31 lines
993 B
Python
31 lines
993 B
Python
import os
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from typing import Any
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from langchain.tools import tool
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from langchain_ollama import OllamaEmbeddings
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from langchain_chroma import Chroma
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from langchain_tavily import TavilySearchResults
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from vectorstore import create_vectorstore
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# Global vector store instance
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VECTORSTORE = create_vectorstore()
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@tool
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def search_local_kb(query: str, top_k: int = 3) -> str:
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"""Semantic search in the local ChromaDB knowledge base."""
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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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if not docs:
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return "No relevant local knowledge found."
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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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"""Web search using Tavily."""
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tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
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results = tavily.run(query)
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if not results:
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return "No web results found."
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return "\n\n".join(f"{r['title']}\n{r['content']}" for r in results)
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