mirror of
https://gitee.com/dify_ai/dify.git
synced 2024-11-30 10:18:13 +08:00
103 lines
4.0 KiB
Python
103 lines
4.0 KiB
Python
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)
|
|
# calc embedding use tokens
|
|
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)
|
|
# add index to db
|
|
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")
|