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Knowledge (#1567)
* initial knowledge * WIP * Adding core knowledge sources * Improve types and better support for file paths * added additional sources * fix linting * update yaml to include optional deps * adding in lorenze feedback * ensure embeddings are persisted * improvements all around Knowledge class * return this * properly reset memory * properly reset memory+knowledge * consolodation and improvements * linted * cleanup rm unused embedder * fix test * fix duplicate * generating cassettes for knowledge test * updated default embedder * None embedder to use default on pipeline cloning * improvements * fixed text_file_knowledge * mypysrc fixes * type check fixes * added extra cassette * just mocks * linted * mock knowledge query to not spin up db * linted * verbose run * put a flag * fix * adding docs * better docs * improvements from review * more docs * linted * rm print * more fixes * clearer docs * added docstrings and type hints for cli --------- Co-authored-by: João Moura <joaomdmoura@gmail.com> Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
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src/crewai/knowledge/source/json_knowledge_source.py
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54
src/crewai/knowledge/source/json_knowledge_source.py
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import json
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from typing import Any, Dict, List
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from pathlib import Path
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from crewai.knowledge.source.base_file_knowledge_source import BaseFileKnowledgeSource
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class JSONKnowledgeSource(BaseFileKnowledgeSource):
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"""A knowledge source that stores and queries JSON file content using embeddings."""
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def load_content(self) -> Dict[Path, str]:
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"""Load and preprocess JSON file content."""
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super().load_content() # Validate the file path
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paths = [self.file_path] if isinstance(self.file_path, Path) else self.file_path
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content: Dict[Path, str] = {}
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for path in paths:
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with open(path, "r", encoding="utf-8") as json_file:
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data = json.load(json_file)
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content[path] = self._json_to_text(data)
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return content
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def _json_to_text(self, data: Any, level: int = 0) -> str:
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"""Recursively convert JSON data to a text representation."""
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text = ""
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indent = " " * level
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if isinstance(data, dict):
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for key, value in data.items():
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text += f"{indent}{key}: {self._json_to_text(value, level + 1)}\n"
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elif isinstance(data, list):
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for item in data:
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text += f"{indent}- {self._json_to_text(item, level + 1)}\n"
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else:
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text += f"{str(data)}"
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return text
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def add(self) -> None:
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"""
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Add JSON file content to the knowledge source, chunk it, compute embeddings,
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and save the embeddings.
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"""
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content_str = (
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str(self.content) if isinstance(self.content, dict) else self.content
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)
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new_chunks = self._chunk_text(content_str)
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self.chunks.extend(new_chunks)
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self.save_documents(metadata=self.metadata)
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def _chunk_text(self, text: str) -> List[str]:
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"""Utility method to split text into chunks."""
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return [
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text[i : i + self.chunk_size]
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for i in range(0, len(text), self.chunk_size - self.chunk_overlap)
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]
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