| import datetime |
| import logging |
| import time |
| import uuid |
|
|
| import click |
| from celery import shared_task |
| from sqlalchemy import func |
|
|
| from core.indexing_runner import IndexingRunner |
| 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 |
|
|
|
|
| @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: |
| dataset = db.session.query(Dataset).filter(Dataset.id == dataset_id).first() |
| if not dataset: |
| raise ValueError("Dataset not exist.") |
|
|
| dataset_document = db.session.query(Document).filter(Document.id == document_id).first() |
| 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, |
| ) |
|
|
| for segment in content: |
| content = segment["content"] |
| doc_id = str(uuid.uuid4()) |
| segment_hash = helper.generate_text_hash(content) |
| |
| tokens = embedding_model.get_text_embedding_num_tokens(texts=[content]) if embedding_model else 0 |
| max_position = ( |
| db.session.query(func.max(DocumentSegment.position)) |
| .filter(DocumentSegment.document_id == dataset_document.id) |
| .scalar() |
| ) |
| 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 + 1 if max_position else 1, |
| content=content, |
| word_count=len(content), |
| tokens=tokens, |
| created_by=user_id, |
| indexing_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), |
| status="completed", |
| completed_at=datetime.datetime.now(datetime.timezone.utc).replace(tzinfo=None), |
| ) |
| if dataset_document.doc_form == "qa_model": |
| segment_document.answer = segment["answer"] |
| db.session.add(segment_document) |
| document_segments.append(segment_document) |
| |
| indexing_runner = IndexingRunner() |
| indexing_runner.batch_add_segments(document_segments, dataset) |
| db.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:{}".format(str(e))) |
| redis_client.setex(indexing_cache_key, 600, "error") |
|
|
