import os
from typing import Any

from langchain.tools import tool
from langchain_ollama import OllamaEmbeddings
from langchain_chroma import Chroma
from langchain_tavily import TavilySearchResults

from vectorstore import create_vectorstore

# Global vector store instance
VECTORSTORE = create_vectorstore()

@tool
def search_local_kb(query: str, top_k: int = 3) -> str:
    """Semantic search in the local ChromaDB knowledge base."""
    retriever = VECTORSTORE.as_retriever(search_kwargs={"k": top_k})
    docs = retriever.get_relevant_documents(query)
    if not docs:
        return "No relevant local knowledge found."
    return "\n\n".join(doc.page_content for doc in docs)

@tool
def web_search(query: str) -> str:
    """Web search using Tavily."""
    tavily = TavilySearchResults(api_key=os.getenv("TAVILY_API_KEY"))
    results = tavily.run(query)
    if not results:
        return "No web results found."
    return "\n\n".join(f"{r['title']}\n{r['content']}" for r in results)