From b3185ad90c1cf1fae84f3db793ac1cd0acca9a08 Mon Sep 17 00:00:00 2001 From: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com> Date: Mon, 23 Dec 2024 10:19:58 -0800 Subject: [PATCH] Feat/docling-support (#1763) * added tool for docling support * docling support installation * use file_paths instead of file_path * fix import * organized imports * run_type docs * needs to be list * fixed logic * logged but file_path is backwards compatible * use file_paths instead of file_path 2 * added test for multiple sources for file_paths * fix run-types * enabling local files to work and type cleanup * linted * fix test and types * fixed run types * fix types * renamed to CrewDoclingSource * linted * added docs * resolve conflicts --------- Co-authored-by: Brandon Hancock (bhancock_ai) <109994880+bhancockio@users.noreply.github.com> Co-authored-by: Brandon Hancock --- docs/concepts/knowledge.mdx | 49 + pyproject.toml | 3 + .../source/base_file_knowledge_source.py | 53 +- .../knowledge/source/base_knowledge_source.py | 2 +- .../knowledge/source/crew_docling_source.py | 120 +++ .../source/string_knowledge_source.py | 4 +- tests/knowledge/knowledge_test.py | 59 +- uv.lock | 911 +++++++++++++++++- 8 files changed, 1166 insertions(+), 35 deletions(-) create mode 100644 src/crewai/knowledge/source/crew_docling_source.py diff --git a/docs/concepts/knowledge.mdx b/docs/concepts/knowledge.mdx index 8af9b70a2..8df6f623f 100644 --- a/docs/concepts/knowledge.mdx +++ b/docs/concepts/knowledge.mdx @@ -79,6 +79,55 @@ crew = Crew( result = crew.kickoff(inputs={"question": "What city does John live in and how old is he?"}) ``` + +Here's another example with the `CrewDoclingSource` +```python Code +from crewai import LLM, Agent, Crew, Process, Task +from crewai.knowledge.source.crew_docling_source import CrewDoclingSource + +# Create a knowledge source +content_source = CrewDoclingSource( + file_paths=[ + "https://lilianweng.github.io/posts/2024-11-28-reward-hacking", + "https://lilianweng.github.io/posts/2024-07-07-hallucination", + ], +) + +# Create an LLM with a temperature of 0 to ensure deterministic outputs +llm = LLM(model="gpt-4o-mini", temperature=0) + +# Create an agent with the knowledge store +agent = Agent( + role="About papers", + goal="You know everything about the papers.", + backstory="""You are a master at understanding papers and their content.""", + verbose=True, + allow_delegation=False, + llm=llm, +) +task = Task( + description="Answer the following questions about the papers: {question}", + expected_output="An answer to the question.", + agent=agent, +) + +crew = Crew( + agents=[agent], + tasks=[task], + verbose=True, + process=Process.sequential, + knowledge_sources=[ + content_source + ], # Enable knowledge by adding the sources here. You can also add more sources to the sources list. +) + +result = crew.kickoff( + inputs={ + "question": "What is the reward hacking paper about? Be sure to provide sources." + } +) +``` + ## Knowledge Configuration ### Chunking Configuration diff --git a/pyproject.toml b/pyproject.toml index 27a4785b4..f9533a6f9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -51,6 +51,9 @@ openpyxl = [ "openpyxl>=3.1.5", ] mem0 = ["mem0ai>=0.1.29"] +docling = [ + "docling>=2.12.0", +] [tool.uv] dev-dependencies = [ diff --git a/src/crewai/knowledge/source/base_file_knowledge_source.py b/src/crewai/knowledge/source/base_file_knowledge_source.py index ae9b3bf71..e086cbf65 100644 --- a/src/crewai/knowledge/source/base_file_knowledge_source.py +++ b/src/crewai/knowledge/source/base_file_knowledge_source.py @@ -1,8 +1,8 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Dict, List, Union +from typing import Dict, List, Optional, Union -from pydantic import Field +from pydantic import Field, field_validator from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage @@ -14,17 +14,28 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC): """Base class for knowledge sources that load content from files.""" _logger: Logger = Logger(verbose=True) - file_path: Union[Path, List[Path], str, List[str]] = Field( - ..., description="The path to the file" + file_path: Optional[Union[Path, List[Path], str, List[str]]] = Field( + default=None, + description="[Deprecated] The path to the file. Use file_paths instead.", + ) + file_paths: Optional[Union[Path, List[Path], str, List[str]]] = Field( + default_factory=list, description="The path to the file" ) content: Dict[Path, str] = Field(init=False, default_factory=dict) storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage) safe_file_paths: List[Path] = Field(default_factory=list) + @field_validator("file_path", "file_paths", mode="before") + def validate_file_path(cls, v, values): + """Validate that at least one of file_path or file_paths is provided.""" + if v is None and ("file_path" not in values or values.get("file_path") is None): + raise ValueError("Either file_path or file_paths must be provided") + return v + def model_post_init(self, _): """Post-initialization method to load content.""" self.safe_file_paths = self._process_file_paths() - self.validate_paths() + self.validate_content() self.content = self.load_content() @abstractmethod @@ -32,7 +43,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC): """Load and preprocess file content. Should be overridden by subclasses. Assume that the file path is relative to the project root in the knowledge directory.""" pass - def validate_paths(self): + def validate_content(self): """Validate the paths.""" for path in self.safe_file_paths: if not path.exists(): @@ -59,13 +70,29 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC): def _process_file_paths(self) -> List[Path]: """Convert file_path to a list of Path objects.""" - paths = ( - [self.file_path] - if isinstance(self.file_path, (str, Path)) - else self.file_path - ) - if not isinstance(paths, list): - raise ValueError("file_path must be a Path, str, or a list of these types") + # Check if old file_path is being used + if hasattr(self, "file_path") and self.file_path is not None: + self._logger.log( + "warning", + "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.", + color="yellow", + ) + paths = ( + [self.file_path] + if isinstance(self.file_path, (str, Path)) + else self.file_path + ) + else: + if self.file_paths is None: + raise ValueError("Your source must be provided with a file_paths: []") + elif isinstance(self.file_paths, list) and len(self.file_paths) == 0: + raise ValueError("Empty file_paths are not allowed") + else: + paths = ( + [self.file_paths] + if isinstance(self.file_paths, (str, Path)) + else self.file_paths + ) return [self.convert_to_path(path) for path in paths] diff --git a/src/crewai/knowledge/source/base_knowledge_source.py b/src/crewai/knowledge/source/base_knowledge_source.py index 07a9a0f25..88c3ab360 100644 --- a/src/crewai/knowledge/source/base_knowledge_source.py +++ b/src/crewai/knowledge/source/base_knowledge_source.py @@ -21,7 +21,7 @@ class BaseKnowledgeSource(BaseModel, ABC): collection_name: Optional[str] = Field(default=None) @abstractmethod - def load_content(self) -> Dict[Any, str]: + def validate_content(self) -> Any: """Load and preprocess content from the source.""" pass diff --git a/src/crewai/knowledge/source/crew_docling_source.py b/src/crewai/knowledge/source/crew_docling_source.py new file mode 100644 index 000000000..8b197168b --- /dev/null +++ b/src/crewai/knowledge/source/crew_docling_source.py @@ -0,0 +1,120 @@ +from pathlib import Path +from typing import Iterator, List, Optional, Union +from urllib.parse import urlparse + +from docling.datamodel.base_models import InputFormat +from docling.document_converter import DocumentConverter +from docling.exceptions import ConversionError +from docling_core.transforms.chunker.hierarchical_chunker import HierarchicalChunker +from docling_core.types.doc.document import DoclingDocument +from pydantic import Field + +from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource +from crewai.utilities.constants import KNOWLEDGE_DIRECTORY +from crewai.utilities.logger import Logger + + +class CrewDoclingSource(BaseKnowledgeSource): + """Default Source class for converting documents to markdown or json + This will auto support PDF, DOCX, and TXT, XLSX, Images, and HTML files without any additional dependencies and follows the docling package as the source of truth. + """ + + _logger: Logger = Logger(verbose=True) + + file_path: Optional[List[Union[Path, str]]] = Field(default=None) + file_paths: List[Union[Path, str]] = Field(default_factory=list) + chunks: List[str] = Field(default_factory=list) + safe_file_paths: List[Union[Path, str]] = Field(default_factory=list) + content: List[DoclingDocument] = Field(default_factory=list) + document_converter: DocumentConverter = Field( + default_factory=lambda: DocumentConverter( + allowed_formats=[ + InputFormat.MD, + InputFormat.ASCIIDOC, + InputFormat.PDF, + InputFormat.DOCX, + InputFormat.HTML, + InputFormat.IMAGE, + InputFormat.XLSX, + InputFormat.PPTX, + ] + ) + ) + + def model_post_init(self, _) -> None: + if self.file_path: + self._logger.log( + "warning", + "The 'file_path' attribute is deprecated and will be removed in a future version. Please use 'file_paths' instead.", + color="yellow", + ) + self.file_paths = self.file_path + self.safe_file_paths = self.validate_content() + self.content = self._load_content() + + def _load_content(self) -> List[DoclingDocument]: + try: + return self._convert_source_to_docling_documents() + except ConversionError as e: + self._logger.log( + "error", + f"Error loading content: {e}. Supported formats: {self.document_converter.allowed_formats}", + "red", + ) + raise e + except Exception as e: + self._logger.log("error", f"Error loading content: {e}") + raise e + + def add(self) -> None: + if self.content is None: + return + for doc in self.content: + new_chunks_iterable = self._chunk_doc(doc) + self.chunks.extend(list(new_chunks_iterable)) + self._save_documents() + + def _convert_source_to_docling_documents(self) -> List[DoclingDocument]: + conv_results_iter = self.document_converter.convert_all(self.safe_file_paths) + return [result.document for result in conv_results_iter] + + def _chunk_doc(self, doc: DoclingDocument) -> Iterator[str]: + chunker = HierarchicalChunker() + for chunk in chunker.chunk(doc): + yield chunk.text + + def validate_content(self) -> List[Union[Path, str]]: + processed_paths: List[Union[Path, str]] = [] + for path in self.file_paths: + if isinstance(path, str): + if path.startswith(("http://", "https://")): + try: + if self._validate_url(path): + processed_paths.append(path) + else: + raise ValueError(f"Invalid URL format: {path}") + except Exception as e: + raise ValueError(f"Invalid URL: {path}. Error: {str(e)}") + else: + local_path = Path(KNOWLEDGE_DIRECTORY + "/" + path) + if local_path.exists(): + processed_paths.append(local_path) + else: + raise FileNotFoundError(f"File not found: {local_path}") + else: + # this is an instance of Path + processed_paths.append(path) + return processed_paths + + def _validate_url(self, url: str) -> bool: + try: + result = urlparse(url) + return all( + [ + result.scheme in ("http", "https"), + result.netloc, + len(result.netloc.split(".")) >= 2, # Ensure domain has TLD + ] + ) + except Exception: + return False diff --git a/src/crewai/knowledge/source/string_knowledge_source.py b/src/crewai/knowledge/source/string_knowledge_source.py index 1d40ac4b8..614303b1f 100644 --- a/src/crewai/knowledge/source/string_knowledge_source.py +++ b/src/crewai/knowledge/source/string_knowledge_source.py @@ -13,9 +13,9 @@ class StringKnowledgeSource(BaseKnowledgeSource): def model_post_init(self, _): """Post-initialization method to validate content.""" - self.load_content() + self.validate_content() - def load_content(self): + def validate_content(self): """Validate string content.""" if not isinstance(self.content, str): raise ValueError("StringKnowledgeSource only accepts string content") diff --git a/tests/knowledge/knowledge_test.py b/tests/knowledge/knowledge_test.py index 201ddea12..366067587 100644 --- a/tests/knowledge/knowledge_test.py +++ b/tests/knowledge/knowledge_test.py @@ -1,10 +1,12 @@ """Test Knowledge creation and querying functionality.""" from pathlib import Path +from typing import List, Union from unittest.mock import patch import pytest +from crewai.knowledge.source.crew_docling_source import CrewDoclingSource from crewai.knowledge.source.csv_knowledge_source import CSVKnowledgeSource from crewai.knowledge.source.excel_knowledge_source import ExcelKnowledgeSource from crewai.knowledge.source.json_knowledge_source import JSONKnowledgeSource @@ -200,7 +202,7 @@ def test_single_short_file(mock_vector_db, tmpdir): f.write(content) file_source = TextFileKnowledgeSource( - file_path=file_path, metadata={"preference": "personal"} + file_paths=[file_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [file_source] mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] @@ -242,7 +244,7 @@ def test_single_2k_character_file(mock_vector_db, tmpdir): f.write(content) file_source = TextFileKnowledgeSource( - file_path=file_path, metadata={"preference": "personal"} + file_paths=[file_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [file_source] mock_vector_db.query.return_value = [{"context": content, "score": 0.9}] @@ -279,7 +281,7 @@ def test_multiple_short_files(mock_vector_db, tmpdir): file_paths.append((file_path, item["metadata"])) file_sources = [ - TextFileKnowledgeSource(file_path=path, metadata=metadata) + TextFileKnowledgeSource(file_paths=[path], metadata=metadata) for path, metadata in file_paths ] mock_vector_db.sources = file_sources @@ -352,7 +354,7 @@ def test_multiple_2k_character_files(mock_vector_db, tmpdir): file_paths.append(file_path) file_sources = [ - TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"}) for path in file_paths ] mock_vector_db.sources = file_sources @@ -399,7 +401,7 @@ def test_hybrid_string_and_files(mock_vector_db, tmpdir): file_paths.append(file_path) file_sources = [ - TextFileKnowledgeSource(file_path=path, metadata={"preference": "personal"}) + TextFileKnowledgeSource(file_paths=[path], metadata={"preference": "personal"}) for path in file_paths ] @@ -424,7 +426,7 @@ def test_pdf_knowledge_source(mock_vector_db): # Create a PDFKnowledgeSource pdf_source = PDFKnowledgeSource( - file_path=pdf_path, metadata={"preference": "personal"} + file_paths=[pdf_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [pdf_source] mock_vector_db.query.return_value = [ @@ -461,7 +463,7 @@ def test_csv_knowledge_source(mock_vector_db, tmpdir): # Create a CSVKnowledgeSource csv_source = CSVKnowledgeSource( - file_path=csv_path, metadata={"preference": "personal"} + file_paths=[csv_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [csv_source] mock_vector_db.query.return_value = [ @@ -496,7 +498,7 @@ def test_json_knowledge_source(mock_vector_db, tmpdir): # Create a JSONKnowledgeSource json_source = JSONKnowledgeSource( - file_path=json_path, metadata={"preference": "personal"} + file_paths=[json_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [json_source] mock_vector_db.query.return_value = [ @@ -529,7 +531,7 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir): # Create an ExcelKnowledgeSource excel_source = ExcelKnowledgeSource( - file_path=excel_path, metadata={"preference": "personal"} + file_paths=[excel_path], metadata={"preference": "personal"} ) mock_vector_db.sources = [excel_source] mock_vector_db.query.return_value = [ @@ -543,3 +545,42 @@ def test_excel_knowledge_source(mock_vector_db, tmpdir): # Assert that the correct information is retrieved assert any("30" in result["context"] for result in results) mock_vector_db.query.assert_called_once() + + +def test_docling_source(mock_vector_db): + docling_source = CrewDoclingSource( + file_paths=[ + "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", + ], + ) + mock_vector_db.sources = [docling_source] + mock_vector_db.query.return_value = [ + { + "context": "Reward hacking is a technique used to improve the performance of reinforcement learning agents.", + "score": 0.9, + } + ] + # Perform a query + query = "What is reward hacking?" + results = mock_vector_db.query(query) + assert any("reward hacking" in result["context"].lower() for result in results) + mock_vector_db.query.assert_called_once() + + +def test_multiple_docling_sources(): + urls: List[Union[Path, str]] = [ + "https://lilianweng.github.io/posts/2024-11-28-reward-hacking/", + "https://lilianweng.github.io/posts/2024-07-07-hallucination/", + ] + docling_source = CrewDoclingSource(file_paths=urls) + + assert docling_source.file_paths == urls + assert docling_source.content is not None + + +def test_docling_source_with_local_file(): + current_dir = Path(__file__).parent + pdf_path = current_dir / "crewai_quickstart.pdf" + docling_source = CrewDoclingSource(file_paths=[pdf_path]) + assert docling_source.file_paths == [pdf_path] + assert docling_source.content is not None diff --git a/uv.lock b/uv.lock index 5cf51b18c..c37a1fa4e 100644 --- a/uv.lock +++ b/uv.lock @@ -211,6 +211,19 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/e4/0e/38cb7b781371e79e9c697fb78f3ccd18fda8bd547d0a2e76e616561a3792/auth0_python-4.7.2-py3-none-any.whl", hash = "sha256:df2224f9b1e170b3aa12d8bc7ff02eadb7cc229307a09ec6b8a55fd1e0e05dc8", size = 131834 }, ] +[[package]] +name = "autoflake" +version = "2.3.1" 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