From 63c222876b1523aa805d1d3437ce27bf378098dc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=9A=D0=B8=D1=80=D0=B8=D0=BB=D0=BB=20=D0=A0=D0=BE=D0=BC?=
 =?UTF-8?q?=D0=B0=D0=BD=D0=BE=D0=B2?= <kirilinka320@gmail.com>
Date: Wed, 3 Jun 2026 09:04:35 +0000
Subject: [PATCH] Update tools.py

---
 tools.py | 83 ++++++++++++++++++--------------------------------------
 1 file changed, 26 insertions(+), 57 deletions(-)

diff --git a/tools.py b/tools.py
index e6bdad4..7342633 100644
--- a/tools.py
+++ b/tools.py
@@ -1,82 +1,51 @@
-"""Tools for RAG agent.
+"""Tools for the RAG agent.
 
-This module defines two LangChain tools:
-
-* ``search_knowledge_base`` – semantic search in Qdrant.
-* ``add_to_knowledge_base`` – add a document to Qdrant.
-
-The tools use the global ``vector_store`` instance defined in this module.
+Two tools are defined:
+1. search_knowledge_base – performs semantic search in the vector store.
+2. add_to_knowledge_base – adds a new document to the vector store.
 """
 
 from typing import List
-
-from langchain_ollama import OllamaEmbeddings
-from langchain_qdrant import QdrantVectorStore
-from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain.tools import tool
 from langchain.schema import Document
 
-# Global vector store instance
-# We initialise it lazily – the first call to the tools will create the store.
-_vector_store = None
-
-# Embedding model
-_embedding = OllamaEmbeddings(model="nomic-embed-text")
-
-# Text splitter
-_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-
-
-def _get_vector_store() -> QdrantVectorStore:
-    global _vector_store
-    if _vector_store is None:
-        # Create a new collection named "knowledge_base"
-        _vector_store = QdrantVectorStore.from_texts(
-            texts=[],
-            embedding=_embedding,
-            url="http://localhost:6333",
-            collection_name="knowledge_base",
-        )
-    return _vector_store
+from vector_store import store
 
 
 @tool("search_knowledge_base")
-def search_knowledge_base(query: str, max_results: int = 5) -> List[dict]:
-    """Semantic search in the knowledge base.
+async def search_knowledge_base(query: str, max_results: int = 5) -> List[Document]:
+    """Search the knowledge base for relevant chunks.
 
     Parameters
     ----------
     query: str
         The search query.
     max_results: int
-        Maximum number of results to return.
+        Number of top results to return.
 
     Returns
     -------
-    List[dict]
-        List of matching documents with ``content`` and ``metadata``.
+    List[Document]
+        List of documents returned by Qdrant similarity search.
     """
-    store = _get_vector_store()
-    results = store.similarity_search_with_score(query, k=max_results)
-    # Return only content and metadata for simplicity
-    return [
-        {"content": doc.page_content, "metadata": doc.metadata}
-        for doc, _ in results
-    ]
+    return store.search(query, max_results)
 
 
 @tool("add_to_knowledge_base")
-def add_to_knowledge_base(content: str, title: str) -> str:
-    """Add a document to the knowledge base.
+async def add_to_knowledge_base(content: str, title: str) -> str:
+    """Add a new document to the knowledge base.
 
-    The content is split into chunks before being added.
+    Parameters
+    ----------
+    content: str
+        Full text of the document.
+    title: str
+        Title or identifier for the document.
+
+    Returns
+    -------
+    str
+        Confirmation message.
     """
-    store = _get_vector_store()
-    chunks = _splitter.split_text(content)
-    docs: List[Document] = []
-    for i, chunk in enumerate(chunks):
-        docs.append(
-            Document(page_content=chunk, metadata={"title": title, "chunk_index": i})
-        )
-    store.add_documents(docs)
-    return f"Document '{title}' added with {len(chunks)} chunks."
+    store.add_document(content, title)
+    return f"Document '{title}' added to the knowledge base."