import datetime
import logging
import time
import uuid

import click
from celery import shared_task  # type: ignore
from sqlalchemy import func, select
from sqlalchemy.orm import Session

from core.model_manager import ModelManager
from core.model_runtime.entities.model_entities import ModelType
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from libs import helper
from models.dataset import Dataset, Document, DocumentSegment
from services.vector_service import VectorService


@shared_task(queue="dataset")
def batch_create_segment_to_index_task(
    job_id: str,
    content: list,
    dataset_id: str,
    document_id: str,
    tenant_id: str,
    user_id: str,
):
    """
    Async batch create segment to index
    :param job_id:
    :param content:
    :param dataset_id:
    :param document_id:
    :param tenant_id:
    :param user_id:

    Usage: batch_create_segment_to_index_task.delay(segment_id)
    """
    logging.info(click.style("Start batch create segment jobId: {}".format(job_id), fg="green"))
    start_at = time.perf_counter()

    indexing_cache_key = "segment_batch_import_{}".format(job_id)

    try:
        with Session(db.engine) as session:
            dataset = session.get(Dataset, dataset_id)
            if not dataset:
                raise ValueError("Dataset not exist.")

            dataset_document = session.get(Document, document_id)
            if not dataset_document:
                raise ValueError("Document not exist.")

            if (
                not dataset_document.enabled
                or dataset_document.archived
                or dataset_document.indexing_status != "completed"
            ):
                raise ValueError("Document is not available.")
            document_segments = []
            embedding_model = None
            if dataset.indexing_technique == "high_quality":
                model_manager = ModelManager()
                embedding_model = model_manager.get_model_instance(
                    tenant_id=dataset.tenant_id,
                    provider=dataset.embedding_model_provider,
                    model_type=ModelType.TEXT_EMBEDDING,
                    model=dataset.embedding_model,
                )
            word_count_change = 0
            segments_to_insert: list[str] = []
            max_position_stmt = select(func.max(DocumentSegment.position)).where(
                DocumentSegment.document_id == dataset_document.id
            )
            max_position = session.scalar(max_position_stmt) or 1
            for segment in content:
                content_str = segment["content"]
                doc_id = str(uuid.uuid4())
                segment_hash = helper.generate_text_hash(content_str)
                # calc embedding use tokens
                tokens = embedding_model.get_text_embedding_num_tokens(texts=[content_str]) if embedding_model else 0
                segment_document = DocumentSegment(
                    tenant_id=tenant_id,
                    dataset_id=dataset_id,
                    document_id=document_id,
                    index_node_id=doc_id,
                    index_node_hash=segment_hash,
                    position=max_position,
                    content=content_str,
                    word_count=len(content_str),
                    tokens=tokens,
                    created_by=user_id,
                    indexing_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
                    status="completed",
                    completed_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
                )
                max_position += 1
                if dataset_document.doc_form == "qa_model":
                    segment_document.answer = segment["answer"]
                    segment_document.word_count += len(segment["answer"])
                word_count_change += segment_document.word_count
                session.add(segment_document)
                document_segments.append(segment_document)
                segments_to_insert.append(str(segment))  # Cast to string if needed
            # update document word count
            dataset_document.word_count += word_count_change
            session.add(dataset_document)
            # add index to db
            VectorService.create_segments_vector(None, document_segments, dataset, dataset_document.doc_form)
            session.commit()

        redis_client.setex(indexing_cache_key, 600, "completed")
        end_at = time.perf_counter()
        logging.info(
            click.style(
                "Segment batch created job: {} latency: {}".format(job_id, end_at - start_at),
                fg="green",
            )
        )
    except Exception as e:
        logging.exception("Segments batch created index failed")
        redis_client.setex(indexing_cache_key, 600, "error")
