47 lines
1.4 KiB
Python
47 lines
1.4 KiB
Python
"""Tool definitions for the RAG agent.
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The tools are simple wrappers around the vector store functions defined in
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`vector_store.py`. They are decorated with `@tool` from LangChain so that the
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agent can call them.
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"""
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from typing import Any
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from langchain.tools import tool
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from .vector_store import add_document, search
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@tool("search_knowledge_base")
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def search_knowledge_base(query: str, max_results: int = 5) -> Any:
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"""Semantic search in the knowledge base.
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Parameters
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----------
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query: str
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The user query.
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max_results: int, optional
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Number of top results to return. Defaults to 5.
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"""
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results = search(query, max_results)
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# Convert results to a readable string
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formatted = "\n".join(
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f"{i+1}. [{res['metadata'].get('title', 'Unknown')}] {res['content'][:200]}"
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for i, res in enumerate(results)
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)
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return formatted if formatted else "No relevant documents found."
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@tool("add_to_knowledge_base")
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def add_to_knowledge_base(content: str, title: str) -> Any:
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"""Add a new document to the knowledge base.
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Parameters
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----------
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content: str
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The full text of the document.
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title: str
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A short title that will be stored as metadata.
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"""
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add_document(content, title)
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return f"Document '{title}' added to the knowledge base."
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__all__ = ["search_knowledge_base", "add_to_knowledge_base"] |