feat: i18n

[shaohuzhang1]

feat: i18n

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[shaohuzhang1]

The provided code snippet appears to be a set of serializers for handling HTTP requests related to users and their roles within an API application. Here are some observations:

Irregularities, Potential Issues, or Optimization Suggestions

1. Duplicated Code: The get_response_body_api method is duplicated across multiple places (in different classes). Consider consolidating this to avoid redundancy.

2. Unused Imports in RePasswordSerializer:

   from utils.base.exceptions import AppApiException, ErrMessage

3. Redundancy in Error Messages:

   • In SwitchLanguageSerializer, you repeat the same error messages under both error_messages keys and inline comments. This can lead to confusion and inconsistency.
   • Similar issues exist in other classes where duplicate or redundant error messages are present.

4. String Interpolation in Email Content:

   • You use string interpolation (format()) to insert variables into HTML content. While it works, consider using f-strings or .format() with {} syntax for better readability and maintainability.

5. Static Method get_user_profile Overriding Class Method get_response_body_api:

   • The get_user_profile static method overrides the existing get_response_body_api class method. Ensure that this change aligns with your requirements and documentation.

6. Validation Regex Errors:

   • There are repeated regex patterns for validating passwords in different serializers without considering context-specific validation rules (e.g., password complexity constraints).

7. Inconsistent Use of Schema Types:

   • Different serializers may have inconsistent use of schema types or descriptions, which can make them harder to understand and review collaboratively.

Recommendation

While the core logic looks functional, improving consistency and reducing duplication will help ensure code quality and ease future maintenance. Here are some specific recommendations:

Consolidate Duplicated Methods

def get_error_message(err_code, *args, **kwargs):
    return _(ErrMessage.err_code_to_str_get_msg.get(err_code, '{}'))(*args, **kwargs)

# Usage:
err_message = get_error_message(AppErrorCodeConstants.USER_ID_INVALID.value.code)

Remove Redundant Error Messages

• Clean up any unnecessary error messages by combining those defined in the serializer's error_messages dictionary with inline explanations when necessary.

Simplify String Interpolations

html_message = f'<strong>{_("Hello")}</strong>'

Refactor Override Methods

Ensure that overwriting methods like get_user_profile maintains clarity on how it differs from the original definition. For example:

class ImprovedUserProfileSerializer(UserProfileSerializer):
    def get_user_profile(self, user: User):
        # Add additional business logic here if needed
        profile_data = {
            "id": user.id,
            # ... rest of the fields ...
        }
        return profile_data

By implementing these improvements, the codebase becomes more organized, readable, and maintainable.

[shaohuzhang1]

The provided code snippet appears to be part of an OpenAPI schema definition for a REST API. Here are some observations and improvements:

Irregularities/Potential Issues

1. Duplicate stt_model_enable Property: The request body defines two instances of the same property ("stt_model_enable"), which might not be intended.

2. Missing Comma Before Final Property: In the dataset_setting, model_setting, and other nested objects, there is no comma before the final property declaration, which may lead to syntax errors if used directly as JSON or Swagger files.

3. Default String Formatting: The default strings contain newline characters at specific positions within {data} tags, which doesn't align with typical markdown formatting practices.

4. Language Handling: There's a redundant mention of language handling in the get_request_body_api() function description, but it doesn't appear to be necessary or consistently implemented throughout.

5. Prologue and Dialogue Number Documentation: These fields describe the opening remark ("prologue") and the number of multi-round conversations without specifying their types accurately (e.g., "number").

6. No Citation Segmentation Prompt Translation: The translation string for "no_references_prompt" seems incomplete or missing content.

Optimization Suggestions

1. Simplify Default Strings: Simplify the default strings for better readability. For example, remove unnecessary line breaks and ensure consistency in markdown usage.

2. Fix Duplicate Properties: Ensure only one instance of "stt_model_enable" is present.

3. Add Missing Commas: Add commas between each parameter list item to improve clarity when used as JSON or YAML.

4. Consistent Language Usage: Clearly document how the answer should maintain consistency with the provided data and respect specified languages.

Here’s an optimized version of the code:

def get_request_body_api():
    return {
        'operationId': 'create_application',
        'parameters': [
            (
                openai.Parameter('Request Body', 'body'),
                None,
                {}
            ),
        ],
        'responses': {
        },
        'requestBody': {
            'required': True,
            'content': {
                'application/json': {
                    'schema': {
                        '$ref': '#/components/schemas/CreateApplicationReq'
                    }
                }
            }
        }
    }

And the corresponding definition for CreateApplicationReq schema:

def CreateApplicationReq_schema(name,
                             desc, 
                             model_id, 
                             dialogue_number, 
                             dataset_setting, 
                             model_setting, 
                             problem_optimization=False, 
                             type='SIMPLE', 
                             stt_model_enable=True):
    
    components = {"schemas": {}}
    schemas = components['schemas']

    applicationApiDatasetSetting = ApplicationApi.DatasetSetting.get_request_body_api()
    applicationModelSetting = ApplicationApi.ModelSetting.get_request_body_api()

    schemas["CreateApplicationReq"] = {
        "type": "object",
        "properties": {
            "name": {"type": "string", "title": _("Application Name"), "description": _("Application Name")},
            "desc": {"type": "string", "title": _("Application Description"), "description": _("Application Description")},
            "model_id": {"type": "string", "title": _("Model ID"), "description": _("Model ID")},
            "dialogue_number": {"type": "integer", "title": _("Number of Multi-Round Conversations"), "description": _("Number of Multi-Round Conversations")},
            "dataset_ids_list": {"type": "array", "items": {"type": "string"}, "title": _("List of Associated Knowledge Base IDs"), "description": _("List of associated knowledge base IDs")},
            "dataset_setting": {"$ref": "#/components/schemas/ApplicationApiDatasetSetting"},
            "model_setting": {"$ref": "#/components/schemas/ApplicationModelSetting"},
            "problem_optimization": {"type": "boolean", "title": _("Problem Optimization"), "description": _("Problem Optimization"), "default": False},
            "type": {"type": "string", "title": _("Application Type"), "description": _("Application Type (SIMPLE | WORK_FLOW)"}, 
            "problem_optimization_prompt": {"type": "string", "title": "_Question Optimization Tips_",
                                          "description": _("Question optimization tips"),
                                          "default": _("(_contains the user\'s question_) Output a complete question based on the context ({question}). Put it in the `<data></data>` tag")}
        }
    }

    return components

@app.route("/v1/applications", methods=["POST"])
@RestController("/v1")
@api_response(code=201)
@endpoint_description("Creates a new application.")
@parameter(parameter="applicationJson", location="json", required=True,
         description="JSON representation of the application configuration")
@ApiOperation(description="Create a new application.")
@RequestMapping(value="/applications", method={RequestMethod.POST})
public ResponseEntity createNewApplication(CreateApplication Req reqObject){
        // Implementation logic here
        return ResponseEntity.status(HttpStatus.CREATED).build();
}

class CreateApplicationReq{
    private String name;
    private String desc;
    private String model_id;
    private Integer dialogue_number;
    private List<String> dataset_ids_list;
    private ApplicationApiDatasetSetting dataset_setting;
    private ApplicationModelSetting model_setting;
    private Boolean problem_optimization;
    private String type;
    private String problem_optimization_prompt;

    // getters and setters
}

Replace applicationApiDatasetSetting and applicationModelSetting with actual implementations or references as per your requirements. Adjust class names and properties according to your project design needs. This approach improves organization, maintainability, and readability while addressing the identified issues.

[shaohuzhang1]

The given code snippet appears to be configuring models in an application or service that supports various machine learning models from the Ollama library, which is developed by Anthropic. The configuration includes details such as model names, descriptions, types (LLM, Embedding), and credentials needed to access them.

Here are some observations and potential areas for improvement:

1. Duplicated Text: There seem to be multiple instances where similar text segments are repeated with slight minor variations. This can lead to redundancy and make maintenance more challenging. For example:

   • Both "qwen:xxb" and "qwen2:xxb-instruct" have very similar descriptions.

2. Description Clarity: While most descriptions add value, ensuring clarity about each feature is crucial. Some paragraphs might omit essential information. For instance, it would help to specify whether these models support streaming output.

3. Consistent Format: Ensure consistency in formatting throughout the description sections. Using bullet points, indentation, or consistent capitalization can improve readability.

4. Optimization Suggestions:

   • Consider consolidating redundant data if parts of one description can be applied universally across other entries.
   • Evaluate if certain fields like ModelTypeConst.LLM, ollama_llm_model_credential, and OllamaChatModel are necessary in all configurations since they appear unchanged between different entries.

5. Error Handling: Make sure there’s error handling mechanism in place to manage missing or inappropriate credentials, especially those related to LLMs and embeddings.

6. User Feedback Mechanism: Implement a feedback loop so that users can report any issues or suggest improvements directly. This will enhance user satisfaction over time and reduce manual updates needed.

Overall, improving documentation quality and maintaining consistency can significantly enhance usability and reliability in your application.