Redesign OpenAI Function Tool System for Extensibility

Description

Refactor the current function tool implementation to create a plugin-based architecture that enables easy addition of new assistant capabilities without modifying core code.

Current Implementation

The current implementation in generateToolOutputs is hardcoded with limited extensibility. Adding new functions requires modifying the core implementation, making the system difficult to extend and maintain.

Proposed Solution

1. Create a plugin-based architecture for registering AI assistant functions
2. Implement a registry system for dynamically discovering and loading functions
3. Develop proper function parameter validation and error handling
4. Add a configuration system for enabling/disabling specific functions
5. Create standardized interfaces for function implementations
6. Include comprehensive documentation and examples for function developers

Benefits

• Extensibility: New functions can be added without modifying core code
• Maintainability: Clear separation of concerns between core logic and function implementations
• Flexibility: Functions can be conditionally enabled based on user permissions
• Testability: Individual functions can be tested in isolation
• Developer Experience: Easier for others to contribute new functions

Implementation Details

Function Plugin Interface

type FunctionPlugin interface {
    // Metadata returns information about the function
    Metadata() FunctionMetadata
    
    // Execute runs the function with provided parameters
    Execute(ctx context.Context, params map[string]interface{}) (interface{}, error)
    
    // ValidateParameters checks if parameters are valid
    ValidateParameters(params map[string]interface{}) error
}

type FunctionMetadata struct {
    Name        string                  `json:"name"`
    Description string                  `json:"description"`
    Parameters  map[string]ParameterDef `json:"parameters"`
    Required    []string                `json:"required"`
    Version     string                  `json:"version"`
    Author      string                  `json:"author"`
    Tags        []string                `json:"tags"`
}

type ParameterDef struct {
    Type        string      `json:"type"`
    Description string      `json:"description"`
    Enum        []string    `json:"enum,omitempty"`
    Default     interface{} `json:"default,omitempty"`
}

Function Registry

type FunctionRegistry interface {
    // RegisterFunction adds a function to the registry
    RegisterFunction(plugin FunctionPlugin) error
    
    // GetFunction retrieves a function by name
    GetFunction(name string) (FunctionPlugin, error)
    
    // ListFunctions returns all registered functions
    ListFunctions() []FunctionMetadata
    
    // EnableFunction enables a function
    EnableFunction(name string) error
    
    // DisableFunction disables a function
    DisableFunction(name string) error
}

Acceptance Criteria

1. A plugin system is implemented with proper interfaces and documentation
2. Existing functions are migrated to the new plugin architecture
3. At least one example plugin is created as reference implementation
4. Configuration system allows enabling/disabling functions
5. Function parameters are properly validated before execution
6. Unit tests validate function registration and execution
7. Documentation is updated with developer guidelines for creating new functions

Technical Considerations

• Consider using reflection or code generation for parameter validation
• Implement proper versioning for function definitions
• Add security checks to prevent malicious function execution
• Consider compatibility with OpenAI’s function calling format
• Provide mechanisms for function dependency injection
• Add telemetry for function usage and performance