from langchain.tools import tool
from .vector_store import VectorStore
from .splitter import chunk_text

# Shared store instance
_store: VectorStore | None = None

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

    Parameters
    ----------
    content: str
        Text content to add.
    title: str
        Optional title for the document.
    """
    global _store
    if _store is None:
        _store = VectorStore()
    chunks = chunk_text(content)
    _store.add_documents(chunks)
    return f"Added {len(chunks)} chunks to the knowledge base under title '{title}'."

@tool
def search_knowledge_base(query: str, max_results: int = 5) -> list[tuple[str, float]]:
    """Search the knowledge base for relevant chunks.

    Parameters
    ----------
    query: str
        Search query.
    max_results: int
        Number of top results to return.
    """
    global _store
    if _store is None:
        _store = VectorStore()
    results = _store.search(query, max_results=max_results)
    return results