mirror of
https://github.com/crewAIInc/crewAI.git
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d4aa676195
- Add limit and score_threshold to BaseRagConfig, propagate to clients - Update default search params in RAG storage, knowledge, and memory (limit=5, threshold=0.6) - Fix linting (ruff, mypy, PERF203) and refactor save logic - Update tests for new defaults and ChromaDB behavior
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import os
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from typing import Any
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from pydantic import BaseModel, ConfigDict, Field
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from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
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from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
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from crewai.rag.types import SearchResult
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os.environ["TOKENIZERS_PARALLELISM"] = "false" # removes logging from fastembed
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class Knowledge(BaseModel):
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"""
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Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
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Args:
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sources: list[BaseKnowledgeSource] = Field(default_factory=list)
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storage: KnowledgeStorage | None = Field(default=None)
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embedder: dict[str, Any] | None = None
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"""
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sources: list[BaseKnowledgeSource] = Field(default_factory=list)
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model_config = ConfigDict(arbitrary_types_allowed=True)
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storage: KnowledgeStorage | None = Field(default=None)
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embedder: dict[str, Any] | None = None
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collection_name: str | None = None
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def __init__(
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self,
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collection_name: str,
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sources: list[BaseKnowledgeSource],
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embedder: dict[str, Any] | None = None,
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storage: KnowledgeStorage | None = None,
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**data,
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):
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super().__init__(**data)
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if storage:
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self.storage = storage
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else:
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self.storage = KnowledgeStorage(
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embedder=embedder, collection_name=collection_name
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)
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self.sources = sources
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def query(
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self, query: list[str], results_limit: int = 5, score_threshold: float = 0.6
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) -> list[SearchResult]:
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"""
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Query across all knowledge sources to find the most relevant information.
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Returns the top_k most relevant chunks.
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Raises:
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ValueError: If storage is not initialized.
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"""
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if self.storage is None:
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raise ValueError("Storage is not initialized.")
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return self.storage.search(
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query,
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limit=results_limit,
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score_threshold=score_threshold,
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)
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def add_sources(self):
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try:
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for source in self.sources:
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source.storage = self.storage
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source.add()
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except Exception as e:
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raise e
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def reset(self) -> None:
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if self.storage:
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self.storage.reset()
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else:
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raise ValueError("Storage is not initialized.")
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