vectorstore

type ChromemConfig struct {
	// Path is the directory for persistent storage.
	// Default: "~/.config/contextd/vectorstore"
	Path string

	// Compress enables gzip compression for stored data.
	// Note: This defaults to false (Go zero value). Set explicitly if compression is desired.
	Compress bool

	// DefaultCollection is the default collection name.
	// Default: "contextd_default"
	DefaultCollection string

	// VectorSize is the expected embedding dimension.
	// Must match the embedder's output dimension.
	// Default: 384 (for FastEmbed bge-small-en-v1.5)
	VectorSize int
}

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

type CollectionInfo struct {
	// Name is the collection name.
	Name string `json:"name"`

	// PointCount is the number of vectors in the collection.
	PointCount int `json:"point_count"`

	// VectorSize is the dimensionality of vectors in this collection.
	VectorSize int `json:"vector_size"`
}

type CompressionLevel string

type CompressionMetadata struct {
	Level            CompressionLevel `json:"compression_level"`               // Current compression level
	Algorithm        string           `json:"compression_algorithm,omitempty"` // Compression algorithm used
	OriginalSize     int              `json:"original_size"`                   // Original content size (tokens/chars)
	CompressedSize   int              `json:"compressed_size"`                 // Compressed content size
	CompressionRatio float64          `json:"compression_ratio"`               // Compression ratio (original/compressed)
	CompressedAt     *time.Time       `json:"compressed_at,omitempty"`         // When compression was applied
}

type Document struct {
	// ID is the unique identifier for the document
	ID string

	// Content is the text content of the document
	Content string

	// Metadata contains additional key-value pairs for filtering
	// Common fields: owner, project, file, branch, timestamp
	Metadata map[string]interface{}

	// Collection is the target collection name for this document.
	// If empty, uses the service's default collection.
	//
	// Collection naming convention:
	//   - Organization: org_{type} (e.g., org_memories)
	//   - Team: {team}_{type} (e.g., platform_memories)
	//   - Project: {team}_{project}_{type} (e.g., platform_contextd_memories)
	Collection string
}

type Embedder interface {
	// EmbedDocuments generates embeddings for multiple texts.
	// Returns a slice of embeddings (one per input text) or an error.
	EmbedDocuments(ctx context.Context, texts []string) ([][]float32, error)

	// EmbedQuery generates an embedding for a single query.
	// Some models optimize differently for queries vs documents.
	EmbedQuery(ctx context.Context, text string) ([]float32, error)
}

type QdrantConfig struct {
	// Host is the Qdrant server hostname or IP address.
	// Default: "localhost"
	Host string

	// Port is the Qdrant gRPC port (NOT HTTP REST port).
	// Default: 6334 (gRPC), not 6333 (HTTP)
	Port int

	// CollectionName is the default collection for operations.
	// Format: {scope}_{type} for multi-tenancy
	// Examples: org_memories, platform_memories, platform_contextd_memories
	CollectionName string

	// VectorSize is the dimensionality of embeddings.
	// Examples: 384 (BAAI/bge-small-en-v1.5), 768 (BERT), 1536 (OpenAI)
	// MUST match Embedder output dimensions.
	VectorSize uint64

	// Distance is the similarity metric for vector search.
	// Options: Cosine (default), Euclid, Dot
	Distance qdrant.Distance

	// UseTLS enables TLS encryption for gRPC connection.
	// Default: false (MVP), true (production)
	UseTLS bool

	// MaxRetries is the maximum number of retry attempts for transient failures.
	// Default: 3
	MaxRetries int

	// RetryBackoff is the initial backoff duration for retries.
	// Doubles on each retry (exponential backoff).
	// Default: 1 second
	RetryBackoff time.Duration

	// MaxMessageSize is the maximum gRPC message size in bytes.
	// Default: 50MB (to handle large documents)
	MaxMessageSize int

	// CircuitBreakerThreshold is the number of failures before opening circuit.
	// Default: 5
	CircuitBreakerThreshold int
}

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

type SearchResult struct {
	// ID is the document identifier
	ID string

	// Content is the document text content
	Content string

	// Score is the similarity score (higher = more similar)
	Score float32

	// Metadata contains the document metadata
	Metadata map[string]interface{}
}

type Store interface {
	// AddDocuments adds documents to the vector store.
	//
	// Documents are embedded and stored with their metadata. The document ID
	// is used as the unique identifier in the vector store.
	//
	// If Document.Collection is specified, the document is added to that collection.
	// Otherwise, the implementation's default collection is used.
	//
	// Returns the IDs of added documents and an error if the operation fails.
	AddDocuments(ctx context.Context, docs []Document) ([]string, error)

	// Search performs similarity search in the default collection.
	//
	// It searches for documents similar to the query and returns up to k results
	// ordered by similarity score (highest first).
	//
	// Returns search results with scores and metadata, or an error if search fails.
	Search(ctx context.Context, query string, k int) ([]SearchResult, error)

	// SearchWithFilters performs similarity search with metadata filters.
	//
	// Filters are applied to document metadata (e.g., {"owner": "alice"}).
	// Only documents matching ALL filter conditions are returned.
	//
	// Returns filtered search results or an error if search fails.
	SearchWithFilters(ctx context.Context, query string, k int, filters map[string]interface{}) ([]SearchResult, error)

	// SearchInCollection performs similarity search in a specific collection.
	//
	// This supports the hierarchical collection architecture by allowing searches
	// in scope-specific collections (e.g., "org_memories", "platform_contextd_memories").
	//
	// Returns filtered search results from the specified collection, or an error.
	SearchInCollection(ctx context.Context, collectionName string, query string, k int, filters map[string]interface{}) ([]SearchResult, error)

	// DeleteDocuments deletes documents by their IDs from the default collection.
	//
	// Returns an error if deletion fails.
	DeleteDocuments(ctx context.Context, ids []string) error

	// DeleteDocumentsFromCollection deletes documents by their IDs from a specific collection.
	//
	// Returns an error if deletion fails.
	DeleteDocumentsFromCollection(ctx context.Context, collectionName string, ids []string) error

	// CreateCollection creates a new collection with the specified configuration.
	//
	// Collections are namespaces for documents (e.g., project-specific collections).
	// The vectorSize parameter specifies the dimensionality of embeddings.
	//
	// Returns an error if collection creation fails or collection already exists.
	CreateCollection(ctx context.Context, collectionName string, vectorSize int) error

	// DeleteCollection deletes a collection and all its documents.
	//
	// This is a destructive operation that cannot be undone.
	//
	// Returns an error if deletion fails or collection doesn't exist.
	DeleteCollection(ctx context.Context, collectionName string) error

	// CollectionExists checks if a collection exists.
	//
	// Returns true if the collection exists, false otherwise.
	// Returns an error only if the check operation itself fails.
	CollectionExists(ctx context.Context, collectionName string) (bool, error)

	// ListCollections returns a list of all collection names.
	//
	// Returns collection names or an error if listing fails.
	ListCollections(ctx context.Context) ([]string, error)

	// GetCollectionInfo returns metadata about a collection.
	//
	// Returns collection info including point count and vector size.
	// Returns ErrCollectionNotFound if the collection doesn't exist.
	GetCollectionInfo(ctx context.Context, collectionName string) (*CollectionInfo, error)

	// ExactSearch performs brute-force similarity search without using HNSW index.
	//
	// This is a fallback for small datasets (<10 vectors) where HNSW index
	// may not be built. It performs exact cosine similarity on all vectors.
	//
	// Returns search results ordered by similarity score (highest first).
	ExactSearch(ctx context.Context, collectionName string, query string, k int) ([]SearchResult, error)

	// Close closes the vector store connection and releases resources.
	Close() error
}

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