schema

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

type Agent interface {
	Plan(ctx context.Context, intermediateSteps []AgentStep, inputs map[string]string) ([]*AgentAction, *AgentFinish, error)
	InputKeys() []string
	OutputKeys() []string
}

type AgentAction struct {
	Tool       string
	ToolInput  *ToolInput
	Log        string
	MessageLog ChatMessages
}

type AgentActionInput struct {
	*AgentActionManagerInput
	RunID string
}

type AgentActionManagerInput struct {
	Action *AgentAction
}

type AgentFinish struct {
	ReturnValues map[string]any
	Log          string
}

type AgentFinishInput struct {
	*AgentFinishManagerInput
	RunID string
}

type AgentFinishManagerInput struct {
	Finish *AgentFinish
}

type AgentStep struct {
	Action      *AgentAction
	Observation string
}

type CallOptions struct {
	CallbackManger CallbackManagerForChainRun
	Stop           []string
}

type Callback interface {
	AlwaysVerbose() bool
	RaiseError() bool
	OnLLMStart(ctx context.Context, input *LLMStartInput) error
	OnChatModelStart(ctx context.Context, input *ChatModelStartInput) error
	OnModelNewToken(ctx context.Context, input *ModelNewTokenInput) error
	OnModelEnd(ctx context.Context, input *ModelEndInput) error
	OnModelError(ctx context.Context, input *ModelErrorInput) error
	OnChainStart(ctx context.Context, input *ChainStartInput) error
	OnChainEnd(ctx context.Context, input *ChainEndInput) error
	OnChainError(ctx context.Context, input *ChainErrorInput) error
	OnAgentAction(ctx context.Context, input *AgentActionInput) error
	OnAgentFinish(ctx context.Context, input *AgentFinishInput) error
	OnToolStart(ctx context.Context, input *ToolStartInput) error
	OnToolEnd(ctx context.Context, input *ToolEndInput) error
	OnToolError(ctx context.Context, input *ToolErrorInput) error
	OnText(ctx context.Context, input *TextInput) error
}

type CallbackManager interface {
	OnLLMStart(ctx context.Context, input *LLMStartManagerInput) (CallbackManagerForModelRun, error)
	OnChatModelStart(ctx context.Context, input *ChatModelStartManagerInput) (CallbackManagerForModelRun, error)
	OnChainStart(ctx context.Context, input *ChainStartManagerInput) (CallbackManagerForChainRun, error)
	OnToolStart(ctx context.Context, input *ToolStartManagerInput) (CallbackManagerForToolRun, error)
	RunID() string
}

type CallbackManagerForChainRun interface {
	OnChainEnd(ctx context.Context, input *ChainEndManagerInput) error
	OnChainError(ctx context.Context, input *ChainErrorManagerInput) error
	OnAgentAction(ctx context.Context, input *AgentActionManagerInput) error
	OnAgentFinish(ctx context.Context, input *AgentFinishManagerInput) error
	OnText(ctx context.Context, input *TextManagerInput) error
	GetInheritableCallbacks() []Callback
	RunID() string
}

type CallbackManagerForModelRun interface {
	OnModelNewToken(ctx context.Context, input *ModelNewTokenManagerInput) error
	OnModelEnd(ctx context.Context, input *ModelEndManagerInput) error
	OnModelError(ctx context.Context, input *ModelErrorManagerInput) error
	OnText(ctx context.Context, input *TextManagerInput) error
	GetInheritableCallbacks() []Callback
	RunID() string
}

type CallbackManagerForToolRun interface {
	OnToolEnd(ctx context.Context, input *ToolEndManagerInput) error
	OnToolError(ctx context.Context, input *ToolErrorManagerInput) error
	OnText(ctx context.Context, input *TextManagerInput) error
}

type CallbackOptions struct {
	Callbacks []Callback
	Verbose   bool
}

type Chain interface {
	// Call executes the chain with the given context and inputs.
	// It returns the outputs of the chain or an error, if any.
	Call(ctx context.Context, inputs ChainValues, optFns ...func(o *CallOptions)) (ChainValues, error)
	// Type returns the type of the chain.
	Type() string
	// Verbose returns the verbosity setting of the chain.
	Verbose() bool
	// Callbacks returns the callbacks associated with the chain.
	Callbacks() []Callback
	// Memory returns the memory associated with the chain.
	Memory() Memory
	// InputKeys returns the expected input keys.
	InputKeys() []string
	// OutputKeys returns the output keys the chain will return.
	OutputKeys() []string
}

type ChainEndInput struct {
	*ChainEndManagerInput
	RunID string
}

type ChainEndManagerInput struct {
	Outputs ChainValues
}

type ChainErrorInput struct {
	*ChainErrorManagerInput
	RunID string
}

type ChainErrorManagerInput struct {
	Error error
}

type ChainStartInput struct {
	*ChainStartManagerInput
	RunID string
}

type ChainStartManagerInput struct {
	ChainType string
	Inputs    ChainValues
}

type ChainValues map[string]any

type ChatMessage interface {
	Content() string
	Type() ChatMessageType
}

type ChatMessageExtension struct {
	FunctionCall *FunctionCall `json:"functionCall,omitempty"`
}

type ChatMessageHistory interface {
	// Messages returns the messages stored in the store.
	Messages(ctx context.Context) (ChatMessages, error)
	// Add a user message to the store.
	AddUserMessage(ctx context.Context, text string) error
	// Add an AI message to the store.
	AddAIMessage(ctx context.Context, text string) error
	// Add a self-created message to the store.
	AddMessage(ctx context.Context, message ChatMessage) error
	// Remove all messages from the store.
	Clear(ctx context.Context) error
}

type ChatMessageType string

type ChatMessages []ChatMessage

type ChatModel interface {
	Model
	// Generate generates text based on the provided chat messages and options.
	Generate(ctx context.Context, messages ChatMessages, optFns ...func(o *GenerateOptions)) (*ModelResult, error)
}

type ChatModelStartInput struct {
	*ChatModelStartManagerInput
	RunID string
}

type ChatModelStartManagerInput struct {
	ChatModelType    string
	Messages         ChatMessages
	InvocationParams map[string]any
}

type Document struct {
	PageContent string
	Metadata    map[string]any
}

type DocumentLoader interface {
	Load(ctx context.Context) ([]Document, error)
	LoadAndSplit(ctx context.Context, splitter TextSplitter) ([]Document, error)
}

type Embedder interface {
	// EmbedDocuments embeds a list of documents and returns their embeddings.
	EmbedDocuments(ctx context.Context, texts []string) ([][]float64, error)
	// EmbedQuery embeds a single query and returns its embedding.
	EmbedQuery(ctx context.Context, text string) ([]float64, error)
}

type FunctionCall struct {
	Name string `json:"name,omitempty"`
	// call function with arguments in JSON format
	Arguments string `json:"arguments,omitempty"`
}

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

type FunctionDefinition struct {
	Name        string                       `json:"name"`
	Description string                       `json:"description,omitempty"`
	Parameters  FunctionDefinitionParameters `json:"parameters"`
}

type FunctionDefinitionParameters struct {
	Type       string                        `json:"type"`
	Properties map[string]*jsonschema.Schema `json:"properties"`
	Required   []string                      `json:"required"`
}

type GenerateOptions struct {
	CallbackManger CallbackManagerForModelRun
	Stop           []string
	Functions      []FunctionDefinition
}

type Generation struct {
	Text    string
	Message ChatMessage
	Info    map[string]any
}

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

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

type LLM interface {
	Model
	// Generate generates text based on the provided prompt and options.
	Generate(ctx context.Context, prompt string, optFns ...func(o *GenerateOptions)) (*ModelResult, error)
}

type LLMStartInput struct {
	*LLMStartManagerInput
	RunID string
}

type LLMStartManagerInput struct {
	LLMType          string
	Prompt           string
	InvocationParams map[string]any
}

type Memory interface {
	// Input keys this memory class will load dynamically.
	MemoryKeys() []string
	// Return key-value pairs given the text input to the chain.
	// If None, return all memories
	LoadMemoryVariables(ctx context.Context, inputs map[string]any) (map[string]any, error)
	// Save the context of this model run to memory.
	SaveContext(ctx context.Context, inputs map[string]any, outputs map[string]any) error
	// Clear memory contents.
	Clear(ctx context.Context) error
}

type Model interface {
	Tokenizer
	// Type returns the type of the model.
	Type() string
	// Verbose returns the verbosity setting of the model.
	Verbose() bool
	// Callbacks returns the registered callbacks of the model.
	Callbacks() []Callback
	// InvocationParams returns the parameters used in the model invocation.
	InvocationParams() map[string]any
}

type ModelEndInput struct {
	*ModelEndManagerInput
	RunID string
}

type ModelEndManagerInput struct {
	Result *ModelResult
}

type ModelErrorInput struct {
	*ModelErrorManagerInput
	RunID string
}

type ModelErrorManagerInput struct {
	Error error
}

type ModelNewTokenInput struct {
	*ModelNewTokenManagerInput
	RunID string
}

type ModelNewTokenManagerInput struct {
	Token string
}

type ModelResult struct {
	Generations []Generation
	LLMOutput   map[string]any
}

type OutputParser[T any] interface {
	// Parse parses the output of an LLM call.
	ParseResult(result Generation) (any, error)
	// Parse parses the output of an LLM call.
	Parse(text string) (T, error)
	// ParseWithPrompt parses the output of an LLM call with the prompt used.
	ParseWithPrompt(text string, prompt PromptValue) (T, error)
	// GetFormatInstructions returns a string describing the format of the output.
	GetFormatInstructions() string
	// Type returns the string type key uniquely identifying this class of parser
	Type() string
}

type PromptValue interface {
	String() string
	Messages() ChatMessages
}

type Retriever interface {
	GetRelevantDocuments(ctx context.Context, query string) ([]Document, error)
}

type StringifyChatMessagesOptions struct {
	HumanPrefix    string
	AIPrefix       string
	SystemPrefix   string
	FunctionPrefix string
}

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

type TextInput struct {
	*TextManagerInput
	RunID string
}

type TextManagerInput struct {
	Text string
}

type TextSplitter interface {
	SplitDocuments(docs []Document) ([]Document, error)
}

type Tokenizer interface {
	// GetTokenIDs returns the token IDs corresponding to the provided text.
	GetTokenIDs(text string) ([]uint, error)
	// GetNumTokens returns the number of tokens in the provided text.
	GetNumTokens(text string) (uint, error)
	// GetNumTokensFromMessage returns the number of tokens in the provided chat messages.
	GetNumTokensFromMessage(messages ChatMessages) (uint, error)
}

type Tool interface {
	// Name returns the name of the tool.
	Name() string
	// Description returns the description of the tool.
	Description() string
	// Run executes the tool with the given input and returns the output.
	Run(ctx context.Context, input any) (string, error)
	// ArgsType returns the type of the input argument expected by the tool.
	ArgsType() reflect.Type
	// Verbose returns the verbosity setting of the tool.
	Verbose() bool
	// Callbacks returns the registered callbacks of the tool.
	Callbacks() []Callback
}

type ToolEndInput struct {
	*ToolEndManagerInput
	RunID string
}

type ToolEndManagerInput struct {
	Output string
}

type ToolErrorInput struct {
	*ToolErrorManagerInput
	RunID string
}

type ToolErrorManagerInput struct {
	Error error
}

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

type ToolStartInput struct {
	*ToolStartManagerInput
	RunID string
}

type ToolStartManagerInput struct {
	ToolName string
	Input    *ToolInput
}

type VectorStore interface {
	AddDocuments(ctx context.Context, docs []Document) error
	SimilaritySearch(ctx context.Context, query string) ([]Document, error)
}