, - id_idx = id_idx, - link_value = link_value, - id_value = id_value, - item = item)) + for link_idx in range(acceptable.shape[0]): + for id_idx in range(acceptable.shape[1]): + if not acceptable[link_idx, id_idx]: continue + + id_value = id_values[id_idx] + link_value = to_resolve[link_idx] + item = item_by_id[id_value] + + futures.append( + executor.submit( + self.compare, + link_idx = link_idx, + id_idx = id_idx, + link_value = link_value, + id_value = id_value, + item = item)) total_cost = 0 pbar = RichLoopBar( @@ -177,12 +175,14 @@ def validation_fn(response: Dict[str, Any]): ), verbose=self.config.get("verbose", False), ) + if response.validated: output = self.runner.api.parse_llm_response( response.response, schema=schema, manually_fix_errors=self.manually_fix_errors, )[0] + self.console.log("NNNNNNNNNNNNNNNNNNN\n%s\n%s\n\n" % (output,prompt)) if output["is_same"]: self.replacements[link_value] = id_value From 8509ed8e3bc6e04b001e52e4da1d1bcd28862bb0 Mon Sep 17 00:00:00 2001 From: Egil Date: Mon, 21 Oct 2024 14:53:43 +0200 Subject: [PATCH 42/58] Print number of replacements found --- docetl/operations/link_resolve.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index 5be3232d..b9378326 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -128,7 +128,8 @@ def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: self.console.log( - f"[green]Number of replacements found: {len(self.replacements)}[/green]" + f"[green]Number of replacements found: {len(self.replacements)} " + f"({(len(self.replacements) / total_possible_comparisons) * 100:.2f}% of all comparisons)[/green]" ) for item in input_data: From c0d01520827de18a7cfa033ac4cdc702ad68bd20 Mon Sep 17 00:00:00 2001 From: Egil Date: Mon, 21 Oct 2024 15:11:21 +0200 Subject: [PATCH 43/58] Removed debug print --- docetl/operations/link_resolve.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index b9378326..16c84d40 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -183,7 +183,6 @@ def validation_fn(response: Dict[str, Any]): schema=schema, manually_fix_errors=self.manually_fix_errors, )[0] - self.console.log("NNNNNNNNNNNNNNNNNNN\n%s\n%s\n\n" % (output,prompt)) if output["is_same"]: self.replacements[link_value] = id_value From b56c6ff01cace8b05333747a092534868528fec9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Egil=20M=C3=B6ller?= Date: Mon, 21 Oct 2024 20:35:48 +0200 Subject: [PATCH 44/58] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e71189aa..93bd5e6c 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ DocETL is the ideal choice when you're looking to maximize correctness and outpu ## Cool Things People Are Doing with DocETL - [Conversation Generator](https://github.com/redhog/docetl-conversation) +- [Text-to-speech](https://github.com/redhog/docetl-speaker) - [YouTube Transcript Topic Extraction](https://github.com/rajib76/docetl_examples) ## Installation From 2b94331b1a09a4ba69193c5ef362046e103502e1 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Mon, 21 Oct 2024 17:23:59 -0700 Subject: [PATCH 45/58] feat: adding human in the loop for split-map-gather decomp --- docetl/optimizers/map_optimizer/optimizer.py | 4 ++-- .../map_optimizer/plan_generators.py | 3 ++- .../map_optimizer/prompt_generators.py | 19 ++++++++++++++++++- docetl/optimizers/map_optimizer/utils.py | 6 ++++++ 4 files changed, 28 insertions(+), 4 deletions(-) diff --git a/docetl/optimizers/map_optimizer/optimizer.py b/docetl/optimizers/map_optimizer/optimizer.py index deb306de..c7d18875 100644 --- a/docetl/optimizers/map_optimizer/optimizer.py +++ b/docetl/optimizers/map_optimizer/optimizer.py @@ -72,7 +72,7 @@ def __init__( self.k_to_pairwise_compare = 6 self.plan_generator = PlanGenerator( - llm_client, console, config, run_operation, max_threads, is_filter + runner, llm_client, console, config, run_operation, max_threads, is_filter ) self.evaluator = Evaluator( llm_client, @@ -83,7 +83,7 @@ def __init__( is_filter, ) self.prompt_generator = PromptGenerator( - llm_client, console, config, max_threads, is_filter + runner, llm_client, console, config, max_threads, is_filter ) def optimize( diff --git a/docetl/optimizers/map_optimizer/plan_generators.py b/docetl/optimizers/map_optimizer/plan_generators.py index 845ecd35..9f8a5f40 100644 --- a/docetl/optimizers/map_optimizer/plan_generators.py +++ b/docetl/optimizers/map_optimizer/plan_generators.py @@ -17,6 +17,7 @@ class PlanGenerator: def __init__( self, + runner, llm_client: LLMClient, console: Console, config: Dict[str, Any], @@ -34,7 +35,7 @@ def __init__( ) self._run_operation = run_operation self.prompt_generator = PromptGenerator( - llm_client, console, config, max_threads, is_filter + runner, llm_client, console, config, max_threads, is_filter ) self.max_threads = max_threads self.config = config diff --git a/docetl/optimizers/map_optimizer/prompt_generators.py b/docetl/optimizers/map_optimizer/prompt_generators.py index 22988a31..9f58dca5 100644 --- a/docetl/optimizers/map_optimizer/prompt_generators.py +++ b/docetl/optimizers/map_optimizer/prompt_generators.py @@ -4,6 +4,7 @@ from litellm import model_cost from rich.console import Console +from rich.prompt import Prompt from docetl.optimizers.map_optimizer.utils import generate_and_validate_prompt from docetl.optimizers.utils import LLMClient @@ -13,6 +14,7 @@ class PromptGenerator: def __init__( self, + runner: "DSLRunner", llm_client: LLMClient, console: Console, config: Dict[str, Any], @@ -24,6 +26,7 @@ def __init__( self.config = config self.max_threads = max_threads self.is_filter = is_filter + self.runner = runner def _generate_validator_prompt( self, @@ -330,7 +333,7 @@ def _get_combine_prompt( Modify the original prompt to be a prompt that will combine these chunk results to accomplish the original task. Guidelines for your prompt template: - - The only variable you are allowed to use is the inputs variable, which contains all chunk results. Each value is a dictionary with the keys {', '.join(schema_keys)} + - The only variable you are allowed to use is the `inputs` variable, which contains all chunk results. Each value is a dictionary with the keys {', '.join(schema_keys)} - Avoid using filters or complex logic, even though Jinja technically supports it - The prompt template must be a valid Jinja2 template - You must use the {{ inputs }} variable somehow (you can access specific schema keys if you'ld like) @@ -353,9 +356,23 @@ def _get_combine_prompt( config=self.config, max_threads=self.max_threads, console=self.console, + inclusion_strings=["inputs"], ) combine_prompt = result["combine_prompt"] + # Confirm with the user that this prompt is good & ask them to edit + if self.runner.status: + self.runner.status.stop() + + combine_prompt = Prompt.ask( + f"Here is the prompt generated for the reduce operation:\n```\n{combine_prompt}\n```\n\nPress enter to confirm, or type in the prompt you would like to use instead.", + default=combine_prompt, + console=self.console, + ) + + if self.runner.status: + self.runner.status.start() + # Determine if the combine operation is associative system_prompt_associative = ( "You are an AI assistant analyzing data processing tasks." diff --git a/docetl/optimizers/map_optimizer/utils.py b/docetl/optimizers/map_optimizer/utils.py index b1e6d055..105a6628 100644 --- a/docetl/optimizers/map_optimizer/utils.py +++ b/docetl/optimizers/map_optimizer/utils.py @@ -27,6 +27,7 @@ def generate_and_validate_prompt( config: Dict[str, Any], max_threads: int, console: Console, + inclusion_strings: List = [], ) -> Dict[str, Any]: max_retries = 3 attempt = 0 @@ -42,6 +43,9 @@ def generate_and_validate_prompt( parameters, ) result = json.loads(response.choices[0].message.content) + for inclusion_string in inclusion_strings: + assert inclusion_string in str(result) + chat_history += [ {"role": "assistant", "content": result}, ] @@ -78,6 +82,8 @@ def generate_and_validate_prompt( except jinja2.exceptions.TemplateError as e: error_message = f"Invalid Jinja2 template: {str(e)}" + except AssertionError: + error_message = f"Generated prompt does not include all required strings: {inclusion_strings}" except Exception as e: # We only care about jinja errors console.log(f"Error: {e}") From 8fb966caa218a6e9495d18e378ce8cefc6489097 Mon Sep 17 00:00:00 2001 From: Egil Date: Tue, 22 Oct 2024 10:02:39 +0200 Subject: [PATCH 46/58] Wrong op_type :p --- docetl/operations/link_resolve.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docetl/operations/link_resolve.py b/docetl/operations/link_resolve.py index 16c84d40..2669ac66 100644 --- a/docetl/operations/link_resolve.py +++ b/docetl/operations/link_resolve.py @@ -159,7 +159,7 @@ def validation_fn(response: Dict[str, Any]): response = self.runner.api.call_llm( model=self.config.get("comparison_model", self.default_model), - op_type="cluster", + op_type="link_resolve", messages=[{"role": "user", "content": prompt}], output_schema=schema, timeout_seconds=self.config.get("timeout", 120), From a50460e29b7c184822d41a60211c461544e4f7df Mon Sep 17 00:00:00 2001 From: Egil Date: Tue, 22 Oct 2024 14:54:27 +0200 Subject: [PATCH 47/58] Added docs --- docs/operators/link-resolve.md | 116 +++++++++++++++++++++++++++++++++ 1 file changed, 116 insertions(+) create mode 100644 docs/operators/link-resolve.md diff --git a/docs/operators/link-resolve.md b/docs/operators/link-resolve.md new file mode 100644 index 00000000..1f107232 --- /dev/null +++ b/docs/operators/link-resolve.md @@ -0,0 +1,116 @@ +# Cluster operation + +The `link_resolve` operation in DocETL is used to fix links between +items, e.g. in a knoweldge graph. It assumes you have already ensured +that the item id:s themselves are canonical, e.g. by running resolve +first. + +It will examine every id specified in a link from one item to another +and compare it to all item id:s. If it is not present with an exact +match, it is going to be compared to each of them using an llm prompt +to try to find a match. + +Note that this is a one-sided approach, compared to the resolve +operation: It assumes that item id:s are canonical / correct. + +## 🚀 Example: Knowledge graph of boating terms + +```yaml +- name: fix_links + type: link_resolve + id_key: title + link_key: related_to + blocking_threshold: 0.85 + embedding_model: text-embedding-ada-002 + comparison_model: gpt-4o-mini + comparison_prompt: | + Compare the following two concepts: + + Concept 1: [{{ link_value }}] + Concept 2: [{{ id_value }}] + + Are these concepts likely refering to the same thing? When + comparing them, also consider the following description of + concept 2: + + {{ item.description }} + + Respond with "True" if they are likely the same concept, or "False" if they are likely different concepts. +``` + +??? example "Sample Input and Output" + +The above make two replacements in the `related_to` keys: `Main sail` -> `Sail (main)` and `Sailing boat` -> `Sailing vessel`. + + Input: + ```json + [ + { + "title": "Sailing vessel", + "related_to": ["Main sail", "Main mast", "Rudder"], + "description": "A boat or ship propelled by sails. Typically with a very hydrodynamical hull." + }, + { + "title": "Catamaran", + "related_to": ["Sailing boat", "Hull"], + "description": "A type of vessel with two parallel hulls" + }, + { + "title": "Sail (main)", + "related_to": ["Sheet"], + "description": "A cloth set up to catch the force of the wind" + }, + { + "title": "Sheet (sailing)", + "related_to": [], + "description": "Ropes used to trim the angle of the sails, deciding the (angle of) wind force applied to the masts" + }, + { + "title": "Rudder angle", + "related_to": [], + "description": "The rudder angle together with the wind force on the mast(s) decides the rate of turn of the vessel" + } + ] + ``` + + Output: + ```json + [ + { + "title": "Sailing vessel", + "related_to": ["Sail (main)", "Main mast", "Rudder"], + "description": "A boat or ship propelled by sails. Typically with a very hydrodynamical hull." + }, + { + "title": "Catamaran", + "related_to": ["Sailing vessel", "Hull"], + "description": "A type of vessel with two parallel hulls" + }, + { + "title": "Sail (main)", + "related_to": ["Sheet"], + "description": "A cloth set up to catch the force of the wind" + }, + { + "title": "Sheet (sailing)", + "related_to": [], + "description": "Ropes used to trim the angle of the sails, deciding the (angle of) wind force applied to the masts" + }, + { + "title": "Rudder angle", + "related_to": [], + "description": "The rudder angle together with the wind force on the mast(s) decides the rate of turn of the vessel" + } + ] + ``` + +## Required Parameters + +- `name`: A unique name for the operation. +- `type`: Must be set to "link_resolve". +- `id_key`: A key to use for item id:s +- `link_key`: A key to make replacements in +- `blocking_threshold`: Embedding similarity threshold for considering entries as potential matches +- `comparison_prompt`: The prompt template to use for comparing potential matches. + +## Optional Parameters From dc538c331c582f15ae52f6bd4f5c3e25d730e46e Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Tue, 22 Oct 2024 12:19:32 -0700 Subject: [PATCH 48/58] fix: cache partial pipeline runs --- docetl/runner.py | 21 +++++++++++ tests/test_runner_caching.py | 73 ++++++++++++++++++++++++++++++++++++ 2 files changed, 94 insertions(+) diff --git a/docetl/runner.py b/docetl/runner.py index 6c46bdb1..7666e905 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -374,6 +374,27 @@ def execute_step( if self.intermediate_dir: self._save_checkpoint(step["name"], operation_name, input_data) + # Load existing step op hash, if exists, merge self.step_op_hashes[step["name"]][operation_name] into it + # Save the step op hash + intermediate_config_path = os.path.join( + self.intermediate_dir, ".docetl_intermediate_config.json" + ) + if os.path.exists(intermediate_config_path): + with open(intermediate_config_path, "r") as f: + existing_config = json.load(f) + else: + existing_config = {} + + if step["name"] not in existing_config: + existing_config[step["name"]] = {} + existing_config[step["name"]][operation_name] = self.step_op_hashes[ + step["name"] + ][operation_name] + + # Resave + with open(intermediate_config_path, "w") as f: + json.dump(existing_config, f, indent=2) + return input_data, total_cost def _load_from_checkpoint_if_exists( diff --git a/tests/test_runner_caching.py b/tests/test_runner_caching.py index 42f02f2f..65b0fac8 100644 --- a/tests/test_runner_caching.py +++ b/tests/test_runner_caching.py @@ -1,3 +1,4 @@ +import shutil import time import pytest import json @@ -118,3 +119,75 @@ def test_pipeline_rerun_on_operation_change( # Check that the runtime is faster when not modifying assert unmodified_runtime < modified_runtime + + +# Test with an incorrect later operation but correct earlier operation +def test_partial_caching(temp_input_file, temp_output_file, temp_intermediate_dir): + # Create initial pipeline with two operations + initial_pipeline = Pipeline( + name="test_pipeline", + datasets={"test_input": Dataset(type="file", path=temp_input_file)}, + operations=[ + MapOp( + name="first_operation", + type="map", + prompt="Analyze the sentiment of the following text: '{{ input.text }}'", + output={"schema": {"sentiment": "string"}}, + model="gpt-4o-mini", + ), + MapOp( + name="second_operation_bad", + type="map", + prompt="Summarize the following text: '{{ forororororo }}'", + output={"schema": {"summary": "1000"}}, + model="gpt-4o-mini", + ), + ], + steps=[ + PipelineStep( + name="first_step", + input="test_input", + operations=["first_operation", "second_operation_bad"], + ), + ], + output=PipelineOutput( + type="file", path=temp_output_file, intermediate_dir=temp_intermediate_dir + ), + default_model="gpt-4o-mini", + ) + + # Run the initial pipeline + # Run the initial pipeline with an expected error + with pytest.raises(Exception): + initial_cost = initial_pipeline.run() + + new_pipeline_with_only_one_op = Pipeline( + name="test_pipeline", + datasets={"test_input": Dataset(type="file", path=temp_input_file)}, + operations=[ + MapOp( + name="first_operation", + type="map", + prompt="Analyze the sentiment of the following text: '{{ input.text }}'", + output={"schema": {"sentiment": "string"}}, + model="gpt-4o-mini", + ), + ], + steps=[ + PipelineStep( + name="first_step", + input="test_input", + operations=["first_operation"], + ), + ], + output=PipelineOutput( + type="file", path=temp_output_file, intermediate_dir=temp_intermediate_dir + ), + default_model="gpt-4o-mini", + ) + rerun_cost = new_pipeline_with_only_one_op.run() + + # Assert that the cost was 0 when rerunning the pipeline + assert ( + rerun_cost == 0 + ), "Expected zero cost when rerunning the pipeline without changes" From b921d3428ca63f1ecec7f7a97eef7d348836c44e Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Tue, 22 Oct 2024 12:21:04 -0700 Subject: [PATCH 49/58] remove tenacity dep --- poetry.lock | 17 +---------------- pyproject.toml | 1 - 2 files changed, 1 insertion(+), 17 deletions(-) diff --git a/poetry.lock b/poetry.lock index c765dd0e..7522e482 100644 --- a/poetry.lock +++ b/poetry.lock @@ -4006,21 +4006,6 @@ files = [ {file = "soupsieve-2.6.tar.gz", hash = "sha256:e2e68417777af359ec65daac1057404a3c8a5455bb8abc36f1a9866ab1a51abb"}, ] -[[package]] -name = "tenacity" -version = "9.0.0" -description = "Retry code until it succeeds" -optional = false -python-versions = ">=3.8" -files = [ - {file = "tenacity-9.0.0-py3-none-any.whl", hash = "sha256:93de0c98785b27fcf659856aa9f54bfbd399e29969b0621bc7f762bd441b4539"}, - {file = "tenacity-9.0.0.tar.gz", hash = "sha256:807f37ca97d62aa361264d497b0e31e92b8027044942bfa756160d908320d73b"}, -] - -[package.extras] -doc = ["reno", "sphinx"] -test = ["pytest", "tornado (>=4.5)", "typeguard"] - [[package]] name = "termcolor" version = "2.5.0" @@ -4518,4 +4503,4 @@ parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepadd [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "6cfc3498d69d4df1672e110994e4791b4d163615f146f36f041a90dced5846ea" +content-hash = "492e352f2364559d37223d6c88069c6a9d586f2f08e8d02deef65ccde07a2194" diff --git a/pyproject.toml b/pyproject.toml index e8791a2b..16fbd376 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,6 @@ python = "^3.10" litellm = "^1.42.1" tqdm = "^4.66.4" rich = "^13.7.1" -tenacity = "^9.0.0" frozendict = "^2.4.4" diskcache = "^5.6.3" typer = "^0.12.5" From f6e3cd047353ad3328311309cc2c386869ea9996 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Tue, 22 Oct 2024 12:47:31 -0700 Subject: [PATCH 50/58] mark as flaky test --- tests/basic/test_basic_map.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/basic/test_basic_map.py b/tests/basic/test_basic_map.py index bd410634..6bd07c08 100644 --- a/tests/basic/test_basic_map.py +++ b/tests/basic/test_basic_map.py @@ -173,6 +173,7 @@ def generate_random_text(length): ] +@pytest.mark.flaky(reruns=2) def test_map_operation_with_timeout(simple_map_config, simple_sample_data, api_wrapper): # Add timeout to the map configuration map_config_with_timeout = { From 8c40931d552aaf56e03e27a0a774239727532872 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Tue, 22 Oct 2024 15:47:59 -0700 Subject: [PATCH 51/58] only compare distinct pairs in resolve --- docetl/operations/resolve.py | 114 ++++++++++++++++++++++++----------- 1 file changed, 80 insertions(+), 34 deletions(-) diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index ac8bae44..fff71343 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -15,6 +15,13 @@ from docetl.utils import completion_cost, extract_jinja_variables +def find_cluster(item, cluster_map): + while item != cluster_map[item]: + cluster_map[item] = cluster_map[cluster_map[item]] + item = cluster_map[item] + return item + + class ResolveOperation(BaseOperation): class schema(BaseOperation.schema): type: str = "resolve" @@ -33,7 +40,7 @@ class schema(BaseOperation.schema): limit_comparisons: Optional[int] = None optimize: Optional[bool] = None timeout: Optional[int] = None - + def compare_pair( self, comparison_prompt: str, @@ -285,32 +292,33 @@ def get_embeddings_batch( total_cost += sum(costs) - # Initialize clusters - clusters = [{i} for i in range(len(input_data))] - cluster_map = {i: i for i in range(len(input_data))} - - def find_cluster(item): - while item != cluster_map[item]: - cluster_map[item] = cluster_map[cluster_map[item]] - item = cluster_map[item] - return item - - def merge_clusters(item1, item2): - root1, root2 = find_cluster(item1), find_cluster(item2) - if root1 != root2: - if len(clusters[root1]) < len(clusters[root2]): - root1, root2 = root2, root1 - clusters[root1] |= clusters[root2] - cluster_map[root2] = root1 - clusters[root2] = set() - - # Generate all pairs to compare - # TODO: virtualize this if possible - all_pairs = [ - (i, j) - for i in range(len(input_data)) - for j in range(i + 1, len(input_data)) - ] + # Generate all pairs to compare, ensuring no duplicate comparisons + def get_unique_comparison_pairs(): + # Create a mapping of values to their indices + value_to_indices = {} + for i, item in enumerate(input_data): + # Create a hashable key from the blocking keys + key = tuple(str(item.get(k, "")) for k in blocking_keys) + if key not in value_to_indices: + value_to_indices[key] = [] + value_to_indices[key].append(i) + + # Generate pairs for comparison, comparing each unique value combination only once + comparison_pairs = [] + keys = list(value_to_indices.keys()) + + # First, handle comparisons between different values + for i in range(len(keys)): + for j in range(i + 1, len(keys)): + # Only need one comparison between different values + idx1 = value_to_indices[keys[i]][0] + idx2 = value_to_indices[keys[j]][0] + if idx1 < idx2: # Maintain ordering to avoid duplicates + comparison_pairs.append((idx1, idx2)) + + return comparison_pairs, value_to_indices + + comparison_pairs, value_to_indices = get_unique_comparison_pairs() # Filter pairs based on blocking conditions def meets_blocking_conditions(pair): @@ -319,7 +327,7 @@ def meets_blocking_conditions(pair): is_match(input_data[i], input_data[j]) if blocking_conditions else False ) - blocked_pairs = list(filter(meets_blocking_conditions, all_pairs)) + blocked_pairs = list(filter(meets_blocking_conditions, comparison_pairs)) # Apply limit_comparisons to blocked pairs if limit_comparisons is not None and len(blocked_pairs) > limit_comparisons: @@ -328,6 +336,10 @@ def meets_blocking_conditions(pair): ) blocked_pairs = random.sample(blocked_pairs, limit_comparisons) + # Initialize clusters with all indices + clusters = [{i} for i in range(len(input_data))] + cluster_map = {i: i for i in range(len(input_data))} + # If there are remaining comparisons, fill with highest cosine similarities remaining_comparisons = ( limit_comparisons - len(blocked_pairs) @@ -341,8 +353,10 @@ def meets_blocking_conditions(pair): similarity_matrix = cosine_similarity(embeddings) cosine_pairs = [] - for i, j in all_pairs: - if (i, j) not in blocked_pairs and find_cluster(i) != find_cluster(j): + for i, j in comparison_pairs: + if (i, j) not in blocked_pairs and find_cluster( + i, cluster_map + ) != find_cluster(j, cluster_map): similarity = similarity_matrix[i, j] if similarity >= blocking_threshold: cosine_pairs.append((i, j, similarity)) @@ -356,11 +370,43 @@ def meets_blocking_conditions(pair): else: blocked_pairs.extend((i, j) for i, j, _ in cosine_pairs) - filtered_pairs = blocked_pairs + # Modified merge_clusters to handle all indices with the same value + + def merge_clusters(item1, item2): + root1, root2 = find_cluster(item1, cluster_map), find_cluster( + item2, cluster_map + ) + if root1 != root2: + if len(clusters[root1]) < len(clusters[root2]): + root1, root2 = root2, root1 + clusters[root1] |= clusters[root2] + cluster_map[root2] = root1 + clusters[root2] = set() + + # Also merge all other indices that share the same values + key1 = tuple(str(input_data[item1].get(k, "")) for k in blocking_keys) + key2 = tuple(str(input_data[item2].get(k, "")) for k in blocking_keys) + + # Merge all indices with the same values + for idx in value_to_indices.get(key1, []): + if idx != item1: + root_idx = find_cluster(idx, cluster_map) + if root_idx != root1: + clusters[root1] |= clusters[root_idx] + cluster_map[root_idx] = root1 + clusters[root_idx] = set() + + for idx in value_to_indices.get(key2, []): + if idx != item2: + root_idx = find_cluster(idx, cluster_map) + if root_idx != root1: + clusters[root1] |= clusters[root_idx] + cluster_map[root_idx] = root1 + clusters[root_idx] = set() # Calculate and print statistics total_possible_comparisons = len(input_data) * (len(input_data) - 1) // 2 - comparisons_made = len(filtered_pairs) + comparisons_made = len(blocked_pairs) comparisons_saved = total_possible_comparisons - comparisons_made self.console.log( f"[green]Comparisons saved by blocking: {comparisons_saved} " @@ -372,12 +418,12 @@ def meets_blocking_conditions(pair): pair_costs = 0 pbar = RichLoopBar( - range(0, len(filtered_pairs), batch_size), + range(0, len(blocked_pairs), batch_size), desc=f"Processing batches of {batch_size} LLM comparisons", console=self.console, ) for i in pbar: - batch = filtered_pairs[i : i + batch_size] + batch = blocked_pairs[i : i + batch_size] with ThreadPoolExecutor(max_workers=self.max_threads) as executor: future_to_pair = { From 3e96dfa6ebd03483c95dc4a23668e752b3713bd1 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 23 Oct 2024 19:55:35 -0700 Subject: [PATCH 52/58] Add educational threads to README --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 93bd5e6c..aff2dd74 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,12 @@ DocETL is the ideal choice when you're looking to maximize correctness and outpu - [Text-to-speech](https://github.com/redhog/docetl-speaker) - [YouTube Transcript Topic Extraction](https://github.com/rajib76/docetl_examples) +## (Educational) Threads + +- [UI/UX Thoughts](https://x.com/sh_reya/status/1846235904664273201) +- [Using Gleaning to Improve Output Quality](https://x.com/sh_reya/status/1843354256335876262) +- [Deep Dive on Resolve Operator](https://x.com/sh_reya/status/1840796824636121288) + ## Installation See the documentation for installing from PyPI. From 9da7e298091053b4b0c83472450faf91774e44aa Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 23 Oct 2024 20:03:04 -0700 Subject: [PATCH 53/58] docs: update readme --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index aff2dd74..31870594 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ # DocETL: Powering Complex Document Processing Pipelines -[Website (Includes Demo)](https://docetl.org) | [Documentation](https://ucbepic.github.io/docetl) | [Discord](https://discord.gg/fHp7B2X3xx) | [Paper](https://arxiv.org/abs/2410.12189) +[![Website](https://img.shields.io/badge/Website-docetl.org-blue)](https://docetl.org) +[![Documentation](https://img.shields.io/badge/Documentation-docs-green)](https://ucbepic.github.io/docetl) +[Discord](https://discord.gg/fHp7B2X3xx) +[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2410.12189) ![DocETL Figure](docs/assets/readmefig.png) From 0a5d0ab1ccf85929b5870e5095971256d501ea98 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Wed, 23 Oct 2024 20:07:39 -0700 Subject: [PATCH 54/58] update discord link --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 31870594..f61411e5 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Website](https://img.shields.io/badge/Website-docetl.org-blue)](https://docetl.org) [![Documentation](https://img.shields.io/badge/Documentation-docs-green)](https://ucbepic.github.io/docetl) -[Discord](https://discord.gg/fHp7B2X3xx) +[![Discord](https://img.shields.io/discord/1285485891095236608?label=Discord&logo=discord)](https://discord.gg/fHp7B2X3xx) [![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2410.12189) ![DocETL Figure](docs/assets/readmefig.png) From d68a4418f855ad59d3884f85ea91ac80dd36476a Mon Sep 17 00:00:00 2001 From: Michiel van der Ree Date: Thu, 24 Oct 2024 10:51:41 +0200 Subject: [PATCH 55/58] Fix Resolve and Map progress bars --- docetl/operations/map.py | 1 - docetl/operations/resolve.py | 2 -- 2 files changed, 3 deletions(-) diff --git a/docetl/operations/map.py b/docetl/operations/map.py index 7073ff3e..60e8b9ec 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -236,7 +236,6 @@ def validation_fn(response: Dict[str, Any]): } results.append(result) total_cost += item_cost - pbar.update(i) if self.status: self.status.start() diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index fff71343..f0a68a44 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -449,8 +449,6 @@ def merge_clusters(item1, item2): if is_match_result: merge_clusters(pair[0], pair[1]) - pbar.update(i) - total_cost += pair_costs # Collect final clusters From 639781f50c170f96d38c76b304621120309493e4 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Fri, 25 Oct 2024 11:09:35 -0700 Subject: [PATCH 56/58] chore: update lockfile --- poetry.lock | 1189 +++++++++++++++++++++++++++------------------------ 1 file changed, 629 insertions(+), 560 deletions(-) diff --git a/poetry.lock b/poetry.lock index 7522e482..9694fbd8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -137,6 +137,48 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "albucore" +version = "0.0.13" +description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. 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| 8 +- docetl/operations/unnest.py | 10 +- docetl/optimizers/map_optimizer/optimizer.py | 4 +- .../map_optimizer/plan_generators.py | 3 +- .../map_optimizer/prompt_generators.py | 19 +- docetl/optimizers/map_optimizer/utils.py | 6 + docetl/parsing_tools.py | 22 +- docetl/runner.py | 185 ++- docetl/schemas.py | 328 +---- docetl/utils.py | 8 + docs/api-reference/python.md | 27 +- docs/operators/cluster.md | 2 - poetry.lock | 1208 +++++++++-------- pyproject.toml | 4 +- tests/basic/test_basic_map.py | 8 +- tests/basic/test_pipeline_with_parsing.py | 2 +- tests/test_azure_rl.py | 17 +- tests/test_config.py | 2 +- tests/test_parsing_tools.py | 30 +- tests/test_resolve_auto_batch.py | 124 ++ tests/test_runner_caching.py | 74 + tests/test_split.py | 1 - tests/test_synth_resolve.py | 15 +- website/src/app/page.tsx | 6 +- 38 files changed, 1537 insertions(+), 1085 deletions(-) create mode 100644 tests/test_resolve_auto_batch.py diff --git a/README.md b/README.md index 9ac1319d..f61411e5 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,9 @@ # DocETL: Powering Complex Document Processing Pipelines -[Website (Includes Demo)](https://docetl.com) | [Documentation](https://ucbepic.github.io/docetl) | [Discord](https://discord.gg/fHp7B2X3xx) | [NotebookLM Podcast](https://notebooklm.google.com/notebook/ef73248b-5a43-49cd-9976-432d20f9fa4f/audio?pli=1) (thanks Shabie from our Discord community!) | Paper (coming soon!) +[![Website](https://img.shields.io/badge/Website-docetl.org-blue)](https://docetl.org) +[![Documentation](https://img.shields.io/badge/Documentation-docs-green)](https://ucbepic.github.io/docetl) +[![Discord](https://img.shields.io/discord/1285485891095236608?label=Discord&logo=discord)](https://discord.gg/fHp7B2X3xx) +[![Paper](https://img.shields.io/badge/Paper-arXiv-red)](https://arxiv.org/abs/2410.12189) ![DocETL Figure](docs/assets/readmefig.png) @@ -16,6 +19,18 @@ DocETL is the ideal choice when you're looking to maximize correctness and outpu - You're working with long documents that don't fit into a single prompt or are too lengthy for effective LLM reasoning - You have validation criteria and want tasks to automatically retry when the validation fails +## Cool Things People Are Doing with DocETL + +- [Conversation Generator](https://github.com/redhog/docetl-conversation) +- [Text-to-speech](https://github.com/redhog/docetl-speaker) +- [YouTube Transcript Topic Extraction](https://github.com/rajib76/docetl_examples) + +## (Educational) Threads + +- [UI/UX Thoughts](https://x.com/sh_reya/status/1846235904664273201) +- [Using Gleaning to Improve Output Quality](https://x.com/sh_reya/status/1843354256335876262) +- [Deep Dive on Resolve Operator](https://x.com/sh_reya/status/1840796824636121288) + ## Installation See the documentation for installing from PyPI. diff --git a/docetl/api.py b/docetl/api.py index 893a4faf..192222f7 100644 --- a/docetl/api.py +++ b/docetl/api.py @@ -64,17 +64,17 @@ FilterOp, GatherOp, MapOp, + ReduceOp, + ResolveOp, + SplitOp, + UnnestOp, + ClusterOp, + SampleOp, OpType, ParallelMapOp, ParsingTool, -) -from docetl.schemas import ( PipelineOutput, PipelineStep, - ReduceOp, - ResolveOp, - SplitOp, - UnnestOp, ) @@ -197,17 +197,13 @@ def optimize( Pipeline: An optimized version of the pipeline. """ config = self._to_dict() - optimizer = Optimizer( + runner = DSLRunner( config, base_name=os.path.join(os.getcwd(), self.name), yaml_file_suffix=self.name, max_threads=max_threads, - model=model, - timeout=timeout, - resume=resume, ) - optimizer.optimize() - optimized_config = optimizer.clean_optimized_config() + optimized_config = runner.optimize(return_pipeline=False) updated_pipeline = Pipeline( name=self.name, @@ -232,8 +228,13 @@ def run(self, max_threads: Optional[int] = None) -> float: float: The total cost of running the pipeline. """ config = self._to_dict() - runner = DSLRunner(config, max_threads=max_threads) - result = runner.run() + runner = DSLRunner( + config, + base_name=os.path.join(os.getcwd(), self.name), + yaml_file_suffix=self.name, + max_threads=max_threads, + ) + result = runner.load_run_save() return result def to_yaml(self, path: str) -> None: @@ -322,6 +323,10 @@ def _update_from_dict(self, config: Dict[str, Any]): self.operations.append(GatherOp(**op, type=op_type)) elif op_type == "unnest": self.operations.append(UnnestOp(**op, type=op_type)) + elif op_type == "cluster": + self.operations.append(ClusterOp(**op, type=op_type)) + elif op_type == "sample": + self.operations.append(SampleOp(**op, type=op_type)) self.steps = [PipelineStep(**step) for step in config["pipeline"]["steps"]] self.output = PipelineOutput(**config["pipeline"]["output"]) self.default_model = config.get("default_model") diff --git a/docetl/builder.py b/docetl/builder.py index d2ace213..840258e8 100644 --- a/docetl/builder.py +++ b/docetl/builder.py @@ -77,26 +77,11 @@ def items(self): return [(key, self[key]) for key in self.keys()] -class Optimizer(ConfigWrapper): - @classmethod - def from_yaml(cls, yaml_file: str, **kwargs): - # check that file ends with .yaml or .yml - if not yaml_file.endswith(".yaml") and not yaml_file.endswith(".yml"): - raise ValueError( - "Invalid file type. Please provide a YAML file ending with '.yaml' or '.yml'." - ) - - base_name = yaml_file.rsplit(".", 1)[0] - suffix = yaml_file.split("/")[-1].split(".")[0] - return super(Optimizer, cls).from_yaml( - yaml_file, base_name=base_name, yaml_file_suffix=suffix, **kwargs - ) +class Optimizer: def __init__( self, - config: Dict, - base_name: str, - yaml_file_suffix: str, + runner: "DSLRunner", max_threads: Optional[int] = None, model: str = "gpt-4o", resume: bool = False, @@ -136,7 +121,16 @@ def __init__( The method also calls print_optimizer_config() to display the initial configuration. """ - ConfigWrapper.__init__(self, config, max_threads) + self.config = runner.config + self.console = runner.console + self.max_threads = runner.max_threads + + self.base_name = runner.base_name + self.yaml_file_suffix = runner.yaml_file_suffix + self.config = runner.config + self.runner = runner + self.status = runner.status + self.optimized_config = copy.deepcopy(self.config) self.llm_client = LLMClient(model) self.operations_cost = 0 @@ -145,17 +139,11 @@ def __init__( self.samples_taken = defaultdict(dict) self.resume = resume - # create parsing tool map - self.parsing_tool_map = create_parsing_tool_map( - self.config.get("parsing_tools", None) - ) - home_dir = os.path.expanduser("~") - cache_dir = os.path.join(home_dir, f".docetl/cache/{yaml_file_suffix}") + cache_dir = os.path.join(home_dir, f".docetl/cache/{runner.yaml_file_suffix}") os.makedirs(cache_dir, exist_ok=True) self.datasets = DatasetOnDisk(dir=cache_dir, console=self.console) self.optimized_ops_path = f"{cache_dir}/optimized_ops" - self.optimized_config_path = f"{base_name}_opt.yaml" # Update sample size map self.sample_size_map = SAMPLE_SIZE_MAP @@ -191,7 +179,7 @@ def syntax_check(self): try: operation_class = get_operation(operation_type) operation_class( - self, + self.runner, operation_config, self.config.get("default_model", "gpt-4o-mini"), self.max_threads, @@ -976,7 +964,7 @@ def _get_sample_data( type=dataset_config["type"], path_or_data=dataset_config["path"], parsing=dataset_config.get("parsing", []), - user_defined_parsing_tool_map=self.parsing_tool_map, + user_defined_parsing_tool_map=self.runner.parsing_tool_map, ) data = dataset.load() @@ -1115,7 +1103,7 @@ def _optimize_reduce( List[Dict[str, Any]]: The optimized operation configuration. """ reduce_optimizer = ReduceOptimizer( - self, + self.runner, self.config, self.console, self.llm_client, @@ -1158,7 +1146,7 @@ def _optimize_equijoin( new_right_name = right_name for _ in range(max_iterations): join_optimizer = JoinOptimizer( - self, + self.runner, self.config, op_config, self.console, @@ -1310,7 +1298,7 @@ def _optimize_resolve( List[Dict[str, Any]]: The optimized operation configuration. """ optimized_config, cost = JoinOptimizer( - self, + self.runner, self.config, op_config, self.console, @@ -1358,7 +1346,7 @@ def _run_operation( operation_class = get_operation(op_config["type"]) oc_kwargs = { - "runner": self, + "runner": self.runner, "config": op_config, "default_model": self.config["default_model"], "max_threads": self.max_threads, @@ -1418,7 +1406,7 @@ def clean_optimized_config(self): return resolved_config - def save_optimized_config(self): + def save_optimized_config(self, optimized_config_path: str): """ Save the optimized configuration to a YAML file. @@ -1427,10 +1415,10 @@ def save_optimized_config(self): """ resolved_config = self.clean_optimized_config() - with open(self.optimized_config_path, "w") as f: + with open(optimized_config_path, "w") as f: yaml.safe_dump(resolved_config, f, default_flow_style=False, width=80) self.console.log( - f"[green italic]💾 Optimized config saved to {self.optimized_config_path}[/green italic]" + f"[green italic]💾 Optimized config saved to {optimized_config_path}[/green italic]" ) diff --git a/docetl/cli.py b/docetl/cli.py index 2594c862..dbbe09fe 100644 --- a/docetl/cli.py +++ b/docetl/cli.py @@ -4,7 +4,6 @@ import os import typer -from docetl.builder import Optimizer from docetl.operations.utils import clear_cache as cc from docetl.runner import DSLRunner @@ -47,15 +46,10 @@ def build( if os.path.exists(env_file): load_dotenv(env_file) - optimizer = Optimizer.from_yaml( - str(yaml_file), - max_threads=max_threads, - model=model, - timeout=timeout, - resume=resume, + runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads) + runner.optimize( + save=True, return_pipeline=False, model=model, resume=resume, timeout=timeout ) - optimizer.optimize() - optimizer.save_optimized_config() @app.command() @@ -83,7 +77,7 @@ def run( load_dotenv(env_file) runner = DSLRunner.from_yaml(str(yaml_file), max_threads=max_threads) - runner.run() + runner.load_run_save() @app.command() diff --git a/docetl/config_wrapper.py b/docetl/config_wrapper.py index 4e4a5f8d..d1f966e8 100644 --- a/docetl/config_wrapper.py +++ b/docetl/config_wrapper.py @@ -1,3 +1,4 @@ +import datetime import os from docetl.utils import load_config from typing import Any, Dict, List, Optional, Tuple, Union @@ -31,15 +32,35 @@ def get(self, item: pyrate_limiter.RateItem) -> pyrate_limiter.AbstractBucket: class ConfigWrapper(object): + @classmethod def from_yaml(cls, yaml_file: str, **kwargs): + # check that file ends with .yaml or .yml + if not yaml_file.endswith(".yaml") and not yaml_file.endswith(".yml"): + raise ValueError( + "Invalid file type. Please provide a YAML file ending with '.yaml' or '.yml'." + ) + + base_name = yaml_file.rsplit(".", 1)[0] + suffix = yaml_file.split("/")[-1].split(".")[0] config = load_config(yaml_file) - return cls(config, **kwargs) + return cls(config, base_name=base_name, yaml_file_suffix=suffix, **kwargs) - def __init__(self, config: Dict, max_threads: int = None): + def __init__( + self, + config: Dict, + base_name: Optional[str] = None, + yaml_file_suffix: Optional[str] = None, + max_threads: int = None, + console: Console = Console(), + ): self.config = config + self.base_name = base_name + self.yaml_file_suffix = yaml_file_suffix or datetime.datetime.now().strftime( + "%Y%m%d_%H%M%S" + ) self.default_model = self.config.get("default_model", "gpt-4o-mini") - self.console = Console() + self.console = console self.max_threads = max_threads or (os.cpu_count() or 1) * 4 self.status = None diff --git a/docetl/dataset.py b/docetl/dataset.py index c90fda4e..b72398c0 100644 --- a/docetl/dataset.py +++ b/docetl/dataset.py @@ -1,9 +1,10 @@ import os from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Union, Literal +from pydantic import BaseModel from docetl.parsing_tools import get_parser, get_parsing_tools -from docetl.schemas import ParsingTool +from docetl.base_schemas import ParsingTool def create_parsing_tool_map( @@ -39,6 +40,40 @@ class Dataset: user_defined_parsing_tool_map (Dict[str, ParsingTool]): A map of user-defined parsing tools. """ + class schema(BaseModel): + """ + Represents a dataset configuration in the pipeline. + + Attributes: + type (str): The type of the dataset. Must be either 'file' or 'memory'. + path (str): The path to the dataset file or the in-memory data, depending on the type. + source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. + parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool + is represented by a dictionary with 'input_key', 'function', and + 'output_key' keys. Defaults to None. + + Example: + ```yaml + datasets: + my_dataset: + type: file + path: input.json + parsing: + - input_key: file_path + function: txt_to_string + output_key: content + ``` + + Note: + The parsing tools are applied in the order they are listed. Each parsing tool takes the output + of the previous tool as its input, allowing for chained processing of the data. + """ + + type: Literal["file", "memory"] + path: str + source: str = "local" + parsing: Optional[List[Dict[str, str]]] = None + def __init__( self, runner, diff --git a/docetl/operations/__init__.py b/docetl/operations/__init__.py index 99d4fab5..66f91032 100644 --- a/docetl/operations/__init__.py +++ b/docetl/operations/__init__.py @@ -2,6 +2,7 @@ def get_operation(operation_type: str): + """Loads a single operation by name""" try: entrypoint = importlib.metadata.entry_points(group="docetl.operation")[ operation_type @@ -9,3 +10,9 @@ def get_operation(operation_type: str): except KeyError as e: raise KeyError(f"Unrecognized operation {operation_type}") return entrypoint.load() + +def get_operations(): + """Load all available operations and return them as a dictionary""" + return { + op.name: op.load() + for op in importlib.metadata.entry_points(group="docetl.operation")} diff --git a/docetl/operations/base.py b/docetl/operations/base.py index a41f5ddc..c9272d75 100644 --- a/docetl/operations/base.py +++ b/docetl/operations/base.py @@ -2,15 +2,33 @@ The BaseOperation class is an abstract base class for all operations in the docetl framework. It provides a common structure and interface for various data processing operations. """ -from abc import ABC, abstractmethod +from abc import ABC, ABCMeta, abstractmethod from typing import Dict, List, Optional, Tuple from docetl.operations.utils import APIWrapper from rich.console import Console from rich.status import Status +import jsonschema +from pydantic import BaseModel -class BaseOperation(ABC): +# FIXME: This should probably live in some utils module? +class classproperty(object): + def __init__(self, f): + self.f = f + + def __get__(self, obj, owner): + return self.f(owner) + + +class BaseOperationMeta(ABCMeta): + def __new__(cls, *arg, **kw): + self = ABCMeta.__new__(cls, *arg, **kw) + self.schema.__name__ = self.__name__ + return self + + +class BaseOperation(ABC, metaclass=BaseOperationMeta): def __init__( self, runner: "ConfigWrapper", @@ -45,6 +63,21 @@ def __init__( ) self.syntax_check() + # This must be overridden in a subclass + class schema(BaseModel, extra="allow"): + name: str + type: str + + @classproperty + def json_schema(cls): + assert hasattr( + cls.schema, "model_json_schema" + ), "Programming error: %s.schema must be a pydantic object but is a %s" % ( + cls, + type(cls.schema), + ) + return cls.schema.model_json_schema() + @abstractmethod def execute(self, input_data: List[Dict]) -> Tuple[List[Dict], float]: """ @@ -73,7 +106,7 @@ def syntax_check(self) -> None: Raises: ValueError: If the configuration is invalid. """ - pass + jsonschema.validate(instance=self.config, schema=self.json_schema) def gleaning_check(self) -> None: """ diff --git a/docetl/operations/equijoin.py b/docetl/operations/equijoin.py index d6cf6469..3de669bc 100644 --- a/docetl/operations/equijoin.py +++ b/docetl/operations/equijoin.py @@ -7,7 +7,7 @@ from collections import defaultdict from concurrent.futures import ThreadPoolExecutor from multiprocessing import Pool, cpu_count -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional import numpy as np from jinja2 import Template @@ -49,6 +49,24 @@ def process_left_item( class EquijoinOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "equijoin" + left: str + right: str + comparison_prompt: str + output: Optional[Dict[str, Any]] = None + blocking_threshold: Optional[float] = None + blocking_conditions: Optional[Dict[str, List[str]]] = None + limits: Optional[Dict[str, int]] = None + comparison_model: Optional[str] = None + optimize: Optional[bool] = None + embedding_model: Optional[str] = None + embedding_batch_size: Optional[int] = None + compare_batch_size: Optional[int] = None + limit_comparisons: Optional[int] = None + blocking_keys: Optional[Dict[str, List[str]]] = None + timeout: Optional[int] = None + def compare_pair( self, comparison_prompt: str, diff --git a/docetl/operations/filter.py b/docetl/operations/filter.py index 48037eef..08ffba15 100644 --- a/docetl/operations/filter.py +++ b/docetl/operations/filter.py @@ -2,9 +2,11 @@ from concurrent.futures import ThreadPoolExecutor from typing import Any, Dict, List, Optional, Tuple +from pydantic import Field from jinja2 import Template +from docetl.operations.base import BaseOperation from docetl.operations.map import MapOperation from docetl.operations.utils import ( RichLoopBar, @@ -12,6 +14,19 @@ class FilterOperation(MapOperation): + # FIXME: What happens if we actually inherit from MapOperation.schema? + class schema(BaseOperation.schema): + type: str = "filter" + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + model: Optional[str] = None + optimize: Optional[bool] = None + recursively_optimize: Optional[bool] = None + sample_size: Optional[int] = None + validation_rules: Optional[List[str]] = Field(None, alias="validate") + num_retries_on_validate_failure: Optional[int] = None + timeout: Optional[int] = None + def syntax_check(self) -> None: """ Checks the configuration of the FilterOperation for required keys and valid structure. diff --git a/docetl/operations/gather.py b/docetl/operations/gather.py index a2e81879..6d97020c 100644 --- a/docetl/operations/gather.py +++ b/docetl/operations/gather.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional from docetl.operations.base import BaseOperation @@ -15,6 +15,14 @@ class GatherOperation(BaseOperation): 5. Return results containing the rendered chunks with added context, including information about skipped characters and headers. """ + class schema(BaseOperation.schema): + type: str = "gather" + content_key: str + doc_id_key: str + order_key: str + peripheral_chunks: Dict[str, Any] + doc_header_key: Optional[str] = None + def __init__(self, *args: Any, **kwargs: Any) -> None: """ Initialize the GatherOperation. diff --git a/docetl/operations/map.py b/docetl/operations/map.py index 300e8419..60e8b9ec 100644 --- a/docetl/operations/map.py +++ b/docetl/operations/map.py @@ -10,8 +10,9 @@ from docetl.operations.base import BaseOperation from docetl.operations.utils import RichLoopBar -from docetl.schemas import MapOp, Tool, ToolFunction +from docetl.base_schemas import Tool, ToolFunction from docetl.utils import completion_cost +from pydantic import Field, field_validator def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: @@ -28,6 +29,31 @@ def render_jinja_template(template_string: str, data: Dict[str, Any]) -> str: class MapOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "map" + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + model: Optional[str] = None + optimize: Optional[bool] = None + recursively_optimize: Optional[bool] = None + sample_size: Optional[int] = None + tools: Optional[List[Dict[str, Any]]] = ( + None # FIXME: Why isn't this using the Tool data class so validation works automatically? + ) + validation_rules: Optional[List[str]] = Field(None, alias="validate") + num_retries_on_validate_failure: Optional[int] = None + gleaning: Optional[Dict[str, Any]] = None + drop_keys: Optional[List[str]] = None + timeout: Optional[int] = None + batch_size: Optional[int] = None + clustering_method: Optional[str] = None + + @field_validator("drop_keys") + def validate_drop_keys(cls, v): + if isinstance(v, str): + return [v] + return v + def __init__( self, *args, @@ -47,7 +73,7 @@ def syntax_check(self) -> None: ValueError: If required keys are missing or invalid in the configuration. TypeError: If configuration values have incorrect types. """ - config = MapOp(**self.config) + config = self.schema(**self.config) if config.drop_keys: if any(not isinstance(key, str) for key in config.drop_keys): @@ -210,7 +236,6 @@ def validation_fn(response: Dict[str, Any]): } results.append(result) total_cost += item_cost - pbar.update(i) if self.status: self.status.start() @@ -219,6 +244,11 @@ def validation_fn(response: Dict[str, Any]): class ParallelMapOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "parallel_map" + prompts: List[Dict[str, Any]] + output: Dict[str, Any] + def __init__( self, *args, diff --git a/docetl/operations/reduce.py b/docetl/operations/reduce.py index cd3cee78..de535b6c 100644 --- a/docetl/operations/reduce.py +++ b/docetl/operations/reduce.py @@ -12,7 +12,7 @@ from collections import deque from concurrent.futures import ThreadPoolExecutor, as_completed from threading import Lock -from typing import Any, Dict, List, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple, Union import jinja2 import numpy as np @@ -35,6 +35,23 @@ class ReduceOperation(BaseOperation): using various strategies including batch reduce, incremental reduce, and parallel fold and merge. """ + class schema(BaseOperation.schema): + type: str = "reduce" + reduce_key: Union[str, List[str]] + output: Optional[Dict[str, Any]] = None + prompt: Optional[str] = None + optimize: Optional[bool] = None + synthesize_resolve: Optional[bool] = None + model: Optional[str] = None + input: Optional[Dict[str, Any]] = None + pass_through: Optional[bool] = None + associative: Optional[bool] = None + fold_prompt: Optional[str] = None + fold_batch_size: Optional[int] = None + value_sampling: Optional[Dict[str, Any]] = None + verbose: Optional[bool] = None + timeout: Optional[int] = None + def __init__(self, *args, **kwargs): """ Initialize the ReduceOperation. diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index 0184f8b2..4cfabc30 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -3,19 +3,46 @@ """ import random +import time from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import Any, Dict, List, Tuple +from typing import Any, Dict, List, Tuple, Optional import jinja2 from jinja2 import Template from rich.prompt import Confirm +import math from docetl.operations.base import BaseOperation from docetl.operations.utils import RichLoopBar, rich_as_completed from docetl.utils import completion_cost, extract_jinja_variables +def find_cluster(item, cluster_map): + while item != cluster_map[item]: + cluster_map[item] = cluster_map[cluster_map[item]] + item = cluster_map[item] + return item + + class ResolveOperation(BaseOperation): + class schema(BaseOperation.schema): + type: str = "resolve" + comparison_prompt: str + resolution_prompt: str + output: Optional[Dict[str, Any]] = None + embedding_model: Optional[str] = None + resolution_model: Optional[str] = None + comparison_model: Optional[str] = None + blocking_keys: Optional[List[str]] = None + blocking_threshold: Optional[float] = None + blocking_conditions: Optional[List[str]] = None + input: Optional[Dict[str, Any]] = None + embedding_batch_size: Optional[int] = None + compare_batch_size: Optional[int] = None + limit_comparisons: Optional[int] = None + optimize: Optional[bool] = None + timeout: Optional[int] = None + def compare_pair( self, comparison_prompt: str, @@ -267,32 +294,33 @@ def get_embeddings_batch( total_cost += sum(costs) - # Initialize clusters - clusters = [{i} for i in range(len(input_data))] - cluster_map = {i: i for i in range(len(input_data))} - - def find_cluster(item): - while item != cluster_map[item]: - cluster_map[item] = cluster_map[cluster_map[item]] - item = cluster_map[item] - return item - - def merge_clusters(item1, item2): - root1, root2 = find_cluster(item1), find_cluster(item2) - if root1 != root2: - if len(clusters[root1]) < len(clusters[root2]): - root1, root2 = root2, root1 - clusters[root1] |= clusters[root2] - cluster_map[root2] = root1 - clusters[root2] = set() - - # Generate all pairs to compare - # TODO: virtualize this if possible - all_pairs = [ - (i, j) - for i in range(len(input_data)) - for j in range(i + 1, len(input_data)) - ] + # Generate all pairs to compare, ensuring no duplicate comparisons + def get_unique_comparison_pairs(): + # Create a mapping of values to their indices + value_to_indices = {} + for i, item in enumerate(input_data): + # Create a hashable key from the blocking keys + key = tuple(str(item.get(k, "")) for k in blocking_keys) + if key not in value_to_indices: + value_to_indices[key] = [] + value_to_indices[key].append(i) + + # Generate pairs for comparison, comparing each unique value combination only once + comparison_pairs = [] + keys = list(value_to_indices.keys()) + + # First, handle comparisons between different values + for i in range(len(keys)): + for j in range(i + 1, len(keys)): + # Only need one comparison between different values + idx1 = value_to_indices[keys[i]][0] + idx2 = value_to_indices[keys[j]][0] + if idx1 < idx2: # Maintain ordering to avoid duplicates + comparison_pairs.append((idx1, idx2)) + + return comparison_pairs, value_to_indices + + comparison_pairs, value_to_indices = get_unique_comparison_pairs() # Filter pairs based on blocking conditions def meets_blocking_conditions(pair): @@ -301,7 +329,7 @@ def meets_blocking_conditions(pair): is_match(input_data[i], input_data[j]) if blocking_conditions else False ) - blocked_pairs = list(filter(meets_blocking_conditions, all_pairs)) + blocked_pairs = list(filter(meets_blocking_conditions, comparison_pairs)) # Apply limit_comparisons to blocked pairs if limit_comparisons is not None and len(blocked_pairs) > limit_comparisons: @@ -310,6 +338,10 @@ def meets_blocking_conditions(pair): ) blocked_pairs = random.sample(blocked_pairs, limit_comparisons) + # Initialize clusters with all indices + clusters = [{i} for i in range(len(input_data))] + cluster_map = {i: i for i in range(len(input_data))} + # If there are remaining comparisons, fill with highest cosine similarities remaining_comparisons = ( limit_comparisons - len(blocked_pairs) @@ -323,8 +355,10 @@ def meets_blocking_conditions(pair): similarity_matrix = cosine_similarity(embeddings) cosine_pairs = [] - for i, j in all_pairs: - if (i, j) not in blocked_pairs and find_cluster(i) != find_cluster(j): + for i, j in comparison_pairs: + if (i, j) not in blocked_pairs and find_cluster( + i, cluster_map + ) != find_cluster(j, cluster_map): similarity = similarity_matrix[i, j] if similarity >= blocking_threshold: cosine_pairs.append((i, j, similarity)) @@ -338,29 +372,98 @@ def meets_blocking_conditions(pair): else: blocked_pairs.extend((i, j) for i, j, _ in cosine_pairs) - filtered_pairs = blocked_pairs + # Modified merge_clusters to handle all indices with the same value + + def merge_clusters(item1, item2): + root1, root2 = find_cluster(item1, cluster_map), find_cluster( + item2, cluster_map + ) + if root1 != root2: + if len(clusters[root1]) < len(clusters[root2]): + root1, root2 = root2, root1 + clusters[root1] |= clusters[root2] + cluster_map[root2] = root1 + clusters[root2] = set() + + # Also merge all other indices that share the same values + key1 = tuple(str(input_data[item1].get(k, "")) for k in blocking_keys) + key2 = tuple(str(input_data[item2].get(k, "")) for k in blocking_keys) + + # Merge all indices with the same values + for idx in value_to_indices.get(key1, []): + if idx != item1: + root_idx = find_cluster(idx, cluster_map) + if root_idx != root1: + clusters[root1] |= clusters[root_idx] + cluster_map[root_idx] = root1 + clusters[root_idx] = set() + + for idx in value_to_indices.get(key2, []): + if idx != item2: + root_idx = find_cluster(idx, cluster_map) + if root_idx != root1: + clusters[root1] |= clusters[root_idx] + cluster_map[root_idx] = root1 + clusters[root_idx] = set() # Calculate and print statistics total_possible_comparisons = len(input_data) * (len(input_data) - 1) // 2 - comparisons_made = len(filtered_pairs) + comparisons_made = len(blocked_pairs) comparisons_saved = total_possible_comparisons - comparisons_made self.console.log( f"[green]Comparisons saved by blocking: {comparisons_saved} " f"({(comparisons_saved / total_possible_comparisons) * 100:.2f}%)[/green]" ) + # Compute an auto-batch size based on the number of comparisons + def auto_batch(): + M = 500 # should be made dynamic in the future based on the model, but this is the current rate limit for 4o-mini. + # (n-k)(k-1) is approximately len(filtered_pairs) + N = len(input_data) + # -k^2 + (n+1)k- n- fp + k^2/2 - k/2= 0, solve for k + quadratic_discriminant = (N + 0.5) ** 2 - 4 * (-1 / 2) * (-N - len(blocked_pairs)) + K_cands = [(-1 * (N+0.5) + (quadratic_discriminant) ** 0.5 )/ (-1), (-1 * (N + 0.5) - (quadratic_discriminant) ** 0.5)/ (-1)] + K = max(K_cands) + if K < 0: + return M + else: + return min(math.ceil(2*K), M) + # Compare pairs and update clusters in real-time - batch_size = self.config.get("compare_batch_size", 100) + batch_size = self.config.get("compare_batch_size", auto_batch()) pair_costs = 0 pbar = RichLoopBar( - range(0, len(filtered_pairs), batch_size), + range(0, len(blocked_pairs), batch_size), desc=f"Processing batches of {batch_size} LLM comparisons", console=self.console, ) + last_processed = 0 for i in pbar: - batch = filtered_pairs[i : i + batch_size] + batch_end = last_processed + batch_size + batch = blocked_pairs[last_processed:batch_end] + # Filter pairs for the initial batch + better_batch = [ + pair for pair in batch + if find_cluster(pair[0], cluster_map) == pair[0] and find_cluster(pair[1], cluster_map) == pair[1] + ] + + # Expand better_batch if it doesn’t reach batch_size + while len(better_batch) < batch_size and batch_end < len(blocked_pairs): + # Move batch_end forward by batch_size to get more pairs + next_end = batch_end + batch_size + next_batch = blocked_pairs[batch_end:next_end] + + better_batch.extend( + pair for pair in next_batch + if find_cluster(pair[0], cluster_map) == pair[0] and find_cluster(pair[1], cluster_map) == pair[1] + ) + + # Update batch_end to prevent overlapping in the next loop + batch_end = next_end + better_batch = better_batch[:batch_size] + last_processed = batch_end with ThreadPoolExecutor(max_workers=self.max_threads) as executor: future_to_pair = { executor.submit( @@ -375,7 +478,7 @@ def meets_blocking_conditions(pair): "max_retries_per_timeout", 2 ), ): pair - for pair in batch + for pair in better_batch } for future in as_completed(future_to_pair): @@ -385,7 +488,7 @@ def meets_blocking_conditions(pair): if is_match_result: merge_clusters(pair[0], pair[1]) - pbar.update(i) + pbar.update(last_processed//batch_size) total_cost += pair_costs diff --git a/docetl/operations/split.py b/docetl/operations/split.py index 8b9f26a3..c32407ea 100644 --- a/docetl/operations/split.py +++ b/docetl/operations/split.py @@ -1,5 +1,5 @@ import uuid -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Any, Optional import tiktoken @@ -18,6 +18,12 @@ class SplitOperation(BaseOperation): - {name}_id: A unique identifier for each original document. - {name}_chunk_num: The sequential number of the chunk within its original document. """ + class schema(BaseOperation.schema): + type: str = "split" + split_key: str + method: str + method_kwargs: Dict[str, Any] + model: Optional[str] = None def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) diff --git a/docetl/operations/unnest.py b/docetl/operations/unnest.py index 8931407e..a8bcbb38 100644 --- a/docetl/operations/unnest.py +++ b/docetl/operations/unnest.py @@ -1,5 +1,5 @@ import copy -from typing import Dict, List, Tuple +from typing import Dict, List, Tuple, Optional from docetl.operations.base import BaseOperation @@ -56,6 +56,14 @@ class UnnestOperation(BaseOperation): ``` """ + class schema(BaseOperation.schema): + type: str = "unnest" + unnest_key: str + keep_empty: Optional[bool] = None + expand_fields: Optional[List[str]] = None + recursive: Optional[bool] = None + depth: Optional[int] = None + def syntax_check(self) -> None: """ Checks if the required configuration key is present in the operation's config. diff --git a/docetl/optimizers/map_optimizer/optimizer.py b/docetl/optimizers/map_optimizer/optimizer.py index deb306de..c7d18875 100644 --- a/docetl/optimizers/map_optimizer/optimizer.py +++ b/docetl/optimizers/map_optimizer/optimizer.py @@ -72,7 +72,7 @@ def __init__( self.k_to_pairwise_compare = 6 self.plan_generator = PlanGenerator( - llm_client, console, config, run_operation, max_threads, is_filter + runner, llm_client, console, config, run_operation, max_threads, is_filter ) self.evaluator = Evaluator( llm_client, @@ -83,7 +83,7 @@ def __init__( is_filter, ) self.prompt_generator = PromptGenerator( - llm_client, console, config, max_threads, is_filter + runner, llm_client, console, config, max_threads, is_filter ) def optimize( diff --git a/docetl/optimizers/map_optimizer/plan_generators.py b/docetl/optimizers/map_optimizer/plan_generators.py index 845ecd35..9f8a5f40 100644 --- a/docetl/optimizers/map_optimizer/plan_generators.py +++ b/docetl/optimizers/map_optimizer/plan_generators.py @@ -17,6 +17,7 @@ class PlanGenerator: def __init__( self, + runner, llm_client: LLMClient, console: Console, config: Dict[str, Any], @@ -34,7 +35,7 @@ def __init__( ) self._run_operation = run_operation self.prompt_generator = PromptGenerator( - llm_client, console, config, max_threads, is_filter + runner, llm_client, console, config, max_threads, is_filter ) self.max_threads = max_threads self.config = config diff --git a/docetl/optimizers/map_optimizer/prompt_generators.py b/docetl/optimizers/map_optimizer/prompt_generators.py index 22988a31..9f58dca5 100644 --- a/docetl/optimizers/map_optimizer/prompt_generators.py +++ b/docetl/optimizers/map_optimizer/prompt_generators.py @@ -4,6 +4,7 @@ from litellm import model_cost from rich.console import Console +from rich.prompt import Prompt from docetl.optimizers.map_optimizer.utils import generate_and_validate_prompt from docetl.optimizers.utils import LLMClient @@ -13,6 +14,7 @@ class PromptGenerator: def __init__( self, + runner: "DSLRunner", llm_client: LLMClient, console: Console, config: Dict[str, Any], @@ -24,6 +26,7 @@ def __init__( self.config = config self.max_threads = max_threads self.is_filter = is_filter + self.runner = runner def _generate_validator_prompt( self, @@ -330,7 +333,7 @@ def _get_combine_prompt( Modify the original prompt to be a prompt that will combine these chunk results to accomplish the original task. Guidelines for your prompt template: - - The only variable you are allowed to use is the inputs variable, which contains all chunk results. Each value is a dictionary with the keys {', '.join(schema_keys)} + - The only variable you are allowed to use is the `inputs` variable, which contains all chunk results. Each value is a dictionary with the keys {', '.join(schema_keys)} - Avoid using filters or complex logic, even though Jinja technically supports it - The prompt template must be a valid Jinja2 template - You must use the {{ inputs }} variable somehow (you can access specific schema keys if you'ld like) @@ -353,9 +356,23 @@ def _get_combine_prompt( config=self.config, max_threads=self.max_threads, console=self.console, + inclusion_strings=["inputs"], ) combine_prompt = result["combine_prompt"] + # Confirm with the user that this prompt is good & ask them to edit + if self.runner.status: + self.runner.status.stop() + + combine_prompt = Prompt.ask( + f"Here is the prompt generated for the reduce operation:\n```\n{combine_prompt}\n```\n\nPress enter to confirm, or type in the prompt you would like to use instead.", + default=combine_prompt, + console=self.console, + ) + + if self.runner.status: + self.runner.status.start() + # Determine if the combine operation is associative system_prompt_associative = ( "You are an AI assistant analyzing data processing tasks." diff --git a/docetl/optimizers/map_optimizer/utils.py b/docetl/optimizers/map_optimizer/utils.py index b1e6d055..105a6628 100644 --- a/docetl/optimizers/map_optimizer/utils.py +++ b/docetl/optimizers/map_optimizer/utils.py @@ -27,6 +27,7 @@ def generate_and_validate_prompt( config: Dict[str, Any], max_threads: int, console: Console, + inclusion_strings: List = [], ) -> Dict[str, Any]: max_retries = 3 attempt = 0 @@ -42,6 +43,9 @@ def generate_and_validate_prompt( parameters, ) result = json.loads(response.choices[0].message.content) + for inclusion_string in inclusion_strings: + assert inclusion_string in str(result) + chat_history += [ {"role": "assistant", "content": result}, ] @@ -78,6 +82,8 @@ def generate_and_validate_prompt( except jinja2.exceptions.TemplateError as e: error_message = f"Invalid Jinja2 template: {str(e)}" + except AssertionError: + error_message = f"Generated prompt does not include all required strings: {inclusion_strings}" except Exception as e: # We only care about jinja errors console.log(f"Error: {e}") diff --git a/docetl/parsing_tools.py b/docetl/parsing_tools.py index 86014abd..a85290ab 100644 --- a/docetl/parsing_tools.py +++ b/docetl/parsing_tools.py @@ -2,12 +2,16 @@ import io import os from typing import Dict, List, Optional, Any +from functools import wraps + def with_input_output_key(fn): """Decorator that wraps a parser function that takes a single string parameter and return list of strings and makes it a full parser function that takes an item as a dictionary and return a list of dictionaries.""" + + @wraps(fn) def wrapper(item, input_key="text", output_key="text", **kw): if input_key not in item: raise ValueError(f"Input key {input_key} not found in item: {item}") @@ -15,18 +19,22 @@ def wrapper(item, input_key="text", output_key="text", **kw): if not isinstance(result, list): result = [result] return [{output_key: res} for res in result] + return wrapper -def llama_index_simple_directory_reader(item: dict[str, Any], input_key: str ="path") -> List[dict[str, Any]]: + +def llama_index_simple_directory_reader( + item: dict[str, Any], input_key: str = "path" +) -> List[dict[str, Any]]: from llama_index.core import SimpleDirectoryReader documents = SimpleDirectoryReader(item[input_key]).load_data() - return [{"text": doc.text, - "metadata": doc.metadata} - for doc in documents] + return [{"text": doc.text, "metadata": doc.metadata} for doc in documents] -def llama_index_wikipedia_reader(item: dict[str, Any], input_key: str = "pages") -> List[dict[str, Any]]: +def llama_index_wikipedia_reader( + item: dict[str, Any], input_key: str = "pages" +) -> List[dict[str, Any]]: from llama_index.readers.wikipedia import WikipediaReader loader = WikipediaReader() @@ -38,9 +46,7 @@ def llama_index_wikipedia_reader(item: dict[str, Any], input_key: str = "pages") for name, doc in zip(pages, documents): doc.metadata["source"] = "https://en.wikipedia.org/wiki/" + name - return [{"text": doc.text, - "metadata": doc.metadata} - for doc in documents] + return [{"text": doc.text, "metadata": doc.metadata} for doc in documents] @with_input_output_key diff --git a/docetl/runner.py b/docetl/runner.py index 6be5da4d..7666e905 100644 --- a/docetl/runner.py +++ b/docetl/runner.py @@ -2,26 +2,28 @@ import json import os import time -from typing import Dict, List, Optional, Tuple +import functools +from typing import Any, Dict, List, Optional, Tuple, Union +from docetl.builder import Optimizer +from pydantic import BaseModel from dotenv import load_dotenv import hashlib from rich.console import Console from docetl.dataset import Dataset, create_parsing_tool_map -from docetl.operations import get_operation +from docetl.operations import get_operation, get_operations from docetl.operations.utils import flush_cache from docetl.config_wrapper import ConfigWrapper +from . import schemas +from .utils import classproperty load_dotenv() class DSLRunner(ConfigWrapper): """ - A class for executing Domain-Specific Language (DSL) configurations. - - This class is responsible for loading, validating, and executing DSL configurations - defined in YAML files. It manages datasets, executes pipeline steps, and tracks + This class is responsible for running DocETL pipelines. It manages datasets, executes pipeline steps, and tracks the cost of operations. Attributes: @@ -32,14 +34,47 @@ class DSLRunner(ConfigWrapper): datasets (Dict): Storage for loaded datasets. """ - def __init__(self, config: Dict, max_threads: int = None): + @classproperty + def schema(cls): + # Accessing the schema loads all operations, so only do this + # when we actually need it... + # Yes, this means DSLRunner.schema isn't really accessible to + # static type checkers. But it /is/ available for dynamic + # checking, and for generating json schema. + + OpType = functools.reduce( + lambda a, b: a | b, [op.schema for op in get_operations().values()] + ) + # More pythonic implementation of the above, but only works in python 3.11: + # OpType = Union[*[op.schema for op in get_operations().values()]] + + class Pipeline(BaseModel): + config: Optional[dict[str, Any]] + parsing_tools: Optional[list[schemas.ParsingTool]] + datasets: Dict[str, schemas.Dataset] + operations: list[OpType] + pipeline: schemas.PipelineSpec + + return Pipeline + + @classproperty + def json_schema(cls): + return cls.schema.model_json_schema() + + def __init__(self, config: Dict, max_threads: int = None, **kwargs): """ Initialize the DSLRunner with a YAML configuration file. Args: max_threads (int, optional): Maximum number of threads to use. Defaults to None. """ - ConfigWrapper.__init__(self, config, max_threads) + super().__init__( + config, + base_name=kwargs.pop("base_name", None), + yaml_file_suffix=kwargs.pop("yaml_file_suffix", None), + max_threads=max_threads, + **kwargs, + ) self.datasets = {} self.intermediate_dir = ( @@ -51,21 +86,6 @@ def __init__(self, config: Dict, max_threads: int = None): self.config.get("parsing_tools", None) ) - # Check if output path is correctly formatted as JSON - output_path = self.config.get("pipeline", {}).get("output", {}).get("path") - if output_path: - if not ( - output_path.lower().endswith(".json") - or output_path.lower().endswith(".csv") - ): - raise ValueError( - f"Output path '{output_path}' is not a JSON or CSV file. Please provide a path ending with '.json' or '.csv'." - ) - else: - raise ValueError( - "No output path specified in the configuration. Please provide an output path ending with '.json' or '.csv' in the configuration." - ) - self.syntax_check() op_map = {op["name"]: op for op in self.config["operations"]} @@ -90,6 +110,23 @@ def __init__(self, config: Dict, max_threads: int = None): all_ops_str.encode() ).hexdigest() + def get_output_path(self, require=False): + output_path = self.config.get("pipeline", {}).get("output", {}).get("path") + if output_path: + if not ( + output_path.lower().endswith(".json") + or output_path.lower().endswith(".csv") + ): + raise ValueError( + f"Output path '{output_path}' is not a JSON or CSV file. Please provide a path ending with '.json' or '.csv'." + ) + elif require: + raise ValueError( + "No output path specified in the configuration. Please provide an output path ending with '.json' or '.csv' in the configuration to use the save() method." + ) + + return output_path + def syntax_check(self): """ Perform a syntax check on all operations defined in the configuration. @@ -105,6 +142,9 @@ def syntax_check(self): "[yellow]Performing syntax check on all operations...[/yellow]" ) + # Just validate that it's a json file if specified + self.get_output_path() + for operation_config in self.config["operations"]: operation = operation_config["name"] operation_type = operation_config["type"] @@ -131,7 +171,7 @@ def find_operation(self, op_name: str) -> Dict: return operation_config raise ValueError(f"Operation '{op_name}' not found in configuration.") - def run(self) -> float: + def load_run_save(self) -> float: """ Execute the entire pipeline defined in the configuration. @@ -141,9 +181,42 @@ def run(self) -> float: Returns: float: The total cost of executing the pipeline. """ + + # Fail early if we can't save the output... + self.get_output_path(require=True) + self.console.rule("[bold blue]Pipeline Execution[/bold blue]") start_time = time.time() - self.load_datasets() + + output, total_cost = self.run(self.load()) + self.save(output) + + self.console.rule("[bold green]Execution Summary[/bold green]") + self.console.print(f"[bold green]Total cost: [green]${total_cost:.2f}[/green]") + self.console.print( + f"[bold green]Total time: [green]{time.time() - start_time:.2f} seconds[/green]" + ) + + return total_cost + + def run(self, datasets) -> float: + """ + Execute the entire pipeline defined in the configuration on some data. + + Args: + datasets (dict[str, Dataset | List[Dict]]): input datasets to transform + + Returns: + (List[Dict], float): The transformed data and the total cost of execution. + """ + self.datasets = { + name: ( + dataset + if isinstance(dataset, Dataset) + else Dataset(self, "memory", dataset) + ) + for name, dataset in datasets.items() + } total_cost = 0 for step in self.config["pipeline"]["steps"]: step_name = step["name"] @@ -158,10 +231,6 @@ def run(self) -> float: f"Step [cyan]{step_name}[/cyan] completed. Cost: [green]${step_cost:.2f}[/green]" ) - self.save_output( - self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load() - ) - # Save the self.step_op_hashes to a file if self.intermediate_dir exists if self.intermediate_dir: with open( @@ -170,15 +239,12 @@ def run(self) -> float: ) as f: json.dump(self.step_op_hashes, f) - self.console.rule("[bold green]Execution Summary[/bold green]") - self.console.print(f"[bold green]Total cost: [green]${total_cost:.2f}[/green]") - self.console.print( - f"[bold green]Total time: [green]{time.time() - start_time:.2f} seconds[/green]" + return ( + self.datasets[self.config["pipeline"]["steps"][-1]["name"]].load(), + total_cost, ) - return total_cost - - def load_datasets(self): + def load(self): """ Load all datasets defined in the configuration. @@ -188,9 +254,10 @@ def load_datasets(self): ValueError: If an unsupported dataset type is encountered. """ self.console.rule("[cyan]Loading Datasets[/cyan]") + datasets = {} for name, dataset_config in self.config["datasets"].items(): if dataset_config["type"] == "file": - self.datasets[name] = Dataset( + datasets[name] = Dataset( self, "file", dataset_config["path"], @@ -201,8 +268,9 @@ def load_datasets(self): self.console.print(f"Loaded dataset: [bold]{name}[/bold]") else: raise ValueError(f"Unsupported dataset type: {dataset_config['type']}") + return datasets - def save_output(self, data: List[Dict]): + def save(self, data: List[Dict]): """ Save the final output of the pipeline. @@ -212,6 +280,8 @@ def save_output(self, data: List[Dict]): Raises: ValueError: If an unsupported output type is specified in the configuration. """ + self.get_output_path(require=True) + self.console.rule("[cyan]Saving Output[/cyan]") output_config = self.config["pipeline"]["output"] if output_config["type"] == "file": @@ -304,6 +374,27 @@ def execute_step( if self.intermediate_dir: self._save_checkpoint(step["name"], operation_name, input_data) + # Load existing step op hash, if exists, merge self.step_op_hashes[step["name"]][operation_name] into it + # Save the step op hash + intermediate_config_path = os.path.join( + self.intermediate_dir, ".docetl_intermediate_config.json" + ) + if os.path.exists(intermediate_config_path): + with open(intermediate_config_path, "r") as f: + existing_config = json.load(f) + else: + existing_config = {} + + if step["name"] not in existing_config: + existing_config[step["name"]] = {} + existing_config[step["name"]][operation_name] = self.step_op_hashes[ + step["name"] + ][operation_name] + + # Resave + with open(intermediate_config_path, "w") as f: + json.dump(existing_config, f, indent=2) + return input_data, total_cost def _load_from_checkpoint_if_exists( @@ -368,6 +459,24 @@ def _save_checkpoint(self, step_name: str, operation_name: str, data: List[Dict] f"[green]✓ [italic]Intermediate saved for operation '{operation_name}' in step '{step_name}' at {checkpoint_path}[/italic][/green]" ) + def optimize( + self, save: bool = False, return_pipeline: bool = True, **kwargs + ) -> Union[Dict, "DSLRunner"]: + builder = Optimizer( + self, + max_threads=self.max_threads, + **kwargs, + ) + builder.optimize() + if save: + builder.save_optimized_config(f"{self.base_name}_opt.yaml") + self.optimized_config_path = f"{self.base_name}_opt.yaml" + + if return_pipeline: + return DSLRunner(builder.clean_optimized_config(), self.max_threads) + + return builder.clean_optimized_config() + if __name__ == "__main__": runner = DSLRunner("workloads/medical/map_opt.yaml") diff --git a/docetl/schemas.py b/docetl/schemas.py index ce89473e..b54f3438 100644 --- a/docetl/schemas.py +++ b/docetl/schemas.py @@ -1,226 +1,29 @@ -from typing import Any, Dict, List, Optional, Union - -from pydantic import BaseModel, Field, field_validator - - -class ToolFunction(BaseModel): - name: str - description: str - parameters: Dict[str, Any] - - -class Tool(BaseModel): - code: str - function: ToolFunction - - -class ParsingTool(BaseModel): - """ - Represents a parsing tool used for custom data parsing in the pipeline. - - Attributes: - name (str): The name of the parsing tool. This should be unique within the pipeline configuration. - function_code (str): The Python code defining the parsing function. This code will be executed - to parse the input data according to the specified logic. It should return a list of strings, where each string is its own document. - - Example: - ```yaml - parsing_tools: - - name: ocr_parser - function_code: | - import pytesseract - from pdf2image import convert_from_path - def ocr_parser(filename: str) -> List[str]: - images = convert_from_path(filename) - text = "" - for image in images: - text += pytesseract.image_to_string(image) - return [text] - ``` - """ - - name: str - function_code: str - - -class Dataset(BaseModel): - """ - Represents a dataset configuration in the pipeline. - - Attributes: - type (str): The type of the dataset. Must be either 'file' or 'memory'. - path (str): The path to the dataset file or the in-memory data, depending on the type. - source (str): The source of the dataset. Currently, only 'local' is supported. Defaults to 'local'. - parsing (Optional[List[Dict[str, str]]]): A list of parsing tools to apply to the data. Each parsing tool - is represented by a dictionary with 'input_key', 'function', and - 'output_key' keys. Defaults to None. - - Example: - ```yaml - datasets: - my_dataset: - type: file - path: input.json - parsing: - - input_key: file_path - function: txt_to_string - output_key: content - ``` - - Note: - The parsing tools are applied in the order they are listed. Each parsing tool takes the output - of the previous tool as its input, allowing for chained processing of the data. - """ - - type: str - path: str - source: str = "local" - parsing: Optional[List[Dict[str, str]]] = None - - -class BaseOp(BaseModel): - name: str - type: str - - -class MapOp(BaseOp): - type: str = "map" - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - tools: Optional[List[Dict[str, Any]]] = None - validation_rules: Optional[List[str]] = Field(None, alias="validate") - num_retries_on_validate_failure: Optional[int] = None - gleaning: Optional[Dict[str, Any]] = None - drop_keys: Optional[List[str]] = None - timeout: Optional[int] = None - batch_size: Optional[int] = None - clustering_method: Optional[str] = None - - @field_validator("drop_keys") - def validate_drop_keys(cls, v): - if isinstance(v, str): - return [v] - return v - - -class ResolveOp(BaseOp): - type: str = "resolve" - comparison_prompt: str - resolution_prompt: str - output: Optional[Dict[str, Any]] = None - embedding_model: Optional[str] = None - resolution_model: Optional[str] = None - comparison_model: Optional[str] = None - blocking_keys: Optional[List[str]] = None - blocking_threshold: Optional[float] = None - blocking_conditions: Optional[List[str]] = None - input: Optional[Dict[str, Any]] = None - embedding_batch_size: Optional[int] = None - compare_batch_size: Optional[int] = None - limit_comparisons: Optional[int] = None - optimize: Optional[bool] = None - timeout: Optional[int] = None - - -class ReduceOp(BaseOp): - type: str = "reduce" - reduce_key: Union[str, List[str]] - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - optimize: Optional[bool] = None - synthesize_resolve: Optional[bool] = None - model: Optional[str] = None - input: Optional[Dict[str, Any]] = None - pass_through: Optional[bool] = None - associative: Optional[bool] = None - fold_prompt: Optional[str] = None - fold_batch_size: Optional[int] = None - value_sampling: Optional[Dict[str, Any]] = None - verbose: Optional[bool] = None - timeout: Optional[int] = None - - -class ParallelMapOp(BaseOp): - type: str = "parallel_map" - prompts: List[Dict[str, Any]] - output: Optional[Dict[str, Any]] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - drop_keys: Optional[List[str]] = None - timeout: Optional[int] = None - batch_size: Optional[int] = None - clustering_method: Optional[str] = None - - @field_validator("drop_keys") - def validate_drop_keys(cls, v): - if isinstance(v, str): - return [v] - return v - - -class FilterOp(BaseOp): - type: str = "filter" - output: Optional[Dict[str, Any]] = None - prompt: Optional[str] = None - model: Optional[str] = None - optimize: Optional[bool] = None - recursively_optimize: Optional[bool] = None - sample_size: Optional[int] = None - validation_rules: Optional[List[str]] = Field(None, alias="validate") - num_retries_on_validate_failure: Optional[int] = None - timeout: Optional[int] = None - - -class EquijoinOp(BaseOp): - type: str = "equijoin" - left: str - right: str - comparison_prompt: str - output: Optional[Dict[str, Any]] = None - blocking_threshold: Optional[float] = None - blocking_conditions: Optional[Dict[str, List[str]]] = None - limits: Optional[Dict[str, int]] = None - comparison_model: Optional[str] = None - optimize: Optional[bool] = None - embedding_model: Optional[str] = None - embedding_batch_size: Optional[int] = None - compare_batch_size: Optional[int] = None - limit_comparisons: Optional[int] = None - blocking_keys: Optional[Dict[str, List[str]]] = None - timeout: Optional[int] = None - - -class SplitOp(BaseOp): - type: str = "split" - split_key: str - method: str - method_kwargs: Dict[str, Any] - model: Optional[str] = None - - -class GatherOp(BaseOp): - type: str = "gather" - content_key: str - doc_id_key: str - order_key: str - peripheral_chunks: Dict[str, Any] - doc_header_key: Optional[str] = None - - -class UnnestOp(BaseOp): - type: str = "unnest" - unnest_key: str - keep_empty: Optional[bool] = None - expand_fields: Optional[List[str]] = None - recursive: Optional[bool] = None - depth: Optional[int] = None - +from .base_schemas import * + +from .operations import cluster +from .operations import equijoin +from .operations import filter +from .operations import gather +from .operations import map +from .operations import reduce +from .operations import resolve +from .operations import sample +from .operations import split +from .operations import unnest + +from . import dataset + +MapOp = map.MapOperation.schema +ResolveOp = resolve.ResolveOperation.schema +ReduceOp = reduce.ReduceOperation.schema +ParallelMapOp = map.ParallelMapOperation.schema +FilterOp = filter.FilterOperation.schema +EquijoinOp = equijoin.EquijoinOperation.schema +SplitOp = split.SplitOperation.schema +GatherOp = gather.GatherOperation.schema +UnnestOp = unnest.UnnestOperation.schema +ClusterOp = cluster.ClusterOperation.schema +SampleOp = sample.SampleOperation.schema OpType = Union[ MapOp, @@ -234,81 +37,4 @@ class UnnestOp(BaseOp): UnnestOp, ] - -class PipelineStep(BaseModel): - """ - Represents a step in the pipeline. - - Attributes: - name (str): The name of the step. - operations (List[Union[Dict[str, Any], str]]): A list of operations to be applied in this step. - Each operation can be either a string (the name of the operation) or a dictionary - (for more complex configurations). - input (Optional[str]): The input for this step. It can be either the name of a dataset - or the name of a previous step. If not provided, the step will use the output - of the previous step as its input. - - Example: - ```python - # Simple step with a single operation - process_step = PipelineStep( - name="process_step", - input="my_dataset", - operations=["process"] - ) - - # Step with multiple operations - summarize_step = PipelineStep( - name="summarize_step", - input="process_step", - operations=["summarize"] - ) - - # Step with a more complex operation configuration - custom_step = PipelineStep( - name="custom_step", - input="previous_step", - operations=[ - { - "custom_operation": { - "model": "gpt-4", - "prompt": "Perform a custom analysis on the following text:" - } - } - ] - ) - ``` - - These examples show different ways to configure pipeline steps, from simple - single-operation steps to more complex configurations with custom parameters. - """ - - name: str - operations: List[Union[Dict[str, Any], str]] - input: Optional[str] = None - - -class PipelineOutput(BaseModel): - """ - Represents the output configuration for a pipeline. - - Attributes: - type (str): The type of output. This could be 'file', 'database', etc. - path (str): The path where the output will be stored. This could be a file path, - database connection string, etc., depending on the type. - intermediate_dir (Optional[str]): The directory to store intermediate results, - if applicable. Defaults to None. - - Example: - ```python - output = PipelineOutput( - type="file", - path="/path/to/output.json", - intermediate_dir="/path/to/intermediate/results" - ) - ``` - """ - - type: str - path: str - intermediate_dir: Optional[str] = None +Dataset = dataset.Dataset.schema diff --git a/docetl/utils.py b/docetl/utils.py index 61f95379..e1635b1b 100644 --- a/docetl/utils.py +++ b/docetl/utils.py @@ -152,3 +152,11 @@ def truncate_sample_data( return truncated_data return truncated_data + + + +class classproperty(object): + def __init__(self, f): + self.f = f + def __get__(self, obj, owner): + return self.f(owner) diff --git a/docs/api-reference/python.md b/docs/api-reference/python.md index 9ec82bd0..f91f11bc 100644 --- a/docs/api-reference/python.md +++ b/docs/api-reference/python.md @@ -2,15 +2,6 @@ ## Operations -::: docetl.schemas.BaseOp - options: - show_root_heading: true - heading_level: 3 - show_if_no_docstring: false - docstring_options: - ignore_init_summary: false - trim_doctest_flags: true - ::: docetl.schemas.MapOp options: show_root_heading: true @@ -92,6 +83,24 @@ ignore_init_summary: false trim_doctest_flags: true +::: docetl.schemas.SampleOp + options: + show_root_heading: true + heading_level: 3 + show_if_no_docstring: false + docstring_options: + ignore_init_summary: false + trim_doctest_flags: true + +::: docetl.schemas.ClusterOp + options: + show_root_heading: true + heading_level: 3 + show_if_no_docstring: false + docstring_options: + ignore_init_summary: false + trim_doctest_flags: true + ## Dataset and Pipeline ::: docetl.schemas.Dataset diff --git a/docs/operators/cluster.md b/docs/operators/cluster.md index 51ef82a1..e7abb65a 100644 --- a/docs/operators/cluster.md +++ b/docs/operators/cluster.md @@ -181,8 +181,6 @@ and a description, and groups them into a tree of categories. | `output_key` | The name of the output key where the cluster path will be inserted in the items. | "clusters" | | `model` | The language model to use | Falls back to `default_model` | | `embedding_model` | The embedding model to use | "text-embedding-3-small" | -| `tools` | List of tool definitions for LLM use | None | | `timeout` | Timeout for each LLM call in seconds | 120 | | `max_retries_per_timeout` | Maximum number of retries per timeout | 2 | -| `validate` | List of Python expressions to validate the output | None | | `sample` | Number of items to sample for this operation | None | diff --git a/poetry.lock b/poetry.lock index 9926319a..9694fbd8 100644 --- a/poetry.lock +++ b/poetry.lock @@ -137,6 +137,48 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "albucore" +version = "0.0.13" +description = "A high-performance image processing library designed to optimize and extend the Albumentations library with specialized functions for advanced image transformations. 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"sha256:b6f687ced5510a9a2474bbae96a4352e5ace5fa34dc44a217b0537fec1db00b4"}, ] [package.dependencies] @@ -4518,4 +4572,4 @@ parsing = ["azure-ai-documentintelligence", "openpyxl", "paddleocr", "paddlepadd [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "d31c822902e5bba42608323eb0ec787636268bfa35dae5d12daa2d70f2ef55fe" +content-hash = "492e352f2364559d37223d6c88069c6a9d586f2f08e8d02deef65ccde07a2194" diff --git a/pyproject.toml b/pyproject.toml index 54640b3c..3eb67cad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,7 +17,6 @@ python = "^3.10" litellm = "^1.42.1" tqdm = "^4.66.4" rich = "^13.7.1" -tenacity = "^9.0.0" frozendict = "^2.4.4" diskcache = "^5.6.3" typer = "^0.12.5" @@ -34,6 +33,8 @@ azure-ai-documentintelligence = { version = "^1.0.0b4", optional = true } paddlepaddle = { version = "^2.6.2", optional = true } paddleocr = { version = "^2.8.1", optional = true } pymupdf = { version = "^1.24.10", optional = true } +jsonschema = "^4.23.0" +rapidfuzz = "^3.10.0" [tool.poetry.extras] parsing = ["python-docx", "openpyxl", "pydub", "python-pptx", "azure-ai-documentintelligence", "paddlepaddle", "paddleocr", "pymupdf"] @@ -89,6 +90,7 @@ resolve = "docetl.operations.resolve:ResolveOperation" gather = "docetl.operations.gather:GatherOperation" cluster = "docetl.operations.cluster:ClusterOperation" sample = "docetl.operations.sample:SampleOperation" +link_resolve = "docetl.operations.link_resolve:LinkResolveOperation" [tool.poetry.plugins."docetl.parser"] llama_index_simple_directory_reader = "docetl.parsing_tools:llama_index_simple_directory_reader" diff --git a/tests/basic/test_basic_map.py b/tests/basic/test_basic_map.py index 357a90b0..6bd07c08 100644 --- a/tests/basic/test_basic_map.py +++ b/tests/basic/test_basic_map.py @@ -26,8 +26,9 @@ def test_map_operation( assert len(results) == len(map_sample_data) assert all("sentiment" in result for result in results) + valid_sentiments = ["positive", "negative", "neutral"] assert all( - result["sentiment"] in ["positive", "negative", "neutral"] for result in results + any(vs in result["sentiment"] for vs in valid_sentiments) for result in results ) @@ -96,8 +97,9 @@ def test_map_operation_with_batching( assert len(results) == len(map_sample_data) assert all("sentiment" in result for result in results) + valid_sentiments = ["positive", "negative", "neutral"] assert all( - result["sentiment"] in ["positive", "negative", "neutral"] for result in results + any(vs in result["sentiment"] for vs in valid_sentiments) for result in results ) @@ -171,12 +173,14 @@ def generate_random_text(length): ] +@pytest.mark.flaky(reruns=2) def test_map_operation_with_timeout(simple_map_config, simple_sample_data, api_wrapper): # Add timeout to the map configuration map_config_with_timeout = { **simple_map_config, "timeout": 1, "max_retries_per_timeout": 0, + "bypass_cache": True, } operation = MapOperation(api_wrapper, map_config_with_timeout, "gpt-4o-mini", 4) diff --git a/tests/basic/test_pipeline_with_parsing.py b/tests/basic/test_pipeline_with_parsing.py index e10de629..3487a5de 100644 --- a/tests/basic/test_pipeline_with_parsing.py +++ b/tests/basic/test_pipeline_with_parsing.py @@ -97,7 +97,7 @@ def test_pipeline_with_parsing(config_file): # Create and run the DSLRunner runner = DSLRunner.from_yaml(config_file) - total_cost = runner.run() + total_cost = runner.load_run_save() # Check if the output file was created assert os.path.exists(output_file.name), "Output file was not created" diff --git a/tests/test_azure_rl.py b/tests/test_azure_rl.py index be23c387..3f051ff7 100644 --- a/tests/test_azure_rl.py +++ b/tests/test_azure_rl.py @@ -1,3 +1,4 @@ +from docetl.runner import DSLRunner import pytest from docetl.operations.map import MapOperation import random @@ -41,12 +42,20 @@ def test_map_operation_over_15_documents(simple_map_config, sample_documents): os.environ["AZURE_API_VERSION"] = os.getenv("LOW_RES_AZURE_API_VERSION") os.environ["AZURE_API_KEY"] = os.getenv("LOW_RES_AZURE_API_KEY") - operation = MapOperation(api_wrapper, simple_map_config, "azure/gpt-4o", 4) - results, cost = operation.execute(sample_documents) + runner = DSLRunner( + { + "default_model": "gpt-4o-mini", + "operations": [], + "pipeline": {"steps": [], "output": {"path": "/tmp/testingdocetl.json"}}, + }, + max_threads=64, + ) + + operation = MapOperation(runner, simple_map_config, "azure/gpt-4o", 4) + results, cost = operation.execute(sample_documents + sample_documents) - assert len(results) == 15 + assert len(results) == 16 assert all("sentiment" in result for result in results) assert all( result["sentiment"] in ["positive", "negative", "neutral"] for result in results ) - assert cost > 0 diff --git a/tests/test_config.py b/tests/test_config.py index 65102948..afceaed1 100644 --- a/tests/test_config.py +++ b/tests/test_config.py @@ -87,7 +87,7 @@ def test_end_to_end_pipeline(config_file, sample_data_file, tmp_path): # Create and run the DSLRunner runner = DSLRunner.from_yaml(str(config_file)) - total_cost = runner.run() + total_cost = runner.load_run_save() # Check if the output file was created output_path = tmp_path / "output.json" diff --git a/tests/test_parsing_tools.py b/tests/test_parsing_tools.py index 9118156b..5d482d28 100644 --- a/tests/test_parsing_tools.py +++ b/tests/test_parsing_tools.py @@ -74,7 +74,7 @@ def temp_pptx_file(): def test_whisper_speech_to_text(temp_audio_file): - result = parsing_tools.whisper_speech_to_text(temp_audio_file) + result = parsing_tools.whisper_speech_to_text.__wrapped__(temp_audio_file) assert isinstance(result, list) assert len(result) == 1 @@ -83,7 +83,7 @@ def test_whisper_speech_to_text(temp_audio_file): def test_xlsx_to_string(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file) + result = parsing_tools.xlsx_to_string.__wrapped__(temp_xlsx_file) assert isinstance(result, list) assert len(result) == 1 @@ -93,7 +93,7 @@ def test_xlsx_to_string(temp_xlsx_file): def test_xlsx_to_string_row_orientation(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file, orientation="row") + result = parsing_tools.xlsx_to_string.__wrapped__(temp_xlsx_file, orientation="row") assert isinstance(result, list) assert len(result) == 1 @@ -102,7 +102,9 @@ def test_xlsx_to_string_row_orientation(temp_xlsx_file): def test_xlsx_to_string_doc_per_sheet(temp_xlsx_file): - result = parsing_tools.xlsx_to_string(temp_xlsx_file, doc_per_sheet=True) + result = parsing_tools.xlsx_to_string.__wrapped__( + temp_xlsx_file, doc_per_sheet=True + ) assert isinstance(result, list) assert len(result) == 1 # Only one sheet in our test file @@ -112,7 +114,7 @@ def test_xlsx_to_string_doc_per_sheet(temp_xlsx_file): def test_txt_to_string(temp_txt_file): - result = parsing_tools.txt_to_string(temp_txt_file) + result = parsing_tools.txt_to_string.__wrapped__(temp_txt_file) assert isinstance(result, list) assert len(result) == 1 @@ -120,7 +122,7 @@ def test_txt_to_string(temp_txt_file): def test_docx_to_string(temp_docx_file): - result = parsing_tools.docx_to_string(temp_docx_file) + result = parsing_tools.docx_to_string.__wrapped__(temp_docx_file) assert isinstance(result, list) assert len(result) == 1 @@ -129,7 +131,7 @@ def test_docx_to_string(temp_docx_file): def test_pptx_to_string(temp_pptx_file): - result = parsing_tools.pptx_to_string(temp_pptx_file) + result = parsing_tools.pptx_to_string.__wrapped__(temp_pptx_file) assert isinstance(result, list) assert len(result) == 1 @@ -140,7 +142,9 @@ def test_pptx_to_string(temp_pptx_file): def test_pptx_to_string_doc_per_slide(temp_pptx_file): - result = parsing_tools.pptx_to_string(temp_pptx_file, doc_per_slide=True) + result = parsing_tools.pptx_to_string.__wrapped__( + temp_pptx_file, doc_per_slide=True + ) assert isinstance(result, list) assert len(result) == 2 @@ -157,7 +161,7 @@ def pdf_url(): def test_azure_di_read(pdf_url): # Test with default parameters - result = parsing_tools.azure_di_read(pdf_url, use_url=True) + result = parsing_tools.azure_di_read.__wrapped__(pdf_url, use_url=True) assert isinstance(result, list) assert len(result) == 1 @@ -169,7 +173,7 @@ def test_azure_di_read(pdf_url): assert "Enhancing the patient" in content # Test with include_line_numbers=True - result_line_numbers = parsing_tools.azure_di_read( + result_line_numbers = parsing_tools.azure_di_read.__wrapped__( pdf_url, use_url=True, include_line_numbers=True ) assert isinstance(result_line_numbers, list) @@ -179,7 +183,7 @@ def test_azure_di_read(pdf_url): def test_azure_di_read_invoice(): invoice_url = "https://raw.githubusercontent.com/Azure-Samples/cognitive-services-REST-api-samples/master/curl/form-recognizer/sample-invoice.pdf" - result = parsing_tools.azure_di_read(invoice_url, use_url=True) + result = parsing_tools.azure_di_read.__wrapped__(invoice_url, use_url=True) assert isinstance(result, list) assert len(result) == 1 @@ -207,7 +211,7 @@ def pytest_sessionfinish(session, exitstatus): def test_paddleocr_pdf_to_string(): pdf_path = "tests/data/PublicWaterMassMailing.pdf" - result = parsing_tools.paddleocr_pdf_to_string(pdf_path, lang="en") + result = parsing_tools.paddleocr_pdf_to_string.__wrapped__(pdf_path, lang="en") assert isinstance(result, list) assert len(result) == 1 @@ -221,7 +225,7 @@ def test_paddleocr_pdf_to_string(): def test_gptpdf_to_string(): input_pdf = "tests/data/PublicWaterMassMailing.pdf" - result = parsing_tools.gptpdf_to_string( + result = parsing_tools.gptpdf_to_string.__wrapped__( input_path=input_pdf, gpt_model="gpt-4o-mini", api_key=os.environ["OPENAI_API_KEY"], diff --git a/tests/test_resolve_auto_batch.py b/tests/test_resolve_auto_batch.py new file mode 100644 index 00000000..01b91a2c --- /dev/null +++ b/tests/test_resolve_auto_batch.py @@ -0,0 +1,124 @@ + +import pytest +from docetl.operations.resolve import ResolveOperation +from tests.conftest import api_wrapper +@pytest.fixture(scope='session') +def resolve_config(): + return { + "name": "name_email_resolver", + "type": "resolve", + "blocking_keys": ["name"], + "blocking_threshold": 0, + "comparison_prompt": "Compare the following two entries and determine if they likely refer to the same person: Person 1: {{ input1 }} Person 2: {{ input2 }} Return true if they likely match, false otherwise.", + "output": {"schema": {"name": "string", "email": "string"}}, + "embedding_model": "text-embedding-3-small", + "comparison_model": "gpt-4o-mini", + "resolution_model": "gpt-4o-mini", + "resolution_prompt": "Given the following list of similar entries, determine one common name and email. {{ inputs }}", + } + + +@pytest.fixture(scope='session') +def resolve_sample_data(): + return [ + {"name": "John Doe", "email": "john@example.com"}, + {"name": "John D.", "email": "johnd@example.com"}, + {"name": "J. Smith", "email": "jane@example.com"}, + {"name": "J. Smith", "email": "jsmith@example.com"}, + {"name": "Emily Brown", "email": "emilyb@example.com"}, + {"name": "Emily B.", "email": "brown.em@example.com"}, + {"name": "Chris White", "email": "chris@example.com"}, + {"name": "C. White", "email": "cwhite@example.com"}, + {"name": "R. Davis", "email": "rdavis@example.com"}, + {"name": "Laura Martinez", "email": "laura.m@example.com"}, + {"name": "L. Martinez", "email": "lmartinez@example.com"}, + {"name": "William Clark", "email": "willclark@example.com"}, + {"name": "W. Clark", "email": "wclark@example.com"}, + {"name": "Megan Allen", "email": "megan.allen@example.com"}, + {"name": "M. Allen", "email": "mallen@example.com"}, + {"name": "Brian Wilson", "email": "brianw@example.com"}, + {"name": "B. Wilson", "email": "bwilson@example.com"}, + {"name": "Nancy Young", "email": "nancy.young@example.com"}, + {"name": "N. Young", "email": "nyoung@example.com"}, + {"name": "Paul Walker", "email": "paulw@example.com"}, + {"name": "P. Walker", "email": "pwalker@example.com"}, + {"name": "Kimberly Adams", "email": "kim.adams@example.com"}, + {"name": "K. Adams", "email": "kadams@example.com"}, + {"name": "Joshua Green", "email": "josh.green@example.com"}, + {"name": "J. Green", "email": "jgreen@example.com"}, + {"name": "Ethan Phillips", "email": "ethanp@example.com"}, + {"name": "E. Phillips", "email": "ephillips@example.com"}, + {"name": "Victoria King", "email": "victoria.k@example.com"}, + {"name": "V. King", "email": "vking@example.com"}, + {"name": "Olivia Turner", "email": "olivia.turner@example.com"}, + {"name": "O. Turner", "email": "oturner@example.com"}, + {"name": "Daniel Moore", "email": "danmoore@example.com"}, + {"name": "D. Moore", "email": "dmoore@example.com"}, + {"name": "Grace Evans", "email": "grace.e@example.com"}, + {"name": "G. Evans", "email": "gevans@example.com"}, + {"name": "Kevin Hall", "email": "khall@example.com"}, + {"name": "K. Hall", "email": "kevin.h@example.com"}, + {"name": "Michael Black", "email": "m.black@example.com"}, + {"name": "M. Black", "email": "mblack@example.com"}, + {"name": "Sarah White", "email": "sarahw@example.com"}, + {"name": "S. White", "email": "swhite@example.com"}, + {"name": "Samuel Hill", "email": "samuel.h@example.com"}, + {"name": "Sam Hill", "email": "shill@example.com"}, + {"name": "Jessica Green", "email": "jess.green@example.com"}, + {"name": "J. Green", "email": "jgreen1@example.com"}, + {"name": "Alexander Young", "email": "alex.young@example.com"}, + {"name": "A. Young", "email": "ayoung@example.com"}, + {"name": "Rachel Brown", "email": "rachel.brown@example.com"}, + {"name": "R. Brown", "email": "rbrown@example.com"}, + {"name": "Jonathan Adams", "email": "jon.adams@example.com"}, + {"name": "J. Adams", "email": "jadams@example.com"}, + {"name": "Olivia Harris", "email": "oliviah@example.com"}, + {"name": "O. Harris", "email": "oharris@example.com"}, + {"name": "Benjamin Parker", "email": "ben.parker@example.com"}, + {"name": "B. Parker", "email": "bparker@example.com"}, + {"name": "Christina Lewis", "email": "christina.lewis@example.com"}, + {"name": "C. Lewis", "email": "clewis@example.com"}, + {"name": "Ethan Baker", "email": "ethanb@example.com"}, + {"name": "E. Baker", "email": "ebaker@example.com"}, + {"name": "Abigail Carter", "email": "abigail.c@example.com"}, + {"name": "A. Carter", "email": "acarter@example.com"}, + {"name": "C. Scott", "email": "cscott@example.com"}, + {"name": "Sophia King", "email": "sophiak@example.com"}, + {"name": "S. King", "email": "s.king@example.com"}, + {"name": "Jacob Wright", "email": "jacob.wright@example.com"}, + {"name": "J. Wright", "email": "jwright@example.com"}, + {"name": "Isabella Mitchell", "email": "isabella.m@example.com"}, + {"name": "I. Mitchell", "email": "imitchell@example.com"}, + {"name": "Daniel Martinez", "email": "dan.m@example.com"}, + {"name": "D. Martinez", "email": "dmartinez@example.com"}, + {"name": "Emma Perez", "email": "emmap@example.com"}, + {"name": "E. Perez", "email": "eperez@example.com"}, + {"name": "Christopher Nelson", "email": "chris.n@example.com"}, + {"name": "C. Nelson", "email": "cnelson@example.com"}, + {"name": "Grace Carter", "email": "gcarter@example.com"}, + {"name": "G. Carter", "email": "grace.c@example.com"}, + {"name": "Lucas Davis", "email": "lucasd@example.com"}, + {"name": "L. Davis", "email": "ldavis@example.com"}, + {"name": "Mia Robinson", "email": "mia.r@example.com"}, + {"name": "M. Robinson", "email": "mrobinson@example.com"}, + {"name": "Henry Garcia", "email": "henry.g@example.com"}, + {"name": "H. Garcia", "email": "hgarcia@example.com"}, + {"name": "Amelia Rodriguez", "email": "amelia.r@example.com"}, + {"name": "A. Rodriguez", "email": "arodriguez@example.com"}, + {"name": "David Walker", "email": "david.w@example.com"}, + {"name": "D. Walker", "email": "dwalker@example.com"} + + ] + + +def test_resolve_operation( + resolve_config, max_threads, resolve_sample_data, api_wrapper +): + operation = ResolveOperation( + api_wrapper, resolve_config, "text-embedding-3-small", max_threads + ) + results, cost = operation.execute(resolve_sample_data[:25]) + + distinct_names = set(result["name"] for result in results) + print(distinct_names) + assert len(distinct_names) < len(results) diff --git a/tests/test_runner_caching.py b/tests/test_runner_caching.py index fbd973fa..65b0fac8 100644 --- a/tests/test_runner_caching.py +++ b/tests/test_runner_caching.py @@ -1,3 +1,4 @@ +import shutil import time import pytest import json @@ -58,6 +59,7 @@ def create_pipeline(input_file, output_file, intermediate_dir, operation_prompt) ) +@pytest.mark.flaky(reruns=3) def test_pipeline_rerun_on_operation_change( temp_input_file, temp_output_file, temp_intermediate_dir ): @@ -117,3 +119,75 @@ def test_pipeline_rerun_on_operation_change( # Check that the runtime is faster when not modifying assert unmodified_runtime < modified_runtime + + +# Test with an incorrect later operation but correct earlier operation +def test_partial_caching(temp_input_file, temp_output_file, temp_intermediate_dir): + # Create initial pipeline with two operations + initial_pipeline = Pipeline( + name="test_pipeline", + datasets={"test_input": Dataset(type="file", path=temp_input_file)}, + operations=[ + MapOp( + name="first_operation", + type="map", + prompt="Analyze the sentiment of the following text: '{{ input.text }}'", + output={"schema": {"sentiment": "string"}}, + model="gpt-4o-mini", + ), + MapOp( + name="second_operation_bad", + type="map", + prompt="Summarize the following text: '{{ forororororo }}'", + output={"schema": {"summary": "1000"}}, + model="gpt-4o-mini", + ), + ], + steps=[ + PipelineStep( + name="first_step", + input="test_input", + operations=["first_operation", "second_operation_bad"], + ), + ], + output=PipelineOutput( + type="file", path=temp_output_file, intermediate_dir=temp_intermediate_dir + ), + default_model="gpt-4o-mini", + ) + + # Run the initial pipeline + # Run the initial pipeline with an expected error + with pytest.raises(Exception): + initial_cost = initial_pipeline.run() + + new_pipeline_with_only_one_op = Pipeline( + name="test_pipeline", + datasets={"test_input": Dataset(type="file", path=temp_input_file)}, + operations=[ + MapOp( + name="first_operation", + type="map", + prompt="Analyze the sentiment of the following text: '{{ input.text }}'", + output={"schema": {"sentiment": "string"}}, + model="gpt-4o-mini", + ), + ], + steps=[ + PipelineStep( + name="first_step", + input="test_input", + operations=["first_operation"], + ), + ], + output=PipelineOutput( + type="file", path=temp_output_file, intermediate_dir=temp_intermediate_dir + ), + default_model="gpt-4o-mini", + ) + rerun_cost = new_pipeline_with_only_one_op.run() + + # Assert that the cost was 0 when rerunning the pipeline + assert ( + rerun_cost == 0 + ), "Expected zero cost when rerunning the pipeline without changes" diff --git a/tests/test_split.py b/tests/test_split.py index 939563dc..71a89b86 100644 --- a/tests/test_split.py +++ b/tests/test_split.py @@ -137,7 +137,6 @@ def test_split_map_gather_operations( assert len(map_results) == len( split_results ), "Map operation should produce same number of results as split operation" - assert map_cost > 0, "Map operation cost should be greater than zero" for result in map_results: assert "summary" in result, "Each result should have a summary" diff --git a/tests/test_synth_resolve.py b/tests/test_synth_resolve.py index ecdd4321..f2629af3 100644 --- a/tests/test_synth_resolve.py +++ b/tests/test_synth_resolve.py @@ -1,3 +1,4 @@ +from docetl.runner import DSLRunner import pytest import json import tempfile @@ -76,21 +77,20 @@ def config_yaml(sample_data): def test_synth_resolve(config_yaml): # Initialize the optimizer - optimizer = Optimizer.from_yaml(config_yaml) + runner = DSLRunner.from_yaml(config_yaml) # Run the optimization - optimizer.optimize() - optimizer.save_optimized_config() + optimized_config = runner.optimize(save=True, return_pipeline=False) # Check if a resolve operation was synthesized synthesized_resolve_found = False - for step in optimizer.optimized_config["pipeline"]["steps"]: + for step in optimized_config["pipeline"]["steps"]: for op in step["operations"]: if op.startswith("synthesized_resolve_"): synthesized_resolve_found = True synthesized_op = [ operation - for operation in optimizer.optimized_config["operations"] + for operation in optimized_config["operations"] if operation["name"] == op ][0] @@ -99,9 +99,6 @@ def test_synth_resolve(config_yaml): assert "embedding_model" in synthesized_op assert "resolution_model" in synthesized_op assert "comparison_model" in synthesized_op - assert "_intermediates" in synthesized_op - assert "map_prompt" in synthesized_op["_intermediates"] - assert "reduce_key" in synthesized_op["_intermediates"] assert "comparison_prompt" in synthesized_op assert "resolution_prompt" in synthesized_op assert "blocking_threshold" in synthesized_op @@ -116,7 +113,7 @@ def test_synth_resolve(config_yaml): # Clean up temporary files os.remove(config_yaml) - os.remove(optimizer.optimized_config_path) + os.remove(runner.optimized_config_path) os.remove("patient_records.json") diff --git a/website/src/app/page.tsx b/website/src/app/page.tsx index 95366b9b..44ec7ef4 100644 --- a/website/src/app/page.tsx +++ b/website/src/app/page.tsx @@ -61,8 +61,10 @@ export default function Home() { Docs - From aab2afb96dc328be365767d3059c6f4693e62d61 Mon Sep 17 00:00:00 2001 From: Shreya Shankar Date: Sun, 27 Oct 2024 19:08:08 -0700 Subject: [PATCH 58/58] fix: simplify formula for equation --- docetl/operations/resolve.py | 44 ++++++++++++++++++++++-------------- mkdocs.yml | 1 - 2 files changed, 27 insertions(+), 18 deletions(-) diff --git a/docetl/operations/resolve.py b/docetl/operations/resolve.py index 379556ec..11715399 100644 --- a/docetl/operations/resolve.py +++ b/docetl/operations/resolve.py @@ -295,9 +295,9 @@ def get_embeddings_batch( total_cost += sum(costs) # Generate all pairs to compare, ensuring no duplicate comparisons - def get_unique_comparison_pairs(): + def get_unique_comparison_pairs() -> Tuple[List[Tuple[int, int]], Dict[Tuple[str, ...], List[int]]]: # Create a mapping of values to their indices - value_to_indices = {} + value_to_indices: Dict[Tuple[str, ...], List[int]] = {} for i, item in enumerate(input_data): # Create a hashable key from the blocking keys key = tuple(str(item.get(k, "")) for k in blocking_keys) @@ -323,7 +323,7 @@ def get_unique_comparison_pairs(): comparison_pairs, value_to_indices = get_unique_comparison_pairs() # Filter pairs based on blocking conditions - def meets_blocking_conditions(pair): + def meets_blocking_conditions(pair: Tuple[int, int]) -> bool: i, j = pair return ( is_match(input_data[i], input_data[j]) if blocking_conditions else False @@ -374,7 +374,7 @@ def meets_blocking_conditions(pair): # Modified merge_clusters to handle all indices with the same value - def merge_clusters(item1, item2): + def merge_clusters(item1: int, item2: int) -> None: root1, root2 = find_cluster(item1, cluster_map), find_cluster( item2, cluster_map ) @@ -416,21 +416,31 @@ def merge_clusters(item1, item2): ) # Compute an auto-batch size based on the number of comparisons - def auto_batch(): - M = 500 # should be made dynamic in the future based on the model, but this is the current rate limit for 4o-mini. - # (n-k)(k-1) is approximately len(filtered_pairs) - N = len(input_data) - # -k^2 + (n+1)k- n- fp + k^2/2 - k/2= 0, solve for k - quadratic_discriminant = (N + 0.5) ** 2 - 4 * (-1 / 2) * (-N - len(blocked_pairs)) - K_cands = [(-1 * (N+0.5) + (quadratic_discriminant) ** 0.5 )/ (-1), (-1 * (N + 0.5) - (quadratic_discriminant) ** 0.5)/ (-1)] - K = max(K_cands) - if K < 0: - return M - else: - return min(math.ceil(2*K), M) + def auto_batch() -> int: + # Maximum batch size limit for 4o-mini model + M = 500 + + n = len(input_data) + m = len(blocked_pairs) + + # https://www.wolframalpha.com/input?i=k%28k-1%29%2F2+%2B+%28n-k%29%28k-1%29+%3D+m%2C+solve+for+k + # Two possible solutions for k: + # k = -1/2 sqrt((1 - 2n)^2 - 8m) + n + 1/2 + # k = 1/2 (sqrt((1 - 2n)^2 - 8m) + 2n + 1) + + discriminant = (1 - 2*n)**2 - 8*m + sqrt_discriminant = discriminant ** 0.5 + + k1 = -0.5 * sqrt_discriminant + n + 0.5 + k2 = 0.5 * (sqrt_discriminant + 2*n + 1) + + # Take the maximum viable solution + k = max(k1, k2) + return M if k < 0 else min(int(k), M) # Compare pairs and update clusters in real-time batch_size = self.config.get("compare_batch_size", auto_batch()) + self.console.log(f"Using compare batch size: {batch_size}") pair_costs = 0 pbar = RichLoopBar( @@ -441,7 +451,7 @@ def auto_batch(): last_processed = 0 for i in pbar: batch_end = last_processed + batch_size - batch = blocked_pairs[last_processed:batch_end] + batch = blocked_pairs[last_processed : batch_end] # Filter pairs for the initial batch better_batch = [ pair for pair in batch diff --git a/mkdocs.yml b/mkdocs.yml index 988c272f..42622b64 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,5 +1,4 @@ site_name: docetl docs -site_url: https://docetl.com/ # use_directory_urls: false # strict: true # docs_dir: docs