"""Agent tools: local KB search (ChromaDB) and web search (Tavily)."""
import os
from langchain.tools import tool
from vectorstore import similarity_search

@tool
def search_local_kb(query: str, top_k: int = 5) -> str:
    """Search the local ChromaDB knowledge base for relevant information.

    Use this when the question may be answered from locally stored documents.

    Args:
        query: natural language search query
        top_k: number of results to return (default 5)

    Returns:
        numbered list of relevant passages, or a message if nothing found
    """
    docs = similarity_search(query, k=top_k)
    if not docs:
        return "No relevant documents found in local knowledge base."
    results = "\n\n".join(
        f"{i + 1}. {doc.page_content}" for i, doc in enumerate(docs)
    )
    return f"[Source: Local KB]\n{results}"

@tool
def web_search(query: str) -> str:
    """Search the web for current information using Tavily.

    Use this when the question requires up-to-date or general knowledge
    not available in the local knowledge base.

    Args:
        query: search query string

    Returns:
        web search results with titles, URLs and excerpts
    """
    try:
        from tavily import TavilyClient
        api_key = os.getenv("TAVILY_API_KEY", "")
        if not api_key:
            return "[Source: Web] Tavily API key not set. Add TAVILY_API_KEY to .env"
        client = TavilyClient(api_key=api_key)
        response = client.search(query, max_results=5)
        items = response.get("results", [])
        if not items:
            return "[Source: Web] No results found."
        lines = []
        for i, r in enumerate(items, 1):
            title = r.get("title", "No title")
            url = r.get("url", "")
            snippet = r.get("content", "")[:300]
            lines.append(f"{i}. {title}\n   URL: {url}\n   {snippet}")
        return "[Source: Web]\n" + "\n\n".join(lines)
    except Exception as e:
        return f"[Source: Web] Search error: {e}"