prebuilt

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

type CreateAgentOption func(*CreateAgentOptions)

type CreateAgentOptions struct {
	Verbose       bool
	SystemMessage string
	StateModifier func(messages []llms.MessageContent) []llms.MessageContent
	Checkpointer  graph.CheckpointStore
	// contains filtered or unexported fields
}

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

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

type DocumentWithScore struct {
	Document Document
	Score    float64
}

type Embedder interface {
	EmbedDocuments(ctx context.Context, texts []string) ([][]float64, error)
	EmbedQuery(ctx context.Context, text string) ([]float64, error)
}

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

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

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

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

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

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

type Memory interface {
	// SaveContext saves the context from this conversation to buffer
	SaveContext(ctx context.Context, inputValues map[string]any, outputValues map[string]any) error
	// LoadMemoryVariables loads memory variables
	LoadMemoryVariables(ctx context.Context, inputs map[string]any) (map[string]any, error)
	// Clear clears memory contents
	Clear(ctx context.Context) error
	// GetMessages returns all messages in memory
	GetMessages(ctx context.Context) ([]llms.ChatMessage, error)
}

type MockEmbedder struct {
	Dimension int
}

type RAGConfig struct {
	// Retrieval configuration
	TopK           int     // Number of documents to retrieve
	ScoreThreshold float64 // Minimum relevance score
	UseReranking   bool    // Whether to use reranking
	UseFallback    bool    // Whether to use fallback search

	// Generation configuration
	SystemPrompt     string
	IncludeCitations bool
	MaxTokens        int
	Temperature      float64

	// Components
	Loader      DocumentLoader
	Splitter    TextSplitter
	Embedder    Embedder
	VectorStore VectorStore
	Retriever   Retriever
	Reranker    Reranker
	LLM         llms.Model
}

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

type RAGState struct {
	Query              string
	Documents          []Document
	RetrievedDocuments []Document
	RankedDocuments    []DocumentWithScore
	Context            string
	Answer             string
	Citations          []string
	Metadata           map[string]interface{}
}

type Reranker interface {
	Rerank(ctx context.Context, query string, documents []Document) ([]DocumentWithScore, error)
}

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

type SimpleReranker struct {
}

type SimpleTextSplitter struct {
	ChunkSize    int
	ChunkOverlap int
	Separator    string
}

type StaticDocumentLoader struct {
	Documents []Document
}

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

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

type ToolInvocation struct {
	Tool      string `json:"tool"`
	ToolInput string `json:"tool_input"`
}

type ToolNode struct {
	Executor *ToolExecutor
}

type VectorStore interface {
	AddDocuments(ctx context.Context, documents []Document, embeddings [][]float64) error
	SimilaritySearch(ctx context.Context, query string, k int) ([]Document, error)
	SimilaritySearchWithScore(ctx context.Context, query string, k int) ([]DocumentWithScore, error)
}

type VectorStoreRetriever struct {
	VectorStore VectorStore
	TopK        int
}