Autocomplete Role

The autocomplete role is specialized for real-time code completion and smart suggestions, providing fast and accurate code recommendations.

Configuration

Configure in config.yaml or ~/.bytebuddy/config.yaml:

models:
  - name: "autocomplete-engine"
    provider: "together"
    model: "codellama/CodeLlama-13b-Instruct-hf"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.1
      maxTokens: 256

Core Features

Fast Response

• Low Latency: Millisecond-level response time
• Real-Time Completion: Instant code suggestions
• Lightweight Models: Optimized small models

Context Awareness

• File Understanding: Understands current file type and structure
• Syntax Analysis: Supports multiple programming language syntax
• Project Context: Considers entire project structure

Intelligent Prediction

• Code Patterns: Recognizes common code patterns
• API Suggestions: Recommends API calls and methods
• Variable Prediction: Predicts variable names and types

Supported Programming Languages

Mainstream Languages

• JavaScript/TypeScript: Complete frontend development support
• Python: Data science and web development
• Java: Enterprise application development
• C++/C: System-level programming
• Go: Cloud-native and backend development
• Rust: Systems programming and performance optimization

Other Languages

• PHP: Web development
• Ruby: Scripting and web development
• Swift: iOS development
• Kotlin: Android and server development
• SQL: Database queries
• Shell: Script writing

Configuration Examples

JavaScript Autocomplete

models:
  - name: "autocomplete-js"
    provider: "together"
    model: "codellama/CodeLlama-13b-Instruct-hf"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.2
      maxTokens: 128

Python Autocomplete

models:
  - name: "autocomplete-python"
    provider: "together"
    model: "codellama/CodeLlama-7b-Instruct-hf"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.1
      maxTokens: 256

Universal Multi-Language Configuration

models:
  - name: "autocomplete-universal"
    provider: "together"
    model: "bigcode/starcoder"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.15
      maxTokens: 200

Performance Optimization

Basic Optimization Configuration

models:
  - name: "autocomplete-optimized"
    provider: "together"
    model: "codellama/CodeLlama-13b-Instruct-hf"
    apiKey: "${TOGETHER_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.1
      maxTokens: 128

Fast Response Configuration

models:
  - name: "autocomplete-fast"
    provider: "groq"
    model: "llama-3.1-8b-instant"
    apiKey: "${GROQ_API_KEY}"
    roles: ["autocomplete"]
    defaultCompletionOptions:
      temperature: 0.1
      maxTokens: 128

Best Practices

1. Choose the Right Model

• Use lightweight models for fast response
• Consider language-specific models
• Balance quality and performance

2. Optimize Parameter Settings

• Use lower temperature values (0.05-0.2)
• Limit maximum token count (128-256)
• Set reasonable timeout periods

3. Context Management

• Keep context concise
• Focus on relevant code areas
• Avoid excessively long history

4. Performance Monitoring

• Monitor response time
• Track completion quality
• Optimize model selection

Troubleshooting

Common Issues

1. Slow Response

   • Check model size and hardware resources
   • Consider using faster providers
   • Reduce context length

2. Inaccurate Suggestions

   • Adjust temperature parameters
   • Add more context information
   • Try different models

3. High Costs

   • Use open-source models
   • Optimize request frequency
   • Set reasonable token limits

Environment Variables

# ~/.bashrc or ~/.zshrc
export TOGETHER_API_KEY="your-together-api-key"
export GROQ_API_KEY="your-groq-api-key"

Through proper autocomplete role configuration, you can significantly improve coding efficiency and development experience.