"""Tools for the RAG agent.

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.tools import tool
from langchain.schema import Document

from vector_store import store


@tool("search_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
        Number of top results to return.

    Returns
    -------
    List[Document]
        List of documents returned by Qdrant similarity search.
    """
    return store.search(query, max_results)


@tool("add_to_knowledge_base")
async 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 identifier for the document.

    Returns
    -------
    str
        Confirmation message.
    """
    store.add_document(content, title)
    return f"Document '{title}' added to the knowledge base."