"""
RAG query endpoints.

POST /v1/query
    Standard JSON response.  Runs the full hybrid search pipeline via
    engine.query() and returns answer + numbered citations + RAGAS scores
    (null until the background Celery task completes).  Uses Groq as the
    LLM — no Gemini API key required.

POST /v1/query/stream
    Server-Sent Events streaming response.  Uses the Gemini SDK to stream
    answer tokens as they are generated.  Requires GEMINI_API_KEY to be set.
    Both endpoints share _resolve_chunks_and_context() for retrieval so the
    confidence gate and hybrid search behaviour are identical.
"""

import json
import uuid
from typing import Optional

from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, field_validator
from sqlmodel import Session, select

from app.deps import get_current_user, get_db
from app.models.db import Job, JobStatus, User

router = APIRouter()


class QueryRequest(BaseModel):
    question: str
    job_ids: Optional[list[uuid.UUID]] = None

    @field_validator("question")
    @classmethod
    def question_not_empty(cls, v):
        if not v or not v.strip():
            raise ValueError("question must not be empty")
        return v.strip()


@router.post("/query")
def query_documents(
    req: QueryRequest,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db),
):
    from app.config import settings
    from app.rag import engine

    if req.job_ids is not None:
        for jid in req.job_ids:
            job = db.get(Job, jid)
            if not job:
                raise HTTPException(status_code=404, detail=f"Job {jid} not found")
            if job.status != JobStatus.completed:
                raise HTTPException(status_code=400, detail=f"Job {jid} is not COMPLETED")
        job_ids_str = [str(j) for j in req.job_ids]
    else:
        job_ids_str = None

    return engine.query(
        question=req.question,
        job_ids=job_ids_str,
        user_id=current_user.id,
        db=db,
        settings=settings,
    )


@router.post("/query/stream")
def query_stream(
    req: QueryRequest,
    current_user: User = Depends(get_current_user),
    db: Session = Depends(get_db),
):
    """Streaming RAG query — returns Server-Sent Events with answer chunks."""
    from app.config import settings as _settings

    if req.job_ids is not None:
        for jid in req.job_ids:
            job = db.get(Job, jid)
            if not job:
                raise HTTPException(status_code=404, detail=f"Job {jid} not found")
            if job.status != JobStatus.completed:
                raise HTTPException(status_code=400, detail=f"Job {jid} is not COMPLETED")
        job_ids_str = [str(j) for j in req.job_ids]
    else:
        # No filter — search all completed documents (shared knowledge base)
        job_ids_str = None

    def event_stream():
        from app.rag.engine import RAG_SYSTEM_PROMPT, _resolve_chunks_and_context
        from google import genai
        from google.genai import types as genai_types

        try:
            result = _resolve_chunks_and_context(req.question, job_ids_str, _settings)
            if result.get("early_return"):
                yield f"data: {json.dumps({'type': 'done', **result['payload']})}\n\n"
                return

            chunks = result["chunks"]
            user_prompt = result["user_prompt"]
            avg_score = result["avg_score"]

            genai_client = genai.Client(api_key=_settings.GEMINI_API_KEY)
            full_text = ""
            for chunk in genai_client.models.generate_content_stream(
                model=_settings.GEMINI_MODEL,
                contents=user_prompt,
                config=genai_types.GenerateContentConfig(system_instruction=RAG_SYSTEM_PROMPT),
            ):
                piece = chunk.text or ""
                if piece:
                    full_text += piece
                    yield f"data: {json.dumps({'type': 'chunk', 'text': piece})}\n\n"

            citations = [
                {"index": i + 1, "filename": c["filename"], "page_or_segment": c["page_or_segment"], "excerpt": c["text"][:200]}
                for i, c in enumerate(chunks)
            ]
            yield f"data: {json.dumps({'type': 'done', 'answer': full_text, 'citations': citations, 'confidence_gate_passed': True, 'avg_similarity_score': avg_score, 'ragas_scores': None})}\n\n"
        except Exception as exc:
            yield f"data: {json.dumps({'type': 'error', 'message': str(exc)})}\n\n"

    return StreamingResponse(event_stream(), media_type="text/event-stream")