Add rag_tools.py

rag_tools.py

@@ -0,0 +1,57 @@
+"""Tools for the RAG agent: local KB search and web search via Tavily."""
+
+from typing import List, Dict
+
+from langchain_ollama import ChatOllama
+from langchain_chroma import Chroma
+from langchain_tavily import TavilySearchResults
+from langchain.tools import tool
+
+# --- Local KB search tool -----------------------------------------------------
+
+@tool("search_local_kb")
+def search_local_kb(query: str, top_k: int = 3, vectorstore: Chroma = None) -> List[Dict]:
+    """Perform a semantic search in the local Chroma vector store.
+
+    Parameters
+    ----------
+    query: str
+        The user query.
+    top_k: int
+        Number of top results to return.
+    vectorstore: Chroma
+        The vector store to search.
+
+    Returns
+    -------
+    List[Dict]
+        List of dictionaries containing ``content`` and ``metadata``.
+    """
+    if vectorstore is None:
+        raise ValueError("vectorstore must be provided")
+    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
+    docs = retriever.get_relevant_documents(query)
+    return [{"content": doc.page_content, "metadata": doc.metadata} for doc in docs]
+
+# --- Web search tool ---------------------------------------------------------
+
+@tool("web_search")
+def web_search(query: str, top_k: int = 3) -> List[Dict]:
+    """Search the web using Tavily.
+
+    Parameters
+    ----------
+    query: str
+        The user query.
+    top_k: int
+        Number of top results to return.
+
+    Returns
+    -------
+    List[Dict]
+        List of dictionaries containing ``title``, ``url`` and ``content``.
+    """
+    tavily = TavilySearchResults(max_results=top_k)
+    results = tavily.run(query)
+    # Tavily returns a list of dicts with keys: title, url, content
+    return results