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https://github.com/infiniflow/ragflow.git
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Fix: Create dataset performance unmatched between HTTP api and web ui (#10960)
### What problem does this PR solve? Fix: Create dataset performance unmatched between HTTP api and web ui #10925 ### Type of change - [x] Bug Fix (non-breaking change which fixes an issue)
This commit is contained in:
Billy Bao
@ -22,7 +22,6 @@ from flask_login import login_required, current_user
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import numpy as np
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from api.db import LLMType
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from api.db.services import duplicate_name
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from api.db.services.llm_service import LLMBundle
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from api.db.services.document_service import DocumentService, queue_raptor_o_graphrag_tasks
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from api.db.services.file2document_service import File2DocumentService
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@ -31,7 +30,6 @@ from api.db.services.pipeline_operation_log_service import PipelineOperationLogS
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from api.db.services.task_service import TaskService, GRAPH_RAPTOR_FAKE_DOC_ID
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from api.db.services.user_service import TenantService, UserTenantService
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from api.utils.api_utils import get_error_data_result, server_error_response, get_data_error_result, validate_request, not_allowed_parameters
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from common.misc_utils import get_uuid
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from api.db import PipelineTaskType, StatusEnum, FileSource, VALID_FILE_TYPES, VALID_TASK_STATUS
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.db_models import File
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@ -50,63 +48,17 @@ from rag.utils.doc_store_conn import OrderByExpr
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@validate_request("name")
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def create():
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req = request.json
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dataset_name = req["name"]
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if not isinstance(dataset_name, str):
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return get_data_error_result(message="Dataset name must be string.")
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if dataset_name.strip() == "":
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return get_data_error_result(message="Dataset name can't be empty.")
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if len(dataset_name.encode("utf-8")) > DATASET_NAME_LIMIT:
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return get_data_error_result(
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message=f"Dataset name length is {len(dataset_name)} which is larger than {DATASET_NAME_LIMIT}")
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req = KnowledgebaseService.create_with_name(
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name = req.pop("name", None),
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tenant_id = current_user.id,
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parser_id = req.pop("parser_id", None),
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**req
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)
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dataset_name = dataset_name.strip()
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dataset_name = duplicate_name(
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KnowledgebaseService.query,
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name=dataset_name,
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tenant_id=current_user.id,
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status=StatusEnum.VALID.value)
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try:
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req["id"] = get_uuid()
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req["name"] = dataset_name
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req["tenant_id"] = current_user.id
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req["created_by"] = current_user.id
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if not req.get("parser_id"):
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req["parser_id"] = "naive"
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e, t = TenantService.get_by_id(current_user.id)
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if not e:
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return get_data_error_result(message="Tenant not found.")
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req["parser_config"] = {
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"layout_recognize": "DeepDOC",
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"chunk_token_num": 512,
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"delimiter": "\n",
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"auto_keywords": 0,
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"auto_questions": 0,
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"html4excel": False,
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"topn_tags": 3,
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"raptor": {
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"use_raptor": True,
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"prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize.",
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"max_token": 256,
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"threshold": 0.1,
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"max_cluster": 64,
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"random_seed": 0
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},
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"graphrag": {
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"use_graphrag": True,
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"entity_types": [
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"organization",
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"person",
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"geo",
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"event",
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"category"
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],
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"method": "light"
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}
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}
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if not KnowledgebaseService.save(**req):
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return get_data_error_result()
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return get_json_result(data={"kb_id": req["id"]})
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return get_json_result(data={"kb_id":req["id"]})
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except Exception as e:
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return server_error_response(e)
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@ -28,12 +28,10 @@ from api.db.services.file2document_service import File2DocumentService
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from api.db.services.file_service import FileService
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from api.db.services.knowledgebase_service import KnowledgebaseService
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from api.db.services.user_service import TenantService
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from common.misc_utils import get_uuid
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from api.utils.api_utils import (
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deep_merge,
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get_error_argument_result,
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get_error_data_result,
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get_error_operating_result,
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get_error_permission_result,
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get_parser_config,
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get_result,
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@ -80,29 +78,28 @@ def create(tenant_id):
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properties:
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name:
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type: string
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description: Name of the dataset.
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description: Dataset name (required).
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avatar:
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type: string
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description: Base64 encoding of the avatar.
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description: Optional base64-encoded avatar image.
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description:
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type: string
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description: Description of the dataset.
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description: Optional dataset description.
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embedding_model:
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type: string
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description: Embedding model Name.
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description: Optional embedding model name; if omitted, the tenant's default embedding model is used.
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permission:
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type: string
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enum: ['me', 'team']
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description: Dataset permission.
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description: Visibility of the dataset (private to me or shared with team).
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chunk_method:
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type: string
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enum: ["naive", "book", "email", "laws", "manual", "one", "paper",
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"picture", "presentation", "qa", "table", "tag"
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]
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description: Chunking method.
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"picture", "presentation", "qa", "table", "tag"]
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description: Chunking method; if omitted, defaults to "naive".
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parser_config:
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type: object
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description: Parser configuration.
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description: Optional parser configuration; server-side defaults will be applied.
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responses:
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200:
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description: Successful operation.
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@ -117,44 +114,43 @@ def create(tenant_id):
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# |----------------|-------------|
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# | embedding_model| embd_id |
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# | chunk_method | parser_id |
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req, err = validate_and_parse_json_request(request, CreateDatasetReq)
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if err is not None:
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return get_error_argument_result(err)
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req = KnowledgebaseService.create_with_name(
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name = req.pop("name", None),
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tenant_id = tenant_id,
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parser_id = req.pop("parser_id", None),
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**req
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)
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# Insert embedding model(embd id)
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ok, t = TenantService.get_by_id(tenant_id)
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if not ok:
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return get_error_permission_result(message="Tenant not found")
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if not req.get("embd_id"):
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req["embd_id"] = t.embd_id
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else:
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ok, err = verify_embedding_availability(req["embd_id"], tenant_id)
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if not ok:
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return err
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try:
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if KnowledgebaseService.get_or_none(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
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return get_error_operating_result(message=f"Dataset name '{req['name']}' already exists")
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req["parser_config"] = get_parser_config(req["parser_id"], req["parser_config"])
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req["id"] = get_uuid()
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req["tenant_id"] = tenant_id
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req["created_by"] = tenant_id
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ok, t = TenantService.get_by_id(tenant_id)
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if not ok:
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return get_error_permission_result(message="Tenant not found")
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if not req.get("embd_id"):
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req["embd_id"] = t.embd_id
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else:
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ok, err = verify_embedding_availability(req["embd_id"], tenant_id)
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if not ok:
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return err
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if not KnowledgebaseService.save(**req):
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return get_error_data_result(message="Create dataset error.(Database error)")
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ok, k = KnowledgebaseService.get_by_id(req["id"])
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if not ok:
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return get_error_data_result(message="Dataset created failed")
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response_data = remap_dictionary_keys(k.to_dict())
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return get_result(data=response_data)
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except OperationalError as e:
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if not KnowledgebaseService.save(**req):
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return get_error_data_result()
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ok, k = KnowledgebaseService.get_by_id(req["id"])
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if not ok:
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return get_error_data_result(message="Dataset created failed")
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response_data = remap_dictionary_keys(k.to_dict())
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return get_result(data=response_data)
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except Exception as e:
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logging.exception(e)
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return get_error_data_result(message="Database operation failed")
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@manager.route("/datasets", methods=["DELETE"]) # noqa: F821
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@token_required
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def delete(tenant_id):
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@ -21,7 +21,11 @@ from api.db import StatusEnum, TenantPermission
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from api.db.db_models import DB, Document, Knowledgebase, User, UserTenant, UserCanvas
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from api.db.services.common_service import CommonService
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from common.time_utils import current_timestamp, datetime_format
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from api.db.services import duplicate_name
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from api.db.services.user_service import TenantService
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from common.misc_utils import get_uuid
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from api.constants import DATASET_NAME_LIMIT
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from api.utils.api_utils import get_parser_config, get_data_error_result
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class KnowledgebaseService(CommonService):
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"""Service class for managing knowledge base operations.
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@ -363,6 +367,64 @@ class KnowledgebaseService(CommonService):
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# List of all knowledge base IDs
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return [m["id"] for m in cls.model.select(cls.model.id).dicts()]
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@classmethod
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@DB.connection_context()
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def create_with_name(
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cls,
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*,
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name: str,
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tenant_id: str,
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parser_id: str | None = None,
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**kwargs
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):
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"""Create a dataset (knowledgebase) by name with kb_app defaults.
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This encapsulates the creation logic used in kb_app.create so other callers
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(including RESTful endpoints) can reuse the same behavior.
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Returns:
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(ok: bool, model_or_msg): On success, returns (True, Knowledgebase model instance);
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on failure, returns (False, error_message).
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"""
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# Validate name
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if not isinstance(name, str):
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return get_data_error_result(message="Dataset name must be string.")
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dataset_name = name.strip()
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if dataset_name == "":
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return get_data_error_result(message="Dataset name can't be empty.")
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if len(dataset_name.encode("utf-8")) > DATASET_NAME_LIMIT:
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return get_data_error_result(message=f"Dataset name length is {len(dataset_name)} which is larger than {DATASET_NAME_LIMIT}")
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# Deduplicate name within tenant
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dataset_name = duplicate_name(
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cls.query,
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name=dataset_name,
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tenant_id=tenant_id,
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status=StatusEnum.VALID.value,
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)
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# Verify tenant exists
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ok, _t = TenantService.get_by_id(tenant_id)
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if not ok:
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return False, "Tenant not found."
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# Build payload
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kb_id = get_uuid()
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payload = {
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"id": kb_id,
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"name": dataset_name,
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"tenant_id": tenant_id,
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"created_by": tenant_id,
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"parser_id": (parser_id or "naive"),
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**kwargs
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}
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# Default parser_config (align with kb_app.create) — do not accept external overrides
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payload["parser_config"] = get_parser_config(parser_id, kwargs.get("parser_config"))
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return payload
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@classmethod
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@DB.connection_context()
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def get_list(cls, joined_tenant_ids, user_id,
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@ -298,8 +298,34 @@ def get_parser_config(chunk_method, parser_config):
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# Define default configurations for each chunking method
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key_mapping = {
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"naive": {"chunk_token_num": 512, "delimiter": r"\n", "html4excel": False, "layout_recognize": "DeepDOC",
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"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
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"naive": {
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"layout_recognize": "DeepDOC",
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"chunk_token_num": 512,
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"delimiter": "\n",
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"auto_keywords": 0,
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"auto_questions": 0,
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"html4excel": False,
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"topn_tags": 3,
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"raptor": {
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"use_raptor": True,
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"prompt": "Please summarize the following paragraphs. Be careful with the numbers, do not make things up. Paragraphs as following:\n {cluster_content}\nThe above is the content you need to summarize.",
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"max_token": 256,
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"threshold": 0.1,
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"max_cluster": 64,
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"random_seed": 0,
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},
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"graphrag": {
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"use_graphrag": True,
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"entity_types": [
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"organization",
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"person",
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"geo",
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"event",
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"category",
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],
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"method": "light",
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},
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},
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"qa": {"raptor": {"use_raptor": False}, "graphrag": {"use_graphrag": False}},
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"tag": None,
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"resume": None,
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Block a user