assistants

type Assistant[O chatmodel.ContentProvider] struct {
	LLM          llms.Model
	OutputParser chatmodel.OutputParser[O]
	// contains filtered or unexported fields
}

type Callback interface {
	tools.Callback
	OnAssistantStart(ctx context.Context, agent IAssistant, input string)
	OnAssistantEnd(ctx context.Context, agent IAssistant, input string, resp *llms.ContentResponse)
	OnAssistantError(ctx context.Context, agent IAssistant, input string, err error)
	OnAssistantLLMCall(ctx context.Context, agent IAssistant, payload []llms.MessageContent)
}

type Config struct {
	// Model is the model to use in an LLM call.
	Model string

	// MaxTokens is the maximum number of tokens to generate to use in an LLM call.
	MaxTokens int

	// Temperature is the temperature for sampling to use in an LLM call, between 0 and 1.
	Temperature float64

	// StopWords is a list of words to stop on to use in an LLM call.
	StopWords []string

	// TopK is the number of tokens to consider for top-k sampling in an LLM call.
	TopK int

	// TopP is the cumulative probability for top-p sampling in an LLM call.
	TopP float64

	// Seed is a seed for deterministic sampling in an LLM call.
	Seed int

	// MinLength is the minimum length of the generated text in an LLM call.
	MinLength int

	// MaxLength is the maximum length of the generated text in an LLM call.
	MaxLength int

	// RepetitionPenalty is the repetition penalty for sampling in an LLM call.
	RepetitionPenalty float64

	// CallbackHandler is the callback handler for Chain
	CallbackHandler Callback

	// Tools is a list of tools to use. Each tool can be a specific tool or a function.
	Tools []llms.Tool

	// ToolChoice is the choice of tool to use, it can either be "none", "auto" (the default behavior), or a specific tool as described in the ToolChoice type.
	ToolChoice any

	JSONMode bool

	// StreamingFunc is a function to be called for each chunk of a streaming response.
	// Return an error to stop streaming early.
	StreamingFunc func(ctx context.Context, chunk []byte) error

	Store       store.MessageStore
	PromptInput map[string]any
	Examples    chatmodel.FewShotExamples
	Mode        encoding.Mode
	// SkipMessageHistory is a flag to skip adding Assistant messages to History.
	SkipMessageHistory bool
	// IsGeneric is a flag to indicate that the assistant should add a generic message to the history,
	// instead of the human
	IsGeneric bool
	// contains filtered or unexported fields
}

type HasCallback interface {
	GetCallback() Callback
}

type IAssistant interface {
	// Name returns the name of the Assistant.
	Name() string
	// Description returns the description of the Assistant, to be used in the prompt of other Assistants or LLMs.
	// Should not exceed LLM model limit.
	Description() string
	// FormatPrompter returns the format prompter for the Assistant.
	FormatPrompt(values map[string]any) (llms.PromptValue, error)
	GetPromptInputVariables() []string

	Call(ctx context.Context, input string, promptInputs map[string]any, options ...Option) (*llms.ContentResponse, error)
}

type IAssistantTool interface {
	// CallAssistant allows the tool to call the assistant with the given input and options.
	CallAssistant(ctx context.Context, input string, options ...Option) (string, error)
}

type IMCPAssistant interface {
	IAssistant
	RegisterMCP(registrator McpServerRegistrator) error
	CallMCP(context.Context, chatmodel.MCPInputRequest) (*mcp.PromptResponse, error)
}

type McpServerRegistrator interface {
	RegisterPrompt(name string, description string, handler any) error
}

type NoopCallback struct{}

type Option func(*Config)

type PackageLoggerCallback struct {
	// contains filtered or unexported fields
}

type PrinterCallback struct {
	Out io.Writer
}

type ProvidePromptInputsFunc func(input string) (map[string]any, error)

type TypeableAssistant[O chatmodel.ContentProvider] interface {
	IAssistant
	HasCallback
	// Run executes the assistant with the given input and prompt inputs.
	// Do not use this method directly, use the Run function instead.
	Run(ctx context.Context, input string, promptInputs map[string]any, optionalOutputType *O, options ...Option) (*llms.ContentResponse, error)
}