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 <kirillkutlahmetov10@gmail.com>
Date: Thu, 28 May 2026 13:32:57 +0000
Subject: [PATCH] =?UTF-8?q?=D0=A3=D0=B4=D0=B0=D0=BB=D0=B8=D1=82=D1=8C=20to?=
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---
 tools.py | 31 -------------------------------
 1 file changed, 31 deletions(-)
 delete mode 100644 tools.py

diff --git a/tools.py b/tools.py
deleted file mode 100644
index 1967392..0000000
--- a/tools.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""
-Tools for the RAG agent.
-
-search_knowledge_base and add_to_knowledge_base are implemented using VectorStore.
-"""
-from typing import List, Dict
-from langchain.tools import tool
-from vector_store import VectorStore
-
-# Instantiate a global store
-store = VectorStore()
-
-@tool
-def search_knowledge_base(query: str, max_results: int = 5) -> str:
-    """Semantic search in the knowledge base."""
-    results = store.similarity_search(query, k=max_results)
-    if not results:
-        return "No relevant documents found."
-    out_lines = []
-    for i, r in enumerate(results, 1):
-        out_lines.append(f"{i}. {r['content'][:200]}... (source: {r['metadata'].get('title', 'unknown')})")
-    return "\n".join(out_lines)
-
-@tool
-def add_to_knowledge_base(content: str, title: str = "document") -> str:
-    """Add a document to the knowledge base.
-
-    The content is split into chunks and stored with metadata.
-    """
-    store.add_documents([content], [{"title": title}])
-    return f"Document '{title}' added to knowledge base."