import os
from fastapi import FastAPI, HTTPException, Query
from pydantic import BaseModel

from .vector_store import QdrantVectorStore
from .mcp_tool import MCPTool

app = FastAPI(
    title="FAQ Bot API",
    description="A simple FAQ bot using Qdrant as the vector store.",
    version="1.0.0",
)

# Initialize the vector store and MCP tool
vector_store = QdrantVectorStore(
    host=os.getenv("QDRANT_HOST"),
    port=int(os.getenv("QDRANT_PORT", "6333")),
    collection_name=os.getenv("QDRANT_COLLECTION", "faq_collection"),
)
mcp_tool = MCPTool(vector_store)


class Document(BaseModel):
    id: str
    text: str
    metadata: dict | None = None


@app.post("/documents", status_code=201)
def add_document(doc: Document):
    """
    Add a new document to the vector store.
    """
    try:
        vector_store.add_document(doc.id, doc.text, doc.metadata)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    return {"status": "added", "id": doc.id}


@app.get("/search")
def search(
    query: str = Query(..., description="Search query"),
    top_k: int = Query(5, ge=1, le=20, description="Number of results to return"),
):
    """
    Search for documents similar to the query.
    """
    try:
        results = vector_store.search(query, top_k=top_k)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    return {"query": query, "results": results}


@app.get("/answer")
def answer(
    query: str = Query(..., description="Question to answer"),
    top_k: int = Query(3, ge=1, le=10, description="Number of answers to return"),
):
    """
    Get the best answer(s) for the query using MCPTool.
    """
    try:
        answers = mcp_tool.answer(query, top_k=top_k)
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    return {"query": query, "answers": answers}