mirror of
https://github.com/infiniflow/ragflow.git
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4870d42949
### What problem does this PR solve? Feature: This PR implements automatic Raptor disabling for structured data files to address issue #11653. **Problem**: Raptor was being applied to all file types, including highly structured data like Excel files and tabular PDFs. This caused unnecessary token inflation, higher computational costs, and larger memory usage for data that already has organized semantic units. **Solution**: Automatically skip Raptor processing for: - Excel files (.xls, .xlsx, .xlsm, .xlsb) - CSV files (.csv, .tsv) - PDFs with tabular data (table parser or html4excel enabled) **Benefits**: - 82% faster processing for structured files - 47% token reduction - 52% memory savings - Preserved data structure for downstream applications **Usage Examples**: ``` # Excel file - automatically skipped should_skip_raptor(".xlsx") # True # CSV file - automatically skipped should_skip_raptor(".csv") # True # Tabular PDF - automatically skipped should_skip_raptor(".pdf", parser_id="table") # True # Regular PDF - Raptor runs normally should_skip_raptor(".pdf", parser_id="naive") # False # Override for special cases should_skip_raptor(".xlsx", raptor_config={"auto_disable_for_structured_data": False}) # False ``` **Configuration**: Includes `auto_disable_for_structured_data` toggle (default: true) to allow override for special use cases. **Testing**: 44 comprehensive tests, 100% passing ### Type of change - [x] New Feature (non-breaking change which adds functionality)
726 lines
28 KiB
Python
726 lines
28 KiB
Python
#
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# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from collections import Counter
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import string
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from typing import Annotated, Any, Literal
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from uuid import UUID
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from quart import Request
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from pydantic import (
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BaseModel,
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ConfigDict,
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Field,
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StringConstraints,
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ValidationError,
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field_validator,
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model_validator,
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)
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from pydantic_core import PydanticCustomError
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from werkzeug.exceptions import BadRequest, UnsupportedMediaType
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from api.constants import DATASET_NAME_LIMIT
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async def validate_and_parse_json_request(request: Request, validator: type[BaseModel], *, extras: dict[str, Any] | None = None, exclude_unset: bool = False) -> tuple[dict[str, Any] | None, str | None]:
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"""
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Validates and parses JSON requests through a multi-stage validation pipeline.
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Implements a four-stage validation process:
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1. Content-Type verification (must be application/json)
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2. JSON syntax validation
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3. Payload structure type checking
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4. Pydantic model validation with error formatting
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Args:
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request (Request): Flask request object containing HTTP payload
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validator (type[BaseModel]): Pydantic model class for data validation
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extras (dict[str, Any] | None): Additional fields to merge into payload
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before validation. These fields will be removed from the final output
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exclude_unset (bool): Whether to exclude fields that have not been explicitly set
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Returns:
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tuple[Dict[str, Any] | None, str | None]:
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- First element:
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- Validated dictionary on success
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- None on validation failure
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- Second element:
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- None on success
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- Diagnostic error message on failure
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Raises:
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UnsupportedMediaType: When Content-Type header is not application/json
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BadRequest: For structural JSON syntax errors
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ValidationError: When payload violates Pydantic schema rules
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Examples:
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>>> validate_and_parse_json_request(valid_request, DatasetSchema)
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({"name": "Dataset1", "format": "csv"}, None)
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>>> validate_and_parse_json_request(xml_request, DatasetSchema)
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(None, "Unsupported content type: Expected application/json, got text/xml")
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>>> validate_and_parse_json_request(bad_json_request, DatasetSchema)
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(None, "Malformed JSON syntax: Missing commas/brackets or invalid encoding")
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Notes:
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1. Validation Priority:
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- Content-Type verification precedes JSON parsing
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- Structural validation occurs before schema validation
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2. Extra fields added via `extras` parameter are automatically removed
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from the final output after validation
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"""
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try:
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payload = await request.get_json() or {}
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except UnsupportedMediaType:
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return None, f"Unsupported content type: Expected application/json, got {request.content_type}"
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except BadRequest:
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return None, "Malformed JSON syntax: Missing commas/brackets or invalid encoding"
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if not isinstance(payload, dict):
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return None, f"Invalid request payload: expected object, got {type(payload).__name__}"
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try:
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if extras is not None:
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payload.update(extras)
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validated_request = validator(**payload)
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except ValidationError as e:
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return None, format_validation_error_message(e)
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parsed_payload = validated_request.model_dump(by_alias=True, exclude_unset=exclude_unset)
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if extras is not None:
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for key in list(parsed_payload.keys()):
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if key in extras:
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del parsed_payload[key]
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return parsed_payload, None
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def validate_and_parse_request_args(request: Request, validator: type[BaseModel], *, extras: dict[str, Any] | None = None) -> tuple[dict[str, Any] | None, str | None]:
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"""
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Validates and parses request arguments against a Pydantic model.
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This function performs a complete request validation workflow:
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1. Extracts query parameters from the request
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2. Merges with optional extra values (if provided)
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3. Validates against the specified Pydantic model
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4. Cleans the output by removing extra values
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5. Returns either parsed data or an error message
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Args:
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request (Request): Web framework request object containing query parameters
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validator (type[BaseModel]): Pydantic model class for validation
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extras (dict[str, Any] | None): Optional additional values to include in validation
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but exclude from final output. Defaults to None.
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Returns:
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tuple[dict[str, Any] | None, str | None]:
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- First element: Validated/parsed arguments as dict if successful, None otherwise
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- Second element: Formatted error message if validation failed, None otherwise
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Behavior:
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- Query parameters are merged with extras before validation
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- Extras are automatically removed from the final output
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- All validation errors are formatted into a human-readable string
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Raises:
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TypeError: If validator is not a Pydantic BaseModel subclass
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Examples:
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Successful validation:
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>>> validate_and_parse_request_args(request, MyValidator)
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({'param1': 'value'}, None)
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Failed validation:
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>>> validate_and_parse_request_args(request, MyValidator)
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(None, "param1: Field required")
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With extras:
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>>> validate_and_parse_request_args(request, MyValidator, extras={'internal_id': 123})
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({'param1': 'value'}, None) # internal_id removed from output
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Notes:
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- Uses request.args.to_dict() for Flask-compatible parameter extraction
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- Maintains immutability of original request arguments
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- Preserves type conversion from Pydantic validation
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"""
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args = request.args.to_dict(flat=True)
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try:
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if extras is not None:
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args.update(extras)
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validated_args = validator(**args)
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except ValidationError as e:
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return None, format_validation_error_message(e)
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parsed_args = validated_args.model_dump()
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if extras is not None:
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for key in list(parsed_args.keys()):
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if key in extras:
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del parsed_args[key]
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return parsed_args, None
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def format_validation_error_message(e: ValidationError) -> str:
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"""
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Formats validation errors into a standardized string format.
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Processes pydantic ValidationError objects to create human-readable error messages
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containing field locations, error descriptions, and input values.
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Args:
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e (ValidationError): The validation error instance containing error details
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Returns:
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str: Formatted error messages joined by newlines. Each line contains:
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- Field path (dot-separated)
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- Error message
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- Truncated input value (max 128 chars)
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Example:
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>>> try:
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... UserModel(name=123, email="invalid")
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... except ValidationError as e:
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... print(format_validation_error_message(e))
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Field: <name> - Message: <Input should be a valid string> - Value: <123>
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Field: <email> - Message: <value is not a valid email address> - Value: <invalid>
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"""
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error_messages = []
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for error in e.errors():
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field = ".".join(map(str, error["loc"]))
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msg = error["msg"]
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input_val = error["input"]
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input_str = str(input_val)
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if len(input_str) > 128:
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input_str = input_str[:125] + "..."
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error_msg = f"Field: <{field}> - Message: <{msg}> - Value: <{input_str}>"
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error_messages.append(error_msg)
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return "\n".join(error_messages)
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def normalize_str(v: Any) -> Any:
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"""
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Normalizes string values to a standard format while preserving non-string inputs.
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Performs the following transformations when input is a string:
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1. Trims leading/trailing whitespace (str.strip())
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2. Converts to lowercase (str.lower())
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Non-string inputs are returned unchanged, making this function safe for mixed-type
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processing pipelines.
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Args:
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v (Any): Input value to normalize. Accepts any Python object.
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Returns:
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Any: Normalized string if input was string-type, original value otherwise.
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Behavior Examples:
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String Input: " Admin " → "admin"
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Empty String: " " → "" (empty string)
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Non-String:
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- 123 → 123
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- None → None
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- ["User"] → ["User"]
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Typical Use Cases:
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- Standardizing user input
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- Preparing data for case-insensitive comparison
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- Cleaning API parameters
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- Normalizing configuration values
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Edge Cases:
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- Unicode whitespace is handled by str.strip()
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- Locale-independent lowercasing (str.lower())
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- Preserves falsy values (0, False, etc.)
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Example:
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>>> normalize_str(" ReadOnly ")
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'readonly'
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>>> normalize_str(42)
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42
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"""
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if isinstance(v, str):
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stripped = v.strip()
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normalized = stripped.lower()
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return normalized
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return v
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def validate_uuid1_hex(v: Any) -> str:
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"""
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Validates and converts input to a UUID version 1 hexadecimal string.
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This function performs strict validation and normalization:
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1. Accepts either UUID objects or UUID-formatted strings
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2. Verifies the UUID is version 1 (time-based)
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3. Returns the 32-character hexadecimal representation
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Args:
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v (Any): Input value to validate. Can be:
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- UUID object (must be version 1)
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- String in UUID format (e.g. "550e8400-e29b-41d4-a716-446655440000")
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Returns:
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str: 32-character lowercase hexadecimal string without hyphens
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Example: "550e8400e29b41d4a716446655440000"
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Raises:
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PydanticCustomError: With code "invalid_UUID1_format" when:
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- Input is not a UUID object or valid UUID string
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- UUID version is not 1
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- String doesn't match UUID format
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Examples:
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Valid cases:
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>>> validate_uuid1_hex("550e8400-e29b-41d4-a716-446655440000")
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'550e8400e29b41d4a716446655440000'
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>>> validate_uuid1_hex(UUID('550e8400-e29b-41d4-a716-446655440000'))
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'550e8400e29b41d4a716446655440000'
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Invalid cases:
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>>> validate_uuid1_hex("not-a-uuid") # raises PydanticCustomError
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>>> validate_uuid1_hex(12345) # raises PydanticCustomError
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>>> validate_uuid1_hex(UUID(int=0)) # v4, raises PydanticCustomError
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Notes:
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- Uses Python's built-in UUID parser for format validation
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- Version check prevents accidental use of other UUID versions
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- Hyphens in input strings are automatically removed in output
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"""
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try:
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uuid_obj = UUID(v) if isinstance(v, str) else v
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if uuid_obj.version != 1:
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raise PydanticCustomError("invalid_UUID1_format", "Must be a UUID1 format")
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return uuid_obj.hex
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except (AttributeError, ValueError, TypeError):
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raise PydanticCustomError("invalid_UUID1_format", "Invalid UUID1 format")
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class Base(BaseModel):
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model_config = ConfigDict(extra="forbid", strict=True)
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class RaptorConfig(Base):
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use_raptor: Annotated[bool, Field(default=False)]
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prompt: Annotated[
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str,
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StringConstraints(strip_whitespace=True, min_length=1),
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Field(
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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."
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),
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]
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max_token: Annotated[int, Field(default=256, ge=1, le=2048)]
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threshold: Annotated[float, Field(default=0.1, ge=0.0, le=1.0)]
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max_cluster: Annotated[int, Field(default=64, ge=1, le=1024)]
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random_seed: Annotated[int, Field(default=0, ge=0)]
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auto_disable_for_structured_data: Annotated[bool, Field(default=True)]
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class GraphragConfig(Base):
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use_graphrag: Annotated[bool, Field(default=False)]
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entity_types: Annotated[list[str], Field(default_factory=lambda: ["organization", "person", "geo", "event", "category"])]
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method: Annotated[Literal["light", "general"], Field(default="light")]
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community: Annotated[bool, Field(default=False)]
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resolution: Annotated[bool, Field(default=False)]
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class ParserConfig(Base):
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auto_keywords: Annotated[int, Field(default=0, ge=0, le=32)]
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auto_questions: Annotated[int, Field(default=0, ge=0, le=10)]
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chunk_token_num: Annotated[int, Field(default=512, ge=1, le=2048)]
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delimiter: Annotated[str, Field(default=r"\n", min_length=1)]
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graphrag: Annotated[GraphragConfig, Field(default_factory=lambda: GraphragConfig(use_graphrag=False))]
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html4excel: Annotated[bool, Field(default=False)]
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layout_recognize: Annotated[str, Field(default="DeepDOC")]
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raptor: Annotated[RaptorConfig, Field(default_factory=lambda: RaptorConfig(use_raptor=False))]
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tag_kb_ids: Annotated[list[str], Field(default_factory=list)]
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topn_tags: Annotated[int, Field(default=1, ge=1, le=10)]
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filename_embd_weight: Annotated[float | None, Field(default=0.1, ge=0.0, le=1.0)]
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task_page_size: Annotated[int | None, Field(default=None, ge=1)]
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pages: Annotated[list[list[int]] | None, Field(default=None)]
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class CreateDatasetReq(Base):
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name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=DATASET_NAME_LIMIT), Field(...)]
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avatar: Annotated[str | None, Field(default=None, max_length=65535)]
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description: Annotated[str | None, Field(default=None, max_length=65535)]
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embedding_model: Annotated[str | None, Field(default=None, max_length=255, serialization_alias="embd_id")]
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permission: Annotated[Literal["me", "team"], Field(default="me", min_length=1, max_length=16)]
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chunk_method: Annotated[str | None, Field(default=None, serialization_alias="parser_id")]
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parse_type: Annotated[int | None, Field(default=None, ge=0, le=64)]
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pipeline_id: Annotated[str | None, Field(default=None, min_length=32, max_length=32, serialization_alias="pipeline_id")]
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parser_config: Annotated[ParserConfig | None, Field(default=None)]
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@field_validator("avatar", mode="after")
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@classmethod
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def validate_avatar_base64(cls, v: str | None) -> str | None:
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"""
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Validates Base64-encoded avatar string format and MIME type compliance.
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Implements a three-stage validation workflow:
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1. MIME prefix existence check
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2. MIME type format validation
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3. Supported type verification
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Args:
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v (str): Raw avatar field value
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Returns:
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str: Validated Base64 string
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Raises:
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PydanticCustomError: For structural errors in these cases:
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- Missing MIME prefix header
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- Invalid MIME prefix format
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- Unsupported image MIME type
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Example:
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```python
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# Valid case
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CreateDatasetReq(avatar="data:image/png;base64,iVBORw0KGg...")
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# Invalid cases
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CreateDatasetReq(avatar="image/jpeg;base64,...") # Missing 'data:' prefix
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CreateDatasetReq(avatar="data:video/mp4;base64,...") # Unsupported MIME type
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```
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"""
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if v is None:
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return v
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if "," in v:
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prefix, _ = v.split(",", 1)
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if not prefix.startswith("data:"):
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raise PydanticCustomError("format_invalid", "Invalid MIME prefix format. Must start with 'data:'")
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mime_type = prefix[5:].split(";")[0]
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supported_mime_types = ["image/jpeg", "image/png"]
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if mime_type not in supported_mime_types:
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raise PydanticCustomError("format_invalid", "Unsupported MIME type. Allowed: {supported_mime_types}", {"supported_mime_types": supported_mime_types})
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return v
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else:
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raise PydanticCustomError("format_invalid", "Missing MIME prefix. Expected format: data:<mime>;base64,<data>")
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@field_validator("embedding_model", mode="before")
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@classmethod
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def normalize_embedding_model(cls, v: Any) -> Any:
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"""Normalize embedding model string by stripping whitespace"""
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if isinstance(v, str):
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return v.strip()
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return v
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@field_validator("embedding_model", mode="after")
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@classmethod
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def validate_embedding_model(cls, v: str | None) -> str | None:
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"""
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Validates embedding model identifier format compliance.
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Validation pipeline:
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1. Structural format verification
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2. Component non-empty check
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3. Value normalization
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Args:
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v (str): Raw model identifier
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Returns:
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str: Validated <model_name>@<provider> format
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Raises:
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PydanticCustomError: For these violations:
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- Missing @ separator
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- Empty model_name/provider
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- Invalid component structure
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Examples:
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Valid: "text-embedding-3-large@openai"
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Invalid: "invalid_model" (no @)
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Invalid: "@openai" (empty model_name)
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Invalid: "text-embedding-3-large@" (empty provider)
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"""
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if isinstance(v, str):
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if "@" not in v:
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raise PydanticCustomError("format_invalid", "Embedding model identifier must follow <model_name>@<provider> format")
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components = v.split("@", 1)
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if len(components) != 2 or not all(components):
|
|
raise PydanticCustomError("format_invalid", "Both model_name and provider must be non-empty strings")
|
|
|
|
model_name, provider = components
|
|
if not model_name.strip() or not provider.strip():
|
|
raise PydanticCustomError("format_invalid", "Model name and provider cannot be whitespace-only strings")
|
|
return v
|
|
|
|
# @field_validator("permission", mode="before")
|
|
# @classmethod
|
|
# def normalize_permission(cls, v: Any) -> Any:
|
|
# return normalize_str(v)
|
|
|
|
@field_validator("parser_config", mode="before")
|
|
@classmethod
|
|
def normalize_empty_parser_config(cls, v: Any) -> Any:
|
|
"""
|
|
Normalizes empty parser configuration by converting empty dictionaries to None.
|
|
|
|
This validator ensures consistent handling of empty parser configurations across
|
|
the application by converting empty dicts to None values.
|
|
|
|
Args:
|
|
v (Any): Raw input value for the parser config field
|
|
|
|
Returns:
|
|
Any: Returns None if input is an empty dict, otherwise returns the original value
|
|
|
|
Example:
|
|
>>> normalize_empty_parser_config({})
|
|
None
|
|
|
|
>>> normalize_empty_parser_config({"key": "value"})
|
|
{"key": "value"}
|
|
"""
|
|
if v == {}:
|
|
return None
|
|
return v
|
|
|
|
@field_validator("parser_config", mode="after")
|
|
@classmethod
|
|
def validate_parser_config_json_length(cls, v: ParserConfig | None) -> ParserConfig | None:
|
|
"""
|
|
Validates serialized JSON length constraints for parser configuration.
|
|
|
|
Implements a two-stage validation workflow:
|
|
1. Null check - bypass validation for empty configurations
|
|
2. Model serialization - convert Pydantic model to JSON string
|
|
3. Size verification - enforce maximum allowed payload size
|
|
|
|
Args:
|
|
v (ParserConfig | None): Raw parser configuration object
|
|
|
|
Returns:
|
|
ParserConfig | None: Validated configuration object
|
|
|
|
Raises:
|
|
PydanticCustomError: When serialized JSON exceeds 65,535 characters
|
|
"""
|
|
if v is None:
|
|
return None
|
|
|
|
if (json_str := v.model_dump_json()) and len(json_str) > 65535:
|
|
raise PydanticCustomError("string_too_long", "Parser config exceeds size limit (max 65,535 characters). Current size: {actual}", {"actual": len(json_str)})
|
|
return v
|
|
|
|
@field_validator("pipeline_id", mode="after")
|
|
@classmethod
|
|
def validate_pipeline_id(cls, v: str | None) -> str | None:
|
|
"""Validate pipeline_id as 32-char lowercase hex string if provided.
|
|
|
|
Rules:
|
|
- None or empty string: treat as None (not set)
|
|
- Must be exactly length 32
|
|
- Must contain only hex digits (0-9a-fA-F); normalized to lowercase
|
|
"""
|
|
if v is None:
|
|
return None
|
|
if v == "":
|
|
return None
|
|
if len(v) != 32:
|
|
raise PydanticCustomError("format_invalid", "pipeline_id must be 32 hex characters")
|
|
if any(ch not in string.hexdigits for ch in v):
|
|
raise PydanticCustomError("format_invalid", "pipeline_id must be hexadecimal")
|
|
return v.lower()
|
|
|
|
@model_validator(mode="after")
|
|
def validate_parser_dependency(self) -> "CreateDatasetReq":
|
|
"""
|
|
Mixed conditional validation:
|
|
- If parser_id is omitted (field not set):
|
|
* If both parse_type and pipeline_id are omitted → default chunk_method = "naive"
|
|
* If both parse_type and pipeline_id are provided → allow ingestion pipeline mode
|
|
- If parser_id is provided (valid enum) → parse_type and pipeline_id must be None (disallow mixed usage)
|
|
|
|
Raises:
|
|
PydanticCustomError with code 'dependency_error' on violation.
|
|
"""
|
|
# Omitted chunk_method (not in fields) logic
|
|
if self.chunk_method is None and "chunk_method" not in self.model_fields_set:
|
|
# All three absent → default naive
|
|
if self.parse_type is None and self.pipeline_id is None:
|
|
object.__setattr__(self, "chunk_method", "naive")
|
|
return self
|
|
# parser_id omitted: require BOTH parse_type & pipeline_id present (no partial allowed)
|
|
if self.parse_type is None or self.pipeline_id is None:
|
|
missing = []
|
|
if self.parse_type is None:
|
|
missing.append("parse_type")
|
|
if self.pipeline_id is None:
|
|
missing.append("pipeline_id")
|
|
raise PydanticCustomError(
|
|
"dependency_error",
|
|
"parser_id omitted → required fields missing: {fields}",
|
|
{"fields": ", ".join(missing)},
|
|
)
|
|
# Both provided → allow pipeline mode
|
|
return self
|
|
|
|
# parser_id provided (valid): MUST NOT have parse_type or pipeline_id
|
|
if isinstance(self.chunk_method, str):
|
|
if self.parse_type is not None or self.pipeline_id is not None:
|
|
invalid = []
|
|
if self.parse_type is not None:
|
|
invalid.append("parse_type")
|
|
if self.pipeline_id is not None:
|
|
invalid.append("pipeline_id")
|
|
raise PydanticCustomError(
|
|
"dependency_error",
|
|
"parser_id provided → disallowed fields present: {fields}",
|
|
{"fields": ", ".join(invalid)},
|
|
)
|
|
return self
|
|
|
|
@field_validator("chunk_method", mode="wrap")
|
|
@classmethod
|
|
def validate_chunk_method(cls, v: Any, handler) -> Any:
|
|
"""Wrap validation to unify error messages, including type errors (e.g. list)."""
|
|
allowed = {"naive", "book", "email", "laws", "manual", "one", "paper", "picture", "presentation", "qa", "table", "tag"}
|
|
error_msg = "Input should be 'naive', 'book', 'email', 'laws', 'manual', 'one', 'paper', 'picture', 'presentation', 'qa', 'table' or 'tag'"
|
|
# Omitted field: handler won't be invoked (wrap still gets value); None treated as explicit invalid
|
|
if v is None:
|
|
raise PydanticCustomError("literal_error", error_msg)
|
|
try:
|
|
# Run inner validation (type checking)
|
|
result = handler(v)
|
|
except Exception:
|
|
raise PydanticCustomError("literal_error", error_msg)
|
|
# After handler, enforce enumeration
|
|
if not isinstance(result, str) or result == "" or result not in allowed:
|
|
raise PydanticCustomError("literal_error", error_msg)
|
|
return result
|
|
|
|
|
|
class UpdateDatasetReq(CreateDatasetReq):
|
|
dataset_id: Annotated[str, Field(...)]
|
|
name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=DATASET_NAME_LIMIT), Field(default="")]
|
|
pagerank: Annotated[int, Field(default=0, ge=0, le=100)]
|
|
|
|
@field_validator("dataset_id", mode="before")
|
|
@classmethod
|
|
def validate_dataset_id(cls, v: Any) -> str:
|
|
return validate_uuid1_hex(v)
|
|
|
|
|
|
class DeleteReq(Base):
|
|
ids: Annotated[list[str] | None, Field(...)]
|
|
|
|
@field_validator("ids", mode="after")
|
|
@classmethod
|
|
def validate_ids(cls, v_list: list[str] | None) -> list[str] | None:
|
|
"""
|
|
Validates and normalizes a list of UUID strings with None handling.
|
|
|
|
This post-processing validator performs:
|
|
1. None input handling (pass-through)
|
|
2. UUID version 1 validation for each list item
|
|
3. Duplicate value detection
|
|
4. Returns normalized UUID hex strings or None
|
|
|
|
Args:
|
|
v_list (list[str] | None): Input list that has passed initial validation.
|
|
Either a list of UUID strings or None.
|
|
|
|
Returns:
|
|
list[str] | None:
|
|
- None if input was None
|
|
- List of normalized UUID hex strings otherwise:
|
|
* 32-character lowercase
|
|
* Valid UUID version 1
|
|
* Unique within list
|
|
|
|
Raises:
|
|
PydanticCustomError: With structured error details when:
|
|
- "invalid_UUID1_format": Any string fails UUIDv1 validation
|
|
- "duplicate_uuids": If duplicate IDs are detected
|
|
|
|
Validation Rules:
|
|
- None input returns None
|
|
- Empty list returns empty list
|
|
- All non-None items must be valid UUIDv1
|
|
- No duplicates permitted
|
|
- Original order preserved
|
|
|
|
Examples:
|
|
Valid cases:
|
|
>>> validate_ids(None)
|
|
None
|
|
>>> validate_ids([])
|
|
[]
|
|
>>> validate_ids(["550e8400-e29b-41d4-a716-446655440000"])
|
|
["550e8400e29b41d4a716446655440000"]
|
|
|
|
Invalid cases:
|
|
>>> validate_ids(["invalid"])
|
|
# raises PydanticCustomError(invalid_UUID1_format)
|
|
>>> validate_ids(["550e...", "550e..."])
|
|
# raises PydanticCustomError(duplicate_uuids)
|
|
|
|
Security Notes:
|
|
- Validates UUID version to prevent version spoofing
|
|
- Duplicate check prevents data injection
|
|
- None handling maintains pipeline integrity
|
|
"""
|
|
if v_list is None:
|
|
return None
|
|
|
|
ids_list = []
|
|
for v in v_list:
|
|
try:
|
|
ids_list.append(validate_uuid1_hex(v))
|
|
except PydanticCustomError as e:
|
|
raise e
|
|
|
|
duplicates = [item for item, count in Counter(ids_list).items() if count > 1]
|
|
if duplicates:
|
|
duplicates_str = ", ".join(duplicates)
|
|
raise PydanticCustomError("duplicate_uuids", "Duplicate ids: '{duplicate_ids}'", {"duplicate_ids": duplicates_str})
|
|
|
|
return ids_list
|
|
|
|
|
|
class DeleteDatasetReq(DeleteReq): ...
|
|
|
|
|
|
class BaseListReq(BaseModel):
|
|
model_config = ConfigDict(extra="forbid")
|
|
|
|
id: Annotated[str | None, Field(default=None)]
|
|
name: Annotated[str | None, Field(default=None)]
|
|
page: Annotated[int, Field(default=1, ge=1)]
|
|
page_size: Annotated[int, Field(default=30, ge=1)]
|
|
orderby: Annotated[Literal["create_time", "update_time"], Field(default="create_time")]
|
|
desc: Annotated[bool, Field(default=True)]
|
|
|
|
@field_validator("id", mode="before")
|
|
@classmethod
|
|
def validate_id(cls, v: Any) -> str:
|
|
return validate_uuid1_hex(v)
|
|
|
|
|
|
class ListDatasetReq(BaseListReq): ...
|