import os
from langchain.tools import tool
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_core.documents import Document
from rag_store import KnowledgeBase

kb = KnowledgeBase()

splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)

@tool
def search_knowledge_base(query: str, max_results: int = 3) -> str:
    """Search the knowledge base for relevant information."""
    docs = kb.similarity_search(query, k=max_results)
    if not docs:
        return "No results found."
    return "\n\n".join(
        f"Title: {doc.metadata.get('title', 'N/A')}\nContent: {doc.page_content}"
        for doc in docs
    )

@tool
def add_to_knowledge_base(content: str, title: str = "Untitled") -> str:
    """Add content to the knowledge base."""
    chunks = splitter.split_text(content)
    docs = [Document(page_content=chunk, metadata={"title": title}) for chunk in chunks]
    kb.add_documents(docs)
    return f"Added document '{title}'."