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* Semantic search implementation for conversation history\n *\n * Provides vector-based semantic search using OpenAI embeddings API.\n * Supports similarity search, ranking, and filtering across conversations.\n */\n\nimport OpenAI from 'openai'\nimport { Message } from '../types'\nimport { ConversationStore } from '../store/ConversationStore'\nimport {\n EmbeddingConfig,\n SearchOptions,\n SearchResult,\n SimilarMessageOptions,\n ConversationSearchOptions,\n BatchEmbeddingRequest,\n BatchEmbeddingResponse,\n EmbeddingCacheEntry,\n SearchStats,\n SearchFilter,\n EmbeddingModel,\n} from './types'\n\n/**\n * Semantic search engine for conversation messages\n */\nexport class SemanticSearch {\n private openai: OpenAI\n private config: Required>\n private store: ConversationStore\n private embeddingCache: Map\n private stats: SearchStats\n\n constructor(store: ConversationStore, config: EmbeddingConfig) {\n if (!config.apiKey) {\n throw new Error('OpenAI API key is required')\n }\n\n this.store = store\n this.openai = new OpenAI({\n apiKey: config.apiKey,\n maxRetries: config.maxRetries ?? 3,\n timeout: config.timeout ?? 30000,\n })\n\n this.config = {\n model: config.model ?? 'text-embedding-3-small',\n batchSize: config.batchSize ?? 100,\n dimensions: config.dimensions ?? 1536,\n maxRetries: config.maxRetries ?? 3,\n timeout: config.timeout ?? 30000,\n }\n\n this.embeddingCache = new Map()\n this.stats = {\n totalMessages: 0,\n totalConversations: 0,\n totalEmbeddings: 0,\n cacheHitRate: 0,\n avgEmbeddingTime: 0,\n }\n }\n\n /**\n * Generate embedding for a single text\n */\n private async generateEmbedding(text: string): Promise {\n const cacheKey = this.getCacheKey(text, this.config.model)\n const cached = this.embeddingCache.get(cacheKey)\n\n if (cached && !this.isCacheExpired(cached)) {\n return cached.embedding\n }\n\n const startTime = Date.now()\n\n try {\n const response = await this.openai.embeddings.create({\n model: this.config.model,\n input: text,\n dimensions: this.config.dimensions,\n })\n\n const embedding = response.data[0]?.embedding\n if (!embedding) {\n throw new Error('Failed to generate embedding')\n }\n const duration = Date.now() - startTime\n\n // Update cache\n this.embeddingCache.set(cacheKey, {\n text,\n embedding,\n model: this.config.model,\n cachedAt: Date.now(),\n ttl: 3600000, // 1 hour\n })\n\n // Update stats\n this.stats.totalEmbeddings++\n this.updateAvgEmbeddingTime(duration)\n\n return embedding\n } catch (error) {\n throw new Error(\n `Failed to generate embedding: ${error instanceof Error ? error.message : String(error)}`\n )\n }\n }\n\n /**\n * Generate embeddings for multiple texts in batches\n */\n async generateBatchEmbeddings(\n request: BatchEmbeddingRequest\n ): Promise {\n const { texts, model, dimensions } = request\n const embedModel = model ?? this.config.model\n const embedDimensions = dimensions ?? this.config.dimensions\n\n if (texts.length === 0) {\n return {\n embeddings: [],\n model: embedModel,\n usage: { promptTokens: 0, totalTokens: 0 },\n }\n }\n\n const embeddings: number[][] = []\n let totalPromptTokens = 0\n let totalTokens = 0\n\n // Process in batches\n for (let i = 0; i < texts.length; i += this.config.batchSize) {\n const batch = texts.slice(i, i + this.config.batchSize)\n const batchEmbeddings: number[][] = []\n\n // Check cache first\n const uncachedTexts: string[] = []\n const uncachedIndices: number[] = []\n\n batch.forEach((text, index) => {\n const cacheKey = this.getCacheKey(text, embedModel)\n const cached = this.embeddingCache.get(cacheKey)\n\n if (cached && !this.isCacheExpired(cached)) {\n batchEmbeddings[index] = cached.embedding\n } else {\n uncachedTexts.push(text)\n uncachedIndices.push(index)\n }\n })\n\n // Generate embeddings for uncached texts\n if (uncachedTexts.length > 0) {\n const response = await this.openai.embeddings.create({\n model: embedModel,\n input: uncachedTexts,\n dimensions: embedDimensions,\n })\n\n uncachedIndices.forEach((index, i) => {\n const embedding = response.data[i]?.embedding\n if (embedding) {\n batchEmbeddings[index] = embedding\n\n // Cache the result\n const text = uncachedTexts[i]\n if (text) {\n this.embeddingCache.set(this.getCacheKey(text, embedModel), {\n text,\n embedding,\n model: embedModel,\n cachedAt: Date.now(),\n ttl: 3600000,\n })\n }\n }\n })\n\n totalPromptTokens += response.usage.prompt_tokens\n totalTokens += response.usage.total_tokens\n this.stats.totalEmbeddings += uncachedTexts.length\n }\n\n embeddings.push(...batchEmbeddings)\n }\n\n this.updateCacheHitRate()\n\n return {\n embeddings,\n model: embedModel,\n usage: {\n promptTokens: totalPromptTokens,\n totalTokens,\n },\n }\n }\n\n /**\n * Search messages by semantic similarity\n */\n async searchMessages(\n query: string,\n options: SearchOptions = {}\n ): Promise {\n const {\n topK = 10,\n threshold = 0.0,\n filter,\n } = options\n\n // Generate query embedding\n const queryEmbedding = await this.generateEmbedding(query)\n\n // Get messages from store based on filter\n const messages = await this.getFilteredMessages(filter)\n\n if (messages.length === 0) {\n return []\n }\n\n // Generate embeddings for all messages\n const messageEmbeddings = await this.generateBatchEmbeddings({\n texts: messages.map((m) => this.getMessageContentAsString(m.content)),\n model: this.config.model,\n dimensions: this.config.dimensions,\n })\n\n // Calculate similarities and create results\n const results: SearchResult[] = []\n\n messages.forEach((message, index) => {\n const embedding = messageEmbeddings.embeddings[index]\n if (!embedding) return\n const similarity = this.cosineSimilarity(queryEmbedding, embedding)\n\n if (similarity >= threshold) {\n results.push({\n message,\n similarity: {\n score: similarity,\n distance: 1 - similarity,\n rank: 0, // Will be set after sorting\n },\n conversationId: (message as any).conversationId,\n })\n }\n })\n\n // Sort by similarity (descending)\n results.sort((a, b) => b.similarity.score - a.similarity.score)\n\n // Set ranks and limit results\n const topResults = results.slice(0, topK)\n topResults.forEach((result, index) => {\n result.similarity.rank = index + 1\n })\n\n return topResults\n }\n\n /**\n * Find similar messages to a given message\n */\n async findSimilarMessages(\n messageId: string,\n options: SimilarMessageOptions = {}\n ): Promise {\n const {\n topK = 5,\n threshold = 0.5,\n includeSelf = false,\n filter,\n } = options\n\n // Find the source message\n const sourceMessage = await this.findMessageById(messageId)\n if (!sourceMessage) {\n throw new Error(`Message with id ${messageId} not found`)\n }\n\n // Search using the source message content\n const results = await this.searchMessages(\n this.getMessageContentAsString(sourceMessage.content),\n {\n topK: includeSelf ? topK : topK + 1,\n threshold,\n filter,\n }\n )\n\n // Filter out the source message if not included\n const filteredResults = includeSelf\n ? results\n : results.filter((r) => r.message.id !== messageId)\n\n return filteredResults.slice(0, topK)\n }\n\n /**\n * Search across multiple conversations\n */\n async searchConversations(\n query: string,\n options: ConversationSearchOptions = {}\n ): Promise {\n const {\n topK = 10,\n threshold = 0.0,\n filter,\n conversationIds,\n maxConversations,\n } = options\n\n // Get list of conversations to search\n let targetConversationIds: string[]\n\n if (conversationIds && conversationIds.length > 0) {\n targetConversationIds = conversationIds\n } else {\n const allConversationIds = await this.store.list()\n targetConversationIds = maxConversations\n ? allConversationIds.slice(0, maxConversations)\n : allConversationIds\n }\n\n // Update filter to include conversation IDs\n const updatedFilter: SearchFilter = {\n ...filter,\n }\n\n // Search messages\n const results = await this.searchMessages(query, {\n topK,\n threshold,\n filter: updatedFilter,\n })\n\n // Filter results by conversation IDs\n const filteredResults = conversationIds\n ? results.filter((r) =>\n conversationIds.includes(r.conversationId || '')\n )\n : results\n\n // Update stats\n this.stats.totalConversations = targetConversationIds.length\n\n return filteredResults\n }\n\n /**\n * Convert message content to a string for embedding\n */\n private getMessageContentAsString(\n content: Message['content']\n ): string {\n if (typeof content === 'string') {\n return content\n }\n if (Array.isArray(content)) {\n return content\n .map((part) => {\n if (typeof part === 'string') return part\n if (part && typeof part === 'object' && 'text' in part) {\n return (part as { text: string }).text\n }\n return ''\n })\n .join(' ')\n }\n if (content && typeof content === 'object' && 'text' in content) {\n return (content as { text: string }).text\n }\n return String(content)\n }\n\n /**\n * Calculate cosine similarity between two vectors\n */\n private cosineSimilarity(a: number[], b: number[]): number {\n if (a.length !== b.length) {\n throw new Error('Vectors must have the same length')\n }\n\n let dotProduct = 0\n let normA = 0\n let normB = 0\n\n for (let i = 0; i < a.length; i++) {\n const aVal = a[i] ?? 0\n const bVal = b[i] ?? 0\n dotProduct += aVal * bVal\n normA += aVal * aVal\n normB += bVal * bVal\n }\n\n const denominator = Math.sqrt(normA) * Math.sqrt(normB)\n\n if (denominator === 0) {\n return 0\n }\n\n return dotProduct / denominator\n }\n\n /**\n * Get filtered messages from the store\n */\n private async getFilteredMessages(\n filter?: SearchFilter\n ): Promise<(Message & { conversationId?: string })[]> {\n const conversationIds = await this.store.list()\n const allMessages: (Message & { conversationId?: string })[] = []\n\n for (const conversationId of conversationIds) {\n const conversation = await this.store.load(conversationId)\n\n if (!conversation) {\n continue\n }\n\n let messages = conversation.messages\n\n // Apply filters\n if (filter) {\n messages = messages.filter((message) => {\n // Filter by conversation ID\n if (\n filter.conversationId &&\n conversationId !== filter.conversationId\n ) {\n return false\n }\n\n // Filter by role\n if (filter.role && message.role !== filter.role) {\n return false\n }\n\n // Filter by date range\n if (filter.startDate && message.timestamp < filter.startDate) {\n return false\n }\n\n if (filter.endDate && message.timestamp > filter.endDate) {\n return false\n }\n\n return true\n })\n }\n\n // Add conversation ID to messages\n const messagesWithConvId = messages.map((m) => ({\n ...m,\n conversationId,\n }))\n\n allMessages.push(...messagesWithConvId)\n }\n\n this.stats.totalMessages = allMessages.length\n\n return allMessages\n }\n\n /**\n * Find a message by ID across all conversations\n */\n private async findMessageById(\n messageId: string\n ): Promise {\n const conversationIds = await this.store.list()\n\n for (const conversationId of conversationIds) {\n const conversation = await this.store.load(conversationId)\n\n if (!conversation) {\n continue\n }\n\n const message = conversation.messages.find((m) => m.id === messageId)\n\n if (message) {\n return message\n }\n }\n\n return null\n }\n\n /**\n * Generate cache key for an embedding\n */\n private getCacheKey(text: string, model: EmbeddingModel): string {\n // Simple hash function for the cache key\n const hash = this.simpleHash(text)\n return `${model}:${hash}`\n }\n\n /**\n * Simple hash function for cache keys\n */\n private simpleHash(str: string): string {\n let hash = 0\n for (let i = 0; i < str.length; i++) {\n const char = str.charCodeAt(i)\n hash = (hash << 5) - hash + char\n hash = hash & hash // Convert to 32bit integer\n }\n return Math.abs(hash).toString(36)\n }\n\n /**\n * Check if a cache entry is expired\n */\n private isCacheExpired(entry: EmbeddingCacheEntry): boolean {\n if (!entry.ttl) {\n return false\n }\n\n return Date.now() - entry.cachedAt > entry.ttl\n }\n\n /**\n * Update average embedding time\n */\n private updateAvgEmbeddingTime(duration: number): void {\n const totalTime =\n (this.stats.avgEmbeddingTime || 0) * (this.stats.totalEmbeddings - 1) +\n duration\n this.stats.avgEmbeddingTime = totalTime / this.stats.totalEmbeddings\n }\n\n /**\n * Update cache hit rate\n */\n private updateCacheHitRate(): void {\n const totalRequests = this.stats.totalEmbeddings\n const cacheSize = this.embeddingCache.size\n\n if (totalRequests > 0) {\n this.stats.cacheHitRate = cacheSize / totalRequests\n }\n }\n\n /**\n * Get search statistics\n */\n getStats(): SearchStats {\n return { ...this.stats }\n }\n\n /**\n * Clear the embedding cache\n */\n clearCache(): void {\n this.embeddingCache.clear()\n }\n\n /**\n * Get the current cache size\n */\n getCacheSize(): number {\n return this.embeddingCache.size\n }\n}\n"]}