"""Production answer path: retrieval -> model selection -> guard -> rendered text."""
from __future__ import annotations

import threading
from pathlib import Path

from tools.rag.common import DB_FILE
from tools.rag.rag_utils import build_rag_context
from tools.rag.llm_context import compact_rag_payload
from tools.graphrag.graph_search import search_graph
from tools.answer_evaluation.graph_context import graph_context
from tools.answer_evaluation.checked_generation import generate_checked_answer
from tools.answer_evaluation.verified_rag import generate_verified_rag
from tools.answer_evaluation.rag_evidence import expand_comparison_sources
from tools.sync.index_state import index_snapshot
from tools.answers.model_client import complete_with_configured_model

_capacity = threading.BoundedSemaphore(2)


def deliver_answer(question: str, *, engine: str = 'rag', limit: int = 5,
                   complete=None, db_file: Path = DB_FILE, include_diagnostics: bool = False) -> dict:
    if not _capacity.acquire(blocking=False):
        return {'status': 'error', 'reason': 'answer_capacity_exceeded',
                'accepted_for_delivery': False, 'answer': 'Agent práve spracúva iné odpovede. Skúste to o chvíľu.'}
    try:
        model = complete or complete_with_configured_model
        if engine == 'rag':
            with index_snapshot(db_file.parent, db_file=db_file):
                retrieved = build_rag_context(db_file, question, limit=limit)
                retrieved = expand_comparison_sources(db_file, question, retrieved)
                retrieved = compact_rag_payload(retrieved)
            result = generate_verified_rag(question, retrieved, model)
            context = retrieved
        elif engine == 'graphrag':
            context = graph_context(search_graph(question))
            prompt = (Path(__file__).resolve().parents[1] / 'answer_evaluation/graph_answer_prompt.txt').read_text()
            generated = generate_checked_answer(question, context, prompt, model)
            if generated.get('accepted_for_delivery'):
                result = {**generated['answer'], 'accepted_for_delivery': True, 'reason': None,
                    'index_version': context.get('index_version'), 'complete': context.get('complete'),
                    'truncated': context.get('truncated'), 'truncation_reasons': context.get('truncation_reasons'),
                    'attempts': generated['attempts']}
            else:
                result = {'status': 'error', 'reason': 'answer_rejected', 'accepted_for_delivery': False,
                    'answer': 'Odpoveď neprešla kontrolou podkladov; neoverené údaje sa nezobrazia.',
                    'index_version': context.get('index_version'), 'attempts': generated['attempts']}
        else:
            raise ValueError('Unsupported answer engine')
        return {**result, 'engine': engine,
                **({'retrieval_context': context} if include_diagnostics else {})}
    finally:
        _capacity.release()