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https://github.com/crewAIInc/crewAI.git
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4f72839fea
- Add KnowledgeConfig class with results_limit, score_threshold, metadata_filter - Add knowledge_config field to Agent - Update Knowledge.query() to forward filter and score_threshold to storage - Update Crew.query_knowledge() to accept and forward filter params - Fix BaseKnowledgeSource._save_documents() to pass self.metadata to storage - Wire Agent.execute_task() to use knowledge_config for both agent and crew queries - Add 10 tests covering all changes Co-Authored-By: João <joao@crewai.com>
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
import os
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from typing import Any, Dict, List, Optional
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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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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 = Field(default_factory=KnowledgeStorage)
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embedder_config: Optional[Dict[str, Any]] = 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 = Field(default_factory=KnowledgeStorage)
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embedder_config: Optional[Dict[str, Any]] = None
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collection_name: Optional[str] = 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_config: Optional[Dict[str, Any]] = None,
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storage: Optional[KnowledgeStorage] = 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_config=embedder_config, collection_name=collection_name
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)
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self.sources = sources
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self.storage.initialize_knowledge_storage()
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for source in sources:
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source.storage = self.storage
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source.add()
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def query(
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self,
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query: List[str],
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limit: int = 3,
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filter: Optional[dict] = None,
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score_threshold: float = 0.35,
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) -> List[Dict[str, Any]]:
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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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"""
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results = self.storage.search(
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query,
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limit,
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filter=filter,
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score_threshold=score_threshold,
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)
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return results
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def _add_sources(self):
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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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