Update rag_tools.py
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"""
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Tools for the RAG agent.
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"""Tools for the RAG agent.
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Two tools are exposed via the ``@tool`` decorator:
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* ``search_knowledge_base`` – semantic search in the Qdrant vector store.
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* ``add_to_knowledge_base`` – add a document to the vector store.
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"""
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from typing import List
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from typing import List, Dict
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from langchain.tools import tool
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from qdrant_store import search, add_document
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from .vector_store import add_document, search_text
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# ---------------------------------------------------------------------------
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# Tool definitions
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# ---------------------------------------------------------------------------
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@tool("search_knowledge_base")
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async def search_knowledge_base(query: str, max_results: int = 5) -> List[str]:
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"""Perform semantic search in the knowledge base.
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"""Return the top *max_results* relevant chunks for *query*.
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Parameters
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----------
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query: str
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Search query.
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max_results: int
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Number of results to return.
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Returns
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-------
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List[str]
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List of retrieved document snippets.
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The function is asynchronous because LangChain expects async tools when
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the agent runs in an async context. The underlying vector store calls
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are synchronous, so we simply wrap the result.
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"""
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docs = search(query, max_results)
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return [doc.page_content for doc in docs]
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return search_text(query, k=max_results)
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@tool("add_to_knowledge_base")
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async def add_to_knowledge_base(content: str, title: str) -> str:
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"""Add a new document to the knowledge base.
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"""Add *content* under *title* to the knowledge base.
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Parameters
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----------
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content: str
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Full text of the document.
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title: str
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Title or identifier for the document.
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Returns
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-------
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str
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Confirmation message.
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Returns a confirmation string.
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"""
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add_document(content, title)
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return f"Document '{title}' added successfully."
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__all__ = ["search_knowledge_base", "add_to_knowledge_base"]
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add_document(title, content)
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return f"Document '{title}' added to knowledge base."
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