feat: solution for 'Экзамен: RAG-агент с ChromaDB и веб-поиском'

tools.py

@@ -0,0 +1,71 @@
+from typing import List
+
+from langchain.tools import tool
+from langchain_ollama import Ollama
+from langchain_qdrant import QdrantVectorStore
+from langchain_tavily import TavilySearchResults
+
+
+@tool
+def search_local_kb(
+    query: str,
+    top_k: int,
+    vectorstore: QdrantVectorStore,
+) -> List[str]:
+    """
+    Perform a semantic search in the local knowledge base stored in Qdrant.
+
+    Parameters
+    ----------
+    query : str
+        The user's query.
+    top_k : int
+        Number of top results to return.
+    vectorstore : QdrantVectorStore
+        The vector store to search.
+
+    Returns
+    -------
+    List[str]
+        List of relevant document snippets.
+    """
+    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
+    docs = retriever.get_relevant_documents(query)
+    return [doc.page_content for doc in docs]
+
+
+@tool
+def web_search(
+    query: str,
+    tavily_api_key: str,
+    max_results: int = 3,
+) -> List[str]:
+    """
+    Perform a web search using Tavily.
+
+    Parameters
+    ----------
+    query : str
+        The user's query.
+    tavily_api_key : str
+        Tavily API key.
+    max_results : int
+        Number of search results to return.
+
+    Returns
+    -------
+    List[str]
+        List of search result snippets.
+    """
+    tavily = TavilySearchResults(
+        api_key=tavily_api_key,
+        max_results=max_results,
+    )
+    results = tavily.run(query)
+    # Extract snippets from results
+    snippets = []
+    for result in results:
+        snippet = result.get("content") or result.get("snippet") or result.get("title")
+        if snippet:
+            snippets.append(snippet)
+    return snippets