Model Configuration

ByteBuddy supports multiple AI models, allowing you to choose the most suitable model according to your needs.

Supported Model Types

Large Language Models

• OpenAI Models: GPT-3.5, GPT-4 series
• Anthropic Models: Claude series models
• Open Source Models: Local models running through Ollama, LM Studio, etc.
• Other Providers: Support for OpenAI-compatible APIs

Model Roles

ByteBuddy supports configuring different models for different roles:

• chat: For conversation interaction and complex task processing
• edit: For code editing tasks
• apply: For code application operations
• autocomplete: For real-time code completion
• embed: For embedding vector generation
• rerank: For search result reranking

Basic Configuration

Configuration File Structure

Configure models in the project root directory or ~/.bytebuddy/config.yaml:

# config.yaml
name: My ByteBuddy Config
version: 0.0.1
schema: v1

models:
  - name: "gpt-4"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles:
      - chat
      - edit
      - apply

  - name: "claude-3-sonnet"
    provider: "anthropic"
    model: "claude-3-sonnet"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles:
      - chat
      - autocomplete

  - name: "local-llama"
    provider: "ollama"
    model: "llama2"
    apiBase: "http://localhost:11434"
    roles:
      - chat

Basic Configuration Parameters

Each model supports the following basic parameters:

• name: Name of the model configuration
• provider: Model provider (openai, anthropic, ollama, etc.)
• model: Specific model name
• apiKey: API key (can use environment variables)
• apiBase: API base URL (optional)
• roles: List of roles the model plays

Advanced Configuration Options

Completion Options Configuration

models:
  - name: "gpt-4-turbo"
    provider: "openai"
    model: "gpt-4-turbo"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    defaultCompletionOptions:
      temperature: 0.7
      maxTokens: 2000
      topP: 0.9
      presencePenalty: 0.1
      frequencyPenalty: 0.1
      stop: ["\n\n", "###"]

Autocomplete Options

models:
  - name: "claude-autocomplete"
    provider: "anthropic"
    model: "claude-3-haiku"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles: ["autocomplete"]
    autocompleteOptions:
      maxPromptTokens: 2000
      debounceDelay: 300
      modelTimeout: 10000
      useCache: true
      useImports: true
      useRecentlyEdited: true

Request Options

models:
  - name: "configured-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    requestOptions:
      timeout: 30000
      verifySsl: true
      headers:
        "User-Agent": "ByteBuddy/1.0"
      extraBodyProperties:
        custom_field: "value"

Environment Variable Configuration

For security reasons, it is recommended to use environment variables to store API keys:

# Environment variable setup
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"

Use in configuration file:

models:
  - name: "secure-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"

Configuration Examples for Different Providers

OpenAI Models

models:
  - name: "gpt-4"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    apiBase: "https://api.openai.com/v1"
    roles: ["chat", "edit"]

Anthropic Models

models:
  - name: "claude-3-opus"
    provider: "anthropic"
    model: "claude-3-opus-20240229"
    apiKey: "${ANTHROPIC_API_KEY}"
    roles: ["chat", "edit"]

Local Ollama Models

models:
  - name: "local-llama3"
    provider: "ollama"
    model: "llama3:8b"
    apiBase: "http://localhost:11434"
    roles: ["chat"]

Custom OpenAI-Compatible API

models:
  - name: "custom-provider"
    provider: "openai-compatible"
    model: "custom-model"
    apiKey: "${CUSTOM_API_KEY}"
    apiBase: "https://your-custom-api.com/v1"
    roles: ["chat"]

Using Company Internal Models

If your company has deployed an internal model service that complies with the OpenAI API specification, you can use it directly with ByteBuddy—even if the model isn't in the official supported provider list!

Simply set provider to "openai", then configure your company's internal model name and API address:

models:
  - name: "company-internal-model"
    provider: "openai" # Key: use openai provider
    model: "your-company-model-name" # Your company's internal model name
    apiKey: "${COMPANY_API_KEY}" # Company internal API key
    apiBase: "https://your-company-ai-api.com/v1" # Company internal API address
    roles: ["chat", "edit", "autocomplete"] # Assign roles as needed

Configuration Key Points:

• provider must be set to "openai": This ensures ByteBuddy uses the standard OpenAI API call format
• model field should contain your company's internal model name: such as "qwen-max", "glm-4", "internlm2", etc.
• apiBase should point to your company's internal API gateway: Make sure the URL includes the /v1 path (if your company's API follows the OpenAI standard)
• apiKey should use your company-assigned key: Also recommended to manage through environment variables

This approach allows you to seamlessly integrate your company's internal AI model services, enjoying the same experience as official OpenAI models while keeping your data secure within your organization.

Model Capability Configuration

ByteBuddy supports specifying specific capabilities for models:

models:
  - name: "gpt-4-vision"
    provider: "openai"
    model: "gpt-4-vision-preview"
    apiKey: "${OPENAI_API_KEY}"
    roles: ["chat"]
    capabilities:
      - "tool_use"
      - "image_input"

Supported model capabilities:

• tool_use: Support for tool calling
• image_input: Support for image input
• next_edit: Support for next edit mode

Cache Configuration

models:
  - name: "cached-model"
    provider: "openai"
    model: "gpt-4"
    apiKey: "${OPENAI_API_KEY}"
    cacheBehavior:
      cacheSystemMessage: true
      cacheConversation: true

Troubleshooting

Common Issue Resolution

1. API Connection Failure

   # Increase timeout
   requestOptions:
     timeout: 60000

2. Slow Model Response

   # Use faster model
   - name: "fast-model"
     provider: "openai"
     model: "gpt-3.5-turbo"
     # ... other configurations

3. API Key Issues

   • Ensure environment variables are set correctly
   • Check API key permissions
   • Verify the model is in the available list

Best Practices

1. Security

   • Always use environment variables to store API keys
   • Rotate API keys regularly
   • Limit API key permission scope

2. Performance Optimization

   • Choose appropriate models for different tasks
   • Reasonably set temperature and maxTokens
   • Enable caching to reduce duplicate requests

3. Cost Control

   • Monitor token usage
   • Use smaller models for simple tasks
   • Set reasonable context length limits

4. Reliability

   • Configure multiple models as backups
   • Set appropriate timeout times
   • Handle API rate limiting errors