mem0ai/dist/oss/index.mjs.map

The Memory Layer For Your AI Apps

{"version":3,"sources":["../../src/oss/src/memory/index.ts","../../src/oss/src/types/index.ts","../../src/oss/src/embeddings/openai.ts","../../src/oss/src/embeddings/ollama.ts","../../src/oss/src/utils/logger.ts","../../src/oss/src/llms/openai.ts","../../src/oss/src/llms/openai_structured.ts","../../src/oss/src/llms/anthropic.ts","../../src/oss/src/llms/groq.ts","../../src/oss/src/llms/mistral.ts","../../src/oss/src/vector_stores/memory.ts","../../src/oss/src/vector_stores/qdrant.ts","../../src/oss/src/vector_stores/redis.ts","../../src/oss/src/llms/ollama.ts","../../src/oss/src/vector_stores/supabase.ts","../../src/oss/src/storage/SQLiteManager.ts","../../src/oss/src/storage/MemoryHistoryManager.ts","../../src/oss/src/storage/SupabaseHistoryManager.ts","../../src/oss/src/embeddings/google.ts","../../src/oss/src/llms/google.ts","../../src/oss/src/llms/azure.ts","../../src/oss/src/embeddings/azure.ts","../../src/oss/src/llms/langchain.ts","../../src/oss/src/prompts/index.ts","../../src/oss/src/graphs/tools.ts","../../src/oss/src/embeddings/langchain.ts","../../src/oss/src/vector_stores/langchain.ts","../../src/oss/src/utils/factory.ts","../../src/oss/src/storage/DummyHistoryManager.ts","../../src/oss/src/config/defaults.ts","../../src/oss/src/config/manager.ts","../../src/oss/src/memory/graph_memory.ts","../../src/oss/src/utils/bm25.ts","../../src/oss/src/graphs/utils.ts","../../src/oss/src/utils/memory.ts","../../src/oss/src/utils/telemetry.ts"],"sourcesContent":["import { v4 as uuidv4 } from \"uuid\";\nimport { createHash } from \"crypto\";\nimport {\n  MemoryConfig,\n  MemoryConfigSchema,\n  MemoryItem,\n  Message,\n  SearchFilters,\n  SearchResult,\n} from \"../types\";\nimport {\n  EmbedderFactory,\n  LLMFactory,\n  VectorStoreFactory,\n  HistoryManagerFactory,\n} from \"../utils/factory\";\nimport {\n  getFactRetrievalMessages,\n  getUpdateMemoryMessages,\n  parseMessages,\n  removeCodeBlocks,\n} from \"../prompts\";\nimport { DummyHistoryManager } from \"../storage/DummyHistoryManager\";\nimport { Embedder } from \"../embeddings/base\";\nimport { LLM } from \"../llms/base\";\nimport { VectorStore } from \"../vector_stores/base\";\nimport { ConfigManager } from \"../config/manager\";\nimport { MemoryGraph } from \"./graph_memory\";\nimport {\n  AddMemoryOptions,\n  SearchMemoryOptions,\n  DeleteAllMemoryOptions,\n  GetAllMemoryOptions,\n} from \"./memory.types\";\nimport { parse_vision_messages } from \"../utils/memory\";\nimport { HistoryManager } from \"../storage/base\";\nimport { captureClientEvent } from \"../utils/telemetry\";\n\nexport class Memory {\n  private config: MemoryConfig;\n  private customPrompt: string | undefined;\n  private embedder: Embedder;\n  private vectorStore: VectorStore;\n  private llm: LLM;\n  private db: HistoryManager;\n  private collectionName: string | undefined;\n  private apiVersion: string;\n  private graphMemory?: MemoryGraph;\n  private enableGraph: boolean;\n  telemetryId: string;\n\n  constructor(config: Partial<MemoryConfig> = {}) {\n    // Merge and validate config\n    this.config = ConfigManager.mergeConfig(config);\n\n    this.customPrompt = this.config.customPrompt;\n    this.embedder = EmbedderFactory.create(\n      this.config.embedder.provider,\n      this.config.embedder.config,\n    );\n    this.vectorStore = VectorStoreFactory.create(\n      this.config.vectorStore.provider,\n      this.config.vectorStore.config,\n    );\n    this.llm = LLMFactory.create(\n      this.config.llm.provider,\n      this.config.llm.config,\n    );\n    if (this.config.disableHistory) {\n      this.db = new DummyHistoryManager();\n    } else {\n      const defaultConfig = {\n        provider: \"sqlite\",\n        config: {\n          historyDbPath: this.config.historyDbPath || \":memory:\",\n        },\n      };\n\n      this.db =\n        this.config.historyStore && !this.config.disableHistory\n          ? HistoryManagerFactory.create(\n              this.config.historyStore.provider,\n              this.config.historyStore,\n            )\n          : HistoryManagerFactory.create(\"sqlite\", defaultConfig);\n    }\n\n    this.collectionName = this.config.vectorStore.config.collectionName;\n    this.apiVersion = this.config.version || \"v1.0\";\n    this.enableGraph = this.config.enableGraph || false;\n    this.telemetryId = \"anonymous\";\n\n    // Initialize graph memory if configured\n    if (this.enableGraph && this.config.graphStore) {\n      this.graphMemory = new MemoryGraph(this.config);\n    }\n\n    // Initialize telemetry if vector store is initialized\n    this._initializeTelemetry();\n  }\n\n  private async _initializeTelemetry() {\n    try {\n      await this._getTelemetryId();\n\n      // Capture initialization event\n      await captureClientEvent(\"init\", this, {\n        api_version: this.apiVersion,\n        client_type: \"Memory\",\n        collection_name: this.collectionName,\n        enable_graph: this.enableGraph,\n      });\n    } catch (error) {}\n  }\n\n  private async _getTelemetryId() {\n    try {\n      if (\n        !this.telemetryId ||\n        this.telemetryId === \"anonymous\" ||\n        this.telemetryId === \"anonymous-supabase\"\n      ) {\n        this.telemetryId = await this.vectorStore.getUserId();\n      }\n      return this.telemetryId;\n    } catch (error) {\n      this.telemetryId = \"anonymous\";\n      return this.telemetryId;\n    }\n  }\n\n  private async _captureEvent(methodName: string, additionalData = {}) {\n    try {\n      await this._getTelemetryId();\n      await captureClientEvent(methodName, this, {\n        ...additionalData,\n        api_version: this.apiVersion,\n        collection_name: this.collectionName,\n      });\n    } catch (error) {\n      console.error(`Failed to capture ${methodName} event:`, error);\n    }\n  }\n\n  static fromConfig(configDict: Record<string, any>): Memory {\n    try {\n      const config = MemoryConfigSchema.parse(configDict);\n      return new Memory(config);\n    } catch (e) {\n      console.error(\"Configuration validation error:\", e);\n      throw e;\n    }\n  }\n\n  async add(\n    messages: string | Message[],\n    config: AddMemoryOptions,\n  ): Promise<SearchResult> {\n    await this._captureEvent(\"add\", {\n      message_count: Array.isArray(messages) ? messages.length : 1,\n      has_metadata: !!config.metadata,\n      has_filters: !!config.filters,\n      infer: config.infer,\n    });\n    const {\n      userId,\n      agentId,\n      runId,\n      metadata = {},\n      filters = {},\n      infer = true,\n    } = config;\n\n    if (userId) filters.userId = metadata.userId = userId;\n    if (agentId) filters.agentId = metadata.agentId = agentId;\n    if (runId) filters.runId = metadata.runId = runId;\n\n    if (!filters.userId && !filters.agentId && !filters.runId) {\n      throw new Error(\n        \"One of the filters: userId, agentId or runId is required!\",\n      );\n    }\n\n    const parsedMessages = Array.isArray(messages)\n      ? (messages as Message[])\n      : [{ role: \"user\", content: messages }];\n\n    const final_parsedMessages = await parse_vision_messages(parsedMessages);\n\n    // Add to vector store\n    const vectorStoreResult = await this.addToVectorStore(\n      final_parsedMessages,\n      metadata,\n      filters,\n      infer,\n    );\n\n    // Add to graph store if available\n    let graphResult;\n    if (this.graphMemory) {\n      try {\n        graphResult = await this.graphMemory.add(\n          final_parsedMessages.map((m) => m.content).join(\"\\n\"),\n          filters,\n        );\n      } catch (error) {\n        console.error(\"Error adding to graph memory:\", error);\n      }\n    }\n\n    return {\n      results: vectorStoreResult,\n      relations: graphResult?.relations,\n    };\n  }\n\n  private async addToVectorStore(\n    messages: Message[],\n    metadata: Record<string, any>,\n    filters: SearchFilters,\n    infer: boolean,\n  ): Promise<MemoryItem[]> {\n    if (!infer) {\n      const returnedMemories: MemoryItem[] = [];\n      for (const message of messages) {\n        if (message.content === \"system\") {\n          continue;\n        }\n        const memoryId = await this.createMemory(\n          message.content as string,\n          {},\n          metadata,\n        );\n        returnedMemories.push({\n          id: memoryId,\n          memory: message.content as string,\n          metadata: { event: \"ADD\" },\n        });\n      }\n      return returnedMemories;\n    }\n    const parsedMessages = messages.map((m) => m.content).join(\"\\n\");\n\n    const [systemPrompt, userPrompt] = this.customPrompt\n      ? [this.customPrompt, `Input:\\n${parsedMessages}`]\n      : getFactRetrievalMessages(parsedMessages);\n\n    const response = await this.llm.generateResponse(\n      [\n        { role: \"system\", content: systemPrompt },\n        { role: \"user\", content: userPrompt },\n      ],\n      { type: \"json_object\" },\n    );\n\n    const cleanResponse = removeCodeBlocks(response as string);\n    let facts: string[] = [];\n    try {\n      facts = JSON.parse(cleanResponse).facts || [];\n    } catch (e) {\n      console.error(\n        \"Failed to parse facts from LLM response:\",\n        cleanResponse,\n        e,\n      );\n      facts = [];\n    }\n\n    // Get embeddings for new facts\n    const newMessageEmbeddings: Record<string, number[]> = {};\n    const retrievedOldMemory: Array<{ id: string; text: string }> = [];\n\n    // Create embeddings and search for similar memories\n    for (const fact of facts) {\n      const embedding = await this.embedder.embed(fact);\n      newMessageEmbeddings[fact] = embedding;\n\n      const existingMemories = await this.vectorStore.search(\n        embedding,\n        5,\n        filters,\n      );\n      for (const mem of existingMemories) {\n        retrievedOldMemory.push({ id: mem.id, text: mem.payload.data });\n      }\n    }\n\n    // Remove duplicates from old memories\n    const uniqueOldMemories = retrievedOldMemory.filter(\n      (mem, index) =>\n        retrievedOldMemory.findIndex((m) => m.id === mem.id) === index,\n    );\n\n    // Create UUID mapping for handling UUID hallucinations\n    const tempUuidMapping: Record<string, string> = {};\n    uniqueOldMemories.forEach((item, idx) => {\n      tempUuidMapping[String(idx)] = item.id;\n      uniqueOldMemories[idx].id = String(idx);\n    });\n\n    // Get memory update decisions\n    const updatePrompt = getUpdateMemoryMessages(uniqueOldMemories, facts);\n\n    const updateResponse = await this.llm.generateResponse(\n      [{ role: \"user\", content: updatePrompt }],\n      { type: \"json_object\" },\n    );\n\n    const cleanUpdateResponse = removeCodeBlocks(updateResponse as string);\n    let memoryActions: any[] = [];\n    try {\n      memoryActions = JSON.parse(cleanUpdateResponse).memory || [];\n    } catch (e) {\n      console.error(\n        \"Failed to parse memory actions from LLM response:\",\n        cleanUpdateResponse,\n        e,\n      );\n      memoryActions = [];\n    }\n\n    // Process memory actions\n    const results: MemoryItem[] = [];\n    for (const action of memoryActions) {\n      try {\n        switch (action.event) {\n          case \"ADD\": {\n            const memoryId = await this.createMemory(\n              action.text,\n              newMessageEmbeddings,\n              metadata,\n            );\n            results.push({\n              id: memoryId,\n              memory: action.text,\n              metadata: { event: action.event },\n            });\n            break;\n          }\n          case \"UPDATE\": {\n            const realMemoryId = tempUuidMapping[action.id];\n            await this.updateMemory(\n              realMemoryId,\n              action.text,\n              newMessageEmbeddings,\n              metadata,\n            );\n            results.push({\n              id: realMemoryId,\n              memory: action.text,\n              metadata: {\n                event: action.event,\n                previousMemory: action.old_memory,\n              },\n            });\n            break;\n          }\n          case \"DELETE\": {\n            const realMemoryId = tempUuidMapping[action.id];\n            await this.deleteMemory(realMemoryId);\n            results.push({\n              id: realMemoryId,\n              memory: action.text,\n              metadata: { event: action.event },\n            });\n            break;\n          }\n        }\n      } catch (error) {\n        console.error(`Error processing memory action: ${error}`);\n      }\n    }\n\n    return results;\n  }\n\n  async get(memoryId: string): Promise<MemoryItem | null> {\n    const memory = await this.vectorStore.get(memoryId);\n    if (!memory) return null;\n\n    const filters = {\n      ...(memory.payload.userId && { userId: memory.payload.userId }),\n      ...(memory.payload.agentId && { agentId: memory.payload.agentId }),\n      ...(memory.payload.runId && { runId: memory.payload.runId }),\n    };\n\n    const memoryItem: MemoryItem = {\n      id: memory.id,\n      memory: memory.payload.data,\n      hash: memory.payload.hash,\n      createdAt: memory.payload.createdAt,\n      updatedAt: memory.payload.updatedAt,\n      metadata: {},\n    };\n\n    // Add additional metadata\n    const excludedKeys = new Set([\n      \"userId\",\n      \"agentId\",\n      \"runId\",\n      \"hash\",\n      \"data\",\n      \"createdAt\",\n      \"updatedAt\",\n    ]);\n    for (const [key, value] of Object.entries(memory.payload)) {\n      if (!excludedKeys.has(key)) {\n        memoryItem.metadata![key] = value;\n      }\n    }\n\n    return { ...memoryItem, ...filters };\n  }\n\n  async search(\n    query: string,\n    config: SearchMemoryOptions,\n  ): Promise<SearchResult> {\n    await this._captureEvent(\"search\", {\n      query_length: query.length,\n      limit: config.limit,\n      has_filters: !!config.filters,\n    });\n    const { userId, agentId, runId, limit = 100, filters = {} } = config;\n\n    if (userId) filters.userId = userId;\n    if (agentId) filters.agentId = agentId;\n    if (runId) filters.runId = runId;\n\n    if (!filters.userId && !filters.agentId && !filters.runId) {\n      throw new Error(\n        \"One of the filters: userId, agentId or runId is required!\",\n      );\n    }\n\n    // Search vector store\n    const queryEmbedding = await this.embedder.embed(query);\n    const memories = await this.vectorStore.search(\n      queryEmbedding,\n      limit,\n      filters,\n    );\n\n    // Search graph store if available\n    let graphResults;\n    if (this.graphMemory) {\n      try {\n        graphResults = await this.graphMemory.search(query, filters);\n      } catch (error) {\n        console.error(\"Error searching graph memory:\", error);\n      }\n    }\n\n    const excludedKeys = new Set([\n      \"userId\",\n      \"agentId\",\n      \"runId\",\n      \"hash\",\n      \"data\",\n      \"createdAt\",\n      \"updatedAt\",\n    ]);\n    const results = memories.map((mem) => ({\n      id: mem.id,\n      memory: mem.payload.data,\n      hash: mem.payload.hash,\n      createdAt: mem.payload.createdAt,\n      updatedAt: mem.payload.updatedAt,\n      score: mem.score,\n      metadata: Object.entries(mem.payload)\n        .filter(([key]) => !excludedKeys.has(key))\n        .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),\n      ...(mem.payload.userId && { userId: mem.payload.userId }),\n      ...(mem.payload.agentId && { agentId: mem.payload.agentId }),\n      ...(mem.payload.runId && { runId: mem.payload.runId }),\n    }));\n\n    return {\n      results,\n      relations: graphResults,\n    };\n  }\n\n  async update(memoryId: string, data: string): Promise<{ message: string }> {\n    await this._captureEvent(\"update\", { memory_id: memoryId });\n    const embedding = await this.embedder.embed(data);\n    await this.updateMemory(memoryId, data, { [data]: embedding });\n    return { message: \"Memory updated successfully!\" };\n  }\n\n  async delete(memoryId: string): Promise<{ message: string }> {\n    await this._captureEvent(\"delete\", { memory_id: memoryId });\n    await this.deleteMemory(memoryId);\n    return { message: \"Memory deleted successfully!\" };\n  }\n\n  async deleteAll(\n    config: DeleteAllMemoryOptions,\n  ): Promise<{ message: string }> {\n    await this._captureEvent(\"delete_all\", {\n      has_user_id: !!config.userId,\n      has_agent_id: !!config.agentId,\n      has_run_id: !!config.runId,\n    });\n    const { userId, agentId, runId } = config;\n\n    const filters: SearchFilters = {};\n    if (userId) filters.userId = userId;\n    if (agentId) filters.agentId = agentId;\n    if (runId) filters.runId = runId;\n\n    if (!Object.keys(filters).length) {\n      throw new Error(\n        \"At least one filter is required to delete all memories. If you want to delete all memories, use the `reset()` method.\",\n      );\n    }\n\n    const [memories] = await this.vectorStore.list(filters);\n    for (const memory of memories) {\n      await this.deleteMemory(memory.id);\n    }\n\n    return { message: \"Memories deleted successfully!\" };\n  }\n\n  async history(memoryId: string): Promise<any[]> {\n    return this.db.getHistory(memoryId);\n  }\n\n  async reset(): Promise<void> {\n    await this._captureEvent(\"reset\");\n    await this.db.reset();\n\n    // Check provider before attempting deleteCol\n    if (this.config.vectorStore.provider.toLowerCase() !== \"langchain\") {\n      try {\n        await this.vectorStore.deleteCol();\n      } catch (e) {\n        console.error(\n          `Failed to delete collection for provider '${this.config.vectorStore.provider}':`,\n          e,\n        );\n        // Decide if you want to re-throw or just log\n      }\n    } else {\n      console.warn(\n        \"Memory.reset(): Skipping vector store collection deletion as 'langchain' provider is used. Underlying Langchain vector store data is not cleared by this operation.\",\n      );\n    }\n\n    if (this.graphMemory) {\n      await this.graphMemory.deleteAll({ userId: \"default\" }); // Assuming this is okay, or needs similar check?\n    }\n\n    // Re-initialize factories/clients based on the original config\n    this.embedder = EmbedderFactory.create(\n      this.config.embedder.provider,\n      this.config.embedder.config,\n    );\n    // Re-create vector store instance - crucial for Langchain to reset wrapper state if needed\n    this.vectorStore = VectorStoreFactory.create(\n      this.config.vectorStore.provider,\n      this.config.vectorStore.config, // This will pass the original client instance back\n    );\n    this.llm = LLMFactory.create(\n      this.config.llm.provider,\n      this.config.llm.config,\n    );\n    // Re-init DB if needed (though db.reset() likely handles its state)\n    // Re-init Graph if needed\n\n    // Re-initialize telemetry\n    this._initializeTelemetry();\n  }\n\n  async getAll(config: GetAllMemoryOptions): Promise<SearchResult> {\n    await this._captureEvent(\"get_all\", {\n      limit: config.limit,\n      has_user_id: !!config.userId,\n      has_agent_id: !!config.agentId,\n      has_run_id: !!config.runId,\n    });\n    const { userId, agentId, runId, limit = 100 } = config;\n\n    const filters: SearchFilters = {};\n    if (userId) filters.userId = userId;\n    if (agentId) filters.agentId = agentId;\n    if (runId) filters.runId = runId;\n\n    const [memories] = await this.vectorStore.list(filters, limit);\n\n    const excludedKeys = new Set([\n      \"userId\",\n      \"agentId\",\n      \"runId\",\n      \"hash\",\n      \"data\",\n      \"createdAt\",\n      \"updatedAt\",\n    ]);\n    const results = memories.map((mem) => ({\n      id: mem.id,\n      memory: mem.payload.data,\n      hash: mem.payload.hash,\n      createdAt: mem.payload.createdAt,\n      updatedAt: mem.payload.updatedAt,\n      metadata: Object.entries(mem.payload)\n        .filter(([key]) => !excludedKeys.has(key))\n        .reduce((acc, [key, value]) => ({ ...acc, [key]: value }), {}),\n      ...(mem.payload.userId && { userId: mem.payload.userId }),\n      ...(mem.payload.agentId && { agentId: mem.payload.agentId }),\n      ...(mem.payload.runId && { runId: mem.payload.runId }),\n    }));\n\n    return { results };\n  }\n\n  private async createMemory(\n    data: string,\n    existingEmbeddings: Record<string, number[]>,\n    metadata: Record<string, any>,\n  ): Promise<string> {\n    const memoryId = uuidv4();\n    const embedding =\n      existingEmbeddings[data] || (await this.embedder.embed(data));\n\n    const memoryMetadata = {\n      ...metadata,\n      data,\n      hash: createHash(\"md5\").update(data).digest(\"hex\"),\n      createdAt: new Date().toISOString(),\n    };\n\n    await this.vectorStore.insert([embedding], [memoryId], [memoryMetadata]);\n    await this.db.addHistory(\n      memoryId,\n      null,\n      data,\n      \"ADD\",\n      memoryMetadata.createdAt,\n    );\n\n    return memoryId;\n  }\n\n  private async updateMemory(\n    memoryId: string,\n    data: string,\n    existingEmbeddings: Record<string, number[]>,\n    metadata: Record<string, any> = {},\n  ): Promise<string> {\n    const existingMemory = await this.vectorStore.get(memoryId);\n    if (!existingMemory) {\n      throw new Error(`Memory with ID ${memoryId} not found`);\n    }\n\n    const prevValue = existingMemory.payload.data;\n    const embedding =\n      existingEmbeddings[data] || (await this.embedder.embed(data));\n\n    const newMetadata = {\n      ...metadata,\n      data,\n      hash: createHash(\"md5\").update(data).digest(\"hex\"),\n      createdAt: existingMemory.payload.createdAt,\n      updatedAt: new Date().toISOString(),\n      ...(existingMemory.payload.userId && {\n        userId: existingMemory.payload.userId,\n      }),\n      ...(existingMemory.payload.agentId && {\n        agentId: existingMemory.payload.agentId,\n      }),\n      ...(existingMemory.payload.runId && {\n        runId: existingMemory.payload.runId,\n      }),\n    };\n\n    await this.vectorStore.update(memoryId, embedding, newMetadata);\n    await this.db.addHistory(\n      memoryId,\n      prevValue,\n      data,\n      \"UPDATE\",\n      newMetadata.createdAt,\n      newMetadata.updatedAt,\n    );\n\n    return memoryId;\n  }\n\n  private async deleteMemory(memoryId: string): Promise<string> {\n    const existingMemory = await this.vectorStore.get(memoryId);\n    if (!existingMemory) {\n      throw new Error(`Memory with ID ${memoryId} not found`);\n    }\n\n    const prevValue = existingMemory.payload.data;\n    await this.vectorStore.delete(memoryId);\n    await this.db.addHistory(\n      memoryId,\n      prevValue,\n      null,\n      \"DELETE\",\n      undefined,\n      undefined,\n      1,\n    );\n\n    return memoryId;\n  }\n}\n","import { z } from \"zod\";\n\nexport interface MultiModalMessages {\n  type: \"image_url\";\n  image_url: {\n    url: string;\n  };\n}\n\nexport interface Message {\n  role: string;\n  content: string | MultiModalMessages;\n}\n\nexport interface EmbeddingConfig {\n  apiKey?: string;\n  model?: string | any;\n  url?: string;\n  modelProperties?: Record<string, any>;\n}\n\nexport interface VectorStoreConfig {\n  collectionName?: string;\n  dimension?: number;\n  client?: any;\n  instance?: any;\n  [key: string]: any;\n}\n\nexport interface HistoryStoreConfig {\n  provider: string;\n  config: {\n    historyDbPath?: string;\n    supabaseUrl?: string;\n    supabaseKey?: string;\n    tableName?: string;\n  };\n}\n\nexport interface LLMConfig {\n  provider?: string;\n  config?: Record<string, any>;\n  apiKey?: string;\n  model?: string | any;\n  modelProperties?: Record<string, any>;\n}\n\nexport interface Neo4jConfig {\n  url: string;\n  username: string;\n  password: string;\n}\n\nexport interface GraphStoreConfig {\n  provider: string;\n  config: Neo4jConfig;\n  llm?: LLMConfig;\n  customPrompt?: string;\n}\n\nexport interface MemoryConfig {\n  version?: string;\n  embedder: {\n    provider: string;\n    config: EmbeddingConfig;\n  };\n  vectorStore: {\n    provider: string;\n    config: VectorStoreConfig;\n  };\n  llm: {\n    provider: string;\n    config: LLMConfig;\n  };\n  historyStore?: HistoryStoreConfig;\n  disableHistory?: boolean;\n  historyDbPath?: string;\n  customPrompt?: string;\n  graphStore?: GraphStoreConfig;\n  enableGraph?: boolean;\n}\n\nexport interface MemoryItem {\n  id: string;\n  memory: string;\n  hash?: string;\n  createdAt?: string;\n  updatedAt?: string;\n  score?: number;\n  metadata?: Record<string, any>;\n}\n\nexport interface SearchFilters {\n  userId?: string;\n  agentId?: string;\n  runId?: string;\n  [key: string]: any;\n}\n\nexport interface SearchResult {\n  results: MemoryItem[];\n  relations?: any[];\n}\n\nexport interface VectorStoreResult {\n  id: string;\n  payload: Record<string, any>;\n  score?: number;\n}\n\nexport const MemoryConfigSchema = z.object({\n  version: z.string().optional(),\n  embedder: z.object({\n    provider: z.string(),\n    config: z.object({\n      modelProperties: z.record(z.string(), z.any()).optional(),\n      apiKey: z.string().optional(),\n      model: z.union([z.string(), z.any()]).optional(),\n    }),\n  }),\n  vectorStore: z.object({\n    provider: z.string(),\n    config: z\n      .object({\n        collectionName: z.string().optional(),\n        dimension: z.number().optional(),\n        client: z.any().optional(),\n      })\n      .passthrough(),\n  }),\n  llm: z.object({\n    provider: z.string(),\n    config: z.object({\n      apiKey: z.string().optional(),\n      model: z.union([z.string(), z.any()]).optional(),\n      modelProperties: z.record(z.string(), z.any()).optional(),\n    }),\n  }),\n  historyDbPath: z.string().optional(),\n  customPrompt: z.string().optional(),\n  enableGraph: z.boolean().optional(),\n  graphStore: z\n    .object({\n      provider: z.string(),\n      config: z.object({\n        url: z.string(),\n        username: z.string(),\n        password: z.string(),\n      }),\n      llm: z\n        .object({\n          provider: z.string(),\n          config: z.record(z.string(), z.any()),\n        })\n        .optional(),\n      customPrompt: z.string().optional(),\n    })\n    .optional(),\n  historyStore: z\n    .object({\n      provider: z.string(),\n      config: z.record(z.string(), z.any()),\n    })\n    .optional(),\n  disableHistory: z.boolean().optional(),\n});\n","import OpenAI from \"openai\";\nimport { Embedder } from \"./base\";\nimport { EmbeddingConfig } from \"../types\";\n\nexport class OpenAIEmbedder implements Embedder {\n  private openai: OpenAI;\n  private model: string;\n\n  constructor(config: EmbeddingConfig) {\n    this.openai = new OpenAI({ apiKey: config.apiKey });\n    this.model = config.model || \"text-embedding-3-small\";\n  }\n\n  async embed(text: string): Promise<number[]> {\n    const response = await this.openai.embeddings.create({\n      model: this.model,\n      input: text,\n    });\n    return response.data[0].embedding;\n  }\n\n  async embedBatch(texts: string[]): Promise<number[][]> {\n    const response = await this.openai.embeddings.create({\n      model: this.model,\n      input: texts,\n    });\n    return response.data.map((item) => item.embedding);\n  }\n}\n","import { Ollama } from \"ollama\";\nimport { Embedder } from \"./base\";\nimport { EmbeddingConfig } from \"../types\";\nimport { logger } from \"../utils/logger\";\n\nexport class OllamaEmbedder implements Embedder {\n  private ollama: Ollama;\n  private model: string;\n  // Using this variable to avoid calling the Ollama server multiple times\n  private initialized: boolean = false;\n\n  constructor(config: EmbeddingConfig) {\n    this.ollama = new Ollama({\n      host: config.url || \"http://localhost:11434\",\n    });\n    this.model = config.model || \"nomic-embed-text:latest\";\n    this.ensureModelExists().catch((err) => {\n      logger.error(`Error ensuring model exists: ${err}`);\n    });\n  }\n\n  async embed(text: string): Promise<number[]> {\n    try {\n      await this.ensureModelExists();\n    } catch (err) {\n      logger.error(`Error ensuring model exists: ${err}`);\n    }\n    const response = await this.ollama.embeddings({\n      model: this.model,\n      prompt: text,\n    });\n    return response.embedding;\n  }\n\n  async embedBatch(texts: string[]): Promise<number[][]> {\n    const response = await Promise.all(texts.map((text) => this.embed(text)));\n    return response;\n  }\n\n  private async ensureModelExists(): Promise<boolean> {\n    if (this.initialized) {\n      return true;\n    }\n    const local_models = await this.ollama.list();\n    if (!local_models.models.find((m: any) => m.name === this.model)) {\n      logger.info(`Pulling model ${this.model}...`);\n      await this.ollama.pull({ model: this.model });\n    }\n    this.initialized = true;\n    return true;\n  }\n}\n","export interface Logger {\n  info: (message: string) => void;\n  error: (message: string) => void;\n  debug: (message: string) => void;\n  warn: (message: string) => void;\n}\n\nexport const logger: Logger = {\n  info: (message: string) => console.log(`[INFO] ${message}`),\n  error: (message: string) => console.error(`[ERROR] ${message}`),\n  debug: (message: string) => console.debug(`[DEBUG] ${message}`),\n  warn: (message: string) => console.warn(`[WARN] ${message}`),\n};\n","import OpenAI from \"openai\";\nimport { LLM, LLMResponse } from \"./base\";\nimport { LLMConfig, Message } from \"../types\";\n\nexport class OpenAILLM implements LLM {\n  private openai: OpenAI;\n  private model: string;\n\n  constructor(config: LLMConfig) {\n    this.openai = new OpenAI({ apiKey: config.apiKey });\n    this.model = config.model || \"gpt-4o-mini\";\n  }\n\n  async generateResponse(\n    messages: Message[],\n    responseFormat?: { type: string },\n    tools?: any[],\n  ): Promise<string | LLMResponse> {\n    const completion = await this.openai.chat.completions.create({\n      messages: messages.map((msg) => {\n        const role = msg.role as \"system\" | \"user\" | \"assistant\";\n        return {\n          role,\n          content:\n            typeof msg.content === \"string\"\n              ? msg.content\n              : JSON.stringify(msg.content),\n        };\n      }),\n      model: this.model,\n      response_format: responseFormat as { type: \"text\" | \"json_object\" },\n      ...(tools && { tools, tool_choice: \"auto\" }),\n    });\n\n    const response = completion.choices[0].message;\n\n    if (response.tool_calls) {\n      return {\n        content: response.content || \"\",\n        role: response.role,\n        toolCalls: response.tool_calls.map((call) => ({\n          name: call.function.name,\n          arguments: call.function.arguments,\n        })),\n      };\n    }\n\n    return response.content || \"\";\n  }\n\n  async generateChat(messages: Message[]): Promise<LLMResponse> {\n    const completion = await this.openai.chat.completions.create({\n      messages: messages.map((msg) => {\n        const role = msg.role as \"system\" | \"user\" | \"assistant\";\n        return {\n          role,\n          content:\n            typeof msg.content === \"string\"\n              ? msg.content\n              : JSON.stringify(msg.content),\n        };\n      }),\n      model: this.model,\n    });\n    const response = completion.choices[0].message;\n    return {\n      content: response.content || \"\",\n      role: response.role,\n    };\n  }\n}\n","import OpenAI from \"openai\";\nimport { LLM, LLMResponse } from \"./base\";\nimport { LLMConfig, Message } from \"../types\";\n\nexport class OpenAIStructuredLLM implements LLM {\n  private openai: OpenAI;\n  private model: string;\n\n  constructor(config: LLMConfig) {\n    this.openai = new OpenAI({ apiKey: config.apiKey });\n    this.model = config.model || \"gpt-4-turbo-preview\";\n  }\n\n  async generateResponse(\n    messages: Message[],\n    responseFormat?: { type: string } | null,\n    tools?: any[],\n  ): Promise<string | LLMResponse> {\n    const completion = await this.openai.chat.completions.create({\n      messages: messages.map((msg) => ({\n        role: msg.role as \"system\" | \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : JSON.stringify(msg.content),\n      })),\n      model: this.model,\n      ...(tools\n        ? {\n            tools: tools.map((tool) => ({\n              type: \"function\",\n              function: {\n                name: tool.function.name,\n                description: tool.function.description,\n                parameters: tool.function.parameters,\n              },\n            })),\n            tool_choice: \"auto\" as const,\n          }\n        : responseFormat\n          ? {\n              response_format: {\n                type: responseFormat.type as \"text\" | \"json_object\",\n              },\n            }\n          : {}),\n    });\n\n    const response = completion.choices[0].message;\n\n    if (response.tool_calls) {\n      return {\n        content: response.content || \"\",\n        role: response.role,\n        toolCalls: response.tool_calls.map((call) => ({\n          name: call.function.name,\n          arguments: call.function.arguments,\n        })),\n      };\n    }\n\n    return response.content || \"\";\n  }\n\n  async generateChat(messages: Message[]): Promise<LLMResponse> {\n    const completion = await this.openai.chat.completions.create({\n      messages: messages.map((msg) => ({\n        role: msg.role as \"system\" | \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : JSON.stringify(msg.content),\n      })),\n      model: this.model,\n    });\n    const response = completion.choices[0].message;\n    return {\n      content: response.content || \"\",\n      role: response.role,\n    };\n  }\n}\n","import Anthropic from \"@anthropic-ai/sdk\";\nimport { LLM, LLMResponse } from \"./base\";\nimport { LLMConfig, Message } from \"../types\";\n\nexport class AnthropicLLM implements LLM {\n  private client: Anthropic;\n  private model: string;\n\n  constructor(config: LLMConfig) {\n    const apiKey = config.apiKey || process.env.ANTHROPIC_API_KEY;\n    if (!apiKey) {\n      throw new Error(\"Anthropic API key is required\");\n    }\n    this.client = new Anthropic({ apiKey });\n    this.model = config.model || \"claude-3-sonnet-20240229\";\n  }\n\n  async generateResponse(\n    messages: Message[],\n    responseFormat?: { type: string },\n  ): Promise<string> {\n    // Extract system message if present\n    const systemMessage = messages.find((msg) => msg.role === \"system\");\n    const otherMessages = messages.filter((msg) => msg.role !== \"system\");\n\n    const response = await this.client.messages.create({\n      model: this.model,\n      messages: otherMessages.map((msg) => ({\n        role: msg.role as \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : msg.content.image_url.url,\n      })),\n      system:\n        typeof systemMessage?.content === \"string\"\n          ? systemMessage.content\n          : undefined,\n      max_tokens: 4096,\n    });\n\n    return response.content[0].text;\n  }\n\n  async generateChat(messages: Message[]): Promise<LLMResponse> {\n    const response = await this.generateResponse(messages);\n    return {\n      content: response,\n      role: \"assistant\",\n    };\n  }\n}\n","import { Groq } from \"groq-sdk\";\nimport { LLM, LLMResponse } from \"./base\";\nimport { LLMConfig, Message } from \"../types\";\n\nexport class GroqLLM implements LLM {\n  private client: Groq;\n  private model: string;\n\n  constructor(config: LLMConfig) {\n    const apiKey = config.apiKey || process.env.GROQ_API_KEY;\n    if (!apiKey) {\n      throw new Error(\"Groq API key is required\");\n    }\n    this.client = new Groq({ apiKey });\n    this.model = config.model || \"llama3-70b-8192\";\n  }\n\n  async generateResponse(\n    messages: Message[],\n    responseFormat?: { type: string },\n  ): Promise<string> {\n    const response = await this.client.chat.completions.create({\n      model: this.model,\n      messages: messages.map((msg) => ({\n        role: msg.role as \"system\" | \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : JSON.stringify(msg.content),\n      })),\n      response_format: responseFormat as { type: \"text\" | \"json_object\" },\n    });\n\n    return response.choices[0].message.content || \"\";\n  }\n\n  async generateChat(messages: Message[]): Promise<LLMResponse> {\n    const response = await this.client.chat.completions.create({\n      model: this.model,\n      messages: messages.map((msg) => ({\n        role: msg.role as \"system\" | \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : JSON.stringify(msg.content),\n      })),\n    });\n\n    const message = response.choices[0].message;\n    return {\n      content: message.content || \"\",\n      role: message.role,\n    };\n  }\n}\n","import { Mistral } from \"@mistralai/mistralai\";\nimport { LLM, LLMResponse } from \"./base\";\nimport { LLMConfig, Message } from \"../types\";\n\nexport class MistralLLM implements LLM {\n  private client: Mistral;\n  private model: string;\n\n  constructor(config: LLMConfig) {\n    if (!config.apiKey) {\n      throw new Error(\"Mistral API key is required\");\n    }\n    this.client = new Mistral({\n      apiKey: config.apiKey,\n    });\n    this.model = config.model || \"mistral-tiny-latest\";\n  }\n\n  // Helper function to convert content to string\n  private contentToString(content: any): string {\n    if (typeof content === \"string\") {\n      return content;\n    }\n    if (Array.isArray(content)) {\n      // Handle ContentChunk array - extract text content\n      return content\n        .map((chunk) => {\n          if (chunk.type === \"text\") {\n            return chunk.text;\n          } else {\n            return JSON.stringify(chunk);\n          }\n        })\n        .join(\"\");\n    }\n    return String(content || \"\");\n  }\n\n  async generateResponse(\n    messages: Message[],\n    responseFormat?: { type: string },\n    tools?: any[],\n  ): Promise<string | LLMResponse> {\n    const response = await this.client.chat.complete({\n      model: this.model,\n      messages: messages.map((msg) => ({\n        role: msg.role as \"system\" | \"user\" | \"assistant\",\n        content:\n          typeof msg.content === \"string\"\n            ? msg.content\n            : JSON.stringify(msg.content),\n      })),\n      ...(tools && { tools }),\n      ...(responseFormat && { response_format: responseFormat }),\n    });\n\n    if (!response || !response.choices || response.choices.length === 0) {\n      return \"\";\n    }\n\n    const message = response.choices[0].message;\n\n    if (!message) {\n      return \"\";\n    }\n\n    if (message.toolCalls && message.toolCalls.length > 0) {\n      return {\n        content: this.contentToString(message.content),\n        role: message.role || \"assistant\",\n        toolCalls: message.toolCalls.map((call) => ({\n          name: call.function.name,\n          arguments:\n            typeof call.function.arguments === \"string\"\n              ? call.function.arguments\n              : JSON.stringify(call.function.arguments),\n        })),\n      };\n    }\n\n    return this.contentToString(message.content);\n  }\n\n  async generateChat(messages: Message[]): Promise<LLMResponse> {\n    const formattedMessages = messages.map((msg) => ({\n      role: msg.role as \"system\" | \"user\" | \"assistant\",\n      content:\n        typeof msg.content === \"string\"\n          ? msg.content\n          : JSON.stringify(msg.content),\n    }));\n\n    const response = await this.client.chat.complete({\n      model: this.model,\n      messages: formattedMessages,\n    });\n\n    if (!response || !response.choices || response.choices.length === 0) {\n      return {\n        content: \"\",\n        role: \"assistant\",\n      };\n    }\n\n    const message = response.choices[0].message;\n\n    return {\n      content: this.contentToString(message.content),\n      role: message.role || \"assistant\",\n    };\n  }\n}\n","import { VectorStore } from \"./base\";\nimport { SearchFilters, VectorStoreConfig, VectorStoreResult } from \"../types\";\nimport sqlite3 from \"sqlite3\";\nimport path from \"path\";\n\ninterface MemoryVector {\n  id: string;\n  vector: number[];\n  payload: Record<string, any>;\n}\n\nexport class MemoryVectorStore implements VectorStore {\n  private db: sqlite3.Database;\n  private dimension: number;\n  private dbPath: string;\n\n  constructor(config: VectorStoreConfig) {\n    this.dimension = config.dimension || 1536; // Default OpenAI dimension\n    this.dbPath = path.join(process.cwd(), \"vector_store.db\");\n    if (config.dbPath) {\n      this.dbPath = config.dbPath;\n    }\n    this.db = new sqlite3.Database(this.dbPath);\n    this.init().catch(console.error);\n  }\n\n  private async init() {\n    await this.run(`\n      CREATE TABLE IF NOT EXISTS vectors (\n        id TEXT PRIMARY KEY,\n        vector BLOB NOT NULL,\n        payload TEXT NOT NULL\n      )\n    `);\n\n    await this.run(`\n      CREATE TABLE IF NOT EXISTS memory_migrations (\n        id INTEGER PRIMARY KEY AUTOINCREMENT,\n        user_id TEXT NOT NULL UNIQUE\n      )\n    `);\n  }\n\n  private async run(sql: string, params: any[] = []): Promise<void> {\n    return new Promise((resolve, reject) => {\n      this.db.run(sql, params, (err) => {\n        if (err) reject(err);\n        else resolve();\n      });\n    });\n  }\n\n  private async all(sql: string, params: any[] = []): Promise<any[]> {\n    return new Promise((resolve, reject) => {\n      this.db.all(sql, params, (err, rows) => {\n        if (err) reject(err);\n        else resolve(rows);\n      });\n    });\n  }\n\n  private async getOne(sql: string, params: any[] = []): Promise<any> {\n    return new Promise((resolve, reject) => {\n      this.db.get(sql, params, (err, row) => {\n        if (err) reject(err);\n        else resolve(row);\n      });\n    });\n  }\n\n  private cosineSimilarity(a: number[], b: number[]): number {\n    let dotProduct = 0;\n    let normA = 0;\n    let normB = 0;\n    for (let i = 0; i < a.length; i++) {\n      dotProduct += a[i] * b[i];\n      normA += a[i] * a[i];\n      normB += b[i] * b[i];\n    }\n    return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));\n  }\n\n  private filterVector(vector: MemoryVector, filters?: SearchFilters): boolean {\n    if (!filters) return true;\n    return Object.entries(filters).every(\n      ([key, value]) => vector.payload[key] === value,\n    );\n  }\n\n  async insert(\n    vectors: number[][],\n    ids: string[],\n    payloads: Record<string, any>[],\n  ): Promise<void> {\n    for (let i = 0; i < vectors.length; i++) {\n      if (vectors[i].length !== this.dimension) {\n        throw new Error(\n          `Vector dimension mismatch. Expected ${this.dimension}, got ${vectors[i].length}`,\n        );\n      }\n      const vectorBuffer = Buffer.from(new Float32Array(vectors[i]).buffer);\n      await this.run(\n        `INSERT OR REPLACE INTO vectors (id, vector, payload) VALUES (?, ?, ?)`,\n        [ids[i], vectorBuffer, JSON.stringify(payloads[i])],\n      );\n    }\n  }\n\n  async search(\n    query: number[],\n    limit: number = 10,\n    filters?: SearchFilters,\n  ): Promise<VectorStoreResult[]> {\n    if (query.length !== this.dimension) {\n      throw new Error(\n        `Query dimension mismatch. Expected ${this.dimension}, got ${query.length}`,\n      );\n    }\n\n    const rows = await this.all(`SELECT * FROM vectors`);\n    const results: VectorStoreResult[] = [];\n\n    for (const row of rows) {\n      const vector = new Float32Array(row.vector.buffer);\n      const payload = JSON.parse(row.payload);\n      const memoryVector: MemoryVector = {\n        id: row.id,\n        vector: Array.from(vector),\n        payload,\n      };\n\n      if (this.filterVector(memoryVector, filters)) {\n        const score = this.cosineSimilarity(query, Array.from(vector));\n        results.push({\n          id: memoryVector.id,\n          payload: memoryVector.payload,\n          score,\n        });\n      }\n    }\n\n    results.sort((a, b) => (b.score || 0) - (a.score || 0));\n    return results.slice(0, limit);\n  }\n\n  async get(vectorId: string): Promise<VectorStoreResult | null> {\n    const row = await this.getOne(`SELECT * FROM vectors WHERE id = ?`, [\n      vectorId,\n    ]);\n    if (!row) return null;\n\n    const payload = JSON.parse(row.payload);\n    return {\n      id: row.id,\n      payload,\n    };\n  }\n\n  async update(\n    vectorId: string,\n    vector: number[],\n    payload: Record<string, any>,\n  ): Promise<void> {\n    if (vector.length !== this.dimension) {\n      throw new Error(\n        `Vector dimension mismatch. Expected ${this.dimension}, got ${vector.length}`,\n      );\n    }\n    const vectorBuffer = Buffer.from(new Float32Array(vector).buffer);\n    await this.run(`UPDATE vectors SET vector = ?, payload = ? WHERE id = ?`, [\n      vectorBuffer,\n      JSON.stringify(payload),\n      vectorId,\n    ]);\n  }\n\n  async delete(vectorId: string): Promise<void> {\n    await this.run(`DELETE FROM vectors WHERE id = ?`, [vectorId]);\n  }\n\n  async deleteCol(): Promise<void> {\n    await this.run(`DROP TABLE IF EXISTS vectors`);\n    await this.init();\n  }\n\n  async list(\n    filters?: SearchFilters,\n    limit: number = 100,\n  ): Promise<[VectorStoreResult[], number]> {\n    const rows = await this.all(`SELECT * FROM vectors`);\n    const results: VectorStoreResult[] = [];\n\n    for (const row of rows) {\n      const payload = JSON.parse(row.payload);\n      const memoryVector: MemoryVector = {\n        id: row.id,\n        vector: Array.from(new Float32Array(row.vector.buffer)),\n        payload,\n      };\n\n      if (this.filterVector(memoryVector, filters)) {\n        results.push({\n          id: memoryVector.id,\n          payload: memoryVector.payload,\n        });\n      }\n    }\n\n    return [results.slice(0, limit), results.length];\n  }\n\n  async getUserId(): Promise<string> {\n    const row = await this.getOne(\n      `SELECT user_id FROM memory_migrations LIMIT 1`,\n    );\n    if (row) {\n      return row.user_id;\n    }\n\n    // Generate a random user_id if none exists\n    const randomUserId =\n      Math.random().toString(36).substring(2, 15) +\n      Math.random().toString(36).substring(2, 15);\n    await this.run(`INSERT INTO memory_migrations (user_id) VALUES (?)`, [\n      randomUserId,\n    ]);\n    return randomUserId;\n  }\n\n  async setUserId(userId: string): Promise<void> {\n    await this.run(`DELETE FROM memory_migrations`);\n    await this.run(`INSERT INTO memory_migrations (user_id) VALUES (?)`, [\n      userId,\n    ]);\n  }\n\n  async initialize(): Promise<void> {\n    await this.init();\n  }\n}\n","import { QdrantClient } from \"@qdrant/js-client-rest\";\nimport { VectorStore } from \"./base\";\nimport { SearchFilters, VectorStoreConfig, VectorStoreResult } from \"../types\";\nimport * as fs from \"fs\";\n\ninterface QdrantConfig extends VectorStoreConfig {\n  client?: QdrantClient;\n  host?: string;\n  port?: number;\n  path?: string;\n  url?: string;\n  apiKey?: string;\n  onDisk?: boolean;\n  collectionName: string;\n  embeddingModelDims: number;\n  dimension?: number;\n}\n\ninterface QdrantFilter {\n  must?: QdrantCondition[];\n  must_not?: QdrantCondition[];\n  should?: QdrantCondition[];\n}\n\ninterface QdrantCondition {\n  key: string;\n  match?: { value: any };\n  range?: { gte?: number; gt?: number; lte?: number; lt?: number };\n}\n\nexport class Qdrant implements VectorStore {\n  private client: QdrantClient;\n  private readonly collectionName: string;\n  private dimension: number;\n\n  constructor(config: QdrantConfig) {\n    if (config.client) {\n      this.client = config.client;\n    } else {\n      const params: Record<string, any> = {};\n      if (config.apiKey) {\n        params.apiKey = config.apiKey;\n      }\n      if (config.url) {\n        params.url = config.url;\n      }\n      if (config.host && config.port) {\n        params.host = config.host;\n        params.port = config.port;\n      }\n      if (!Object.keys(params).length) {\n        params.path = config.path;\n        if (!config.onDisk && config.path) {\n          if (\n            fs.existsSync(config.path) &&\n            fs.statSync(config.path).isDirectory()\n          ) {\n            fs.rmSync(config.path, { recursive: true });\n          }\n        }\n      }\n\n      this.client = new QdrantClient(params);\n    }\n\n    this.collectionName = config.collectionName;\n    this.dimension = config.dimension || 1536; // Default OpenAI dimension\n    this.initialize().catch(console.error);\n  }\n\n  private createFilter(filters?: SearchFilters): QdrantFilter | undefined {\n    if (!filters) return undefined;\n\n    const conditions: QdrantCondition[] = [];\n    for (const [key, value] of Object.entries(filters)) {\n      if (\n        typeof value === \"object\" &&\n        value !== null &&\n        \"gte\" in value &&\n        \"lte\" in value\n      ) {\n        conditions.push({\n          key,\n          range: {\n            gte: value.gte,\n            lte: value.lte,\n          },\n        });\n      } else {\n        conditions.push({\n          key,\n          match: {\n            value,\n