"""Tools for the RAG agent.

Two tools are exposed via the ``@tool`` decorator:
* ``search_knowledge_base`` – semantic search in the Qdrant vector store.
* ``add_to_knowledge_base`` – add a document to the vector store.
"""

from typing import List, Dict

from langchain.tools import tool

from .vector_store import add_document, search_text

# ---------------------------------------------------------------------------
# Tool definitions
# ---------------------------------------------------------------------------

@tool("search_knowledge_base")
async def search_knowledge_base(query: str, max_results: int = 5) -> List[str]:
    """Return the top *max_results* relevant chunks for *query*.

    The function is asynchronous because LangChain expects async tools when
    the agent runs in an async context. The underlying vector store calls
    are synchronous, so we simply wrap the result.
    """
    return search_text(query, k=max_results)

@tool("add_to_knowledge_base")
async def add_to_knowledge_base(content: str, title: str) -> str:
    """Add *content* under *title* to the knowledge base.

    Returns a confirmation string.
    """
    add_document(title, content)
    return f"Document '{title}' added to knowledge base."