"""
Tools for the RAG agent.

search_knowledge_base and add_to_knowledge_base are implemented using VectorStore.
"""
from typing import List, Dict
from langchain.tools import tool
from vector_store import VectorStore

# Instantiate a global store
store = VectorStore()

@tool
def search_knowledge_base(query: str, max_results: int = 5) -> str:
    """Semantic search in the knowledge base."""
    results = store.similarity_search(query, k=max_results)
    if not results:
        return "No relevant documents found."
    out_lines = []
    for i, r in enumerate(results, 1):
        out_lines.append(f"{i}. {r['content'][:200]}... (source: {r['metadata'].get('title', 'unknown')})")
    return "\n".join(out_lines)

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

    The content is split into chunks and stored with metadata.
    """
    store.add_documents([content], [{"title": title}])
    return f"Document '{title}' added to knowledge base."