import { C as ConversationStore } from '../ConversationStore-DgHjCpwv.mjs';
import { M as Message } from '../streaming.d-CUzaq8hR.mjs';
import '../common.d-iR60eBef.mjs';

/**
 * Type definitions for semantic search
 */

/**
 * Supported OpenAI embedding models
 */
type EmbeddingModel = 'text-embedding-3-small' | 'text-embedding-3-large' | 'text-embedding-ada-002';
/**
 * Configuration for embeddings
 */
interface EmbeddingConfig {
    /** OpenAI API key */
    apiKey: string;
    /** Embedding model to use */
    model?: EmbeddingModel;
    /** Batch size for embedding generation */
    batchSize?: number;
    /** Dimensions for the embedding (only for text-embedding-3-* models) */
    dimensions?: number;
    /** Maximum retries for API calls */
    maxRetries?: number;
    /** Timeout in milliseconds */
    timeout?: number;
}
/**
 * A message with its vector embedding
 */
interface MessageWithEmbedding extends Message {
    /** Vector embedding of the message content */
    embedding: number[];
}
/**
 * Filter criteria for search
 */
interface SearchFilter {
    /** Filter by conversation ID */
    conversationId?: string;
    /** Filter by message role */
    role?: 'user' | 'assistant' | 'system';
    /** Filter by date range (start timestamp) */
    startDate?: number;
    /** Filter by date range (end timestamp) */
    endDate?: number;
    /** Filter by custom metadata */
    metadata?: Record<string, any>;
}
/**
 * Options for search operations
 */
interface SearchOptions {
    /** Number of results to return */
    topK?: number;
    /** Minimum similarity threshold (0-1) */
    threshold?: number;
    /** Filter criteria */
    filter?: SearchFilter;
    /** Whether to include the query message in results */
    includeQuery?: boolean;
}
/**
 * Similarity score with additional metadata
 */
interface SimilarityScore {
    /** Cosine similarity score (0-1) */
    score: number;
    /** Distance metric (1 - cosine similarity) */
    distance: number;
    /** Rank in results (1-based) */
    rank: number;
}
/**
 * Search result containing a message and its similarity score
 */
interface SearchResult {
    /** The matched message */
    message: Message;
    /** Similarity score and metadata */
    similarity: SimilarityScore;
    /** Conversation ID the message belongs to */
    conversationId?: string;
}
/**
 * Options for finding similar messages
 */
interface SimilarMessageOptions {
    /** Number of similar messages to return */
    topK?: number;
    /** Minimum similarity threshold (0-1) */
    threshold?: number;
    /** Whether to include the source message in results */
    includeSelf?: boolean;
    /** Filter criteria */
    filter?: SearchFilter;
}
/**
 * Options for searching across conversations
 */
interface ConversationSearchOptions extends SearchOptions {
    /** Specific conversation IDs to search within */
    conversationIds?: string[];
    /** Maximum number of conversations to search */
    maxConversations?: number;
}
/**
 * Batch embedding request
 */
interface BatchEmbeddingRequest {
    /** Array of texts to embed */
    texts: string[];
    /** Model to use for embeddings */
    model?: EmbeddingModel;
    /** Dimensions for the embedding */
    dimensions?: number;
}
/**
 * Batch embedding response
 */
interface BatchEmbeddingResponse {
    /** Array of embeddings corresponding to input texts */
    embeddings: number[][];
    /** Model used for embeddings */
    model: string;
    /** Total tokens used */
    usage: {
        promptTokens: number;
        totalTokens: number;
    };
}
/**
 * Cache entry for embeddings
 */
interface EmbeddingCacheEntry {
    /** The original text */
    text: string;
    /** The generated embedding */
    embedding: number[];
    /** Model used */
    model: string;
    /** Timestamp when cached */
    cachedAt: number;
    /** TTL in milliseconds */
    ttl?: number;
}
/**
 * Statistics for semantic search operations
 */
interface SearchStats {
    /** Total number of messages indexed */
    totalMessages: number;
    /** Total number of conversations indexed */
    totalConversations: number;
    /** Total number of embeddings generated */
    totalEmbeddings: number;
    /** Cache hit rate (0-1) */
    cacheHitRate?: number;
    /** Average embedding generation time (ms) */
    avgEmbeddingTime?: number;
}

/**
 * Semantic search implementation for conversation history
 *
 * Provides vector-based semantic search using OpenAI embeddings API.
 * Supports similarity search, ranking, and filtering across conversations.
 */

/**
 * Semantic search engine for conversation messages
 */
declare class SemanticSearch {
    private openai;
    private config;
    private store;
    private embeddingCache;
    private stats;
    constructor(store: ConversationStore, config: EmbeddingConfig);
    /**
     * Generate embedding for a single text
     */
    private generateEmbedding;
    /**
     * Generate embeddings for multiple texts in batches
     */
    generateBatchEmbeddings(request: BatchEmbeddingRequest): Promise<BatchEmbeddingResponse>;
    /**
     * Search messages by semantic similarity
     */
    searchMessages(query: string, options?: SearchOptions): Promise<SearchResult[]>;
    /**
     * Find similar messages to a given message
     */
    findSimilarMessages(messageId: string, options?: SimilarMessageOptions): Promise<SearchResult[]>;
    /**
     * Search across multiple conversations
     */
    searchConversations(query: string, options?: ConversationSearchOptions): Promise<SearchResult[]>;
    /**
     * Convert message content to a string for embedding
     */
    private getMessageContentAsString;
    /**
     * Calculate cosine similarity between two vectors
     */
    private cosineSimilarity;
    /**
     * Get filtered messages from the store
     */
    private getFilteredMessages;
    /**
     * Find a message by ID across all conversations
     */
    private findMessageById;
    /**
     * Generate cache key for an embedding
     */
    private getCacheKey;
    /**
     * Simple hash function for cache keys
     */
    private simpleHash;
    /**
     * Check if a cache entry is expired
     */
    private isCacheExpired;
    /**
     * Update average embedding time
     */
    private updateAvgEmbeddingTime;
    /**
     * Update cache hit rate
     */
    private updateCacheHitRate;
    /**
     * Get search statistics
     */
    getStats(): SearchStats;
    /**
     * Clear the embedding cache
     */
    clearCache(): void;
    /**
     * Get the current cache size
     */
    getCacheSize(): number;
}

export { type BatchEmbeddingRequest, type BatchEmbeddingResponse, type ConversationSearchOptions, type EmbeddingCacheEntry, type EmbeddingConfig, type EmbeddingModel, type MessageWithEmbedding, type SearchFilter, type SearchOptions, type SearchResult, type SearchStats, SemanticSearch, type SimilarMessageOptions, type SimilarityScore };