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108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
import datetime
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import logging
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import time
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import uuid
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import click
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from celery import shared_task
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from sqlalchemy import func
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from core.indexing_runner import IndexingRunner
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from core.model_manager import ModelManager
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from core.model_runtime.entities.model_entities import ModelType
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from extensions.ext_database import db
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from extensions.ext_redis import redis_client
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from libs import helper
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from models.dataset import Dataset, Document, DocumentSegment
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@shared_task(queue="dataset")
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def batch_create_segment_to_index_task(
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job_id: str, content: list, dataset_id: str, document_id: str, tenant_id: str, user_id: str
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):
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"""
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Async batch create segment to index
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:param job_id:
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:param content:
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:param dataset_id:
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:param document_id:
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:param tenant_id:
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:param user_id:
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Usage: batch_create_segment_to_index_task.delay(segment_id)
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"""
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logging.info(click.style("Start batch create segment jobId: {}".format(job_id), fg="green"))
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start_at = time.perf_counter()
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indexing_cache_key = "segment_batch_import_{}".format(job_id)
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try:
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dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first()
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if not dataset:
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raise ValueError("Dataset not exist.")
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dataset_document = db.session.query(Document).filter(Document.id == document_id).first()
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if not dataset_document:
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raise ValueError("Document not exist.")
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if not dataset_document.enabled or dataset_document.archived or dataset_document.indexing_status != "completed":
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raise ValueError("Document is not available.")
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document_segments = []
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embedding_model = None
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if dataset.indexing_technique == "high_quality":
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model_manager = ModelManager()
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embedding_model = model_manager.get_model_instance(
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tenant_id=dataset.tenant_id,
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provider=dataset.embedding_model_provider,
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model_type=ModelType.TEXT_EMBEDDING,
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model=dataset.embedding_model,
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)
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word_count_change = 0
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for segment in content:
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content = segment["content"]
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doc_id = str(uuid.uuid4())
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segment_hash = helper.generate_text_hash(content)
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# calc embedding use tokens
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tokens = embedding_model.get_text_embedding_num_tokens(texts=[content]) if embedding_model else 0
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max_position = (
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db.session.query(func.max(DocumentSegment.position))
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.filter(DocumentSegment.document_id == dataset_document.id)
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.scalar()
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)
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segment_document = DocumentSegment(
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tenant_id=tenant_id,
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dataset_id=dataset_id,
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document_id=document_id,
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index_node_id=doc_id,
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index_node_hash=segment_hash,
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position=max_position + 1 if max_position else 1,
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content=content,
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word_count=len(content),
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tokens=tokens,
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created_by=user_id,
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indexing_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
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status="completed",
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completed_at=datetime.datetime.now(datetime.UTC).replace(tzinfo=None),
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)
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if dataset_document.doc_form == "qa_model":
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segment_document.answer = segment["answer"]
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segment_document.word_count += len(segment["answer"])
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word_count_change += segment_document.word_count
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db.session.add(segment_document)
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document_segments.append(segment_document)
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# update document word count
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dataset_document.word_count += word_count_change
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db.session.add(dataset_document)
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# add index to db
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indexing_runner = IndexingRunner()
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indexing_runner.batch_add_segments(document_segments, dataset)
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db.session.commit()
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redis_client.setex(indexing_cache_key, 600, "completed")
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end_at = time.perf_counter()
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logging.info(
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click.style("Segment batch created job: {} latency: {}".format(job_id, end_at - start_at), fg="green")
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)
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except Exception as e:
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logging.exception("Segments batch created index failed")
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redis_client.setex(indexing_cache_key, 600, "error")
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