21 lines
938 B
Python
21 lines
938 B
Python
from langchain.tools import tool
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from langchain_chroma import Chroma
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from langchain_ollama import OllamaEmbeddings
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from langchain_tavily import TavilySearchResults
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@tool
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def search_local_kb(query: str, top_k: int = 3) -> str:
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"""Поиск в локальной базе знаний (ChromaDB) по запросу."""
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persist_directory = "./chroma_db"
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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vectorstore = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
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retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
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docs = retriever.get_relevant_documents(query)
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return "\n\n".join([doc.page_content for doc in docs])
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@tool
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def web_search(query: str) -> str:
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"""Поиск в интернете через Tavily."""
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search = TavilySearchResults()
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results = search.run(query)
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return "\n\n".join([result.get("content", "") for result in results]) |