"""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."