@daitanjs/intelligence/dist/index.cjs.map

A modular library for advanced LLM orchestration, stateful RAG, multi-step agentic workflows, and tool use, built on LangChain.js.

{
  "version": 3,
  "sources": ["../src/index.js", "../src/orchestration/daitanOrchestrator.js", "../src/intelligence/core/llmOrchestrator.js", "../src/intelligence/core/promptBuilder.js", "../src/intelligence/core/expertModels.js", "../src/intelligence/core/llmPricing.js", "../src/intelligence/core/tokenUtils.js", "../src/services/llmService.js", "../src/intelligence/index.js", "../src/intelligence/core/embeddingGenerator.js", "../src/intelligence/core/toolFactory.js", "../src/intelligence/rag/index.js", "../src/intelligence/rag/retrieval.js", "../src/intelligence/rag/vectorStoreFactory.js", "../src/intelligence/rag/chromaVectorStoreAdapter.js", "../src/intelligence/rag/chromaClient.js", "../src/intelligence/rag/memoryVectorStoreAdapter.js", "../src/intelligence/rag/chatMemory.js", "../src/intelligence/rag/chat.js", "../src/intelligence/rag/embed.js", "../src/intelligence/rag/documentLoader.js", "../src/intelligence/metadata/parse.js", "../src/intelligence/metadata/index.js", "../src/intelligence/rag/printStats.js", "../src/intelligence/search/specializedSearch.js", "../src/intelligence/tools/tool-registries.js", "../src/intelligence/tools/calculatorTool.js", "../src/intelligence/tools/wikipediaSearchTool.js", "../src/intelligence/tools/cliTool.js", "../src/intelligence/tools/webSearchTool.js", "../src/intelligence/tools/userManagementTool.js", "../src/intelligence/tools/csvQueryTool.js", "../src/intelligence/tools/createPaymentIntentTool.js", "../src/intelligence/tools/youtubeSearchTool.js", "../src/intelligence/tools/processYoutubeAudioTool.js", "../src/intelligence/tools/imageGenerationTool.js", "../src/intelligence/tools/gmailTools.js", "../src/intelligence/tools/calendarTool.js", "../src/intelligence/tools/googleDriveTools.js", "../src/intelligence/tools/ragTool.js", "../src/intelligence/tools/baseTool.js", "../src/intelligence/agents/agentRunner.js", "../src/intelligence/workflows/graphRunner.js", "../src/intelligence/workflows/planAndExecuteAgentGraph.js", "../src/intelligence/workflows/langGraphManager.js", "../src/intelligence/workflows/reactWithReflectionAgentGraph.js", "../src/intelligence/agents/agentExecutor.js", "../src/intelligence/agents/prompts/generalAgentPrompt.js", "../src/memory/inMemoryChatHistoryStore.js", "../src/intelligence/agents/baseAgent.js", "../src/intelligence/agents/chat/index.js", "../src/intelligence/agents/chat/choreographerAgent.js", "../src/intelligence/agents/chat/coachAgent.js", "../src/intelligence/agents/chat/openingPhraseAgent.js", "../src/intelligence/agents/chat/participantAgent.js", "../src/intelligence/workflows/index.js", "../src/intelligence/workflows/presets/deepResearchAgent.js", "../src/intelligence/workflows/presets/searchAndUnderstand.js", "../src/intelligence/workflows/presets/automatedResearchAgent.js", "../src/intelligence/core/ollamaUtils.js", "../src/caching/cacheManager.js", "../src/language/index.js"],
  "sourcesContent": ["// src/index.js\n/**\n * @file Main public entry point for the @daitanjs/intelligence package.\n * @module @daitanjs/intelligence\n */\nimport { getLogger } from '@daitanjs/development';\n\nconst mainIndexLogger = getLogger('daitan-intelligence-main-index');\n\nmainIndexLogger.debug(\n  'Initializing DaitanJS Intelligence main package exports...'\n);\n\n// --- Core Abstractions & Services ---\nexport { DaitanOrchestrator } from './orchestration/daitanOrchestrator.js';\nexport { LLMService } from './services/llmService.js';\n\n// --- Configuration Management ---\nexport {\n  getConfigManager,\n  initializeConfigManager,\n  DaitanConfigManagerClass,\n} from '@daitanjs/config';\n\n// --- Core Intelligence & Utilities ---\nexport {\n  generateIntelligence,\n  generateEmbedding,\n  askWithRetrieval,\n  createRagChatInstance,\n  loadAndEmbedFile,\n  printStoreStats,\n  getVectorStore,\n  vectorStoreCollectionExists,\n  checkChromaConnection,\n  runToolCallingAgent,\n  runGraphAgent,\n  runDeepResearchAgent,\n  runAutomatedResearchWorkflow,\n  searchAndUnderstand,\n  // --- New Export for Specialized Search ---\n  searchNews,\n  searchGeneralWeb,\n  searchAcademic,\n  BaseAgent,\n  ChoreographerAgent,\n  CoachAgent,\n  OpeningPhraseAgent,\n  ParticipantAgent,\n  createDaitanTool,\n  getDefaultTools,\n  getDaitanPlatformTools,\n  BaseTool,\n  DaitanLangGraph,\n  createGraphRunner,\n  estimateLlmCost,\n  countTokens,\n  countTokensForMessages,\n  checkOllamaStatus,\n} from './intelligence/index.js';\n\n// --- Caching ---\nexport {\n  getCache,\n  generateCacheKey,\n  clearCache,\n} from './caching/cacheManager.js';\n\n// --- Memory Management ---\nexport { InMemoryChatMessageHistoryStore } from './memory/inMemoryChatHistoryStore.js';\n\n// --- Language Services ---\nexport { translate } from './language/index.js';\n\nmainIndexLogger.info(\n  'DaitanJS Intelligence package main exports configured and ready.'\n);\n", "// intelligence/src/orchestration/daitanOrchestrator.js\n/**\n * @file High-level orchestrator facade for the DaitanJS Intelligence library.\n * @module @daitanjs/orchestration/daitanOrchestrator\n *\n * @description\n * The DaitanOrchestrator provides a simplified, unified interface to the most common\n * and powerful functionalities of the @daitanjs/intelligence package. It is designed\n * to be the primary entry point for developers, abstracting away the underlying\n * complexity of service instantiation, graph compilation, and state management.\n * It now includes direct access to the most powerful research agent.\n */\nimport { getLogger } from '@daitanjs/development';\nimport { getConfigManager } from '@daitanjs/config';\nimport { LLMService } from '../services/llmService.js';\nimport {\n  askWithRetrieval,\n  loadAndEmbedFile,\n  printStoreStats,\n  runToolCallingAgent,\n  runDeepResearchAgent, // NEW: Direct import of the best research agent\n  getDefaultTools,\n  createGraphRunner,\n  createPlanAndExecuteAgentGraph,\n  createReActAgentGraph,\n  runAutomatedResearchWorkflow,\n  InMemoryChatMessageHistoryStore,\n} from '../intelligence/index.js'; // Use the main barrel file\nimport { HumanMessage } from '@langchain/core/messages';\n\nconst logger = getLogger('daitan-orchestrator');\n\n/**\n * @typedef {import('../services/llmService.js').LLMServiceConfig} LLMServiceConfig\n * @typedef {import('../intelligence/rag/interfaces.js').AskWithRetrievalOptions} AskWithRetrievalOptions\n * @typedef {import('../intelligence/rag/interfaces.js').LoadAndEmbedOptions} LoadAndEmbedOptions\n * @typedef {import('../intelligence/agents/agentExecutor.js').RunDaitanAgentParams} RunToolCallingAgentParams\n * @typedef {import('../intelligence/workflows/planAndExecuteAgentGraph.js').PlanAndExecuteAgentState} PlanAndExecuteAgentState\n * @typedef {import('../intelligence/workflows/reactWithReflectionAgentGraph.js').ReActAgentState} ReActAgentState\n * @typedef {import('../intelligence/workflows/presets/automatedResearchAgent.js').ResearchState} AutomatedResearchState\n * @typedef {import('../intelligence/workflows/graphRunner.js').CreateGraphRunnerOptions} CreateGraphRunnerOptions\n */\n\nexport class DaitanOrchestrator {\n  /**\n   * @param {Object} [orchestratorConfig={}]\n   * @param {LLMServiceConfig} [orchestratorConfig.llmServiceConfig] - Configuration for the internal LLMService.\n   * @param {LLMService} [orchestratorConfig.llmServiceInstance] - An existing LLMService instance.\n   * @param {boolean} [orchestratorConfig.verboseGlobal] - Global verbosity for orchestrator operations.\n   */\n  constructor(orchestratorConfig = {}) {\n    const configManager = getConfigManager();\n    this.verbose =\n      orchestratorConfig.verboseGlobal ??\n      (configManager.get('DEBUG_ORCHESTRATOR', false) ||\n        configManager.get('DEBUG_INTELLIGENCE', false));\n\n    this.llmService =\n      orchestratorConfig.llmServiceInstance instanceof LLMService\n        ? orchestratorConfig.llmServiceInstance\n        : new LLMService({\n            ...orchestratorConfig.llmServiceConfig,\n            verbose: this.verbose,\n          });\n\n    this.compiledGraphs = {};\n    this.defaultHistoryStore = new InMemoryChatMessageHistoryStore();\n\n    logger.info('DaitanOrchestrator fully initialized.');\n  }\n\n  /**\n   * Direct call to the underlying LLM service.\n   * @param {import('../services/llmService.js').GenerateIntelligenceParams} params\n   * @returns {Promise<import('../intelligence/core/llmOrchestrator.js').GenerateIntelligenceResult<any>>}\n   */\n  async llmCall(params) {\n    if (this.verbose) {\n      logger.info('Orchestrator: Initiating direct LLM call.', {\n        summary: params.metadata?.summary,\n      });\n    }\n    // The LLMService's generate method handles merging defaults correctly.\n    return this.llmService.generate(params);\n  }\n\n  /**\n   * Performs a RAG query with advanced options.\n   * @param {string} query - The user's query.\n   * @param {AskWithRetrievalOptions} [ragOptions] - Options for askWithRetrieval.\n   * @returns {Promise<import('../intelligence/rag/retrieval.js').RetrievalResult>}\n   */\n  async ragQuery(query, ragOptions = {}) {\n    if (this.verbose)\n      logger.info(\n        `Orchestrator: Initiating RAG query for: \"${query.substring(\n          0,\n          50\n        )}...\"`,\n        { collection: ragOptions.collectionName }\n      );\n    return askWithRetrieval(query, {\n      localVerbose: this.verbose,\n      ...ragOptions,\n    });\n  }\n\n  /**\n   * Loads and embeds a file into the vector store.\n   * @param {string} filePath - Path to the file.\n   * @param {Object} [customMetadata={}] - Custom metadata to add.\n   * @param {LoadAndEmbedOptions} [embedOptions={}] - Options for loadAndEmbedFile.\n   * @returns {Promise<object>} An object indicating success and embedding stats.\n   */\n  async embedFile(filePath, customMetadata = {}, embedOptions = {}) {\n    if (this.verbose)\n      logger.info(\n        `Orchestrator: Initiating file embedding for: \"${filePath}\"`,\n        { collection: embedOptions.collectionName }\n      );\n    return loadAndEmbedFile({\n      filePath,\n      customMetadata,\n      options: { localVerbose: this.verbose, ...embedOptions },\n    });\n  }\n\n  /**\n   * Prints statistics for a RAG collection.\n   * @param {string} [collectionName] - Name of the collection.\n   * @returns {Promise<void>}\n   */\n  async getRagStats(collectionName) {\n    if (this.verbose)\n      logger.info(\n        `Orchestrator: Requesting RAG stats for collection: \"${\n          collectionName || 'default'\n        }\"`\n      );\n    return printStoreStats({ collectionName, localVerbose: this.verbose });\n  }\n\n  /**\n   * Runs the most advanced, multi-step research agent to answer a complex query.\n   * @param {string} query - The complex question or research topic.\n   * @param {import('../intelligence/workflows/presets/deepResearchAgent.js').runDeepResearchAgent} options - Options for the deep research agent, including `thinkingLevel`, `collectionName`, `onProgress`, and `chatHistory`.\n   * @returns {Promise<{finalAnswer: string, sources: string[], plan: any[]}>} The comprehensive answer and its sources.\n   */\n  async research(query, options) {\n    if (this.verbose) {\n      logger.info(\n        `Orchestrator: Initiating deep research for topic: \"${query}\"`\n      );\n    }\n    // Directly call the powerful, imported research agent\n    return runDeepResearchAgent(query, options);\n  }\n\n  /**\n   * Runs a general-purpose tool-using agent.\n   * @param {RunToolCallingAgentParams} params - The parameters for the agent run.\n   * @returns {Promise<Object>}\n   */\n  async runToolAgent(params) {\n    if (this.verbose)\n      logger.info(`Orchestrator: Running tool-calling Agent.`, {\n        input: params.input.substring(0, 50) + '...',\n        sessionId: params.sessionId,\n      });\n\n    return runToolCallingAgent({\n      historyStore: this.defaultHistoryStore,\n      verbose: this.verbose,\n      ...params,\n    });\n  }\n\n  // The more granular agent runners below are kept for advanced use cases or backward compatibility.\n  // The top-level `research` method is now the recommended entry point for research tasks.\n\n  /**\n   * Runs the Plan-and-Execute agent graph.\n   * @param {string} query - The user's original query.\n   * @param {object} [options={}] - Options for the run.\n   * @returns {Promise<PlanAndExecuteAgentState>}\n   */\n  async runPlanAndExecuteAgent(query, options = {}) {\n    const { tools, initialState = {}, sessionId, onStateUpdate } = options;\n    const effectiveSessionId = sessionId || `plan-exec-${Date.now()}`;\n    if (this.verbose)\n      logger.info(\n        `Orchestrator: Running Plan-and-Execute Agent for query: \"${query.substring(\n          0,\n          50\n        )}...\"`,\n        { sessionId: effectiveSessionId }\n      );\n\n    if (!this.compiledGraphs.planAndExecute) {\n      this.compiledGraphs.planAndExecute = await createPlanAndExecuteAgentGraph(\n        this.llmService,\n        tools || getDefaultTools()\n      );\n      logger.info('Compiled Plan-and-Execute graph for the first time.');\n    }\n\n    const runner = createGraphRunner(this.compiledGraphs.planAndExecute, {\n      verbose: this.verbose,\n      onStateUpdate,\n    });\n    const effectiveTools = tools || getDefaultTools();\n\n    const finalInitialState = {\n      originalQuery: query,\n      inputMessage: new HumanMessage(query),\n      llmServiceInstance: this.llmService,\n      toolsMap: effectiveTools.reduce(\n        (map, tool) => ({ ...map, [tool.name]: tool }),\n        {}\n      ),\n      verbose: this.verbose,\n      ...initialState,\n    };\n    return runner(finalInitialState, {\n      configurable: { thread_id: effectiveSessionId },\n    });\n  }\n}\n", "// intelligence/src/intelligence/core/llmOrchestrator.js\n/**\n * @file The final, stable, and feature-complete LLM orchestrator.\n * @module @daitanjs/intelligence/core/llmOrchestrator\n *\n * @description\n * This file contains the fully restored logic for the intelligence orchestrator.\n * It is built on a proven stable core and safely integrates multi-provider support,\n * expert profile resolution, configuration, and robust error handling.\n * For architectural guidance, see the README.md in this directory.\n */\nimport { ChatOpenAI } from '@langchain/openai';\nimport { ChatAnthropic } from '@langchain/anthropic';\nimport { ChatGroq } from '@langchain/groq';\nimport {\n  StringOutputParser,\n  JsonOutputParser,\n} from '@langchain/core/output_parsers';\nimport { getConfigManager } from '@daitanjs/config';\nimport { DaitanApiError, DaitanConfigurationError } from '@daitanjs/error';\nimport { buildLlmMessages } from './promptBuilder.js';\nimport { getExpertModelDefinition } from './expertModels.js';\nimport { estimateLlmCost } from './llmPricing.js';\nimport { countTokensForMessages, countTokens } from './tokenUtils.js';\nimport { getLogger } from '@daitanjs/development';\n\nconst logger = getLogger('daitan-llm-orchestrator');\n\n/**\n * Extracts a JSON object from a string, especially if it's wrapped in markdown code fences.\n * @param {string} text - The input string which might contain a JSON object.\n * @returns {string} - The extracted JSON string or the original text if no JSON is found.\n */\nfunction extractJsonFromString(text) {\n  if (typeof text !== 'string') return text;\n\n  // This regex finds JSON within ```json ... ``` or just the first valid { ... } or [ ... ]\n  const jsonRegex = /```(?:json)?\\s*([\\s\\S]+?)\\s*```|({[\\s\\S]*}|\\[[\\s\\S]*\\])/m;\n  const match = text.match(jsonRegex);\n\n  // If a match is found, return the captured group (either from the code block or the raw object/array).\n  // Otherwise, return the original text to let the parser try.\n  return match ? match[1] || match[2] || text : text;\n}\n\nexport const generateIntelligence = async ({\n  prompt = {},\n  config = {},\n  callbacks,\n  metadata = {}, // Add metadata to destructuring\n}) => {\n  const {\n    llm: llmConfig = {},\n    response: responseConfig = {},\n    ...otherConfigs\n  } = config;\n\n  if (!prompt.user && (!prompt.shots || prompt.shots.length === 0)) {\n    throw new DaitanConfigurationError(\n      'A `prompt.user` message or messages in `prompt.shots` are required.'\n    );\n  }\n\n  let llm;\n  let providerName;\n  let modelName;\n  let temperature;\n\n  try {\n    const configManager = getConfigManager();\n    const rawTarget =\n      llmConfig.target ||\n      configManager.get('DEFAULT_EXPERT_PROFILE') ||\n      configManager.get('LLM_PROVIDER') ||\n      'FAST_TASKER';\n\n    const expertDef = getExpertModelDefinition(rawTarget);\n    if (expertDef) {\n      providerName = expertDef.provider.toLowerCase();\n      modelName = expertDef.model;\n      temperature = expertDef.temperature;\n    } else {\n      const [p, m] = rawTarget.split('|');\n      providerName = p ? p.toLowerCase() : 'openai';\n      modelName = m;\n    }\n\n    const commonConfig = {\n      temperature: temperature ?? llmConfig.temperature ?? 0.7,\n      maxRetries: config.retry?.maxAttempts ?? 2,\n      timeout: llmConfig.requestTimeout,\n      modelName: modelName,\n    };\n\n    switch (providerName) {\n      case 'openai': {\n        const apiKey = llmConfig.apiKey || configManager.get('OPENAI_API_KEY');\n        if (!apiKey)\n          throw new DaitanConfigurationError('OPENAI_API_KEY not found.');\n        llm = new ChatOpenAI({ ...commonConfig, apiKey });\n        break;\n      }\n      case 'anthropic': {\n        const apiKey =\n          llmConfig.apiKey || configManager.get('ANTHROPIC_API_KEY');\n        if (!apiKey)\n          throw new DaitanConfigurationError('ANTHROPIC_API_KEY not found.');\n        llm = new ChatAnthropic({ ...commonConfig, apiKey });\n        break;\n      }\n      case 'groq': {\n        const apiKey = llmConfig.apiKey || configManager.get('GROQ_API_KEY');\n        if (!apiKey)\n          throw new DaitanConfigurationError('GROQ_API_KEY not found.');\n        llm = new ChatGroq({ ...commonConfig, apiKey });\n        break;\n      }\n      default:\n        throw new DaitanConfigurationError(\n          `Unsupported provider in orchestrator: '${providerName}'`\n        );\n    }\n\n    // --- DEFINITIVE FIX: Use the new `buildLlmMessages` which correctly parses the structured prompt object ---\n    const messages = buildLlmMessages(prompt);\n    // --- END OF FIX ---\n\n    const result = await llm.invoke(messages, { callbacks });\n    const rawContent = result.content ?? '';\n\n    let contentToParse = rawContent;\n    if (responseConfig.format === 'json') {\n      contentToParse = extractJsonFromString(rawContent);\n    }\n\n    const parser =\n      responseConfig.format === 'json'\n        ? new JsonOutputParser()\n        : new StringOutputParser();\n\n    const finalResponse = await parser.parse(contentToParse);\n\n    const usageMetadata = result.usage_metadata ?? {};\n    const usage = {\n      inputTokens:\n        usageMetadata.inputTokens ??\n        (await countTokensForMessages(messages, modelName, providerName)),\n      outputTokens:\n        usageMetadata.outputTokens ??\n        (await countTokens(\n          typeof finalResponse === 'string'\n            ? finalResponse\n            : JSON.stringify(finalResponse ?? ''),\n          modelName,\n          providerName\n        )),\n    };\n    usage.totalTokens = (usage.inputTokens || 0) + (usage.outputTokens || 0);\n    const cost = estimateLlmCost(\n      providerName,\n      modelName,\n      usage.inputTokens,\n      usage.outputTokens\n    );\n\n    return {\n      response: finalResponse,\n      usage: { ...usage, ...cost },\n      rawResponse: rawContent,\n    };\n  } catch (error) {\n    logger.error(\n      `LLM Interaction Failed. Summary: ${\n        metadata.summary || 'N/A'\n      }. Provider: ${providerName}, Model: ${modelName}. Error: ${\n        error.message\n      }`,\n      { errorStack: error.stack }\n    );\n\n    throw new DaitanApiError(\n      `An unrecoverable error occurred during the LLM interaction with provider '${\n        providerName || 'unknown'\n      }'.`,\n      providerName || 'unknown',\n      error?.status,\n      { model: modelName, summary: metadata.summary },\n      error\n    );\n  }\n};\n", "// intelligence/src/intelligence/core/promptBuilder.js\n/**\n * @file Contains helpers for building and constructing prompt message arrays for LLMs.\n * @module @daitanjs/intelligence/core/promptBuilder\n *\n * @description\n * This module centralizes the logic for creating the list of messages that will be sent\n * to a large language model. It handles the assembly of system messages, few-shot examples\n * (`shots`), and the final user prompt into a structured format compatible with\n * LangChain's chat models.\n */\n\nimport {\n  HumanMessage,\n  SystemMessage,\n  AIMessage,\n  BaseMessage,\n} from '@langchain/core/messages';\nimport { getLogger } from '@daitanjs/development';\n\nconst promptBuilderLogger = getLogger('daitan-prompt-builder');\n\n/**\n * Converts an array of DaitanJS-style message objects into LangChain `BaseMessage` instances.\n * @private\n * @param {Array<Object>} messages - Array of message objects (e.g., `{role: 'user', content: '...'}`).\n * @returns {BaseMessage[]} An array of LangChain message instances.\n */\nfunction convertToLangChainMessages(messages) {\n  if (!Array.isArray(messages)) {\n    return [];\n  }\n  return messages.reduce((acc, msg) => {\n    if (msg instanceof BaseMessage) {\n      acc.push(msg);\n    } else if (\n      typeof msg === 'object' &&\n      msg !== null &&\n      typeof msg.role === 'string' &&\n      (typeof msg.content === 'string' ||\n        Array.isArray(msg.content) ||\n        typeof msg.content === 'object') // Allow object for JSON few-shot\n    ) {\n      const role = msg.role.toLowerCase();\n      try {\n        if (role === 'system')\n          acc.push(new SystemMessage({ content: msg.content }));\n        else if (role === 'user' || role === 'human')\n          acc.push(new HumanMessage({ content: msg.content }));\n        else if (role === 'assistant' || role === 'ai')\n          acc.push(new AIMessage({ content: msg.content }));\n        else {\n          promptBuilderLogger.warn(\n            `Unknown message role \"${msg.role}\". Treating as human.`\n          );\n          acc.push(new HumanMessage({ content: msg.content }));\n        }\n      } catch (e) {\n        promptBuilderLogger.error(\n          'Failed to create LangChain message from object.',\n          { messageObject: msg, error: e.message }\n        );\n        // ignore malformed message\n      }\n    }\n    return acc;\n  }, []);\n}\n\n/**\n * Builds the final array of message objects to be sent to the LLM from a structured prompt object.\n *\n * @param {Object} prompt - The structured prompt object.\n * @param {Object} [prompt.system] - An object containing parts of the system message (e.g., persona, task).\n * @param {string | object} [prompt.user] - The final user query.\n * @param {Array} [prompt.shots=[]] - Few-shot examples in `{role, content}` format.\n * @returns {BaseMessage[]} An array of LangChain `BaseMessage` objects ready for an LLM.\n */\nexport const buildLlmMessages = (prompt = {}) => {\n  const systemConfig = prompt.system || {};\n  const userContent = prompt.user;\n  const fewShotExamples = prompt.shots || [];\n\n  // This order defines the structure of the system prompt.\n  const systemInstructionParts = [\n    systemConfig.persona,\n    systemConfig.whoYouAre, // Legacy support\n    systemConfig.task,\n    systemConfig.whatYouDo, // Legacy support\n    systemConfig.guidelines,\n    systemConfig.vitals,\n    systemConfig.scoring,\n    systemConfig.writingStyle,\n    systemConfig.outputFormat,\n    systemConfig.outputFormatDescription, // Legacy support\n    systemConfig.promptingTips,\n    systemConfig.reiteration,\n  ];\n\n  const systemInstructionContent = systemInstructionParts\n    .filter(Boolean)\n    .join('\\n\\n');\n\n  let messages = [];\n  if (systemInstructionContent) {\n    messages.push({ role: 'system', content: systemInstructionContent });\n  }\n\n  if (Array.isArray(fewShotExamples) && fewShotExamples.length > 0) {\n    messages.push(\n      ...fewShotExamples.filter(\n        (s) =>\n          s &&\n          typeof s.role === 'string' &&\n          (typeof s.content === 'string' ||\n            Array.isArray(s.content) ||\n            typeof s.content === 'object')\n      )\n    );\n  }\n\n  if (\n    userContent &&\n    (typeof userContent === 'string' || typeof userContent === 'object')\n  ) {\n    messages.push({ role: 'user', content: userContent });\n  }\n\n  return convertToLangChainMessages(messages);\n};\n", "// intelligence/src/intelligence/core/expertModels.js\nimport { getEnvVariable } from '@daitanjs/development';\n\nexport const DEFAULT_EXPERT_PROFILE_NAME = getEnvVariable(\n  'DEFAULT_EXPERT_PROFILE',\n  'FAST_TASKER'\n);\n\nconst getExpertConfig = (envVarKey, defaultValue) => {\n  const combinedValue = getEnvVariable(envVarKey, defaultValue);\n  const parts = combinedValue.split('|');\n  const provider = parts[0]?.trim();\n  const model = parts[1]?.trim();\n  if (!provider || !model) {\n    const [defaultProvider, defaultModel] = defaultValue.split('|');\n    return { provider: defaultProvider.trim(), model: defaultModel.trim() };\n  }\n  return { provider, model };\n};\n\nexport const EXPERT_MODELS = {\n  MASTER_COMMUNICATOR: {\n    ...getExpertConfig('LLM_EXPERT_MASTER_COMMUNICATOR', 'openai|gpt-4o-mini'),\n    description: 'Expert in clear, concise, and engaging communication.',\n    temperature: 0.7,\n  },\n  CREATIVE_WRITER: {\n    ...getExpertConfig('LLM_EXPERT_CREATIVE_WRITER', 'openai|gpt-4-turbo'),\n    description: 'Expert in creative writing, storytelling, and brainstorming.',\n    temperature: 0.9,\n  },\n  FAST_TASKER: {\n    ...getExpertConfig('LLM_EXPERT_FAST_TASKER', 'openai|gpt-4o-mini'),\n    description: 'Optimized for speed on less complex tasks.',\n    temperature: 0.5,\n  },\n  LOCAL_DEFAULT: {\n    ...getExpertConfig('LLM_EXPERT_LOCAL_DEFAULT', 'ollama|llama3:instruct'),\n    description: 'A general-purpose model running locally via Ollama.',\n    temperature: 0.7,\n  },\n  MASTER_CODER: {\n    ...getExpertConfig(\n      'LLM_EXPERT_MASTER_CODER',\n      'anthropic|claude-3-opus-20240229'\n    ),\n    description: 'Expert in code generation, debugging, and explanation.',\n    temperature: 0.3,\n  },\n  CODING_STUDENT: {\n    ...getExpertConfig('LLM_EXPERT_CODING_STUDENT', 'openai|gpt-4o-mini'),\n    description: 'Capable, cost-effective coding assistant for simpler tasks.',\n    temperature: 0.5,\n  },\n  SENTIMENT_WIZARD: {\n    ...getExpertConfig('LLM_EXPERT_SENTIMENT_WIZARD', 'openai|gpt-3.5-turbo'),\n    description:\n      'Specialized in sentiment analysis and understanding nuanced text.',\n    temperature: 0.2,\n  },\n  TRANSLATION_MULTILINGUAL: {\n    ...getExpertConfig(\n      'LLM_EXPERT_TRANSLATION_MULTILINGUAL',\n      'openai|gpt-4o-mini'\n    ),\n    description: 'Expert in multilingual translation.',\n    temperature: 0.1,\n  },\n  DATA_ANALYSIS_EXPERT: {\n    ...getExpertConfig('LLM_EXPERT_DATA_ANALYSIS', 'openai|gpt-4-turbo'),\n    description: 'Expert in interpreting data and generating insights.',\n    temperature: 0.4,\n  },\n  RESEARCH_ASSISTANT: {\n    ...getExpertConfig('LLM_EXPERT_RESEARCH_ASSISTANT', 'openai|gpt-4-turbo'),\n    description:\n      'Specialized in synthesizing information and research queries.',\n    temperature: 0.5,\n  },\n};\n\nexport const getExpertModelDefinition = (expertName) => {\n  if (typeof expertName !== 'string' || !expertName.trim()) return undefined;\n  return EXPERT_MODELS[expertName.toUpperCase()];\n};\n\nexport const getDefaultExpertProfile = () => {\n  return getExpertModelDefinition(DEFAULT_EXPERT_PROFILE_NAME);\n};\n", "// intelligence/src/intelligence/core/llmPricing.js\n// This is the full, original code for this file.\n\nimport { getLogger } from '@daitanjs/development';\n\nconst logger = getLogger('llm-pricing');\n\nexport const PROVIDER_MODEL_PRICING = {\n  openai: {\n    'gpt-4o': { inputCostPer1MTokens: 5.0, outputCostPer1MTokens: 15.0 },\n    'gpt-4o-mini': { inputCostPer1MTokens: 0.15, outputCostPer1MTokens: 0.6 },\n    'gpt-4-turbo': { inputCostPer1MTokens: 10.0, outputCostPer1MTokens: 30.0 },\n    'gpt-4': { inputCostPer1MTokens: 30.0, outputCostPer1MTokens: 60.0 },\n    'gpt-3.5-turbo': { inputCostPer1MTokens: 0.5, outputCostPer1MTokens: 1.5 },\n    'text-embedding-3-large': {\n      inputCostPer1MTokens: 0.13,\n      outputCostPer1MTokens: 0.0,\n    },\n    'text-embedding-3-small': {\n      inputCostPer1MTokens: 0.02,\n      outputCostPer1MTokens: 0.0,\n    },\n    'text-embedding-ada-002': {\n      inputCostPer1MTokens: 0.1,\n      outputCostPer1MTokens: 0.0,\n    },\n  },\n  anthropic: {\n    'claude-3-opus-20240229': {\n      inputCostPer1MTokens: 15.0,\n      outputCostPer1MTokens: 75.0,\n    },\n    'claude-3-sonnet-20240229': {\n      inputCostPer1MTokens: 3.0,\n      outputCostPer1MTokens: 15.0,\n    },\n    'claude-3-haiku-20240307': {\n      inputCostPer1MTokens: 0.25,\n      outputCostPer1MTokens: 1.25,\n    },\n  },\n  groq: {\n    'llama3-8b-8192': {\n      inputCostPer1MTokens: 0.05,\n      outputCostPer1MTokens: 0.1,\n    },\n    'llama3-70b-8192': {\n      inputCostPer1MTokens: 0.59,\n      outputCostPer1MTokens: 0.79,\n    },\n    'mixtral-8x7b-32768': {\n      inputCostPer1MTokens: 0.27,\n      outputCostPer1MTokens: 0.27,\n    },\n  },\n  ollama: {\n    'llama3:instruct': {\n      inputCostPer1MTokens: 0,\n      outputCostPer1MTokens: 0,\n      details: 'Local model, no cost.',\n    },\n    'nomic-embed-text': {\n      inputCostPer1MTokens: 0,\n      outputCostPer1MTokens: 0,\n      details: 'Local model, no cost.',\n    },\n  },\n};\n\nexport const estimateLlmCost = (\n  provider,\n  model,\n  inputTokens = 0,\n  outputTokens = 0\n) => {\n  const providerKey = provider?.toLowerCase();\n  const modelKey = model?.toLowerCase();\n  const result = {\n    estimatedCostUSD: null,\n    currency: 'USD',\n    details: 'No pricing information available.',\n  };\n\n  if (!providerKey || !modelKey) {\n    logger.debug('Provider or model key missing for cost estimation.');\n    return result;\n  }\n\n  const providerPricing = PROVIDER_MODEL_PRICING[providerKey];\n  if (!providerPricing) {\n    result.details = `No pricing for provider '${providerKey}'.`;\n    return result;\n  }\n\n  // Find a matching model, allowing for variants like 'gpt-4-turbo-2024-04-09' to match 'gpt-4-turbo'\n  const baseModelKey = Object.keys(providerPricing).find((key) =>\n    modelKey.startsWith(key)\n  );\n\n  if (!baseModelKey) {\n    result.details = `No pricing for model '${modelKey}' under provider '${providerKey}'.`;\n    return result;\n  }\n\n  const modelPricing = providerPricing[baseModelKey];\n\n  if (\n    modelPricing.inputCostPer1MTokens === 0 &&\n    modelPricing.outputCostPer1MTokens === 0\n  ) {\n    result.estimatedCostUSD = 0;\n    result.details = modelPricing.details || 'Model is free or local.';\n    return result;\n  }\n\n  const inputCost =\n    (inputTokens / 1_000_000) * (modelPricing.inputCostPer1MTokens || 0);\n  const outputCost =\n    (outputTokens / 1_000_000) * (modelPricing.outputCostPer1MTokens || 0);\n\n  result.estimatedCostUSD = inputCost + outputCost;\n  result.details = `Cost calculated for ${providerKey}/${baseModelKey}.`;\n\n  return result;\n};\n", "// intelligence/src/intelligence/core/tokenUtils.js\n// This is the full, original code for this file.\n\nimport { get_encoding } from 'tiktoken';\nimport { getLogger } from '@daitanjs/development';\n\nconst logger = getLogger('token-utils');\nconst DEFAULT_CHAR_TO_TOKEN_RATIO = 4;\nconst tiktokenCache = new Map();\n\nconst getTiktokenForModel = (modelNameInput) => {\n  const modelName = String(modelNameInput || '');\n  if (tiktokenCache.has(modelName)) {\n    return tiktokenCache.get(modelName);\n  }\n\n  let encodingName;\n  if (\n    modelName.startsWith('gpt-4') ||\n    modelName.startsWith('gpt-3.5-turbo') ||\n    modelName.startsWith('text-embedding-3') ||\n    modelName.startsWith('gpt-4o')\n  ) {\n    encodingName = 'cl100k_base';\n  } else if (modelName.includes('text-embedding-ada-002')) {\n    encodingName = 'p50k_base';\n  } else {\n    logger.debug(\n      `No specific tiktoken encoding for model \"${modelName}\". Defaulting to cl100k_base.`\n    );\n    encodingName = 'cl100k_base';\n  }\n\n  try {\n    const encoding = get_encoding(encodingName);\n    tiktokenCache.set(modelName, encoding);\n    return encoding;\n  } catch (error) {\n    logger.warn(\n      `Could not initialize tiktoken for model \"${modelName}\" (encoding: ${encodingName}): ${error.message}.`\n    );\n    return null;\n  }\n};\n\nexport const countTokens = (text, modelName, providerName = 'openai') => {\n  if (typeof text !== 'string' || text === '') return 0;\n\n  const openaiCompatibleProviders = [\n    'openai',\n    'groq',\n    'openrouter',\n    'anthropic',\n  ];\n  if (openaiCompatibleProviders.includes(providerName?.toLowerCase() || '')) {\n    const tiktokenInstance = getTiktokenForModel(modelName);\n    if (tiktokenInstance) {\n      try {\n        return tiktokenInstance.encode(text).length;\n      } catch (error) {\n        logger.warn(\n          `Tiktoken encoding failed for model \"${modelName}\". Falling back to char count.`\n        );\n      }\n    }\n  }\n\n  const estimatedTokens = Math.ceil(text.length / DEFAULT_CHAR_TO_TOKEN_RATIO);\n  logger.debug(\n    `Using char-based token approximation for model \"${modelName}\" (provider: ${providerName}). Chars: ${text.length}, Approx Tokens: ${estimatedTokens}`\n  );\n  return estimatedTokens;\n};\n\nexport const countTokensForMessages = (\n  messages,\n  modelName,\n  providerName = 'openai'\n) => {\n  if (!Array.isArray(messages) || messages.length === 0) return 0;\n\n  const tiktokenInstance = getTiktokenForModel(modelName);\n  if (!tiktokenInstance) {\n    let totalChars = 0;\n    messages.forEach((msg) => {\n      if (typeof msg.content === 'string') {\n        totalChars += msg.content.length;\n      }\n    });\n    return (\n      Math.ceil(totalChars / DEFAULT_CHAR_TO_TOKEN_RATIO) + messages.length * 2\n    );\n  }\n\n  let tokensPerMessage = 3;\n  let tokensPerName = 1;\n\n  let numTokens = 0;\n  messages.forEach((message) => {\n    numTokens += tokensPerMessage;\n    for (const key in message) {\n      // Note: LangChain message objects have content as a direct property.\n      if (key === 'content' && typeof message.content === 'string') {\n        numTokens += tiktokenInstance.encode(message.content).length;\n      } else if (key === 'name' && message[key]) {\n        numTokens += tokensPerName;\n      }\n    }\n  });\n\n  numTokens += 3; // every reply is primed with <|start|>assistant<|message|>\n  return numTokens;\n};\n", "// intelligence/src/services/llmService.js\n/**\n * @file Provides a service class for simplified and consistent interaction with LLMs.\n * @module @daitanjs/intelligence/services/llmService\n */\nimport { generateIntelligence } from '../intelligence/core/llmOrchestrator.js';\nimport { getLogger } from '@daitanjs/development';\nimport { getConfigManager } from '@daitanjs/config';\nimport { DaitanInvalidInputError } from '@daitanjs/error';\n\nconst logger = getLogger('daitan-llm-service');\n\n/**\n * @typedef {import('../intelligence/core/llmOrchestrator.js').LLMUsageInfo} LLMUsageInfo\n * @typedef {import('../intelligence/core/llmOrchestrator.js').LLMCallbacks} LLMCallbacks\n * @typedef {import('../intelligence/core/llmOrchestrator.js').GenerateIntelligenceParams} GenerateIntelligenceParams\n */\n\n/**\n * @typedef {Object} LLMServiceConfig\n * @property {string} [target] - Default LLM target, as an expert profile name (e.g., 'FAST_TASKER') or a 'provider|model' string.\n * @property {string} [apiKey] - Default API key (overrides configManager).\n * @property {string} [baseURL] - Default base URL (overrides configManager).\n * @property {number} [temperature] - Default temperature.\n * @property {number} [maxTokens] - Default max_tokens.\n * @property {boolean} [verbose] - Default verbosity for LLM calls.\n * @property {boolean} [trace] - Default LangSmith tracing enablement.\n * @property {boolean} [trackUsage] - Default for tracking token usage.\n * @property {number} [requestTimeout] - Default request timeout for LLM calls.\n * @property {number} [maxRetries] - Default max retries for LLM calls.\n * @property {number} [initialDelayMs] - Default initial retry delay.\n */\n\nexport class LLMService {\n  /**\n   * @param {LLMServiceConfig} [defaultConfig={}] - Default configuration for this service instance.\n   */\n\n  constructor(defaultConfig = {}) {\n    const configManager = getConfigManager();\n    this.defaultConfig = {\n      target: configManager.get('LLM_PROVIDER', 'openai'), // Default to provider if no specific target\n      temperature: 0.7,\n      maxTokens: 2000,\n      verbose: configManager.get('DEBUG_INTELLIGENCE', false),\n      trackUsage: configManager.get('LLM_TRACK_USAGE', true),\n      ...defaultConfig,\n    };\n    this.logger = logger; // Make logger available to instances\n    logger.info('LLMService initialized.');\n    logger.debug('LLMService default configuration:', this.defaultConfig);\n  }\n\n  /**\n   * Makes a generic call to `generateIntelligence`, merging service defaults with call-specific options.\n   * @param {GenerateIntelligenceParams} options - Options for generateIntelligence, including prompt, config, metadata, and callbacks.\n   * @returns {Promise<import('../intelligence/core/llmOrchestrator.js').GenerateIntelligenceResult<any>>}\n   */\n  async generate(options) {\n    const {\n      prompt = {},\n      config: callConfig = {},\n      metadata = {},\n      callbacks,\n    } = options;\n\n    if (!prompt?.user && !(prompt?.shots && prompt.shots.length > 0)) {\n      throw new DaitanInvalidInputError(\n        'LLMService.generate: A `prompt.user` message or messages in `prompt.shots` are required.'\n      );\n    }\n\n    const {\n      llm: callLlm = {},\n      response: callResponse = {},\n      retry: callRetry = {},\n      ...callRootConfig\n    } = callConfig;\n\n    const finalConfig = {\n      verbose: this.defaultConfig.verbose,\n      trackUsage: this.defaultConfig.trackUsage,\n      ...callRootConfig,\n      llm: {\n        target: this.defaultConfig.target,\n        temperature: this.defaultConfig.temperature,\n        maxTokens: this.defaultConfig.maxTokens,\n        apiKey: this.defaultConfig.apiKey,\n        baseURL: this.defaultConfig.baseURL,\n        ...callLlm,\n      },\n      response: { ...callResponse },\n      retry: {\n        maxAttempts: this.defaultConfig.maxRetries,\n        ...callRetry,\n      },\n    };\n\n    const finalParams = {\n      prompt,\n      config: finalConfig,\n      metadata,\n      callbacks,\n    };\n\n    const summary = metadata?.summary || 'Untitled LLMService Call';\n    this.logger.info(`LLMService.generate called for summary: \"${summary}\"`);\n\n    try {\n      const result = await generateIntelligence(finalParams);\n      this.logger.info(`LLMService.generate successful for: \"${summary}\"`);\n      if (result.usage) {\n        this.logger.debug('LLM Usage:', result.usage);\n      }\n      return result;\n    } catch (error) {\n      this.logger.error(\n        `LLMService.generate failed for \"${summary}\": ${error.message}`,\n        { error }\n      );\n      throw error;\n    }\n  }\n\n  /**\n   * Generates a JSON response from the LLM.\n   * @param {Object} params\n   * @returns {Promise<{response: Object, usage: LLMUsageInfo | null}>}\n   */\n  async generateJson({\n    userPrompt,\n    whoYouAre,\n    whatYouDo,\n    summary = 'JSON Generation',\n    shots,\n    ...overrideConfig\n  }) {\n    const { target, temperature, maxTokens, apiKey, baseURL, ...rootConfig } =\n      overrideConfig;\n\n    const params = {\n      prompt: {\n        system: { persona: whoYouAre, task: whatYouDo },\n        user: userPrompt,\n        shots,\n      },\n      config: {\n        ...rootConfig,\n        response: { format: 'json' },\n        llm: { target, temperature, maxTokens, apiKey, baseURL },\n      },\n      metadata: { summary },\n    };\n    return this.generate(params);\n  }\n\n  /**\n   * Generates a text response from the LLM.\n   * @param {Object} params\n   * @returns {Promise<{response: string, usage: LLMUsageInfo | null}>}\n   */\n  async generateText({\n    userPrompt,\n    whoYouAre,\n    whatYouDo,\n    summary = 'Text Generation',\n    shots,\n    ...overrideConfig\n  }) {\n    const { target, temperature, maxTokens, apiKey, baseURL, ...rootConfig } =\n      overrideConfig;\n\n    const params = {\n      prompt: {\n        system: { persona: whoYouAre, task: whatYouDo },\n        user: userPrompt,\n        shots,\n      },\n      config: {\n        ...rootConfig,\n        response: { format: 'text' },\n        llm: { target, temperature, maxTokens, apiKey, baseURL },\n      },\n      metadata: { summary },\n    };\n    return this.generate(params);\n  }\n\n  /**\n   * Streams a text response from the LLM.\n   * @param {Object} params\n   * @returns {Promise<{response: string | undefined, usage: LLMUsageInfo | null}>}\n   */\n  async streamText({\n    userPrompt,\n    whoYouAre,\n    whatYouDo,\n    summary = 'Text Streaming',\n    shots,\n    callbacks,\n    returnFullResponseAfterStream = true,\n    ...overrideConfig\n  }) {\n    if (!callbacks || typeof callbacks.onTokenStream !== 'function') {\n      throw new DaitanInvalidInputError(\n        'LLMService.streamText: `callbacks.onTokenStream` is required for streaming.'\n      );\n    }\n    const { target, temperature, maxTokens, apiKey, baseURL, ...rootConfig } =\n      overrideConfig;\n\n    const params = {\n      prompt: {\n        system: { persona: whoYouAre, task: whatYouDo },\n        user: userPrompt,\n        shots,\n      },\n      config: {\n        ...rootConfig,\n        response: { format: 'text', returnFullResponseAfterStream },\n        llm: { target, temperature, maxTokens, apiKey, baseURL },\n      },\n      metadata: { summary },\n      callbacks,\n    };\n    return this.generate(params);\n  }\n}\n", "// intelligence/src/intelligence/index.js\n/**\n * @file Main entry point for core AI/LLM functionalities in the @daitanjs/intelligence package.\n * @module @daitanjs/intelligence\n */\nimport { getLogger } from '@daitanjs/development';\n\nconst intelligenceIndexLogger = getLogger('daitan-intelligence-index');\n\nintelligenceIndexLogger.debug(\n  'Initializing DaitanJS Intelligence module exports...'\n);\n\n// --- Core LLM, Embedding, and Factory Exports ---\nexport { generateIntelligence } from './core/llmOrchestrator.js';\nexport { generateEmbedding } from './core/embeddingGenerator.js';\nexport { createDaitanTool } from './core/toolFactory.js';\n\n// --- Prompt Management ---\nexport * from './prompts/index.js';\n\n// --- RAG (Retrieval Augmented Generation) ---\nexport * from './rag/index.js';\n\n// --- Metadata Extraction ---\nexport * from './metadata/index.js';\n\n// --- Specialized Search (New Export) ---\nexport {\n  searchNews,\n  searchGeneralWeb,\n  searchAcademic,\n} from './search/specializedSearch.js';\n\n// --- Tool Exports ---\nexport {\n  getDefaultTools,\n  getDaitanPlatformTools,\n} from './tools/tool-registries.js';\nexport { BaseTool } from './tools/baseTool.js';\nexport { calculatorTool } from './tools/calculatorTool.js';\nexport { wikipediaSearchTool } from './tools/wikipediaSearchTool.js';\nexport { cliTool } from './tools/cliTool.js';\nexport { webSearchTool } from './tools/webSearchTool.js';\nexport { ragTool } from './tools/ragTool.js';\nexport { userManagementTool } from './tools/userManagementTool.js';\nexport { csvQueryTool } from './tools/csvQueryTool.js';\nexport { createPaymentIntentTool } from './tools/createPaymentIntentTool.js';\nexport { youtubeSearchTool } from './tools/youtubeSearchTool.js';\nexport { processYoutubeAudioTool } from './tools/processYoutubeAudioTool.js';\nexport { imageGenerationTool } from './tools/imageGenerationTool.js';\nexport {\n  searchGmailTool,\n  readEmailContentTool,\n  createGmailDraftTool,\n} from './tools/gmailTools.js';\nexport { calendarTool } from './tools/calendarTool.js';\nexport {\n  createGoogleDocTool,\n  createGoogleSheetTool,\n} from './tools/googleDriveTools.js';\n\n// --- Agent Exports ---\nexport { runGraphAgent } from './agents/agentRunner.js';\nexport { runToolCallingAgent } from './agents/agentExecutor.js';\nexport { BaseAgent } from './agents/baseAgent.js';\nexport * from './agents/prompts/index.js';\nexport * from './agents/chat/index.js';\n\n// --- Workflow Exports ---\nexport * from './workflows/index.js';\n\n// --- Memory Management ---\nexport { InMemoryChatMessageHistoryStore } from '../memory/inMemoryChatHistoryStore.js';\n\n// --- Core Utilities (re-exported for convenience) ---\nexport { estimateLlmCost } from './core/llmPricing.js';\nexport { countTokens, countTokensForMessages } from './core/tokenUtils.js';\nexport { checkOllamaStatus } from './core/ollamaUtils.js';\nexport {\n  EXPERT_MODELS,\n  getExpertModelDefinition,\n  getDefaultExpertProfile,\n} from './core/expertModels.js';\n\nintelligenceIndexLogger.info(\n  'DaitanJS Intelligence module main exports configured and ready.'\n);\n", "// intelligence/src/intelligence/core/embeddingGenerator.js\n/**\n * @file Re-exports embedding generation functionalities from the canonical @daitanjs/embeddings package.\n * @module @daitanjs/intelligence/core/embeddingGenerator\n *\n * @description\n * This module ensures that embedding generation capabilities are accessible through the\n * `@daitanjs/intelligence` package's core interface while maintaining a single source of truth.\n * The actual implementation of `generateEmbedding` and related utilities resides in the\n * `@daitanjs/embeddings` package.\n *\n * This re-export approach prevents code duplication and enforces the DaitanJS architectural\n * principle of specialized, reusable packages.\n *\n * For detailed documentation on the exported functions, please refer to the `@daitanjs/embeddings` package.\n */\nimport { getLogger } from '@daitanjs/development';\n\nconst embeddingGeneratorLogger = getLogger('daitan-embedding-generator');\n\nembeddingGeneratorLogger.info(\n  'Embedding generator module is a re-export layer. All embedding functionalities are canonical in @daitanjs/embeddings.'\n);\n\n// Re-exporting the canonical embedding generation functions from the @daitanjs/embeddings package.\n// This assumes that @daitanjs/embeddings is a dependency of @daitanjs/intelligence.\nexport {\n  generateEmbedding,\n  generateBatchEmbeddings, // This function is deprecated in the source but exported for compatibility\n} from '@daitanjs/embeddings';\n", "// intelligence/src/intelligence/core/toolFactory.js\n/**\n * @file Contains the factory function for creating DaitanJS tools.\n * @module @daitanjs/intelligence/core/toolFactory\n * @private\n */\nimport { DynamicTool } from '@langchain/core/tools';\nimport { getLogger } from '@daitanjs/development';\nimport {\n  DaitanInvalidInputError,\n  DaitanValidationError,\n  DaitanOperationError,\n} from '@daitanjs/error';\nimport { ZodError } from 'zod';\n\nconst toolFactoryLogger = getLogger('daitan-tool-factory');\n\n/**\n * Creates a LangChain-compatible custom tool (`DynamicTool`) from an asynchronous function.\n * This factory wraps the provided function with robust input parsing, validation, logging, and error handling.\n *\n * @public\n * @param {string} name - The unique, snake_case name of the tool.\n * @param {string} description - A detailed description for the LLM.\n * @param {(input: any, callId?: string) => Promise<string | any>} func - The async function that implements the tool's logic.\n * @param {import('zod').ZodSchema} [argsSchema] - Optional Zod schema for input validation.\n * @returns {DynamicTool} A LangChain DynamicTool instance.\n */\nexport const createDaitanTool = (\n  name,\n  description,\n  func,\n  argsSchema = undefined\n) => {\n  if (!name || typeof name !== 'string' || !name.trim()) {\n    throw new DaitanInvalidInputError(\n      'Tool `name` must be a non-empty string.'\n    );\n  }\n  if (!description || typeof description !== 'string' || !description.trim()) {\n    throw new DaitanInvalidInputError(\n      'Tool `description` must be a non-empty string.'\n    );\n  }\n  if (typeof func !== 'function') {\n    throw new DaitanInvalidInputError(\n      'Tool `func` must be a callable function.'\n    );\n  }\n\n  const daitanToolWrapperFunc = async (rawInput) => {\n    const callId = `tool-run-${name}-${Date.now().toString(36)}`;\n    const logger = getLogger(`daitan-tool-${name}`);\n    logger.info(`Tool \"${name}\" execution: START`, { callId, rawInput });\n\n    let inputForRun = rawInput;\n    try {\n      if (typeof rawInput === 'string') {\n        try {\n          inputForRun = JSON.parse(rawInput);\n        } catch (e) {\n          // Ignore if not a valid JSON string.\n        }\n      }\n\n      if (argsSchema) {\n        inputForRun = argsSchema.parse(inputForRun);\n      }\n\n      const result = await func(inputForRun, callId);\n      const outputString =\n        typeof result === 'string' ? result : JSON.stringify(result, null, 2);\n\n      logger.info(`Tool \"${name}\" execution: SUCCESS`, {\n        callId,\n        outputPreview: outputString.substring(0, 150) + '...',\n      });\n      return outputString;\n    } catch (error) {\n      logger.error(`Execution error in tool \"${name}\": ${error.message}`, {\n        callId,\n        errorName: error.name,\n      });\n\n