from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, HttpUrl
from typing import Optional
import uuid
import logging
from src.database import db
from src.queue_manager import queue_manager
from src.vector_store import vector_store
from src.embeddings import embedding_service
from groq import Groq
from src.config import config

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="RAG System API", version="1.0.0")

class IngestURLRequest(BaseModel):
    url: HttpUrl

class QueryRequest(BaseModel):
    question: str
    top_k: Optional[int] = None

class IngestURLResponse(BaseModel):
    url_id: str
    url: str
    status: str
    message: str

class StatusResponse(BaseModel):
    url_id: str
    url: str
    status: str
    created_at: str
    updated_at: str
    completed_at: Optional[str] = None
    chunk_count: int
    error_message: Optional[str] = None

class QueryResponse(BaseModel):
    question: str
    answer: str
    sources: list

@app.on_event("startup")
async def startup_event():
    logger.info("Starting RAG System API...")
    logger.info(f"Database path: {config.DATABASE_PATH}")
    logger.info(f"Redis: {config.REDIS_HOST}:{config.REDIS_PORT}")
    logger.info(f"Qdrant: {config.QDRANT_HOST}:{config.QDRANT_PORT}")

@app.get("/")
async def root():
    return {
        "message": "RAG System API",
        "version": "1.0.0",
        "endpoints": {
            "ingest": "POST /ingest-url",
            "query": "POST /query",
            "status": "GET /status/{url_id}"
        }
    }

@app.post("/ingest-url", response_model=IngestURLResponse, status_code=202)
async def ingest_url(request: IngestURLRequest):
    try:
        url = str(request.url)
        url_id = str(uuid.uuid4())
        
        db.create_url_record(url_id, url)
        
        job_data = {
            "url_id": url_id,
            "url": url
        }
        queue_manager.enqueue(job_data)
        
        logger.info(f"Enqueued URL for processing: {url} (ID: {url_id})")
        
        return IngestURLResponse(
            url_id=url_id,
            url=url,
            status="pending",
            message="URL has been queued for processing"
        )
    
    except Exception as e:
        logger.error(f"Error enqueueing URL: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")

@app.get("/status/{url_id}", response_model=StatusResponse)
async def get_status(url_id: str):
    record = db.get_url_record(url_id)
    
    if not record:
        raise HTTPException(status_code=404, detail="URL ID not found")
    
    return StatusResponse(
        url_id=record["id"],
        url=record["url"],
        status=record["status"],
        created_at=record["created_at"],
        updated_at=record["updated_at"],
        completed_at=record.get("completed_at"),
        chunk_count=record.get("chunk_count", 0),
        error_message=record.get("error_message")
    )

@app.post("/query", response_model=QueryResponse)
async def query_knowledge_base(request: QueryRequest):
    try:
        if not config.GROQ_API_KEY:
            raise HTTPException(
                status_code=500,
                detail="GROQ_API_KEY not configured. Please set it in your .env file"
            )
        
        question = request.question
        top_k = request.top_k or config.TOP_K_RESULTS
        
        query_embedding = embedding_service.embed_text(question)
        
        search_results = vector_store.search(query_embedding, top_k=top_k)
        
        if not search_results:
            return QueryResponse(
                question=question,
                answer="I don't have enough information in my knowledge base to answer this question. Please ingest relevant URLs first.",
                sources=[]
            )
        
        context_parts = []
        sources = []
        for i, result in enumerate(search_results):
            context_parts.append(f"[Source {i+1}]: {result['text']}")
            sources.append({
                "url": result["url"],
                "score": result["score"],
                "text_snippet": result["text"][:200] + "..." if len(result["text"]) > 200 else result["text"]
            })
        
        context = "\n\n".join(context_parts)
        
        system_prompt = """You are a helpful AI assistant that answers questions based solely on the provided context. 
Your responses must be:
1. Grounded in the provided sources
2. Accurate and factual
3. Clear and concise
4. If the context doesn't contain relevant information, say so explicitly."""

        user_prompt = f"""Context from knowledge base:

{context}

Question: {question}

Please provide a clear, grounded answer based only on the information in the context above. If the context doesn't contain enough information to answer the question, please say so."""

        groq_client = Groq(api_key=config.GROQ_API_KEY)
        
        chat_completion = groq_client.chat.completions.create(
            messages=[
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": user_prompt}
            ],
            model="llama-3.3-70b-versatile",
            temperature=0.3,
            max_tokens=1024
        )
        
        answer = chat_completion.choices[0].message.content
        
        logger.info(f"Query processed: {question[:100]}...")
        
        return QueryResponse(
            question=question,
            answer=answer,
            sources=sources
        )
    
    except HTTPException:
        raise
    except Exception as e:
        logger.error(f"Error processing query: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")

@app.get("/health")
async def health_check():
    redis_ok = queue_manager.ping()
    
    return {
        "status": "healthy",
        "redis_connected": redis_ok,
        "queue_length": queue_manager.get_queue_length() if redis_ok else None
    }