GUIDE.md

Production Guide for the gollm Package

Introduction

This guide provides best practices and recommendations for using the gollm package in a production environment. The gollm package offers a unified interface for interacting with various Language Model (LLM) providers, making it easier to integrate LLMs into your Go applications.

Getting Started

1. Install the package:

   go get github.com/teilomillet/gollm
   

2. Import the package in your Go code:

   import "github.com/teilomillet/gollm"

Configuration Management

Do's:

• Create separate configuration files for different environments (development, staging, production).
• Use environment variables to manage sensitive information like API keys.
• Implement a configuration validation step in your deployment pipeline.

Don'ts:

• Don't hardcode configuration values in your application code.
• Avoid committing configuration files with sensitive information to version control.

Example configuration file (~/.gollm/configs/production.yaml):

provider: anthropic
model: claude-3-opus-20240229
temperature: 0.7
max_tokens: 100
log_level: info

LLM Client Initialization

Do's:

• Initialize the LLM client once and reuse it across your application.
• Use a context with timeout for LLM operations to prevent hanging requests.

Don'ts:

• Don't create a new LLM client for each request, as this can be inefficient.

Example:

llm, err := gollm.NewLLM("/path/to/production.yaml")
if err != nil {
    log.Fatalf("Failed to initialize LLM: %v", err)
}

// Use llm throughout your application

Prompt Engineering

Do's:

• Use the Prompt struct to create well-structured prompts.
• Leverage directives to guide the LLM's behavior.
• Set a maximum length for responses to control costs and response times.

Don'ts:

• Avoid sending sensitive or personal information in prompts.
• Don't rely on the LLM for critical decision-making without human oversight.

Example:

prompt := gollm.NewPrompt("Summarize the following text:").
    Directive("Focus on key points").
    Directive("Use simple language").
    MaxLength(150).
    Input(longText)

Error Handling and Logging

Do's:

• Implement robust error handling for all LLM operations.
• Log errors and unexpected behaviors for monitoring and debugging.
• Use structured logging for easier parsing and analysis.

Don'ts:

• Don't ignore errors returned by the gollm package.
• Avoid logging sensitive information.

Example:

response, _, err := llm.Generate(ctx, prompt.String())
if err != nil {
    log.Printf("Error generating response: %v", err)
    // Handle the error appropriately
    return
}

Performance Optimization

Do's:

• Use goroutines for concurrent LLM requests when appropriate.
• Implement caching for frequently requested information.
• Monitor and optimize token usage to control costs.

Don'ts:

• Don't overload the LLM with unnecessary requests.
• Avoid using the LLM for tasks that can be efficiently done with traditional programming.

Example of concurrent requests:

var wg sync.WaitGroup
responses := make(chan string, len(questions))

for _, q := range questions {
    wg.Add(1)
    go func(question string) {
        defer wg.Done()
        response, err := gollm.QuestionAnswer(ctx, llm, question)
        if err != nil {
            log.Printf("Error answering question: %v", err)
            return
        }
        responses <- response
    }(q)
}

wg.Wait()
close(responses)

Security Considerations

Do's:

• Implement input validation and sanitization before sending data to the LLM.
• Use HTTPS for all API communications.
• Regularly update the gollm package to get the latest security patches.

Don'ts:

• Don't expose the LLM directly to user input without proper safeguards.
• Avoid storing or transmitting user data through the LLM unless necessary and compliant with privacy regulations.

Monitoring and Observability

Do's:

• Implement monitoring for LLM request latency, error rates, and token usage.
• Set up alerts for abnormal behavior or exceeded thresholds.
• Use distributed tracing to understand the flow of requests through your system.

Don'ts:

• Don't rely solely on LLM provider dashboards for monitoring.
• Avoid ignoring performance degradation or unexpected behavior.

Conclusion

By following these guidelines, you can effectively leverage the gollm package in a production environment. Remember to continuously monitor, test, and optimize your LLM integration to ensure it meets your application's needs and performance requirements.