"""Tools for the RAG agent.

This module defines two LangChain tools that interact with the
``KnowledgeBase`` defined in :mod:`src.vector_store`.

The tools are decorated with ``@tool`` from ``langchain.tools`` so that
the agent can invoke them automatically.
"""

from langchain.tools import tool
from .vector_store import kb

@tool("search_knowledge_base")
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Search the local knowledge base.

    Parameters
    ----------
    query: str
        The search query.
    max_results: int, optional
        Limit of results to return.

    Returns
    -------
    str
        A formatted string with the search results.
    """
    results = kb.search(query, limit=max_results)
    if not results:
        return "No relevant documents found."
    lines = []
    for i, res in enumerate(results, 1):
        title = res["metadata"].get("title", "Untitled")
        lines.append(f"{i}. Title: {title}\nContent: {res['page_content']}\n")
    return "\n".join(lines)

@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
        The full text of the document.
    title: str
        A short title for the document.

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