21 lines
875 B
Python
21 lines
875 B
Python
from langchain.tools import tool
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from langchain.embeddings.ollama import OllamaEmbeddings
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from langchain.vectorstores import Chroma
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import os
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@tool("search_local_kb")
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def search_local_kb(query: str, top_k: int = 3) -> str:
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"""Semantic search in local ChromaDB."""
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persist_dir = os.getenv("CHROMA_DB_DIR", "./chroma_db")
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embeddings = OllamaEmbeddings(model="nomic-embed-text")
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db = Chroma(embedding_function=embeddings, persist_directory=persist_dir)
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docs = db.similarity_search(query, k=top_k)
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return "\n".join([d.page_content for d in docs]) + f"\n[Source: chromadb]"
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@tool("web_search")
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def web_search(query: str) -> str:
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from tavily import TavilyClient
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client = TavilyClient(api_key=os.getenv("TAVILY_API_KEY"))
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result = client.search(query, max_results=3)
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return "\n".join([r['content'] for r in result]) + f"\n[Source: tavily]"
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