Add src/tools.py

src/tools.py

@@ -0,0 +1,57 @@
+"""Agent tools for interacting with the knowledge base.
+
+This module defines two tools that can be used by the LangChain agent:
+
+* ``search_knowledge_base`` – performs a semantic search in the Qdrant vector store.
+* ``add_to_knowledge_base`` – adds a new document (title + content) to the store.
+
+Both tools are decorated with ``@tool`` from ``langchain.tools`` so that they can be
+exposed to the agent.
+"""
+
+from typing import List, Dict, Any
+
+from langchain.tools import tool
+
+from .vector_store import KnowledgeBase
+
+# Create a single global knowledge base instance that all tools will use.
+# In a real deployment you might want to inject this via dependency injection.
+kb = KnowledgeBase()
+
+@tool("search_knowledge_base")
+def search_knowledge_base(query: str, max_results: int = 5) -> List[Dict[str, Any]]:
+    """Search the knowledge base for relevant chunks.
+
+    Parameters
+    ----------
+    query: str
+        The search query.
+    max_results: int, optional
+        Number of top results to return. Defaults to 5.
+
+    Returns
+    -------
+    List[Dict[str, Any]]
+        A list of dictionaries containing ``content``, ``title``, ``chunk_index`` and ``score``.
+    """
+    return kb.search(query, max_results)
+
+@tool("add_to_knowledge_base")
+def add_to_knowledge_base(content: str, title: str) -> str:
+    """Add a new document to the knowledge base.
+
+    Parameters
+    ----------
+    content: str
+        Full text of the document.
+    title: str
+        Title or name of the document.
+
+    Returns
+    -------
+    str
+        Confirmation message.
+    """
+    kb.add_document(content, title)
+    return f"Document '{title}' added to the knowledge base."