embeddings

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

type EmbeddingProvider interface {
	// CreateEmbedding generates embedding for the given text
	CreateEmbedding(ctx context.Context, text string) ([]float32, error)

	// BatchCreateEmbeddings generates embeddings for multiple texts
	BatchCreateEmbeddings(ctx context.Context, texts []string) ([][]float32, error)

	// Dimensions returns the dimensionality of the embeddings
	Dimensions() int
}

type EmbeddingSearch interface {
	// Store stores documents and handles embedding generation internally
	Store(ctx context.Context, docs []PostDocument) error

	// Search performs a similarity search using the query text
	Search(ctx context.Context, query string, opts SearchOptions) ([]SearchResult, error)

	// Delete removes documents
	Delete(ctx context.Context, postIDs []string) error

	// Clear removes all documents
	Clear(ctx context.Context) error
}

type EmbeddingSearchConfig struct {
	Type              string           `json:"type"`
	VectorStore       UpstreamConfig   `json:"vectorStore"`
	EmbeddingProvider UpstreamConfig   `json:"embeddingProvider"`
	Parameters        json.RawMessage  `json:"parameters"`
	Dimensions        int              `json:"dimensions"`
	ChunkingOptions   chunking.Options `json:"chunkingOptions"`
}

type PostDocument struct {
	PostID    string // ID of the Mattermost post
	CreateAt  int64  // Creation timestamp of the referenced post, not when this was indexed
	TeamID    string
	ChannelID string
	UserID    string
	Content   string

	// Embed chunk info to track if this is a chunk
	chunking.ChunkInfo
}

type SearchOptions struct {
	Limit         int
	MinScore      float32
	TeamID        string
	ChannelID     string
	UserID        string // User ID for permission checks
	CreatedAfter  int64
	CreatedBefore int64
}

type SearchResult struct {
	Document PostDocument
	Score    float32
}

type UpstreamConfig struct {
	Type       string          `json:"type"`
	Parameters json.RawMessage `json:"parameters"`
}

type VectorStore interface {
	// Store stores documents and their embeddings
	Store(ctx context.Context, docs []PostDocument, embeddings [][]float32) error

	// Search performs a similarity search using the provided embedding
	Search(ctx context.Context, embedding []float32, opts SearchOptions) ([]SearchResult, error)

	// Delete removes documents from the vector store
	Delete(ctx context.Context, postIDs []string) error

	// Clear removes all documents from the vector store
	Clear(ctx context.Context) error
}