import logging
import time

import click
from celery import shared_task
from sqlalchemy import select
from werkzeug.exceptions import NotFound

from core.db.session_factory import session_factory
from core.rag.datasource.vdb.vector_factory import Vector
from core.rag.index_processor.constant.index_type import IndexTechniqueType
from core.rag.models.document import Document
from extensions.ext_redis import redis_client
from models.dataset import Dataset
from models.model import App, AppAnnotationSetting, MessageAnnotation
from services.dataset_service import DatasetCollectionBindingService

logger = logging.getLogger(__name__)


@shared_task(queue="dataset")
def batch_import_annotations_task(job_id: str, content_list: list[dict], app_id: str, tenant_id: str, user_id: str):
    """
    Add annotation to index.
    :param job_id: job_id
    :param content_list: content list
    :param app_id: app id
    :param tenant_id: tenant id
    :param user_id: user_id

    """
    logger.info(click.style(f"Start batch import annotation: {job_id}", fg="green"))
    start_at = time.perf_counter()
    indexing_cache_key = f"app_annotation_batch_import_{str(job_id)}"
    active_jobs_key = f"annotation_import_active:{tenant_id}"

    with session_factory.create_session() as session:
        # get app info
        app = session.scalar(
            select(App).where(App.id == app_id, App.tenant_id == tenant_id, App.status == "normal").limit(1)
        )

        if app:
            try:
                documents = []
                for content in content_list:
                    annotation = MessageAnnotation(
                        app_id=app.id, content=content["answer"], question=content["question"], account_id=user_id
                    )
                    session.add(annotation)
                    session.flush()

                    document = Document(
                        page_content=content["question"],
                        metadata={"annotation_id": annotation.id, "app_id": app_id, "doc_id": annotation.id},
                    )
                    documents.append(document)
                # if annotation reply is enabled , batch add annotations' index
                app_annotation_setting = session.scalar(
                    select(AppAnnotationSetting).where(AppAnnotationSetting.app_id == app_id).limit(1)
                )

                if app_annotation_setting:
                    dataset_collection_binding = (
                        DatasetCollectionBindingService.get_dataset_collection_binding_by_id_and_type(
                            app_annotation_setting.collection_binding_id, session, "annotation"
                        )
                    )
                    if not dataset_collection_binding:
                        raise NotFound("App annotation setting not found")
                    dataset = Dataset(
                        id=app_id,
                        tenant_id=tenant_id,
                        indexing_technique=IndexTechniqueType.HIGH_QUALITY,
                        embedding_model_provider=dataset_collection_binding.provider_name,
                        embedding_model=dataset_collection_binding.model_name,
                        collection_binding_id=dataset_collection_binding.id,
                    )

                    vector = Vector(dataset, attributes=["doc_id", "annotation_id", "app_id"], session=session)
                    vector.create(documents, duplicate_check=True)

                session.commit()
                redis_client.setex(indexing_cache_key, 600, "completed")
                end_at = time.perf_counter()
                logger.info(
                    click.style(
                        "Build index successful for batch import annotation: {} latency: {}".format(
                            job_id, end_at - start_at
                        ),
                        fg="green",
                    )
                )
            except Exception as e:
                session.rollback()
                redis_client.setex(indexing_cache_key, 600, "error")
                indexing_error_msg_key = f"app_annotation_batch_import_error_msg_{str(job_id)}"
                redis_client.setex(indexing_error_msg_key, 600, str(e))
                logger.exception("Build index for batch import annotations failed")
            finally:
                # Clean up active job tracking to release concurrency slot
                try:
                    redis_client.zrem(active_jobs_key, job_id)
                    logger.debug("Released concurrency slot for job: %s", job_id)
                except Exception as cleanup_error:
                    # Log but don't fail if cleanup fails - the job will be auto-expired
                    logger.warning("Failed to clean up active job tracking for %s: %s", job_id, cleanup_error)
