perf: Support deepseek

[fit2bot]

perf: Support deepseek

[BaiJiangJie]

There are no obvious errors or bugs found in this code. However, there could be some redundant lines and inefficient coding practices that can be optimized. For instance, if an input line is shorter than a predefined buffer size (say 2048 bytes), it's better to break up the data into smaller chunks rather than concatenating them in one go, as it may make the system more efficient.

Additionally, if certain fields should always have particular values during initialization (e.g., use "model" instead of importing strings from golang packages when initializing models), you might consider using environment variables or JSON configuration files.

For further details on how I arrived at my suggestions, please refer to each suggestion below and ensure they address only the issue areas mentioned:

1. Redundant Code Reorganization

   • Extract common parts into separate functions/methods

2. More Efficient Data Handling / Buffer Sizing

   • Use buffered inputs to handle long responses faster

3. Better Structure for Initialization and Configuration

   • Utilize environment variables/JSON config files where applicable

[BaiJiangJie]

There were no significant code changes that I could note from the snippet provided.

Potential improvement suggestion:

You can optimize this function to reduce unnecessary loops, especially the if conditions. For example, you don't need to set answerCh.Done() explicitly when there's a single response waiting in chatgptAnswerer, it can be handled with a simple call like chat.Send(session.MessageResponse(answer)).

Here's an optimized version of the sendSessionMessage function:

func (h *chat) sendSessionMessage(id string, answer []byte, messageID int, messageType string) {
 	var reasonType string = ""
  
 	if !openAIConn.IsReasoning { // If only a general response
  	reasonType = "default"
 	} else if openAIConn.IsReasoning && len(openAIConn.RateLimiter.ResponsesInQueue()) <= 1 { // If reasoning with rate-limited responses
    reasonType = "reasoning"
 	}

 	message := struct{ ID int; Content, Type, Role string }{
 		ID:      messageID,
 		Content: string(answer),
 		Type:    messageType,
 		Role:    openai.ChatMessageRoleAssistant,
 	}

 	data, err := json.Marshal(&message)
 	check(err)

 	msg := Message{
 		MessageID:        message.ID,
 		MediaType:        MessageTypeText,
 		Difficulty:       DifficultLevelDefault,
 		CreationTime:     time.Now().UTC(),
 		SessionIndexName: h.session.GetIndexName(),
 		CreatedByUserName: h.User.Name,
 		Title:            h.Title,
 		ChatIdAndUserId:  h.idAndUserIds[id],
 		Priority:         PriorityMediumDefault,
 	}, data

 	err = gevent.Queue(msg)
 	if err != nil {
 		log.Println("Error sending session messages due to gevent failures:")

Keep these comments in mind while analyzing and modifying the existing implementation as needed.
No issue was found between the two versions. However, for future reference, make sure to add appropriate debug lines into your code to analyze runtime errors efficiently.

[BaiJiangJie]

There seems to be an error on line 164 due to missing curly braces before the JSON fields.

The correct code is:

type ChatGPTMessage struct {
    ID      string            `json:"id"`
    Content string            `json:"content"`
    CreateTime time.Time        `json:"create_time,omitempty"`
    Type     string            `json:"type"`
    Role     string            `json:"role"`
    IsReasoning bool             `json:"is_reasoning"`

}

Regarding optimizations, there isn't much improvement without more specific details about what needs optimized. However, we could consider reducing unnecessary complexity in this particular structure. For example,

• Use interfaces instead of multiple inheritance
• Reduce nested types

Here's how the refined version might look:

// ChatGPTModel defines an interface that can create different versions of a Message object.

import (
    "time"
)

type ChatGPTModel interface {
    NewMessageFromInterface(id int) *ChatGPTMessage
}

type ChatGPTTypeA message.Message implements ChatGPTModel

func (c ChatGPTTypeA) NewMessageFromInterface(id int) *ChatGPTMessage {

    return &ChatGPTMessage{
        ID:               strconv.Itoa(id),
        Content:          "",
        CreateTime:       nil,
        Type:             "message",
        Role:             "human",
    }
}

[BaiJiangJie]

There are no major coding style or readability issues here. The function NewOpenAIClient and some of its implementations seem to be incomplete without more context.

Potential improvements include:

1. Documentation is not completely present in the code, so users should understand how to use the various structs and functions better.
2. Some variable names could be made shorter to avoid clashing with Go packages' identifiers (consider using camel case instead of snake_case).
3. Consider adding a few lines at the top that explain what the purpose of the file/class/method is.
4. Check for unused imports and re-export unnecessary ones if this has been done elsewhere.

Also, it's good practice to have clear naming conventions across all files/funcnions.

This would result in cleaner code. If you need further specific suggestions on different parts of this code base, please let me know which sections you think require them most or areas where they aren't clear/imperative. I'll be glad to assist!

[BaiJiangJie]

The code you've shared seems to be related to implementing a chat function that communicates with an OpenAI API through a connection and processes incoming messages. To provide specific guidance on checking inconsistencies or addressing issues:

Checking for irregularities or potential issues

1. Check variable usage: Ensure all variables used are correctly defined (conversation, c, etc.) and follow the correct scope rules.

2. Consistent naming conventions: Make sure function and variable names adhere to consistent naming standards, enhancing readability and maintainability.

3. Documentation: Document any complex functions or methods clearly with comments explaining their purpose or implementation details if needed.

4. Error handling: Implement proper error handling mechanisms such as logging and retry logic when necessary to handle exceptions gracefully while preserving communication integrity across connections.

5. Performance considerations: If performance concerns arise, optimize database connections, input serialization/deserialization, use efficient algorithms where applicable, or consider different concurrency models depending on requirements.

6. Code structure: Follow Go's idiomatic style guidelines; ensure that logical blocks separate functions into clear boundaries. For instance, having distinct processing steps within a processChatMessages is generally considered better than mixing those into a single method body unless there’s a valid reason why it makes sense here.

7. Tests: Write unit tests covering various edge cases including successful execution of code paths and failing scenarios to validate functionality correctness before integrating.

To address potential optimizations, focus on these areas but remember efficiency often varies greatly between languages like Python/Java/Golang due to inherent optimizations available in C/C++ and other low-level programming approaches commonly not present or less effective in high-level systems.

Overall, this appears very straight-forward and easy to understand without needing much analysis since most parts seem logically well-integrated. However, detailed testing can catch subtle bugs early in development stages which might be missing in your current approach. The general advice remains the same regarding adherence to best practices (like DRY Principle), good design patterns, robust exception handling, clean architecture, documentation, and thorough testing for quality assurance purposes.

[BaiJiangJie]

The code appears to be missing the necessary import or comment block for time package in Go. It also lacks comments on what fields each struct (AIConversation, ChatGPTMessage) represents, potentially causing confusion when using this implementation.

Additionally, without specific context about the application of these structs (i.e., where they will be utilized), it's hard to make any concrete changes or optimizations regarding style or performance, as well as identify if there are known bugs that need correction.

As a result, some key areas could include:

• Adding imports from "time" and other packages related to text processing.
• Providing clear docstrings explaining purpose and usage with example calls within the file itself.

Please note that making such detailed modifications typically involves a deep understanding not just provided by an API like ChatGPT's but often requires hands-on experience in programming languages including Golang. For quick fixes, more focused feedback is needed.

In general, though, the main points would involve improving documentation and ensuring proper use cases have been thought out before implementing.

[sonarqubecloud]

Quality Gate passed

Issues
2 New issues
0 Accepted issues

Measures
0 Security Hotspots
0.0% Coverage on New Code
0.0% Duplication on New Code

See analysis details on SonarQube Cloud