"""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.answer_evaluation.rag_preparation import prepare_named_content
from tools.retrieval.question_constraints import unsupported_constraint, constraint_message
from tools.sync.index_state import index_snapshot
from tools.answers.model_client import complete_with_configured_model
from tools.answers.answer_errors import public_failure

_capacity = threading.BoundedSemaphore(2)


def retrieval_failure(engine: str) -> dict:
    return public_failure({'reason':'retrieval_unavailable','engine':engine,'index_version':None,
                           'complete':False,'truncated':False,'truncation_reasons':[]})


def name_failure(retrieved: dict, engine: str) -> dict | None:
    resolution = retrieved.get('name_resolution') or {}
    if not resolution or resolution.get('status') == 'ok':
        return None
    return {'status':resolution['status'],'reason':resolution['reason'],'engine':engine,
            'accepted_for_delivery':True,'answer':resolution['message'],'claims':[],'source_urls':[],
            'name_resolution':resolution,'index_version':retrieved.get('index_version'),
            'complete':False,'truncated':False,'truncation_reasons':[]}


def with_name_resolution(result: dict, retrieved: dict) -> dict:
    resolution = retrieved.get('name_resolution') or {}
    if result.get('accepted_for_delivery') and resolution.get('notices'):
        result['answer'] = '\n'.join(resolution['notices'])+'\n\n'+result['answer']
    if resolution:
        result['name_resolution'] = resolution
    return result


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 public_failure({'reason':'answer_capacity_exceeded','engine':engine})
    try:
        model = complete or complete_with_configured_model
        from tools.answer_evaluation.question_intent import unresolved_followup
        if unresolved_followup(question):
            return {'status':'ambiguous','reason':'missing_followup_context','accepted_for_delivery':True,
                'answer':'Uveď osobu a presné označenia prác alebo úplnú otázku. Samotné „obe“ bez predchádzajúcich možností neviem spoľahlivo vyhľadať.',
                'claims':[],'source_urls':[],'engine':engine,'index_version':None,'complete':False,'truncated':False,'truncation_reasons':[]}
        from tools.knowledge.query import (invalid_question, deliver_content,
                                          ContentModelUnavailable, ContentAnswerRejected)
        if invalid_question(question):
            return {'status':'ambiguous','reason':'unintelligible_question','accepted_for_delivery':True,
                    'answer':'Otázke nerozumiem. Napíš, na ktorého študenta, tému alebo výsledok sa pýtaš.',
                    'claims':[],'source_urls':[],'engine':engine,'index_version':None,
                    'complete':False,'truncated':False,'truncation_reasons':[]}
        import re
        from tools.answer_evaluation.context_policy import normalized
        wording=normalized(question)
        if re.search(r'\bsemest\w*\b',wording) and re.search(r'\b(?:aktual\w*|teraz|v akom|v ktorom)\b',wording):
            return {'status':'ambiguous','reason':'enrollment_not_established','accepted_for_delivery':True,
                    'answer':'Aktuálny semester neviem určiť len z roku začiatku štúdia alebo z názvu práce. '
                             'Potrebný je doložený údaj o aktuálnom štúdiu; môžem vyhľadať evidované práce a ich roky.',
                    'claims':[],'source_urls':[],'engine':engine,'index_version':None,
                    'complete':False,'truncated':False,'truncation_reasons':[]}
        unsupported=unsupported_constraint(question)
        if unsupported and unsupported!='year_range':
            return {'status':'ambiguous','reason':'unsupported_constraint','accepted_for_delivery':True,
                    'answer':constraint_message(unsupported),'claims':[],'source_urls':[],
                    'index_version':None,'complete':False,'truncated':False,'truncation_reasons':[],'engine':engine}
        try:
            content_result = deliver_content(question, engine=engine, limit=limit, db_file=db_file, complete=model)
            if content_result is not None:
                return content_result
        except ContentModelUnavailable:
            return public_failure({'reason':'model_unavailable','engine':engine})
        except ContentAnswerRejected:
            return public_failure({'reason':'answer_rejected','engine':engine})
        except Exception:
            return public_failure({'reason':'content_index_unavailable','engine':engine})
        unsupported=unsupported_constraint(question)
        if unsupported:
            return {'status':'ambiguous','reason':'unsupported_constraint','accepted_for_delivery':True,
                    'answer':constraint_message(unsupported),'claims':[],'source_urls':[],
                    'index_version':None,'complete':False,'truncated':False,'truncation_reasons':[], 'engine':engine}
        if engine == 'rag':
            try:
                with index_snapshot(db_file.parent, db_file=db_file):
                    retrieved = build_rag_context(db_file, question, limit=limit)
                    if retrieved.get('status')=='error':
                        return retrieval_failure(engine)
                    if failure := name_failure(retrieved, engine):
                        return failure
                    question = retrieved.get('resolved_query') or question
                    retrieved = expand_comparison_sources(db_file, question, retrieved)
                    retrieved = prepare_named_content(db_file, question, retrieved)
                    retrieved = compact_rag_payload(retrieved)
            except Exception:
                return retrieval_failure(engine)
            result = generate_verified_rag(question, retrieved, model)
            context = retrieved
        elif engine == 'graphrag':
            from tools.answer_evaluation.question_intent import graph_needs_text
            from tools.retrieval.work_selector import parse_work_selector
            if graph_needs_text(question) or parse_work_selector(question).specific:
                return {'status':'ambiguous','reason':'text_evidence_required','accepted_for_delivery':True,
                    'answer':'Táto otázka vyžaduje textové podklady o zadaní, priebehu alebo výsledkoch práce. '
                             'Použi /rag s rovnakou otázkou; graf samotný tieto výňatky neposkytuje.',
                    'claims':[],'source_urls':[],'engine':engine,'index_version':None,
                    'complete':False,'truncated':False,'truncation_reasons':[]}
            try:
                retrieved=search_graph(question, limit=limit)
                if retrieved.get('status')=='error':
                    return retrieval_failure(engine)
                if failure := name_failure(retrieved, engine):
                    return failure
                question = retrieved.get('resolved_query') or question
                context = graph_context(retrieved)
            except Exception:
                return retrieval_failure(engine)
            if context.get('clarification'):
                return with_name_resolution({'status':'ambiguous','reason':context.get('reason'),'accepted_for_delivery':True,
                        'answer':context['clarification'],'claims':[],'source_urls':[], 'engine':engine,
                        'complete':False,'truncated':False,'truncation_reasons':[],
                        'index_version':context.get('index_version')}, retrieved)
            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:
                reason=generated.get('reason','answer_rejected')
                result = public_failure({'reason':reason,
                    'index_version':context.get('index_version'),'attempts':generated['attempts']})
        else:
            raise ValueError('Unsupported answer engine')
        if result.get('status')=='error' or result.get('accepted_for_delivery') is not True:
            result=public_failure(result)
        result = with_name_resolution(result, retrieved)
        return {**result, 'engine': engine,
                **({'retrieval_context': context} if include_diagnostics else {})}
    finally:
        _capacity.release()