"""
Tools for the RAG agent.

- search_local_kb(query, top_k)
- web_search(query)
"""
import os
from typing import List

from langchain_ollama import OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_tavily import TavilySearchResults
from langchain_core.documents import Document

# Global vectorstore – will be set in init_db or main
vectorstore: Chroma = None

def search_local_kb(query: str, top_k: int = 3) -> List[Document]:
    """Semantic search in the local ChromaDB."""
    if vectorstore is None:
        raise RuntimeError("Vectorstore not initialized")
    return vectorstore.similarity_search_with_score(query, k=top_k)

# Tavily client – API key from env
from tavily import TavilyClient
import os
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY", "")
client = TavilyClient(api_key=TAVILY_API_KEY)

def web_search(query: str, max_results: int = 3) -> List[Document]:
    """Search the web via Tavily and return Documents."""
    results = client.search(query=query, max_results=max_results)
    docs = []
    for r in results:
        content = f"{r.title}\n\n{r.content}"
        docs.append(Document(page_content=content, metadata={"source": "tavily", "url": r.url}))
    return docs