"""
Tools for the RAG agent.

Two tools: search_knowledge_base and add_to_knowledge_base.
"""

from typing import List, Dict
from langchain.tools import tool
from vector_store import vector_store
from chunker import split_text

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

    Returns a formatted string of results.
    """
    hits = vector_store.search(query, k=max_results)
    if not hits:
        return "No relevant documents found."
    lines: List[str] = []
    for i, hit in enumerate(hits, 1):
        title = hit["metadata"].get("title", f"doc_{hit['id']}")
        snippet = hit["document"][:200]
        lines.append(f"{i}. {title}: {snippet}...")
    return "\n".join(lines)

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

    Splits content into chunks and stores each with metadata.
    """
    chunks = split_text(content)
    for idx, chunk in enumerate(chunks):
        doc_id = f"{title}_{idx}"
        vector_store.add_document(doc_id=doc_id, text=chunk, metadata={"title": title})
    return f"Added {len(chunks)} chunks from '{title}'."