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Code Issues Packages Projects Releases Wiki Activity

Refa: http API create dataset and test cases (#7393)

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### What problem does this PR solve?

This PR introduces Pydantic-based validation for the create dataset HTTP
API, improving code clarity and robustness. Key changes include:
1. Pydantic Validation
2. ​​Error Handling
3. Test Updates
4. Documentation

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Documentation Update
- [x] Refactoring
This commit is contained in:
liu an 2025-04-29 16:53:57 +08:00 committed by GitHub
parent c88e4b3fc0
commit 78380fa181
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GPG Key ID: B5690EEEBB952194
11 changed files with 1239 additions and 812 deletions
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12
.github/workflows/tests.yml vendored
View File

@ -86,7 +86,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
cd sdk/python && uv sync --python 3.10 --group test --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
- name: Run frontend api tests against Elasticsearch
run: |
@ -96,7 +96,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
cd sdk/python && uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
- name: Run http api tests against Elasticsearch
run: |
@ -106,7 +106,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && pytest -s --tb=short -m "not slow"
cd sdk/python && uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_http_api && pytest -s --tb=short -m "not slow"
- name: Stop ragflow:nightly
if: always() # always run this step even if previous steps failed
@ -125,7 +125,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
cd sdk/python && uv sync --python 3.10 --group test --frozen && uv pip install . && source .venv/bin/activate && cd test/test_sdk_api && pytest -s --tb=short get_email.py t_dataset.py t_chat.py t_session.py t_document.py t_chunk.py
- name: Run frontend api tests against Infinity
run: |
@ -135,7 +135,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
cd sdk/python && uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_frontend_api && pytest -s --tb=short get_email.py test_dataset.py
- name: Run http api tests against Infinity
run: |
@ -145,7 +145,7 @@ jobs:
echo "Waiting for service to be available..."
sleep 5
done
cd sdk/python && uv sync --python 3.10 --frozen && uv pip install . && source .venv/bin/activate && cd test/test_http_api && DOC_ENGINE=infinity pytest -s --tb=short -m "not slow"
cd sdk/python && uv sync --python 3.10 --group test --frozen && source .venv/bin/activate && cd test/test_http_api && DOC_ENGINE=infinity pytest -s --tb=short -m "not slow"
- name: Stop ragflow:nightly
if: always() # always run this step even if previous steps failed

270
api/apps/sdk/dataset.py
View File

@ -14,24 +14,35 @@
# limitations under the License.
#
import logging
from flask import request
from api.db import StatusEnum, FileSource
from peewee import OperationalError
from pydantic import ValidationError
from api import settings
from api.db import FileSource, StatusEnum
from api.db.db_models import File
from api.db.services.document_service import DocumentService
from api.db.services.file2document_service import File2DocumentService
from api.db.services.file_service import FileService
from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import TenantLLMService, LLMService
from api.db.services.llm_service import LLMService, TenantLLMService
from api.db.services.user_service import TenantService
from api import settings
from api.utils import get_uuid
from api.utils.api_utils import (
check_duplicate_ids,
dataset_readonly_fields,
get_error_argument_result,
get_error_data_result,
get_parser_config,
get_result,
token_required,
get_error_data_result,
valid,
get_parser_config, valid_parser_config, dataset_readonly_fields,check_duplicate_ids
valid_parser_config,
)
from api.utils.validation_utils import CreateDatasetReq, format_validation_error_message
@manager.route("/datasets", methods=["POST"]) # noqa: F821
@ -62,16 +73,28 @@ def create(tenant_id):
name:
type: string
description: Name of the dataset.
avatar:
type: string
description: Base64 encoding of the avatar.
description:
type: string
description: Description of the dataset.
embedding_model:
type: string
description: Embedding model Name.
permission:
type: string
enum: ['me', 'team']
description: Dataset permission.
chunk_method:
type: string
enum: ["naive", "manual", "qa", "table", "paper", "book", "laws",
"presentation", "picture", "one", "email", "tag"
enum: ["naive", "book", "email", "laws", "manual", "one", "paper",
"picture", "presentation", "qa", "table", "tag"
]
description: Chunking method.
pagerank:
type: integer
description: Set page rank.
parser_config:
type: object
description: Parser configuration.
@ -84,106 +107,87 @@ def create(tenant_id):
data:
type: object
"""
req = request.json
for k in req.keys():
if dataset_readonly_fields(k):
return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
e, t = TenantService.get_by_id(tenant_id)
permission = req.get("permission")
chunk_method = req.get("chunk_method")
parser_config = req.get("parser_config")
valid_parser_config(parser_config)
valid_permission = ["me", "team"]
valid_chunk_method = [
"naive",
"manual",
"qa",
"table",
"paper",
"book",
"laws",
"presentation",
"picture",
"one",
"email",
"tag"
]
check_validation = valid(
permission,
valid_permission,
chunk_method,
valid_chunk_method,
)
if check_validation:
return check_validation
req["parser_config"] = get_parser_config(chunk_method, parser_config)
if "tenant_id" in req:
return get_error_data_result(message="`tenant_id` must not be provided")
if "chunk_count" in req or "document_count" in req:
return get_error_data_result(
message="`chunk_count` or `document_count` must not be provided"
)
if "name" not in req:
return get_error_data_result(message="`name` is not empty!")
req_i = request.json
if not isinstance(req_i, dict):
return get_error_argument_result(f"Invalid request payload: expected object, got {type(req_i).__name__}")
try:
req_v = CreateDatasetReq(**req_i)
except ValidationError as e:
return get_error_argument_result(format_validation_error_message(e))
# Field name transformations during model dump:
# | Original | Dump Output |
# |----------------|-------------|
# | embedding_model| embd_id |
# | chunk_method | parser_id |
req = req_v.model_dump(by_alias=True)
try:
if KnowledgebaseService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value):
return get_error_argument_result(message=f"Dataset name '{req['name']}' already exists")
except OperationalError as e:
logging.exception(e)
return get_error_data_result(message="Database operation failed")
req["parser_config"] = get_parser_config(req["parser_id"], req["parser_config"])
req["id"] = get_uuid()
req["name"] = req["name"].strip()
if req["name"] == "":
return get_error_data_result(message="`name` is not empty string!")
if len(req["name"]) >= 128:
return get_error_data_result(
message="Dataset name should not be longer than 128 characters."
)
if KnowledgebaseService.query(
name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value
):
return get_error_data_result(
message="Duplicated dataset name in creating dataset."
)
req["tenant_id"] = tenant_id
req["created_by"] = tenant_id
if not req.get("embedding_model"):
req["embedding_model"] = t.embd_id
try:
ok, t = TenantService.get_by_id(tenant_id)
if not ok:
return get_error_data_result(message="Tenant not found")
except OperationalError as e:
logging.exception(e)
return get_error_data_result(message="Database operation failed")
if not req.get("embd_id"):
req["embd_id"] = t.embd_id
else:
valid_embedding_models = [
"BAAI/bge-large-zh-v1.5",
"maidalun1020/bce-embedding-base_v1",
builtin_embedding_models = [
"BAAI/bge-large-zh-v1.5@BAAI",
"maidalun1020/bce-embedding-base_v1@Youdao",
]
embd_model = LLMService.query(
llm_name=req["embedding_model"], model_type="embedding"
)
if embd_model:
if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
if not embd_model:
embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
if not embd_model:
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
is_builtin_model = req["embd_id"] in builtin_embedding_models
try:
# model name must be model_name@model_factory
llm_name, llm_factory = TenantLLMService.split_model_name_and_factory(req["embd_id"])
is_tenant_model = TenantLLMService.query(tenant_id=tenant_id, llm_name=llm_name, llm_factory=llm_factory, model_type="embedding")
is_supported_model = LLMService.query(llm_name=llm_name, fid=llm_factory, model_type="embedding")
if not (is_supported_model and (is_builtin_model or is_tenant_model)):
return get_error_argument_result(f"The embedding_model '{req['embd_id']}' is not supported")
except OperationalError as e:
logging.exception(e)
return get_error_data_result(message="Database operation failed")
try:
if not KnowledgebaseService.save(**req):
return get_error_data_result(message="Database operation failed")
except OperationalError as e:
logging.exception(e)
return get_error_data_result(message="Database operation failed")
try:
ok, k = KnowledgebaseService.get_by_id(req["id"])
if not ok:
return get_error_data_result(message="Dataset created failed")
except OperationalError as e:
logging.exception(e)
return get_error_data_result(message="Database operation failed")
response_data = {}
key_mapping = {
"chunk_num": "chunk_count",
"doc_num": "document_count",
"parser_id": "chunk_method",
"embd_id": "embedding_model",
}
mapped_keys = {
new_key: req[old_key]
for new_key, old_key in key_mapping.items()
if old_key in req
}
req.update(mapped_keys)
flds = list(req.keys())
for f in flds:
if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
del req[f]
if not KnowledgebaseService.save(**req):
return get_error_data_result(message="Create dataset error.(Database error)")
renamed_data = {}
e, k = KnowledgebaseService.get_by_id(req["id"])
for key, value in k.to_dict().items():
new_key = key_mapping.get(key, key)
renamed_data[new_key] = value
return get_result(data=renamed_data)
response_data[new_key] = value
return get_result(data=response_data)
@manager.route("/datasets", methods=["DELETE"]) # noqa: F821
@ -254,29 +258,28 @@ def delete(tenant_id):
]
)
File2DocumentService.delete_by_document_id(doc.id)
FileService.filter_delete(
[File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
FileService.filter_delete([File.source_type == FileSource.KNOWLEDGEBASE, File.type == "folder", File.name == kbs[0].name])
if not KnowledgebaseService.delete_by_id(id):
errors.append(f"Delete dataset error for {id}")
continue
success_count += 1
if errors:
if success_count > 0:
return get_result(
data={"success_count": success_count, "errors": errors},
message=f"Partially deleted {success_count} datasets with {len(errors)} errors"
)
return get_result(data={"success_count": success_count, "errors": errors}, message=f"Partially deleted {success_count} datasets with {len(errors)} errors")
else:
return get_error_data_result(message="; ".join(errors))
if duplicate_messages:
if success_count > 0:
return get_result(message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors", data={"success_count": success_count, "errors": duplicate_messages},)
return get_result(
message=f"Partially deleted {success_count} datasets with {len(duplicate_messages)} errors",
data={"success_count": success_count, "errors": duplicate_messages},
)
else:
return get_error_data_result(message=";".join(duplicate_messages))
return get_result(code=settings.RetCode.SUCCESS)
@manager.route("/datasets/<dataset_id>", methods=["PUT"]) # noqa: F821
@manager.route("/datasets/<dataset_id>", methods=["PUT"]) # noqa: F821
@token_required
def update(tenant_id, dataset_id):
"""
@ -333,7 +336,7 @@ def update(tenant_id, dataset_id):
if dataset_readonly_fields(k):
return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=f"'{k}' is readonly.")
e, t = TenantService.get_by_id(tenant_id)
invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id", "create_date", "create_time", "created_by", "status","token_num","update_date","update_time"}
invalid_keys = {"id", "embd_id", "chunk_num", "doc_num", "parser_id", "create_date", "create_time", "created_by", "status", "token_num", "update_date", "update_time"}
if any(key in req for key in invalid_keys):
return get_error_data_result(message="The input parameters are invalid.")
permission = req.get("permission")
@ -341,20 +344,7 @@ def update(tenant_id, dataset_id):
parser_config = req.get("parser_config")
valid_parser_config(parser_config)
valid_permission = ["me", "team"]
valid_chunk_method = [
"naive",
"manual",
"qa",
"table",
"paper",
"book",
"laws",
"presentation",
"picture",
"one",
"email",
"tag"
]
valid_chunk_method = ["naive", "manual", "qa", "table", "paper", "book", "laws", "presentation", "picture", "one", "email", "tag"]
check_validation = valid(
permission,
valid_permission,
@ -381,18 +371,14 @@ def update(tenant_id, dataset_id):
req.pop("document_count")
if req.get("chunk_method"):
if kb.chunk_num != 0 and req["chunk_method"] != kb.parser_id:
return get_error_data_result(
message="If `chunk_count` is not 0, `chunk_method` is not changeable."
)
return get_error_data_result(message="If `chunk_count` is not 0, `chunk_method` is not changeable.")
req["parser_id"] = req.pop("chunk_method")
if req["parser_id"] != kb.parser_id:
if not req.get("parser_config"):
req["parser_config"] = get_parser_config(chunk_method, parser_config)
if "embedding_model" in req:
if kb.chunk_num != 0 and req["embedding_model"] != kb.embd_id:
return get_error_data_result(
message="If `chunk_count` is not 0, `embedding_model` is not changeable."
)
return get_error_data_result(message="If `chunk_count` is not 0, `embedding_model` is not changeable.")
if not req.get("embedding_model"):
return get_error_data_result("`embedding_model` can't be empty")
valid_embedding_models = [
@ -409,38 +395,26 @@ def update(tenant_id, dataset_id):
"text-embedding-v3",
"maidalun1020/bce-embedding-base_v1",
]
embd_model = LLMService.query(
llm_name=req["embedding_model"], model_type="embedding"
)
embd_model = LLMService.query(llm_name=req["embedding_model"], model_type="embedding")
if embd_model:
if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(
tenant_id=tenant_id,
model_type="embedding",
llm_name=req.get("embedding_model"),
):
return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
if not embd_model:
embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
embd_model = TenantLLMService.query(tenant_id=tenant_id, model_type="embedding", llm_name=req.get("embedding_model"))
if not embd_model:
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
req["embd_id"] = req.pop("embedding_model")
if "name" in req:
req["name"] = req["name"].strip()
if len(req["name"]) >= 128:
return get_error_data_result(
message="Dataset name should not be longer than 128 characters."
)
if (
req["name"].lower() != kb.name.lower()
and len(
KnowledgebaseService.query(
name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value
)
)
> 0
):
return get_error_data_result(
message="Duplicated dataset name in updating dataset."
)
return get_error_data_result(message="Dataset name should not be longer than 128 characters.")
if req["name"].lower() != kb.name.lower() and len(KnowledgebaseService.query(name=req["name"], tenant_id=tenant_id, status=StatusEnum.VALID.value)) > 0:
return get_error_data_result(message="Duplicated dataset name in updating dataset.")
flds = list(req.keys())
for f in flds:
if req[f] == "" and f in ["permission", "parser_id", "chunk_method"]:
@ -511,11 +485,11 @@ def list_datasets(tenant_id):
id = request.args.get("id")
name = request.args.get("name")
if id:
kbs = KnowledgebaseService.get_kb_by_id(id,tenant_id)
kbs = KnowledgebaseService.get_kb_by_id(id, tenant_id)
if not kbs:
return get_error_data_result(f"You don't own the dataset {id}")
if name:
kbs = KnowledgebaseService.get_kb_by_name(name,tenant_id)
kbs = KnowledgebaseService.get_kb_by_name(name, tenant_id)
if not kbs:
return get_error_data_result(f"You don't own the dataset {name}")
page_number = int(request.args.get("page", 1))

52
api/utils/api_utils.py
View File

@ -322,6 +322,10 @@ def get_error_data_result(
return jsonify(response)
def get_error_argument_result(message="Invalid arguments"):
return get_result(code=settings.RetCode.ARGUMENT_ERROR, message=message)
def generate_confirmation_token(tenant_id):
serializer = URLSafeTimedSerializer(tenant_id)
return "ragflow-" + serializer.dumps(get_uuid(), salt=tenant_id)[2:34]
@ -368,46 +372,34 @@ def get_parser_config(chunk_method, parser_config):
return parser_config
def get_data_openai(id=None,
created=None,
model=None,
prompt_tokens= 0,
completion_tokens=0,
content = None,
finish_reason= None,
object="chat.completion",
param=None,
def get_data_openai(
id=None,
created=None,
model=None,
prompt_tokens=0,
completion_tokens=0,
content=None,
finish_reason=None,
object="chat.completion",
param=None,
):
total_tokens= prompt_tokens + completion_tokens
total_tokens = prompt_tokens + completion_tokens
return {
"id":f"{id}",
"id": f"{id}",
"object": object,
"created": int(time.time()) if created else None,
"model": model,
"param":param,
"param": param,
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": total_tokens,
"completion_tokens_details": {
"reasoning_tokens": 0,
"accepted_prediction_tokens": 0,
"rejected_prediction_tokens": 0
}
"completion_tokens_details": {"reasoning_tokens": 0, "accepted_prediction_tokens": 0, "rejected_prediction_tokens": 0},
},
"choices": [
{
"message": {
"role": "assistant",
"content": content
},
"logprobs": None,
"finish_reason": finish_reason,
"index": 0
}
]
}
"choices": [{"message": {"role": "assistant", "content": content}, "logprobs": None, "finish_reason": finish_reason, "index": 0}],
}
def valid_parser_config(parser_config):
if not parser_config:
return

162
api/utils/validation_utils.py Normal file
View File

@ -0,0 +1,162 @@
#
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from enum import auto
from typing import Annotated, List, Optional
from pydantic import BaseModel, Field, StringConstraints, ValidationError, field_validator
from strenum import StrEnum
def format_validation_error_message(e: ValidationError):
error_messages = []
for error in e.errors():
field = ".".join(map(str, error["loc"]))
msg = error["msg"]
input_val = error["input"]
input_str = str(input_val)
if len(input_str) > 128:
input_str = input_str[:125] + "..."
error_msg = f"Field: <{field}> - Message: <{msg}> - Value: <{input_str}>"
error_messages.append(error_msg)
return "\n".join(error_messages)
class PermissionEnum(StrEnum):
me = auto()
team = auto()
class ChunkMethodnEnum(StrEnum):
naive = auto()
book = auto()
email = auto()
laws = auto()
manual = auto()
one = auto()
paper = auto()
picture = auto()
presentation = auto()
qa = auto()
table = auto()
tag = auto()
class GraphragMethodEnum(StrEnum):
light = auto()
general = auto()
class Base(BaseModel):
class Config:
extra = "forbid"
json_schema_extra = {"charset": "utf8mb4", "collation": "utf8mb4_0900_ai_ci"}
class RaptorConfig(Base):
use_raptor: bool = Field(default=False)
prompt: Annotated[
str,
StringConstraints(strip_whitespace=True, min_length=1),
Field(
default="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."
),
]
max_token: int = Field(default=256, ge=1, le=2048)
threshold: float = Field(default=0.1, ge=0.0, le=1.0)
max_cluster: int = Field(default=64, ge=1, le=1024)
random_seed: int = Field(default=0, ge=0, le=10_000)
class GraphragConfig(Base):
use_graphrag: bool = Field(default=False)
entity_types: List[str] = Field(default_factory=lambda: ["organization", "person", "geo", "event", "category"])
method: GraphragMethodEnum = Field(default=GraphragMethodEnum.light)
community: bool = Field(default=False)
resolution: bool = Field(default=False)
class ParserConfig(Base):
auto_keywords: int = Field(default=0, ge=0, le=32)
auto_questions: int = Field(default=0, ge=0, le=10)
chunk_token_num: int = Field(default=128, ge=1, le=2048)
delimiter: str = Field(default=r"\n!?;。；！？", min_length=1)
graphrag: Optional[GraphragConfig] = None
html4excel: bool = False
layout_recognize: str = "DeepDOC"
raptor: Optional[RaptorConfig] = None
tag_kb_ids: List[str] = Field(default_factory=list)
topn_tags: int = Field(default=1, ge=1, le=10)
filename_embd_weight: Optional[float] = Field(default=None, ge=0.0, le=1.0)
task_page_size: Optional[int] = Field(default=None, ge=1, le=10_000)
pages: Optional[List[List[int]]] = None
class CreateDatasetReq(Base):
name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=128), Field(...)]
avatar: Optional[str] = Field(default=None, max_length=65535)
description: Optional[str] = Field(default=None, max_length=65535)
embedding_model: Annotated[Optional[str], StringConstraints(strip_whitespace=True, max_length=255), Field(default=None, serialization_alias="embd_id")]
permission: Annotated[PermissionEnum, StringConstraints(strip_whitespace=True, min_length=1, max_length=16), Field(default=PermissionEnum.me)]
chunk_method: Annotated[ChunkMethodnEnum, StringConstraints(strip_whitespace=True, min_length=1, max_length=32), Field(default=ChunkMethodnEnum.naive, serialization_alias="parser_id")]
pagerank: int = Field(default=0, ge=0, le=100)
parser_config: Optional[ParserConfig] = Field(default=None)
@field_validator("avatar")
@classmethod
def validate_avatar_base64(cls, v: str) -> str:
if v is None:
return v
if "," in v:
prefix, _ = v.split(",", 1)
if not prefix.startswith("data:"):
raise ValueError("Invalid MIME prefix format. Must start with 'data:'")
mime_type = prefix[5:].split(";")[0]
supported_mime_types = ["image/jpeg", "image/png"]
if mime_type not in supported_mime_types:
raise ValueError(f"Unsupported MIME type. Allowed: {supported_mime_types}")
return v
else:
raise ValueError("Missing MIME prefix. Expected format: data:<mime>;base64,<data>")
@field_validator("embedding_model", mode="after")
@classmethod
def validate_embedding_model(cls, v: str) -> str:
if "@" not in v:
raise ValueError("Embedding model must be xxx@yyy")
return v
@field_validator("permission", mode="before")
@classmethod
def permission_auto_lowercase(cls, v: str) -> str:
if isinstance(v, str):
return v.lower()
return v
@field_validator("parser_config", mode="after")
@classmethod
def validate_parser_config_json_length(cls, v: Optional[ParserConfig]) -> Optional[ParserConfig]:
if v is not None:
json_str = v.model_dump_json()
if len(json_str) > 65535:
raise ValueError("Parser config have at most 65535 characters")
return v

114
docs/references/http_api_reference.md
View File

@ -341,6 +341,7 @@ Creates a dataset.
- `"embedding_model"`: `string`
- `"permission"`: `string`
- `"chunk_method"`: `string`
- `"pagerank"`: `int`
- `"parser_config"`: `object`
##### Request example
@ -359,53 +360,83 @@ curl --request POST \
- `"name"`: (*Body parameter*), `string`, *Required*
The unique name of the dataset to create. It must adhere to the following requirements:
- Permitted characters include:
- English letters (a-z, A-Z)
- Digits (0-9)
- "_" (underscore)
- Must begin with an English letter or underscore.
- Maximum 65,535 characters.
- Case-insensitive.
- Basic Multilingual Plane (BMP) only
- Maximum 128 characters
- Case-insensitive
- `"avatar"`: (*Body parameter*), `string`
Base64 encoding of the avatar.
- Maximum 65535 characters
- `"description"`: (*Body parameter*), `string`
A brief description of the dataset to create.
- Maximum 65535 characters
- `"embedding_model"`: (*Body parameter*), `string`
The name of the embedding model to use. For example: `"BAAI/bge-zh-v1.5"`
The name of the embedding model to use. For example: `"BAAI/bge-large-zh-v1.5@BAAI"`
- Maximum 255 characters
- Must follow `model_name@model_factory` format
- `"permission"`: (*Body parameter*), `string`
Specifies who can access the dataset to create. Available options:
- `"me"`: (Default) Only you can manage the dataset.
- `"team"`: All team members can manage the dataset.
- `"pagerank"`: (*Body parameter*), `int`
Set page rank: refer to [Set page rank](https://ragflow.io/docs/dev/set_page_rank)
- Default: `0`
- Minimum: `0`
- Maximum: `100`
- `"chunk_method"`: (*Body parameter*), `enum<string>`
The chunking method of the dataset to create. Available options:
- `"naive"`: General (default)
- `"book"`: Book
- `"email"`: Email
- `"laws"`: Laws
- `"manual"`: Manual
- `"one"`: One
- `"paper"`: Paper
- `"picture"`: Picture
- `"presentation"`: Presentation
- `"qa"`: Q&A
- `"table"`: Table
- `"paper"`: Paper
- `"book"`: Book
- `"laws"`: Laws
- `"presentation"`: Presentation
- `"picture"`: Picture
- `"one"`: One
- `"email"`: Email
- `"tag"`: Tag
- `"parser_config"`: (*Body parameter*), `object`
The configuration settings for the dataset parser. The attributes in this JSON object vary with the selected `"chunk_method"`:
- If `"chunk_method"` is `"naive"`, the `"parser_config"` object contains the following attributes:
- `"chunk_token_count"`: Defaults to `128`.
- `"layout_recognize"`: Defaults to `true`.
- `"html4excel"`: Indicates whether to convert Excel documents into HTML format. Defaults to `false`.
- `"delimiter"`: Defaults to `"\n"`.
- `"task_page_size"`: Defaults to `12`. For PDF only.
- `"raptor"`: RAPTOR-specific settings. Defaults to: `{"use_raptor": false}`.
- `"auto_keywords"`: `int`
- Defaults to `0`
- Minimum: `0`
- Maximum: `32`
- `"auto_questions"`: `int`
- Defaults to `0`
- Minimum: `0`
- Maximum: `10`
- `"chunk_token_num"`: `int`
- Defaults to `128`
- Minimum: `1`
- Maximum: `2048`
- `"delimiter"`: `string`
- Defaults to `"\n"`.
- `"html4excel"`: `bool` Indicates whether to convert Excel documents into HTML format.
- Defaults to `false`
- `"layout_recognize"`: `string`
- Defaults to `DeepDOC`
- `"tag_kb_ids"`: `array<string>` refer to [Use tag set](https://ragflow.io/docs/dev/use_tag_sets)
- Must include a list of dataset IDs, where each dataset is parsed using the ​​Tag Chunk Method
- `"task_page_size"`: `int` For PDF only.
- Defaults to `12`
- Minimum: `1`
- Maximum: `10000`
- `"raptor"`: `object` RAPTOR-specific settings.
- Defaults to: `{"use_raptor": false}`
- `"graphrag"`: `object` GRAPHRAG-specific settings.
- Defaults to: `{"use_graphrag": false}`
- If `"chunk_method"` is `"qa"`, `"manuel"`, `"paper"`, `"book"`, `"laws"`, or `"presentation"`, the `"parser_config"` object contains the following attribute:
- `"raptor"`: RAPTOR-specific settings. Defaults to: `{"use_raptor": false}`.
- `"raptor"`: `object` RAPTOR-specific settings.
- Defaults to: `{"use_raptor": false}`.
- If `"chunk_method"` is `"table"`, `"picture"`, `"one"`, or `"email"`, `"parser_config"` is an empty JSON object.
#### Response
@ -419,33 +450,34 @@ Success:
"avatar": null,
"chunk_count": 0,
"chunk_method": "naive",
"create_date": "Thu, 24 Oct 2024 09:14:07 GMT",
"create_time": 1729761247434,
"created_by": "69736c5e723611efb51b0242ac120007",
"create_date": "Mon, 28 Apr 2025 18:40:41 GMT",
"create_time": 1745836841611,
"created_by": "3af81804241d11f0a6a79f24fc270c7f",
"description": null,
"document_count": 0,
"embedding_model": "BAAI/bge-large-zh-v1.5",
"id": "527fa74891e811ef9c650242ac120006",
"embedding_model": "BAAI/bge-large-zh-v1.5@BAAI",
"id": "3b4de7d4241d11f0a6a79f24fc270c7f",
"language": "English",
"name": "test_1",
"name": "RAGFlow example",
"pagerank": 0,
"parser_config": {
"chunk_token_num": 128,
"delimiter": "\\n",
"html4excel": false,
"layout_recognize": true,
"chunk_token_num": 128,
"delimiter": "\\n!?;。；！？",
"html4excel": false,
"layout_recognize": "DeepDOC",
"raptor": {
"use_raptor": false
}
},
}
},
"permission": "me",
"similarity_threshold": 0.2,
"status": "1",
"tenant_id": "69736c5e723611efb51b0242ac120007",
"tenant_id": "3af81804241d11f0a6a79f24fc270c7f",
"token_num": 0,
"update_date": "Thu, 24 Oct 2024 09:14:07 GMT",
"update_time": 1729761247434,
"vector_similarity_weight": 0.3
}
"update_date": "Mon, 28 Apr 2025 18:40:41 GMT",
"update_time": 1745836841611,
"vector_similarity_weight": 0.3,
},
}
```
@ -453,8 +485,8 @@ Failure:
```json
{
"code": 102,
"message": "Duplicated knowledgebase name in creating dataset."
"code": 101,
"message": "Dataset name 'RAGFlow example' already exists"
}
```

17
docs/references/python_api_reference.md
View File

@ -95,11 +95,12 @@ else:
```python
RAGFlow.create_dataset(
name: str,
avatar: str = "",
description: str = "",
embedding_model: str = "BAAI/bge-large-zh-v1.5",
avatar: Optional[str] = None,
description: Optional[str] = None,
embedding_model: Optional[str] = "BAAI/bge-large-zh-v1.5@BAAI",
permission: str = "me",
chunk_method: str = "naive",
pagerank: int = 0,
parser_config: DataSet.ParserConfig = None
) -> DataSet
```
@ -112,16 +113,16 @@ Creates a dataset.
The unique name of the dataset to create. It must adhere to the following requirements:
- Maximum 65,535 characters.
- Maximum 128 characters.
- Case-insensitive.
##### avatar: `str`
Base64 encoding of the avatar. Defaults to `""`
Base64 encoding of the avatar. Defaults to `None`
##### description: `str`
A brief description of the dataset to create. Defaults to `""`.
A brief description of the dataset to create. Defaults to `None`.
##### permission
@ -147,6 +148,10 @@ The chunking method of the dataset to create. Available options:
- `"one"`: One
- `"email"`: Email
##### pagerank, `int`
The pagerank of the dataset to create. Defaults to `0`.
##### parser_config
The parser configuration of the dataset. A `ParserConfig` object's attributes vary based on the selected `chunk_method`:

31
sdk/python/pyproject.toml
View File

@ -2,31 +2,26 @@
name = "ragflow-sdk"
version = "0.18.0"
description = "Python client sdk of [RAGFlow](https://github.com/infiniflow/ragflow). RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding."
authors = [
{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }
]
authors = [{ name = "Zhichang Yu", email = "yuzhichang@gmail.com" }]
license = { text = "Apache License, Version 2.0" }
readme = "README.md"
requires-python = ">=3.10,<3.13"
dependencies = [
"requests>=2.30.0,<3.0.0",
"beartype>=0.18.5,<0.19.0",
"pytest>=8.0.0,<9.0.0",
"requests-toolbelt>=1.0.0",
"python-docx>=1.1.2",
dependencies = ["requests>=2.30.0,<3.0.0", "beartype>=0.18.5,<0.19.0"]
[dependency-groups]
test = [
"hypothesis>=6.131.9",
"openpyxl>=3.1.5",
"python-pptx>=1.0.2",
"pillow>=11.1.0",
"pytest>=8.3.5",
"python-docx>=1.1.2",
"python-pptx>=1.0.2",
"reportlab>=4.3.1",
"requests>=2.32.3",
"requests-toolbelt>=1.0.0",
]
[project.optional-dependencies]
test = [
"pytest>=8.0.0,<9.0.0"
]
[tool.pytest.ini_options]
markers = [
"slow: marks tests as slow (deselect with '-m \"not slow\"')",
"wip: marks tests as work in progress (deselect with '-m \"not wip\"')"
]
markers = ["slow: marks tests as slow (deselect with '-m \"not slow\"')"]

224
sdk/python/ragflow_sdk/ragflow.py
View File

@ -13,16 +13,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import requests
from .modules.agent import Agent
from .modules.chat import Chat
from .modules.chunk import Chunk
from .modules.dataset import DataSet
from .modules.agent import Agent
class RAGFlow:
def __init__(self, api_key, base_url, version='v1'):
def __init__(self, api_key, base_url, version="v1"):
"""
api_url: http://<host_address>/api/v1
"""
@ -31,11 +33,11 @@ class RAGFlow:
self.authorization_header = {"Authorization": "{} {}".format("Bearer", self.user_key)}
def post(self, path, json=None, stream=False, files=None):
res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream,files=files)
res = requests.post(url=self.api_url + path, json=json, headers=self.authorization_header, stream=stream, files=files)
return res
def get(self, path, params=None, json=None):
res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header,json=json)
res = requests.get(url=self.api_url + path, params=params, headers=self.authorization_header, json=json)
return res
def delete(self, path, json):
@ -43,54 +45,73 @@ class RAGFlow:
return res
def put(self, path, json):
res = requests.put(url=self.api_url + path, json= json,headers=self.authorization_header)
res = requests.put(url=self.api_url + path, json=json, headers=self.authorization_header)
return res
def create_dataset(self, name: str, avatar: str = "", description: str = "", embedding_model:str = "BAAI/bge-large-zh-v1.5",
language: str = "English",
permission: str = "me",chunk_method: str = "naive",
parser_config: DataSet.ParserConfig = None) -> DataSet:
def create_dataset(
self,
name: str,
avatar: Optional[str] = None,
description: Optional[str] = None,
embedding_model: Optional[str] = "BAAI/bge-large-zh-v1.5@BAAI",
permission: str = "me",
chunk_method: str = "naive",
pagerank: int = 0,
parser_config: DataSet.ParserConfig = None,
) -> DataSet:
if parser_config:
parser_config = parser_config.to_json()
res = self.post("/datasets",
{"name": name, "avatar": avatar, "description": description,"embedding_model":embedding_model,
"language": language,
"permission": permission, "chunk_method": chunk_method,
"parser_config": parser_config
}
)
res = self.post(
"/datasets",
{
"name": name,
"avatar": avatar,
"description": description,
"embedding_model": embedding_model,
"permission": permission,
"chunk_method": chunk_method,
"pagerank": pagerank,
"parser_config": parser_config,
},
)
res = res.json()
if res.get("code") == 0:
return DataSet(self, res["data"])
raise Exception(res["message"])
def delete_datasets(self, ids: list[str] | None = None):
res = self.delete("/datasets",{"ids": ids})
res=res.json()
res = self.delete("/datasets", {"ids": ids})
res = res.json()
if res.get("code") != 0:
raise Exception(res["message"])
def get_dataset(self,name: str):
def get_dataset(self, name: str):
_list = self.list_datasets(name=name)
if len(_list) > 0:
return _list[0]
raise Exception("Dataset %s not found" % name)
def list_datasets(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True,
id: str | None = None, name: str | None = None) -> \
list[DataSet]:
res = self.get("/datasets",
{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
def list_datasets(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None) -> list[DataSet]:
res = self.get(
"/datasets",
{
"page": page,
"page_size": page_size,
"orderby": orderby,
"desc": desc,
"id": id,
"name": name,
},
)
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
for data in res["data"]:
result_list.append(DataSet(self, data))
return result_list
raise Exception(res["message"])
def create_chat(self, name: str, avatar: str = "", dataset_ids=None,
llm: Chat.LLM | None = None, prompt: Chat.Prompt | None = None) -> Chat:
def create_chat(self, name: str, avatar: str = "", dataset_ids=None, llm: Chat.LLM | None = None, prompt: Chat.Prompt | None = None) -> Chat:
if dataset_ids is None:
dataset_ids = []
dataset_list = []
@ -98,25 +119,33 @@ class RAGFlow:
dataset_list.append(id)
if llm is None:
llm = Chat.LLM(self, {"model_name": None,
"temperature": 0.1,
"top_p": 0.3,
"presence_penalty": 0.4,
"frequency_penalty": 0.7,
"max_tokens": 512, })
llm = Chat.LLM(
self,
{
"model_name": None,
"temperature": 0.1,
"top_p": 0.3,
"presence_penalty": 0.4,
"frequency_penalty": 0.7,
"max_tokens": 512,
},
)
if prompt is None:
prompt = Chat.Prompt(self, {"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.7,
"top_n": 8,
"top_k": 1024,
"variables": [{
"key": "knowledge",
"optional": True
}], "rerank_model": "",
"empty_response": None,
"opener": None,
"show_quote": True,
"prompt": None})
prompt = Chat.Prompt(
self,
{
"similarity_threshold": 0.2,
"keywords_similarity_weight": 0.7,
"top_n": 8,
"top_k": 1024,
"variables": [{"key": "knowledge", "optional": True}],
"rerank_model": "",
"empty_response": None,
"opener": None,
"show_quote": True,
"prompt": None,
},
)
if prompt.opener is None:
prompt.opener = "Hi! I'm your assistant, what can I do for you?"
if prompt.prompt is None:
@ -127,70 +156,93 @@ class RAGFlow:
"Answers need to consider chat history.\nHere is the knowledge base:\n{knowledge}\nThe above is the knowledge base."
)
temp_dict = {"name": name,
"avatar": avatar,
"dataset_ids": dataset_list if dataset_list else [],
"llm": llm.to_json(),
"prompt": prompt.to_json()}
temp_dict = {"name": name, "avatar": avatar, "dataset_ids": dataset_list if dataset_list else [], "llm": llm.to_json(), "prompt": prompt.to_json()}
res = self.post("/chats", temp_dict)
res = res.json()
if res.get("code") == 0:
return Chat(self, res["data"])
raise Exception(res["message"])
def delete_chats(self,ids: list[str] | None = None):
res = self.delete('/chats',
{"ids":ids})
def delete_chats(self, ids: list[str] | None = None):
res = self.delete("/chats", {"ids": ids})
res = res.json()
if res.get("code") != 0:
raise Exception(res["message"])
def list_chats(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True,
id: str | None = None, name: str | None = None) -> list[Chat]:
res = self.get("/chats",{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "name": name})
def list_chats(self, page: int = 1, page_size: int = 30, orderby: str = "create_time", desc: bool = True, id: str | None = None, name: str | None = None) -> list[Chat]:
res = self.get(
"/chats",
{
"page": page,
"page_size": page_size,
"orderby": orderby,
"desc": desc,
"id": id,
"name": name,
},
)
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
for data in res["data"]:
result_list.append(Chat(self, data))
return result_list
raise Exception(res["message"])
def retrieve(
self,
dataset_ids,
document_ids=None,
question="",
page=1,
page_size=30,
similarity_threshold=0.2,
vector_similarity_weight=0.3,
top_k=1024,
rerank_id: str | None = None,
keyword: bool = False,
):
if document_ids is None:
document_ids = []
data_json = {
"page": page,
"page_size": page_size,
"similarity_threshold": similarity_threshold,
"vector_similarity_weight": vector_similarity_weight,
"top_k": top_k,
"rerank_id": rerank_id,
"keyword": keyword,
"question": question,
"dataset_ids": dataset_ids,
"document_ids": document_ids,
}
# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
res = self.post("/retrieval", json=data_json)
res = res.json()
if res.get("code") == 0:
chunks = []
for chunk_data in res["data"].get("chunks"):
chunk = Chunk(self, chunk_data)
chunks.append(chunk)
return chunks
raise Exception(res.get("message"))
def retrieve(self, dataset_ids, document_ids=None, question="", page=1, page_size=30, similarity_threshold=0.2, vector_similarity_weight=0.3, top_k=1024, rerank_id: str | None = None, keyword:bool=False, ):
if document_ids is None:
document_ids = []
data_json ={
def list_agents(self, page: int = 1, page_size: int = 30, orderby: str = "update_time", desc: bool = True, id: str | None = None, title: str | None = None) -> list[Agent]:
res = self.get(
"/agents",
{
"page": page,
"page_size": page_size,
"similarity_threshold": similarity_threshold,
"vector_similarity_weight": vector_similarity_weight,
"top_k": top_k,
"rerank_id": rerank_id,
"keyword": keyword,
"question": question,
"dataset_ids": dataset_ids,
"document_ids": document_ids
}
# Send a POST request to the backend service (using requests library as an example, actual implementation may vary)
res = self.post('/retrieval',json=data_json)
res = res.json()
if res.get("code") ==0:
chunks=[]
for chunk_data in res["data"].get("chunks"):
chunk=Chunk(self,chunk_data)
chunks.append(chunk)
return chunks
raise Exception(res.get("message"))
def list_agents(self, page: int = 1, page_size: int = 30, orderby: str = "update_time", desc: bool = True,
id: str | None = None, title: str | None = None) -> list[Agent]:
res = self.get("/agents",{"page": page, "page_size": page_size, "orderby": orderby, "desc": desc, "id": id, "title": title})
"orderby": orderby,
"desc": desc,
"id": id,
"title": title,
},
)
res = res.json()
result_list = []
if res.get("code") == 0:
for data in res['data']:
for data in res["data"]:
result_list.append(Agent(self, data))
return result_list
raise Exception(res["message"])

852
sdk/python/test/test_http_api/test_dataset_mangement/test_create_dataset.py
View File

@ -14,13 +14,26 @@
# limitations under the License.
#
import hypothesis.strategies as st
import pytest
from common import DATASET_NAME_LIMIT, INVALID_API_TOKEN, create_dataset
from hypothesis import example, given, settings
from libs.auth import RAGFlowHttpApiAuth
from libs.utils import encode_avatar
from libs.utils.file_utils import create_image_file
@st.composite
def valid_names(draw):
base_chars = "abcdefghijklmnopqrstuvwxyz_"
first_char = draw(st.sampled_from([c for c in base_chars if c.isalpha() or c == "_"]))
remaining = draw(st.text(alphabet=st.sampled_from(base_chars), min_size=0, max_size=DATASET_NAME_LIMIT - 2))
name = (first_char + remaining)[:128]
return name.encode("utf-8").decode("utf-8")
@pytest.mark.usefixtures("clear_datasets")
class TestAuthorization:
@pytest.mark.parametrize(
@ -33,6 +46,7 @@ class TestAuthorization:
"Authentication error: API key is invalid!",
),
],
ids=["empty_auth", "invalid_api_token"],
)
def test_invalid_auth(self, auth, expected_code, expected_message):
res = create_dataset(auth, {"name": "auth_test"})
@ -42,31 +56,509 @@ class TestAuthorization:
@pytest.mark.usefixtures("clear_datasets")
class TestDatasetCreation:
@given(name=valid_names())
@example("a" * 128)
@settings(max_examples=20)
def test_valid_name(self, get_http_api_auth, name):
res = create_dataset(get_http_api_auth, {"name": name})
assert res["code"] == 0, res
assert res["data"]["name"] == name, res
@pytest.mark.parametrize(
"payload, expected_code",
"name, expected_message",
[
({"name": "valid_name"}, 0),
({"name": "a" * (DATASET_NAME_LIMIT + 1)}, 102),
({"name": 0}, 100),
({"name": ""}, 102),
({"name": "duplicated_name"}, 102),
({"name": "case_insensitive"}, 102),
("", "String should have at least 1 character"),
(" ", "String should have at least 1 character"),
("a" * (DATASET_NAME_LIMIT + 1), "String should have at most 128 characters"),
(0, "Input should be a valid string"),
],
ids=["empty_name", "space_name", "too_long_name", "invalid_name"],
)
def test_basic_scenarios(self, get_http_api_auth, payload, expected_code):
if payload["name"] == "duplicated_name":
create_dataset(get_http_api_auth, payload)
elif payload["name"] == "case_insensitive":
create_dataset(get_http_api_auth, {"name": payload["name"].upper()})
def test_invalid_name(self, get_http_api_auth, name, expected_message):
res = create_dataset(get_http_api_auth, {"name": name})
assert res["code"] == 101, res
assert expected_message in res["message"], res
def test_duplicated_name(self, get_http_api_auth):
name = "duplicated_name"
payload = {"name": name}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert res["message"] == f"Dataset name '{name}' already exists", res
assert res["code"] == expected_code
if expected_code == 0:
assert res["data"]["name"] == payload["name"]
def test_case_insensitive(self, get_http_api_auth):
name = "CaseInsensitive"
res = create_dataset(get_http_api_auth, {"name": name.upper()})
assert res["code"] == 0, res
if payload["name"] in ["duplicated_name", "case_insensitive"]:
assert res["message"] == "Duplicated dataset name in creating dataset."
res = create_dataset(get_http_api_auth, {"name": name.lower()})
assert res["code"] == 101, res
assert res["message"] == f"Dataset name '{name.lower()}' already exists", res
def test_avatar(self, get_http_api_auth, tmp_path):
fn = create_image_file(tmp_path / "ragflow_test.png")
payload = {
"name": "avatar_test",
"avatar": f"data:image/png;base64,{encode_avatar(fn)}",
}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
def test_avatar_none(self, get_http_api_auth, tmp_path):
payload = {"name": "test_avatar_none", "avatar": None}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
assert res["data"]["avatar"] is None, res
def test_avatar_exceeds_limit_length(self, get_http_api_auth):
res = create_dataset(get_http_api_auth, {"name": "exceeds_limit_length_avatar", "avatar": "a" * 65536})
assert res["code"] == 101, res
assert "String should have at most 65535 characters" in res["message"], res
@pytest.mark.parametrize(
"name, avatar_prefix, expected_message",
[
("empty_prefix", "", "Missing MIME prefix. Expected format: data:<mime>;base64,<data>"),
("missing_comma", "data:image/png;base64", "Missing MIME prefix. Expected format: data:<mime>;base64,<data>"),
("unsupported_mine_type", "invalid_mine_prefix:image/png;base64,", "Invalid MIME prefix format. Must start with 'data:'"),
("invalid_mine_type", "data:unsupported_mine_type;base64,", "Unsupported MIME type. Allowed: ['image/jpeg', 'image/png']"),
],
ids=["empty_prefix", "missing_comma", "unsupported_mine_type", "invalid_mine_type"],
)
def test_invalid_avatar_prefix(self, get_http_api_auth, tmp_path, name, avatar_prefix, expected_message):
fn = create_image_file(tmp_path / "ragflow_test.png")
payload = {
"name": name,
"avatar": f"{avatar_prefix}{encode_avatar(fn)}",
}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert expected_message in res["message"], res
def test_description_none(self, get_http_api_auth):
payload = {"name": "test_description_none", "description": None}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
assert res["data"]["description"] is None, res
def test_description_exceeds_limit_length(self, get_http_api_auth):
payload = {"name": "exceeds_limit_length_description", "description": "a" * 65536}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert "String should have at most 65535 characters" in res["message"], res
@pytest.mark.parametrize(
"name, embedding_model",
[
("BAAI/bge-large-zh-v1.5@BAAI", "BAAI/bge-large-zh-v1.5@BAAI"),
("maidalun1020/bce-embedding-base_v1@Youdao", "maidalun1020/bce-embedding-base_v1@Youdao"),
("embedding-3@ZHIPU-AI", "embedding-3@ZHIPU-AI"),
("embedding_model_default", None),
],
ids=["builtin_baai", "builtin_youdao", "tenant__zhipu", "default"],
)
def test_valid_embedding_model(self, get_http_api_auth, name, embedding_model):
if embedding_model is None:
payload = {"name": name}
else:
payload = {"name": name, "embedding_model": embedding_model}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
if embedding_model is None:
assert res["data"]["embedding_model"] == "BAAI/bge-large-zh-v1.5@BAAI", res
else:
assert res["data"]["embedding_model"] == embedding_model, res
@pytest.mark.parametrize(
"name, embedding_model",
[
("unknown_llm_name", "unknown@ZHIPU-AI"),
("unknown_llm_factory", "embedding-3@unknown"),
("tenant_no_auth", "deepseek-chat@DeepSeek"),
],
ids=["unknown_llm_name", "unknown_llm_factory", "tenant_no_auth"],
)
def test_invalid_embedding_model(self, get_http_api_auth, name, embedding_model):
payload = {"name": name, "embedding_model": embedding_model}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert res["message"] == f"The embedding_model '{embedding_model}' is not supported", res
@pytest.mark.parametrize(
"name, embedding_model",
[
("builtin_missing_at", "BAAI/bge-large-zh-v1.5"),
("tenant_missing_at", "embedding-3ZHIPU-AI"),
],
ids=["builtin_missing_at", "tenant_missing_at"],
)
def test_embedding_model_missing_at(self, get_http_api_auth, name, embedding_model):
payload = {"name": name, "embedding_model": embedding_model}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert "Embedding model must be xxx@yyy" in res["message"], res
@pytest.mark.parametrize(
"name, permission",
[
("me", "me"),
("team", "team"),
("me_upercase", "ME"),
("team_upercase", "TEAM"),
("permission_default", None),
],
ids=["me", "team", "me_upercase", "team_upercase", "permission_default"],
)
def test_valid_permission(self, get_http_api_auth, name, permission):
if permission is None:
payload = {"name": name}
else:
payload = {"name": name, "permission": permission}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
if permission is None:
assert res["data"]["permission"] == "me", res
else:
assert res["data"]["permission"] == permission.lower(), res
@pytest.mark.parametrize(
"name, permission",
[
("empty", ""),
("unknown", "unknown"),
("type_error", list()),
],
)
def test_invalid_permission(self, get_http_api_auth, name, permission):
payload = {"name": name, "permission": permission}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101
assert "Input should be 'me' or 'team'" in res["message"]
@pytest.mark.parametrize(
"name, chunk_method",
[
("naive", "naive"),
("book", "book"),
("email", "email"),
("laws", "laws"),
("manual", "manual"),
("one", "one"),
("paper", "paper"),
("picture", "picture"),
("presentation", "presentation"),
("qa", "qa"),
("table", "table"),
("tag", "tag"),
("chunk_method_default", None),
],
)
def test_valid_chunk_method(self, get_http_api_auth, name, chunk_method):
if chunk_method is None:
payload = {"name": name}
else:
payload = {"name": name, "chunk_method": chunk_method}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
if chunk_method is None:
assert res["data"]["chunk_method"] == "naive", res
else:
assert res["data"]["chunk_method"] == chunk_method, res
@pytest.mark.parametrize(
"name, chunk_method",
[
("empty", ""),
("unknown", "unknown"),
("type_error", list()),
],
)
def test_invalid_chunk_method(self, get_http_api_auth, name, chunk_method):
payload = {"name": name, "chunk_method": chunk_method}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert "Input should be 'naive', 'book', 'email', 'laws', 'manual', 'one', 'paper', 'picture', 'presentation', 'qa', 'table' or 'tag'" in res["message"], res
@pytest.mark.parametrize(
"name, parser_config",
[
("default_none", None),
("default_empty", {}),
("auto_keywords_min", {"auto_keywords": 0}),
("auto_keywords_mid", {"auto_keywords": 16}),
("auto_keywords_max", {"auto_keywords": 32}),
("auto_questions_min", {"auto_questions": 0}),
("auto_questions_mid", {"auto_questions": 5}),
("auto_questions_max", {"auto_questions": 10}),
("chunk_token_num_min", {"chunk_token_num": 1}),
("chunk_token_num_mid", {"chunk_token_num": 1024}),
("chunk_token_num_max", {"chunk_token_num": 2048}),
("delimiter", {"delimiter": "\n"}),
("delimiter_space", {"delimiter": " "}),
("html4excel_true", {"html4excel": True}),
("html4excel_false", {"html4excel": False}),
("layout_recognize_DeepDOC", {"layout_recognize": "DeepDOC"}),
("layout_recognize_navie", {"layout_recognize": "Plain Text"}),
("tag_kb_ids", {"tag_kb_ids": ["1", "2"]}),
("topn_tags_min", {"topn_tags": 1}),
("topn_tags_mid", {"topn_tags": 5}),
("topn_tags_max", {"topn_tags": 10}),
("filename_embd_weight_min", {"filename_embd_weight": 0.1}),
("filename_embd_weight_mid", {"filename_embd_weight": 0.5}),
("filename_embd_weight_max", {"filename_embd_weight": 1.0}),
("task_page_size_min", {"task_page_size": 1}),
("task_page_size_mid", {"task_page_size": 5_000}),
("task_page_size_max", {"task_page_size": 10_000}),
("pages", {"pages": [[1, 100]]}),
("pages_none", None),
("graphrag_true", {"graphrag": {"use_graphrag": True}}),
("graphrag_false", {"graphrag": {"use_graphrag": False}}),
("graphrag_entity_types", {"graphrag": {"entity_types": ["age", "sex", "height", "weight"]}}),
("graphrag_method_general", {"graphrag": {"method": "general"}}),
("graphrag_method_light", {"graphrag": {"method": "light"}}),
("graphrag_community_true", {"graphrag": {"community": True}}),
("graphrag_community_false", {"graphrag": {"community": False}}),
("graphrag_resolution_true", {"graphrag": {"resolution": True}}),
("graphrag_resolution_false", {"graphrag": {"resolution": False}}),
("raptor_true", {"raptor": {"use_raptor": True}}),
("raptor_false", {"raptor": {"use_raptor": False}}),
("raptor_prompt", {"raptor": {"prompt": "Who are you?"}}),
("raptor_max_token_min", {"raptor": {"max_token": 1}}),
("raptor_max_token_mid", {"raptor": {"max_token": 1024}}),
("raptor_max_token_max", {"raptor": {"max_token": 2048}}),
("raptor_threshold_min", {"raptor": {"threshold": 0.0}}),
("raptor_threshold_mid", {"raptor": {"threshold": 0.5}}),
("raptor_threshold_max", {"raptor": {"threshold": 1.0}}),
("raptor_max_cluster_min", {"raptor": {"max_cluster": 1}}),
("raptor_max_cluster_mid", {"raptor": {"max_cluster": 512}}),
("raptor_max_cluster_max", {"raptor": {"max_cluster": 1024}}),
("raptor_random_seed_min", {"raptor": {"random_seed": 0}}),
("raptor_random_seed_mid", {"raptor": {"random_seed": 5_000}}),
("raptor_random_seed_max", {"raptor": {"random_seed": 10_000}}),
],
ids=[
"default_none",
"default_empty",
"auto_keywords_min",
"auto_keywords_mid",
"auto_keywords_max",
"auto_questions_min",
"auto_questions_mid",
"auto_questions_max",
"chunk_token_num_min",
"chunk_token_num_mid",
"chunk_token_num_max",
"delimiter",
"delimiter_space",
"html4excel_true",
"html4excel_false",
"layout_recognize_DeepDOC",
"layout_recognize_navie",
"tag_kb_ids",
"topn_tags_min",
"topn_tags_mid",
"topn_tags_max",
"filename_embd_weight_min",
"filename_embd_weight_mid",
"filename_embd_weight_max",
"task_page_size_min",
"task_page_size_mid",
"task_page_size_max",
"pages",
"pages_none",
"graphrag_true",
"graphrag_false",
"graphrag_entity_types",
"graphrag_method_general",
"graphrag_method_light",
"graphrag_community_true",
"graphrag_community_false",
"graphrag_resolution_true",
"graphrag_resolution_false",
"raptor_true",
"raptor_false",
"raptor_prompt",
"raptor_max_token_min",
"raptor_max_token_mid",
"raptor_max_token_max",
"raptor_threshold_min",
"raptor_threshold_mid",
"raptor_threshold_max",
"raptor_max_cluster_min",
"raptor_max_cluster_mid",
"raptor_max_cluster_max",
"raptor_random_seed_min",
"raptor_random_seed_mid",
"raptor_random_seed_max",
],
)
def test_valid_parser_config(self, get_http_api_auth, name, parser_config):
if parser_config is None:
payload = {"name": name}
else:
payload = {"name": name, "parser_config": parser_config}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, res
if parser_config is None:
assert res["data"]["parser_config"] == {
"chunk_token_num": 128,
"delimiter": r"\n!?;。；！？",
"html4excel": False,
"layout_recognize": "DeepDOC",
"raptor": {"use_raptor": False},
}
elif parser_config == {}:
assert res["data"]["parser_config"] == {
"auto_keywords": 0,
"auto_questions": 0,
"chunk_token_num": 128,
"delimiter": r"\n!?;。；！？",
"filename_embd_weight": None,
"graphrag": None,
"html4excel": False,
"layout_recognize": "DeepDOC",
"pages": None,
"raptor": None,
"tag_kb_ids": [],
"task_page_size": None,
"topn_tags": 1,
}
else:
for k, v in parser_config.items():
if isinstance(v, dict):
for kk, vv in v.items():
assert res["data"]["parser_config"][k][kk] == vv
else:
assert res["data"]["parser_config"][k] == v
@pytest.mark.parametrize(
"name, parser_config, expected_message",
[
("auto_keywords_min_limit", {"auto_keywords": -1}, "Input should be greater than or equal to 0"),
("auto_keywords_max_limit", {"auto_keywords": 33}, "Input should be less than or equal to 32"),
("auto_keywords_float_not_allowed", {"auto_keywords": 3.14}, "Input should be a valid integer, got a number with a fractional part"),
("auto_keywords_type_invalid", {"auto_keywords": "string"}, "Input should be a valid integer, unable to parse string as an integer"),
("auto_questions_min_limit", {"auto_questions": -1}, "Input should be greater than or equal to 0"),
("auto_questions_max_limit", {"auto_questions": 11}, "Input should be less than or equal to 10"),
("auto_questions_float_not_allowed", {"auto_questions": 3.14}, "Input should be a valid integer, got a number with a fractional part"),
("auto_questions_type_invalid", {"auto_questions": "string"}, "Input should be a valid integer, unable to parse string as an integer"),
("chunk_token_num_min_limit", {"chunk_token_num": 0}, "Input should be greater than or equal to 1"),
("chunk_token_num_max_limit", {"chunk_token_num": 2049}, "Input should be less than or equal to 2048"),
("chunk_token_num_float_not_allowed", {"chunk_token_num": 3.14}, "Input should be a valid integer, got a number with a fractional part"),
("chunk_token_num_type_invalid", {"chunk_token_num": "string"}, "Input should be a valid integer, unable to parse string as an integer"),
("delimiter_empty", {"delimiter": ""}, "String should have at least 1 character"),
("html4excel_type_invalid", {"html4excel": "string"}, "Input should be a valid boolean, unable to interpret input"),
("tag_kb_ids_not_list", {"tag_kb_ids": "1,2"}, "Input should be a valid list"),
("tag_kb_ids_int_in_list", {"tag_kb_ids": [1, 2]}, "Input should be a valid string"),
("topn_tags_min_limit", {"topn_tags": 0}, "Input should be greater than or equal to 1"),
("topn_tags_max_limit", {"topn_tags": 11}, "Input should be less than or equal to 10"),
("topn_tags_float_not_allowed", {"topn_tags": 3.14}, "Input should be a valid integer, got a number with a fractional part"),
("topn_tags_type_invalid", {"topn_tags": "string"}, "Input should be a valid integer, unable to parse string as an integer"),
("filename_embd_weight_min_limit", {"filename_embd_weight": -1}, "Input should be greater than or equal to 0"),
("filename_embd_weight_max_limit", {"filename_embd_weight": 1.1}, "Input should be less than or equal to 1"),
("filename_embd_weight_type_invalid", {"filename_embd_weight": "string"}, "Input should be a valid number, unable to parse string as a number"),
("task_page_size_min_limit", {"task_page_size": 0}, "Input should be greater than or equal to 1"),
("task_page_size_max_limit", {"task_page_size": 10_001}, "Input should be less than or equal to 10000"),
("task_page_size_float_not_allowed", {"task_page_size": 3.14}, "Input should be a valid integer, got a number with a fractional part"),
("task_page_size_type_invalid", {"task_page_size": "string"}, "Input should be a valid integer, unable to parse string as an integer"),
("pages_not_list", {"pages": "1,2"}, "Input should be a valid list"),
("pages_not_list_in_list", {"pages": ["1,2"]}, "Input should be a valid list"),
("pages_not_int_list", {"pages": [["string1", "string2"]]}, "Input should be a valid integer, unable to parse string as an integer"),
("graphrag_type_invalid", {"graphrag": {"use_graphrag": "string"}}, "Input should be a valid boolean, unable to interpret input"),
("graphrag_entity_types_not_list", {"graphrag": {"entity_types": "1,2"}}, "Input should be a valid list"),
("graphrag_entity_types_not_str_in_list", {"graphrag": {"entity_types": [1, 2]}}, "nput should be a valid string"),
("graphrag_method_unknown", {"graphrag": {"method": "unknown"}}, "Input should be 'light' or 'general'"),
("graphrag_method_none", {"graphrag": {"method": None}}, "Input should be 'light' or 'general'"),
("graphrag_community_type_invalid", {"graphrag": {"community": "string"}}, "Input should be a valid boolean, unable to interpret input"),
("graphrag_resolution_type_invalid", {"graphrag": {"resolution": "string"}}, "Input should be a valid boolean, unable to interpret input"),
("raptor_type_invalid", {"raptor": {"use_raptor": "string"}}, "Input should be a valid boolean, unable to interpret input"),
("raptor_prompt_empty", {"raptor": {"prompt": ""}}, "String should have at least 1 character"),
("raptor_prompt_space", {"raptor": {"prompt": " "}}, "String should have at least 1 character"),
("raptor_max_token_min_limit", {"raptor": {"max_token": 0}}, "Input should be greater than or equal to 1"),
("raptor_max_token_max_limit", {"raptor": {"max_token": 2049}}, "Input should be less than or equal to 2048"),
("raptor_max_token_float_not_allowed", {"raptor": {"max_token": 3.14}}, "Input should be a valid integer, got a number with a fractional part"),
("raptor_max_token_type_invalid", {"raptor": {"max_token": "string"}}, "Input should be a valid integer, unable to parse string as an integer"),
("raptor_threshold_min_limit", {"raptor": {"threshold": -0.1}}, "Input should be greater than or equal to 0"),
("raptor_threshold_max_limit", {"raptor": {"threshold": 1.1}}, "Input should be less than or equal to 1"),
("raptor_threshold_type_invalid", {"raptor": {"threshold": "string"}}, "Input should be a valid number, unable to parse string as a number"),
("raptor_max_cluster_min_limit", {"raptor": {"max_cluster": 0}}, "Input should be greater than or equal to 1"),
("raptor_max_cluster_max_limit", {"raptor": {"max_cluster": 1025}}, "Input should be less than or equal to 1024"),
("raptor_max_cluster_float_not_allowed", {"raptor": {"max_cluster": 3.14}}, "Input should be a valid integer, got a number with a fractional par"),
("raptor_max_cluster_type_invalid", {"raptor": {"max_cluster": "string"}}, "Input should be a valid integer, unable to parse string as an integer"),
("raptor_random_seed_min_limit", {"raptor": {"random_seed": -1}}, "Input should be greater than or equal to 0"),
("raptor_random_seed_max_limit", {"raptor": {"random_seed": 10_001}}, "Input should be less than or equal to 10000"),
("raptor_random_seed_float_not_allowed", {"raptor": {"random_seed": 3.14}}, "Input should be a valid integer, got a number with a fractional part"),
("raptor_random_seed_type_invalid", {"raptor": {"random_seed": "string"}}, "Input should be a valid integer, unable to parse string as an integer"),
("parser_config_type_invalid", {"delimiter": "a" * 65536}, "Parser config have at most 65535 characters"),
],
ids=[
"auto_keywords_min_limit",
"auto_keywords_max_limit",
"auto_keywords_float_not_allowed",
"auto_keywords_type_invalid",
"auto_questions_min_limit",
"auto_questions_max_limit",
"auto_questions_float_not_allowed",
"auto_questions_type_invalid",
"chunk_token_num_min_limit",
"chunk_token_num_max_limit",
"chunk_token_num_float_not_allowed",
"chunk_token_num_type_invalid",
"delimiter_empty",
"html4excel_type_invalid",
"tag_kb_ids_not_list",
"tag_kb_ids_int_in_list",
"topn_tags_min_limit",
"topn_tags_max_limit",
"topn_tags_float_not_allowed",
"topn_tags_type_invalid",
"filename_embd_weight_min_limit",
"filename_embd_weight_max_limit",
"filename_embd_weight_type_invalid",
"task_page_size_min_limit",
"task_page_size_max_limit",
"task_page_size_float_not_allowed",
"task_page_size_type_invalid",
"pages_not_list",
"pages_not_list_in_list",
"pages_not_int_list",
"graphrag_type_invalid",
"graphrag_entity_types_not_list",
"graphrag_entity_types_not_str_in_list",
"graphrag_method_unknown",
"graphrag_method_none",
"graphrag_community_type_invalid",
"graphrag_resolution_type_invalid",
"raptor_type_invalid",
"raptor_prompt_empty",
"raptor_prompt_space",
"raptor_max_token_min_limit",
"raptor_max_token_max_limit",
"raptor_max_token_float_not_allowed",
"raptor_max_token_type_invalid",
"raptor_threshold_min_limit",
"raptor_threshold_max_limit",
"raptor_threshold_type_invalid",
"raptor_max_cluster_min_limit",
"raptor_max_cluster_max_limit",
"raptor_max_cluster_float_not_allowed",
"raptor_max_cluster_type_invalid",
"raptor_random_seed_min_limit",
"raptor_random_seed_max_limit",
"raptor_random_seed_float_not_allowed",
"raptor_random_seed_type_invalid",
"parser_config_type_invalid",
],
)
def test_invalid_parser_config(self, get_http_api_auth, name, parser_config, expected_message):
payload = {"name": name, "parser_config": parser_config}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 101, res
assert expected_message in res["message"], res
@pytest.mark.slow
def test_dataset_10k(self, get_http_api_auth):
@ -74,329 +566,3 @@ class TestDatasetCreation:
payload = {"name": f"dataset_{i}"}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0, f"Failed to create dataset {i}"
def test_avatar(self, get_http_api_auth, tmp_path):
fn = create_image_file(tmp_path / "ragflow_test.png")
payload = {
"name": "avatar_test",
"avatar": encode_avatar(fn),
}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0
def test_description(self, get_http_api_auth):
payload = {"name": "description_test", "description": "a" * 65536}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == 0
@pytest.mark.parametrize(
"name, permission, expected_code",
[
("me", "me", 0),
("team", "team", 0),
("empty_permission", "", 0),
("me_upercase", "ME", 102),
("team_upercase", "TEAM", 102),
("other_permission", "other_permission", 102),
],
)
def test_permission(self, get_http_api_auth, name, permission, expected_code):
payload = {"name": name, "permission": permission}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == expected_code
if expected_code == 0 and permission != "":
assert res["data"]["permission"] == permission
if permission == "":
assert res["data"]["permission"] == "me"
@pytest.mark.parametrize(
"name, chunk_method, expected_code",
[
("naive", "naive", 0),
("manual", "manual", 0),
("qa", "qa", 0),
("table", "table", 0),
("paper", "paper", 0),
("book", "book", 0),
("laws", "laws", 0),
("presentation", "presentation", 0),
("picture", "picture", 0),
("one", "one", 0),
("email", "email", 0),
("tag", "tag", 0),
("empty_chunk_method", "", 0),
("other_chunk_method", "other_chunk_method", 102),
],
)
def test_chunk_method(self, get_http_api_auth, name, chunk_method, expected_code):
payload = {"name": name, "chunk_method": chunk_method}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == expected_code
if expected_code == 0 and chunk_method != "":
assert res["data"]["chunk_method"] == chunk_method
if chunk_method == "":
assert res["data"]["chunk_method"] == "naive"
@pytest.mark.parametrize(
"name, embedding_model, expected_code",
[
("BAAI/bge-large-zh-v1.5", "BAAI/bge-large-zh-v1.5", 0),
(
"maidalun1020/bce-embedding-base_v1",
"maidalun1020/bce-embedding-base_v1",
0,
),
("other_embedding_model", "other_embedding_model", 102),
],
)
def test_embedding_model(self, get_http_api_auth, name, embedding_model, expected_code):
payload = {"name": name, "embedding_model": embedding_model}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == expected_code
if expected_code == 0:
assert res["data"]["embedding_model"] == embedding_model
@pytest.mark.parametrize(
"name, chunk_method, parser_config, expected_code, expected_message",
[
(
"naive_default",
"naive",
{
"chunk_token_num": 128,
"layout_recognize": "DeepDOC",
"html4excel": False,
"delimiter": "\n!?。；！？",
"task_page_size": 12,
"raptor": {"use_raptor": False},
},
0,
"",
),
("naive_empty", "naive", {}, 0, ""),
(
"naive_chunk_token_num_negative",
"naive",
{"chunk_token_num": -1},
100,
"AssertionError('chunk_token_num should be in range from 1 to 100000000')",
),
(
"naive_chunk_token_num_zero",
"naive",
{"chunk_token_num": 0},
100,
"AssertionError('chunk_token_num should be in range from 1 to 100000000')",
),
(
"naive_chunk_token_num_max",
"naive",
{"chunk_token_num": 100000000},
100,
"AssertionError('chunk_token_num should be in range from 1 to 100000000')",
),
(
"naive_chunk_token_num_float",
"naive",
{"chunk_token_num": 3.14},
100,
"AssertionError('chunk_token_num should be int')",
),
(
"naive_chunk_token_num_str",
"naive",
{"chunk_token_num": "1024"},
100,
"AssertionError('chunk_token_num should be int')",
),
(
"naive_layout_recognize_DeepDOC",
"naive",
{"layout_recognize": "DeepDOC"},
0,
"",
),
(
"naive_layout_recognize_Naive",
"naive",
{"layout_recognize": "Naive"},
0,
"",
),
("naive_html4excel_true", "naive", {"html4excel": True}, 0, ""),
("naive_html4excel_false", "naive", {"html4excel": False}, 0, ""),
(
"naive_html4excel_not_bool",
"naive",
{"html4excel": 1},
100,
"AssertionError('html4excel should be True or False')",
),
("naive_delimiter_empty", "naive", {"delimiter": ""}, 0, ""),
("naive_delimiter_backticks", "naive", {"delimiter": "`##`"}, 0, ""),
(
"naive_delimiter_not_str",
"naive",
{"delimiter": 1},
100,
"AssertionError('delimiter should be str')",
),
(
"naive_task_page_size_negative",
"naive",
{"task_page_size": -1},
100,
"AssertionError('task_page_size should be in range from 1 to 100000000')",
),
(
"naive_task_page_size_zero",
"naive",
{"task_page_size": 0},
100,
"AssertionError('task_page_size should be in range from 1 to 100000000')",
),
(
"naive_task_page_size_max",
"naive",
{"task_page_size": 100000000},
100,
"AssertionError('task_page_size should be in range from 1 to 100000000')",
),
(
"naive_task_page_size_float",
"naive",
{"task_page_size": 3.14},
100,
"AssertionError('task_page_size should be int')",
),
(
"naive_task_page_size_str",
"naive",
{"task_page_size": "1024"},
100,
"AssertionError('task_page_size should be int')",
),
("naive_raptor_true", "naive", {"raptor": {"use_raptor": True}}, 0, ""),
("naive_raptor_false", "naive", {"raptor": {"use_raptor": False}}, 0, ""),
(
"invalid_key",
"naive",
{"invalid_key": "invalid_value"},
100,
"""AssertionError("Abnormal \'parser_config\'. Invalid key: invalid_key")""",
),
(
"naive_auto_keywords_negative",
"naive",
{"auto_keywords": -1},
100,
"AssertionError('auto_keywords should be in range from 0 to 32')",
),
(
"naive_auto_keywords_max",
"naive",
{"auto_keywords": 32},
100,
"AssertionError('auto_keywords should be in range from 0 to 32')",
),
(
"naive_auto_keywords_float",
"naive",
{"auto_keywords": 3.14},
100,
"AssertionError('auto_keywords should be int')",
),
(
"naive_auto_keywords_str",
"naive",
{"auto_keywords": "1024"},
100,
"AssertionError('auto_keywords should be int')",
),
(
"naive_auto_questions_negative",
"naive",
{"auto_questions": -1},
100,
"AssertionError('auto_questions should be in range from 0 to 10')",
),
(
"naive_auto_questions_max",
"naive",
{"auto_questions": 10},
100,
"AssertionError('auto_questions should be in range from 0 to 10')",
),
(
"naive_auto_questions_float",
"naive",
{"auto_questions": 3.14},
100,
"AssertionError('auto_questions should be int')",
),
(
"naive_auto_questions_str",
"naive",
{"auto_questions": "1024"},
100,
"AssertionError('auto_questions should be int')",
),
(
"naive_topn_tags_negative",
"naive",
{"topn_tags": -1},
100,
"AssertionError('topn_tags should be in range from 0 to 10')",
),
(
"naive_topn_tags_max",
"naive",
{"topn_tags": 10},
100,
"AssertionError('topn_tags should be in range from 0 to 10')",
),
(
"naive_topn_tags_float",
"naive",
{"topn_tags": 3.14},
100,
"AssertionError('topn_tags should be int')",
),
(
"naive_topn_tags_str",
"naive",
{"topn_tags": "1024"},
100,
"AssertionError('topn_tags should be int')",
),
],
)
def test_parser_configs(
self,
get_http_api_auth,
name,
chunk_method,
parser_config,
expected_code,
expected_message,
):
payload = {
"name": name,
"chunk_method": chunk_method,
"parser_config": parser_config,
}
res = create_dataset(get_http_api_auth, payload)
assert res["code"] == expected_code
if expected_code == 0 and parser_config != {}:
for k, v in parser_config.items():
assert res["data"]["parser_config"][k] == v
if expected_code != 0 or expected_message:
assert res["message"] == expected_message
if parser_config == {}:
assert res["data"]["parser_config"] == {
"chunk_token_num": 128,
"delimiter": "\\n!?;。；！？",
"html4excel": False,
"layout_recognize": "DeepDOC",
"raptor": {"use_raptor": False},
}

11
sdk/python/test/test_sdk_api/t_dataset.py
View File

@ -14,10 +14,11 @@
# limitations under the License.
#
from ragflow_sdk import RAGFlow
import random
import pytest
from common import HOST_ADDRESS
from ragflow_sdk import RAGFlow
def test_create_dataset_with_name(get_api_key_fixture):
@ -32,7 +33,7 @@ def test_create_dataset_with_duplicated_name(get_api_key_fixture):
rag.create_dataset("test_create_dataset_with_duplicated_name")
with pytest.raises(Exception) as exc_info:
rag.create_dataset("test_create_dataset_with_duplicated_name")
assert str(exc_info.value) == "Duplicated dataset name in creating dataset."
assert str(exc_info.value) == "Dataset name 'test_create_dataset_with_duplicated_name' already exists"
def test_create_dataset_with_random_chunk_method(get_api_key_fixture):
@ -46,11 +47,13 @@ def test_create_dataset_with_random_chunk_method(get_api_key_fixture):
def test_create_dataset_with_invalid_parameter(get_api_key_fixture):
API_KEY = get_api_key_fixture
rag = RAGFlow(API_KEY, HOST_ADDRESS)
valid_chunk_methods = ["naive", "manual", "qa", "table", "paper", "book", "laws", "presentation", "picture", "one", "email", "tag"]
chunk_method = "invalid_chunk_method"
with pytest.raises(Exception) as exc_info:
rag.create_dataset("test_create_dataset_with_invalid_chunk_method", chunk_method=chunk_method)
assert str(exc_info.value) == f"'{chunk_method}' is not in {valid_chunk_methods}"
assert (
str(exc_info.value)
== f"Field: <chunk_method> - Message: <Input should be 'naive', 'book', 'email', 'laws', 'manual', 'one', 'paper', 'picture', 'presentation', 'qa', 'table' or 'tag'> - Value: <{chunk_method}>"
)
def test_update_dataset_with_name(get_api_key_fixture):

306
sdk/python/uv.lock generated
View File

@ -1,6 +1,16 @@
version = 1
revision = 1
requires-python = ">=3.10, <3.13"
[[package]]
name = "attrs"
version = "25.3.0"
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/5a/b0/1367933a8532ee6ff8d63537de4f1177af4bff9f3e829baf7331f595bb24/attrs-25.3.0.tar.gz", hash = "sha256:75d7cefc7fb576747b2c81b4442d4d4a1ce0900973527c011d1030fd3bf4af1b" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/77/06/bb80f5f86020c4551da315d78b3ab75e8228f89f0162f2c3a819e407941a/attrs-25.3.0-py3-none-any.whl", hash = "sha256:427318ce031701fea540783410126f03899a97ffc6f61596ad581ac2e40e3bc3" },
]
[[package]]
name = "beartype"
version = "0.18.5"
@ -12,11 +22,11 @@ wheels = [
[[package]]
name = "certifi"
version = "2025.1.31"
version = "2025.4.26"
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/1c/ab/c9f1e32b7b1bf505bf26f0ef697775960db7932abeb7b516de930ba2705f/certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651" }
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/e8/9e/c05b3920a3b7d20d3d3310465f50348e5b3694f4f88c6daf736eef3024c4/certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/38/fc/bce832fd4fd99766c04d1ee0eead6b0ec6486fb100ae5e74c1d91292b982/certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe" },
{ url = "https://mirrors.aliyun.com/pypi/packages/4a/7e/3db2bd1b1f9e95f7cddca6d6e75e2f2bd9f51b1246e546d88addca0106bd/certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3" },
]
[[package]]
@ -103,6 +113,20 @@ wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/02/cc/b7e31358aac6ed1ef2bb790a9746ac2c69bcb3c8588b41616914eb106eaf/exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b" },
]
[[package]]
name = "hypothesis"
version = "6.131.9"
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
dependencies = [
{ name = "attrs" },
{ name = "exceptiongroup", marker = "python_full_version < '3.11'" },
{ name = "sortedcontainers" },
]
sdist = { url = "https://mirrors.aliyun.com/pypi/packages/10/ff/217417d065aa8a4e6815ddc39acee1222f1b67bd0e4803b85de86a837873/hypothesis-6.131.9.tar.gz", hash = "sha256:ee9b0e1403e1121c91921dbdc79d7f509fdb96d457a0389222d2a68d6c8a8f8e" }
wheels = [
{ url = "https://mirrors.aliyun.com/pypi/packages/bd/e5/41a6733bfe11997795669dec3b3d785c28918e06568a2540dcc29f0d3fa7/hypothesis-6.131.9-py3-none-any.whl", hash = "sha256:7c2d9d6382e98e5337b27bd34e5b223bac23956787a827e1d087e00d893561d6" },
]
[[package]]
name = "idna"
version = "3.10"
@ -114,76 +138,76 @@ wheels = [
[[package]]
name = "iniconfig"
version = "2.0.0"
version = "2.1.0"
source = { registry = "https://mirrors.aliyun.com/pypi/simple" }
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