@jmndao/mongoose-ai/dist/index.js

AI-powered Mongoose plugin for intelligent document processing with auto-summarization, semantic search, MongoDB Vector Search, and function calling

"use strict";
var __create = Object.create;
var __defProp = Object.defineProperty;
var __getOwnPropDesc = Object.getOwnPropertyDescriptor;
var __getOwnPropNames = Object.getOwnPropertyNames;
var __getProtoOf = Object.getPrototypeOf;
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        __defProp(to, key, { get: () => from[key], enumerable: !(desc = __getOwnPropDesc(from, key)) || desc.enumerable });
  }
  return to;
};
var __toESM = (mod, isNodeMode, target) => (target = mod != null ? __create(__getProtoOf(mod)) : {}, __copyProps(
  // If the importer is in node compatibility mode or this is not an ESM
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  isNodeMode || !mod || !mod.__esModule ? __defProp(target, "default", { value: mod, enumerable: true }) : target,
  mod
));
var __toCommonJS = (mod) => __copyProps(__defProp({}, "__esModule", { value: true }), mod);

// src/index.ts
var index_exports = {};
__export(index_exports, {
  AnthropicProvider: () => AnthropicProvider,
  DEFAULT_CONFIG: () => DEFAULT_CONFIG,
  OllamaProvider: () => OllamaProvider,
  OpenAIProvider: () => OpenAIProvider,
  QuickFunctions: () => QuickFunctions,
  SUPPORTED_MODELS: () => SUPPORTED_MODELS,
  SUPPORTED_PROVIDERS: () => SUPPORTED_PROVIDERS,
  VERSION: () => VERSION,
  aiPlugin: () => aiPlugin,
  checkEnvironment: () => checkEnvironment,
  cosineSimilarity: () => cosineSimilarity,
  createAIConfig: () => createAIConfig,
  createAdvancedAIConfig: () => createAdvancedAIConfig,
  createFunction: () => createFunction,
  createOllamaConfig: () => createOllamaConfig,
  createVectorIndex: () => createVectorIndex,
  default: () => index_default,
  detectVectorSearchSupport: () => detectVectorSearchSupport,
  estimateCost: () => estimateCost,
  estimateTokenCount: () => estimateTokenCount,
  hasAIDocumentMethods: () => hasAIDocumentMethods,
  hasAIMethods: () => hasAIMethods,
  isSearchResult: () => isSearchResult,
  validateApiKey: () => validateApiKey
});
module.exports = __toCommonJS(index_exports);

// src/config-helpers.ts
var DEFAULT_CONFIG = {
  advanced: {
    maxRetries: 2,
    timeout: 3e4,
    skipOnUpdate: false,
    forceRegenerate: false,
    logLevel: "warn",
    continueOnError: true,
    enableFunctions: false
  },
  openai: {
    chatModel: "gpt-4o-mini",
    embeddingModel: "text-embedding-3-small",
    maxTokens: 200,
    temperature: 0.3
  },
  anthropic: {
    chatModel: "claude-haiku-4-5",
    maxTokens: 200,
    temperature: 0.3
  },
  ollama: {
    chatModel: "llama3.2",
    embeddingModel: "nomic-embed-text",
    maxTokens: 200,
    temperature: 0.3,
    endpoint: "http://localhost:11434"
  },
  vectorSearch: {
    enabled: true,
    indexName: "vector_index",
    autoCreateIndex: true,
    similarity: "cosine"
  }
};
function validateApiKey(apiKey, provider) {
  if (typeof apiKey !== "string" || apiKey.length < 2) {
    return false;
  }
  switch (provider) {
    case "openai":
      return apiKey.startsWith("sk-") && apiKey.length > 20;
    // More strict validation
    case "anthropic":
      return apiKey.startsWith("sk-ant-") && apiKey.length > 30 || apiKey.length > 20;
    case "ollama":
      return apiKey.length >= 2;
    // Ollama just needs non-empty string
    default:
      return false;
  }
}
function createAdvancedAIConfig(options) {
  if (!validateApiKey(options.apiKey, options.provider)) {
    throw new Error(`Invalid API key format for ${options.provider}`);
  }
  const defaultModelConfig = options.provider === "openai" ? DEFAULT_CONFIG.openai : options.provider === "anthropic" ? DEFAULT_CONFIG.anthropic : DEFAULT_CONFIG.ollama;
  return {
    model: options.model,
    provider: options.provider,
    field: options.field,
    credentials: { apiKey: options.apiKey },
    prompt: options.prompt,
    advanced: { ...DEFAULT_CONFIG.advanced, ...options.advanced },
    modelConfig: { ...defaultModelConfig, ...options.modelConfig },
    vectorSearch: { ...DEFAULT_CONFIG.vectorSearch, ...options.vectorSearch },
    includeFields: options.includeFields,
    excludeFields: options.excludeFields,
    functions: options.functions
  };
}
function createAIConfig(options) {
  return createAdvancedAIConfig({
    ...options,
    provider: "openai"
    // Default to OpenAI for backward compatibility
  });
}
function createOllamaConfig(options) {
  return createAdvancedAIConfig({
    ...options,
    apiKey: "local",
    // Placeholder for Ollama
    provider: "ollama",
    modelConfig: {
      endpoint: options.endpoint,
      chatModel: options.chatModel,
      embeddingModel: options.embeddingModel
    }
  });
}
function estimateTokenCount(text) {
  if (!text || typeof text !== "string") return 0;
  return Math.ceil(text.length / 4);
}
function estimateCost(tokenCount, model, provider = "openai") {
  const pricing = {
    openai: {
      "gpt-3.5-turbo": 15e-4,
      "gpt-4": 0.03,
      "gpt-4o": 5e-3,
      "gpt-4o-mini": 15e-5,
      "text-embedding-3-small": 2e-5,
      "text-embedding-3-large": 13e-5
    },
    anthropic: {
      "claude-haiku-4-5": 1e-3,
      "claude-sonnet-4-6": 3e-3,
      "claude-opus-4-7": 0.015,
      "claude-3-haiku-20240307": 25e-5,
      "claude-3-sonnet-20240229": 3e-3,
      "claude-3-opus-20240229": 0.015
    },
    ollama: {
      // Local models have no API costs
      "llama3.2": 0,
      llama2: 0,
      mistral: 0,
      "nomic-embed-text": 0
    }
  };
  const providerPricing = pricing[provider];
  const pricePerToken = providerPricing?.[model] || 0;
  return tokenCount / 1e3 * pricePerToken;
}
function checkEnvironment() {
  const missing = [];
  const warnings = [];
  const isNode = typeof globalThis !== "undefined" && typeof globalThis.process !== "undefined" && globalThis.process.versions?.node;
  if (!isNode) {
    warnings.push("Not running in Node.js environment");
    return { isValid: false, missing, warnings };
  }
  const env = globalThis.process.env;
  if (!env.OPENAI_API_KEY && !env.ANTHROPIC_API_KEY) {
    missing.push(
      "OPENAI_API_KEY, ANTHROPIC_API_KEY environment variable, or use Ollama for local processing"
    );
  }
  if (env.OPENAI_API_KEY && !validateApiKey(env.OPENAI_API_KEY, "openai")) {
    warnings.push("OPENAI_API_KEY format appears invalid");
  }
  if (env.ANTHROPIC_API_KEY && !validateApiKey(env.ANTHROPIC_API_KEY, "anthropic")) {
    warnings.push("ANTHROPIC_API_KEY format appears invalid");
  }
  return {
    isValid: missing.length === 0,
    missing,
    warnings
  };
}

// src/plugin.ts
var import_mongoose = require("mongoose");

// src/providers/openai.ts
var import_openai = __toESM(require("openai"));

// src/providers/base.ts
var BaseProvider = class {
  constructor(credentials, advancedOptions = {}) {
    this.validateCredentials(credentials);
    this.advanced = {
      maxRetries: advancedOptions.maxRetries ?? 2,
      timeout: advancedOptions.timeout ?? 3e4,
      skipOnUpdate: advancedOptions.skipOnUpdate ?? false,
      forceRegenerate: advancedOptions.forceRegenerate ?? false,
      logLevel: advancedOptions.logLevel || "warn",
      continueOnError: advancedOptions.continueOnError ?? true,
      enableFunctions: advancedOptions.enableFunctions ?? false
    };
  }
  /**
   * Execute functions with error handling
   */
  async executeFunctions(functions, functionCalls, document) {
    const results = [];
    for (const call of functionCalls) {
      const func = functions.find((f) => f.name === call.name);
      if (!func) {
        results.push({
          name: call.name,
          success: false,
          error: "Function not found",
          executedAt: /* @__PURE__ */ new Date()
        });
        continue;
      }
      try {
        const result = await func.handler(call.arguments || {}, document);
        results.push({
          name: call.name,
          success: true,
          result: call.arguments,
          // Store the arguments that were applied
          executedAt: /* @__PURE__ */ new Date()
        });
        this.log(
          "debug",
          `Function ${call.name} executed successfully. Document updated.`
        );
      } catch (error) {
        results.push({
          name: call.name,
          success: false,
          error: error instanceof Error ? error.message : "Unknown error",
          executedAt: /* @__PURE__ */ new Date()
        });
        this.log("error", `Function ${call.name} failed:`, error);
      }
    }
    return results;
  }
  /**
   * Convert document to clean text
   */
  prepareText(document) {
    if (!document || typeof document !== "object") {
      return "";
    }
    const clean = { ...document };
    const systemFields = ["_id", "__v", "createdAt", "updatedAt", "id"];
    systemFields.forEach((field) => delete clean[field]);
    try {
      return JSON.stringify(clean, null, 2).replace(/[{}",[\]]/g, " ").replace(/\s+/g, " ").trim();
    } catch (error) {
      this.log("warn", "Failed to stringify document, using fallback");
      return String(document).trim();
    }
  }
  /**
   * Truncate text to specified length
   */
  truncateText(text, maxLength) {
    if (!text || typeof text !== "string") {
      return "";
    }
    if (text.length <= maxLength) return text;
    const truncated = text.substring(0, maxLength);
    const lastSpace = truncated.lastIndexOf(" ");
    return lastSpace > maxLength * 0.8 ? truncated.substring(0, lastSpace) + "..." : truncated + "...";
  }
  /**
   * Extract error message
   */
  getErrorMessage(error) {
    if (typeof error === "string") return error;
    if (error?.message) return error.message;
    if (error?.error?.message) return error.error.message;
    if (error?.response?.data?.error?.message)
      return error.response.data.error.message;
    return "Unknown error occurred";
  }
  /**
   * Log messages based on level
   */
  log(level, message, error) {
    const levels = { debug: 0, info: 1, warn: 2, error: 3 };
    const currentLevel = levels[this.advanced.logLevel];
    if (levels[level] >= currentLevel) {
      const timestamp = (/* @__PURE__ */ new Date()).toISOString();
      const prefix = `[${timestamp}] [mongoose-ai] [${level.toUpperCase()}]`;
      if (error && level === "error") {
        console[level](`${prefix} ${message}`, error);
      } else if (level === "debug" && typeof error !== "undefined") {
        console[level](`${prefix} ${message}`, error);
      } else {
        console[level](`${prefix} ${message}`);
      }
    }
  }
};

// src/providers/openai.ts
var OpenAIProvider = class extends BaseProvider {
  constructor(credentials, modelConfig = {}, advancedOptions = {}) {
    super(credentials, advancedOptions);
    this.config = {
      chatModel: modelConfig.chatModel || "gpt-4o-mini",
      embeddingModel: modelConfig.embeddingModel || "text-embedding-3-small",
      maxTokens: modelConfig.maxTokens || 200,
      temperature: modelConfig.temperature || 0.3
    };
    try {
      this.client = new import_openai.default({
        apiKey: credentials.apiKey,
        organization: credentials.organizationId,
        timeout: this.advanced.timeout,
        maxRetries: this.advanced.maxRetries
      });
      this.log("info", "OpenAI provider initialized successfully");
    } catch (error) {
      throw new Error(
        `Failed to initialize OpenAI client: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Generate summary for document with optional function calling
   */
  async summarize(document, customPrompt, functions) {
    const startTime = Date.now();
    try {
      const text = this.prepareText(document);
      if (!text.trim()) {
        throw new Error("No content to summarize");
      }
      const prompt = customPrompt || "Summarize this content in 2-3 clear sentences:";
      const tools = this.prepareFunctionTools(functions);
      const requestParams = {
        model: this.config.chatModel,
        messages: [
          { role: "system", content: prompt },
          { role: "user", content: text }
        ],
        max_tokens: this.config.maxTokens,
        temperature: this.config.temperature
      };
      if (tools.length > 0 && this.advanced.enableFunctions) {
        requestParams.tools = tools;
        requestParams.tool_choice = "auto";
      }
      const response = await this.client.chat.completions.create(requestParams);
      const choice = response.choices[0];
      if (!choice) {
        throw new Error("No response choice received from OpenAI");
      }
      let summary = choice.message?.content?.trim() || "";
      let functionResults = [];
      const toolCalls = choice.message?.tool_calls;
      if (toolCalls && toolCalls.length > 0 && functions && this.advanced.enableFunctions) {
        functionResults = await this.executeFunctions(
          functions,
          toolCalls.map((call) => ({
            name: call.function.name,
            arguments: JSON.parse(call.function.arguments || "{}")
          })),
          document
        );
      }
      if (!summary && functionResults.length > 0) {
        summary = "Analysis completed with automated classifications applied.";
        this.log(
          "info",
          "No summary content provided, using default summary due to tool calls"
        );
      }
      if (!summary) {
        throw new Error("Failed to generate summary - no content returned");
      }
      const processingTime = Date.now() - startTime;
      this.log("info", `Summary generated in ${processingTime}ms`);
      return {
        summary,
        generatedAt: /* @__PURE__ */ new Date(),
        model: this.config.chatModel,
        tokenCount: response.usage?.total_tokens,
        processingTime,
        ...functionResults.length > 0 && { functionResults }
      };
    } catch (error) {
      const processingTime = Date.now() - startTime;
      this.log(
        "error",
        `Summary generation failed after ${processingTime}ms:`,
        error
      );
      throw new Error(
        `Summary generation failed: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Generate embedding for text
   */
  async generateEmbedding(text) {
    const startTime = Date.now();
    try {
      if (!text || typeof text !== "string") {
        throw new Error("Text is required for embedding");
      }
      const processedText = this.truncateText(text, 8e3);
      const response = await this.client.embeddings.create({
        model: this.config.embeddingModel,
        input: processedText
      });
      const embedding = response.data[0]?.embedding;
      if (!embedding || !Array.isArray(embedding)) {
        throw new Error(
          "Failed to generate embedding - no embedding data returned"
        );
      }
      const processingTime = Date.now() - startTime;
      this.log("info", `Embedding generated in ${processingTime}ms`);
      return {
        embedding,
        generatedAt: /* @__PURE__ */ new Date(),
        model: this.config.embeddingModel,
        dimensions: embedding.length,
        processingTime
      };
    } catch (error) {
      const processingTime = Date.now() - startTime;
      this.log(
        "error",
        `Embedding generation failed after ${processingTime}ms:`,
        error
      );
      throw new Error(
        `Embedding generation failed: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Prepare function tools for OpenAI API
   */
  prepareFunctionTools(functions) {
    if (!functions || !this.advanced.enableFunctions) {
      return [];
    }
    return functions.map((func) => {
      const cleanParameters = {};
      const requiredFields = [];
      Object.entries(func.parameters).forEach(([key, param]) => {
        if (param.required === true) {
          requiredFields.push(key);
        }
        const { required, ...cleanParam } = param;
        cleanParameters[key] = cleanParam;
      });
      return {
        type: "function",
        function: {
          name: func.name,
          description: func.description,
          parameters: {
            type: "object",
            properties: cleanParameters,
            required: requiredFields
          }
        }
      };
    });
  }
  /**
   * Validate credentials
   */
  validateCredentials(credentials) {
    if (!credentials || typeof credentials !== "object") {
      throw new Error("Credentials object is required");
    }
    if (!credentials.apiKey || typeof credentials.apiKey !== "string") {
      throw new Error("OpenAI API key is required");
    }
    if (!credentials.apiKey.startsWith("sk-")) {
      throw new Error('Invalid OpenAI API key format - must start with "sk-"');
    }
    if (credentials.apiKey.length < 20) {
      throw new Error("OpenAI API key appears to be invalid - too short");
    }
  }
  /**
   * Get provider information
   */
  getProviderInfo() {
    return {
      name: "OpenAI",
      version: "1.1.0",
      models: this.config,
      advanced: this.advanced
    };
  }
};

// src/providers/anthropic.ts
var AnthropicProvider = class extends BaseProvider {
  constructor(credentials, modelConfig = {}, advancedOptions = {}) {
    super(credentials, advancedOptions);
    this.apiKey = credentials.apiKey;
    this.config = {
      chatModel: modelConfig.chatModel || "claude-haiku-4-5",
      maxTokens: modelConfig.maxTokens || 200,
      temperature: modelConfig.temperature || 0.3
    };
    this.log("info", "Anthropic provider initialized successfully");
  }
  /**
   * Generate summary for document with optional function calling
   */
  async summarize(document, customPrompt, functions) {
    const startTime = Date.now();
    try {
      const text = this.prepareText(document);
      if (!text.trim()) {
        throw new Error("No content to summarize");
      }
      const prompt = customPrompt || "Summarize this content in 2-3 clear sentences:";
      const tools = this.prepareFunctionTools(functions);
      const requestBody = {
        model: this.config.chatModel,
        max_tokens: this.config.maxTokens,
        temperature: this.config.temperature,
        messages: [
          {
            role: "user",
            content: `${prompt}

Content to analyze:
${text}`
          }
        ]
      };
      if (tools.length > 0 && this.advanced.enableFunctions) {
        requestBody.tools = tools;
      }
      const response = await this.makeAnthropicRequest(requestBody);
      const textContent = response.content?.find((c) => c.type === "text");
      let summary = textContent?.text?.trim() || "";
      const functionResults = [];
      const toolUse = response.content?.filter((c) => c.type === "tool_use") || [];
      if (toolUse.length > 0 && functions && this.advanced.enableFunctions) {
        for (const use of toolUse) {
          try {
            const func = functions.find((f) => f.name === use.name);
            if (!func) {
              functionResults.push({
                name: use.name,
                success: false,
                error: "Function not found",
                executedAt: /* @__PURE__ */ new Date()
              });
              continue;
            }
            const docInstance = document;
            await func.handler(use.input || {}, docInstance);
            functionResults.push({
              name: use.name,
              success: true,
              result: use.input,
              executedAt: /* @__PURE__ */ new Date()
            });
            this.log(
              "debug",
              `Function ${use.name} executed successfully. Document updated.`
            );
          } catch (error) {
            functionResults.push({
              name: use.name,
              success: false,
              error: error instanceof Error ? error.message : "Unknown error",
              executedAt: /* @__PURE__ */ new Date()
            });
            this.log("error", `Function ${use.name} failed:`, error);
          }
        }
        if (!summary && functionResults.length > 0) {
          summary = "Analysis completed with automated classifications applied.";
          this.log(
            "info",
            "No summary content provided, using default summary due to tool calls"
          );
        }
      }
      if (!summary) {
        throw new Error("Failed to generate summary - no content returned");
      }
      const processingTime = Date.now() - startTime;
      this.log("info", `Summary generated in ${processingTime}ms`);
      return {
        summary,
        generatedAt: /* @__PURE__ */ new Date(),
        model: this.config.chatModel,
        tokenCount: response.usage?.input_tokens + response.usage?.output_tokens,
        processingTime,
        ...functionResults.length > 0 && { functionResults }
      };
    } catch (error) {
      const processingTime = Date.now() - startTime;
      this.log(
        "error",
        `Summary generation failed after ${processingTime}ms:`,
        error
      );
      throw new Error(
        `Summary generation failed: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Generate embedding for text - Note: Anthropic doesn't provide embeddings
   */
  async generateEmbedding(text) {
    throw new Error(
      "Anthropic provider does not support embeddings. Use OpenAI provider for embedding models."
    );
  }
  /**
   * Make API request to Anthropic
   */
  async makeAnthropicRequest(body) {
    try {
      const response = await fetch("https://api.anthropic.com/v1/messages", {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
          "x-api-key": this.apiKey,
          "anthropic-version": "2023-06-01"
        },
        body: JSON.stringify(body),
        signal: AbortSignal.timeout(this.advanced.timeout)
      });
      if (!response.ok) {
        const errorText = await response.text();
        let errorData = {};
        try {
          errorData = JSON.parse(errorText);
        } catch {
          errorData = { error: { message: errorText } };
        }
        const errorMessage = errorData.error?.message || errorData.message || `HTTP ${response.status} ${response.statusText}`;
        throw new Error(`Anthropic API error: ${errorMessage}`);
      }
      return await response.json();
    } catch (error) {
      if (error instanceof Error) {
        throw error;
      }
      throw new Error(`Request failed: ${String(error)}`);
    }
  }
  /**
   * Prepare function tools for Anthropic API
   */
  prepareFunctionTools(functions) {
    if (!functions || !this.advanced.enableFunctions) {
      return [];
    }
    return functions.map((func) => {
      const cleanProperties = {};
      const requiredFields = [];
      Object.entries(func.parameters).forEach(([key, param]) => {
        if (param.required === true) {
          requiredFields.push(key);
        }
        const { required, ...cleanParam } = param;
        cleanProperties[key] = cleanParam;
      });
      return {
        name: func.name,
        description: func.description,
        input_schema: {
          type: "object",
          properties: cleanProperties,
          required: requiredFields
        }
      };
    });
  }
  /**
   * Validate credentials
   */
  validateCredentials(credentials) {
    if (!credentials || typeof credentials !== "object") {
      throw new Error("Credentials object is required");
    }
    if (!credentials.apiKey || typeof credentials.apiKey !== "string") {
      throw new Error("Anthropic API key is required");
    }
    if (credentials.apiKey.length < 20) {
      throw new Error("Anthropic API key appears to be invalid - too short");
    }
    if (!credentials.apiKey.startsWith("sk-ant-") && credentials.apiKey.length < 40) {
      this.log(
        "warn",
        "Anthropic API key format may be invalid - expected to start with 'sk-ant-'"
      );
    }
  }
  /**
   * Get provider information
   */
  getProviderInfo() {
    return {
      name: "Anthropic",
      version: "1.1.0",
      models: this.config,
      advanced: this.advanced
    };
  }
};

// src/providers/ollama.ts
var OllamaProvider = class extends BaseProvider {
  constructor(credentials, modelConfig = {}, advancedOptions = {}) {
    super(credentials, advancedOptions);
    this.config = {
      chatModel: modelConfig.chatModel || "llama3.2",
      embeddingModel: modelConfig.embeddingModel || "nomic-embed-text",
      maxTokens: modelConfig.maxTokens || 200,
      temperature: modelConfig.temperature || 0.3,
      endpoint: modelConfig.endpoint || "http://localhost:11434"
    };
    this.endpoint = this.config.endpoint;
    this.log("info", "Ollama provider initialized successfully");
  }
  /**
   * Generate summary for document with optional function calling
   */
  async summarize(document, customPrompt, functions) {
    const startTime = Date.now();
    try {
      const text = this.prepareText(document);
      if (!text.trim()) {
        throw new Error("No content to summarize");
      }
      const prompt = customPrompt || "Summarize this content in 2-3 clear sentences:";
      let fullPrompt = `${prompt}

Content to analyze:
${text}`;
      let functionResults = [];
      if (functions && functions.length > 0 && this.advanced.enableFunctions) {
        const functionInstructions = this.prepareFunctionInstructions(functions);
        fullPrompt += `

${functionInstructions}`;
      }
      const requestBody = {
        model: this.config.chatModel,
        prompt: fullPrompt,
        options: {
          num_predict: this.config.maxTokens,
          temperature: this.config.temperature
        },
        stream: false
      };
      const response = await this.makeOllamaRequest(
        "/api/generate",
        requestBody
      );
      let summary = response.response?.trim() || "";
      if (functions && functions.length > 0 && this.advanced.enableFunctions) {
        functionResults = await this.parseFunctionCalls(
          summary,
          functions,
          document
        );
        if (!summary && functionResults.length > 0) {
          summary = "Analysis completed with automated classifications applied.";
          this.log(
            "info",
            "No summary content provided, using default summary due to function calls"
          );
        }
      }
      if (!summary) {
        throw new Error("Failed to generate summary - no content returned");
      }
      const processingTime = Date.now() - startTime;
      this.log("info", `Summary generated in ${processingTime}ms`);
      return {
        summary,
        generatedAt: /* @__PURE__ */ new Date(),
        model: this.config.chatModel,
        tokenCount: this.estimateTokenCount(summary),
        processingTime,
        ...functionResults.length > 0 && { functionResults }
      };
    } catch (error) {
      const processingTime = Date.now() - startTime;
      this.log(
        "error",
        `Summary generation failed after ${processingTime}ms:`,
        error
      );
      throw new Error(
        `Summary generation failed: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Generate embedding for text
   */
  async generateEmbedding(text) {
    const startTime = Date.now();
    try {
      if (!text || typeof text !== "string") {
        throw new Error("Text is required for embedding");
      }
      const processedText = this.truncateText(text, 8e3);
      const requestBody = {
        model: this.config.embeddingModel,
        prompt: processedText
      };
      const response = await this.makeOllamaRequest(
        "/api/embeddings",
        requestBody
      );
      const embedding = response.embedding;
      if (!embedding || !Array.isArray(embedding)) {
        throw new Error(
          "Failed to generate embedding - no embedding data returned"
        );
      }
      const processingTime = Date.now() - startTime;
      this.log("info", `Embedding generated in ${processingTime}ms`);
      return {
        embedding,
        generatedAt: /* @__PURE__ */ new Date(),
        model: this.config.embeddingModel,
        dimensions: embedding.length,
        processingTime
      };
    } catch (error) {
      const processingTime = Date.now() - startTime;
      this.log(
        "error",
        `Embedding generation failed after ${processingTime}ms:`,
        error
      );
      throw new Error(
        `Embedding generation failed: ${this.getErrorMessage(error)}`
      );
    }
  }
  /**
   * Make API request to Ollama
   */
  async makeOllamaRequest(endpoint, body) {
    try {
      const url = `${this.endpoint}${endpoint}`;
      const response = await fetch(url, {
        method: "POST",
        headers: {
          "Content-Type": "application/json"
        },
        body: JSON.stringify(body),
        signal: AbortSignal.timeout(this.advanced.timeout)
      });
      if (!response.ok) {
        const errorText = await response.text();
        let errorData = {};
        try {
          errorData = JSON.parse(errorText);
        } catch {
          errorData = { error: { message: errorText } };
        }
        const errorMessage = errorData.error?.message || errorData.message || `HTTP ${response.status} ${response.statusText}`;
        throw new Error(`Ollama API error: ${errorMessage}`);
      }
      return await response.json();
    } catch (error) {
      if (error instanceof Error) {
        if (error.name === "AbortError") {
          throw new Error("Ollama request timeout - is Ollama running?");
        }
        if (error.message.includes("fetch")) {
          throw new Error(
            "Cannot connect to Ollama - is it running on " + this.endpoint + "?"
          );
        }
        throw error;
      }
      throw new Error(`Request failed: ${String(error)}`);
    }
  }
  /**
   * Prepare function calling instructions for Ollama
   */
  prepareFunctionInstructions(functions) {
    let instructions = "\nAvailable actions you can perform:\n";
    functions.forEach((func, index) => {
      instructions += `${index + 1}. ${func.name}: ${func.description}
`;
      Object.entries(func.parameters).forEach(([key, param]) => {
        instructions += `   - ${key}: ${param.description}`;
        if (param.enum) {
          instructions += ` (options: ${param.enum.join(", ")})`;
        }
        instructions += "\n";
      });
    });
    instructions += "\nAfter your summary, if appropriate, specify actions using this format:\n";
    instructions += "ACTIONS:\n";
    instructions += "- function_name: parameter_value\n";
    instructions += "- another_function: another_value\n";
    return instructions;
  }
  /**
   * Parse function calls from Ollama response
   */
  async parseFunctionCalls(response, functions, document) {
    const results = [];
    try {
      const actionsMatch = response.match(/ACTIONS:\s*([\s\S]*?)(?:\n\n|$)/i);
      if (!actionsMatch) {
        return results;
      }
      const actionsText = actionsMatch[1];
      const actionLines = actionsText.split("\n").filter((line) => line.trim().startsWith("-")).map((line) => line.replace(/^-\s*/, "").trim());
      for (const actionLine of actionLines) {
        const [functionName, ...valueParts] = actionLine.split(":");
        const value = valueParts.join(":").trim();
        const func = functions.find((f) => f.name === functionName.trim());
        if (!func) {
          results.push({
            name: functionName.trim(),
            success: false,
            error: "Function not found",
            executedAt: /* @__PURE__ */ new Date()
          });
          continue;
        }
        try {
          const args = this.parseSimpleArgs(func, value);
          await func.handler(args, document);
          results.push({
            name: func.name,
            success: true,
            result: args,
            executedAt: /* @__PURE__ */ new Date()
          });
          this.log("debug", `Function ${func.name} executed successfully`);
        } catch (error) {
          results.push({
            name: func.name,
            success: false,
            error: error instanceof Error ? error.message : "Unknown error",
            executedAt: /* @__PURE__ */ new Date()
          });
          this.log("error", `Function ${func.name} failed:`, error);
        }
      }
    } catch (error) {
      this.log("error", "Function parsing failed:", error);
    }
    return results;
  }
  /**
   * Parse simple function arguments
   */
  parseSimpleArgs(func, value) {
    const paramKeys = Object.keys(func.parameters);
    if (paramKeys.length === 1) {
      const paramKey = paramKeys[0];
      const param = func.parameters[paramKey];
      if (param.type === "number") {
        return { [paramKey]: parseFloat(value) };
      } else if (param.type === "array") {
        return { [paramKey]: value.split(",").map((v) => v.trim()) };
      } else {
        return { [paramKey]: value };
      }
    }
    try {
      return JSON.parse(value);
    } catch {
      return { [paramKeys[0]]: value };
    }
  }
  /**
   * Estimate token count (rough approximation)
   */
  estimateTokenCount(text) {
    return Math.ceil(text.length / 4);
  }
  /**
   * Validate credentials
   */
  validateCredentials(credentials) {
    if (!credentials || typeof credentials !== "object") {
      throw new Error("Credentials object is required");
    }
    if (!credentials.apiKey) {
      credentials.apiKey = "local";
    }
  }
  /**
   * Get provider information
   */
  getProviderInfo() {
    return {
      name: "Ollama",
      version: "1.4.0",
      models: this.config,
      advanced: this.advanced,
      endpoint: this.endpoint
    };
  }
};

// src/providers/factory.ts
function createProvider(provider, credentials, modelConfig, advancedOptions) {
  switch (provider) {
    case "openai":
      return new OpenAIProvider(
        credentials,
        modelConfig,
        advancedOptions
      );
    case "anthropic":
      return new AnthropicProvider(
        credentials,
        modelConfig,
        advancedOptions
      );
    case "ollama":
      return new OllamaProvider(
        credentials,
        modelConfig,
        advancedOptions
      );
    default:
      throw new Error(`Unsupported provider: ${provider}`);
  }
}
function validateProviderModel(provider, model) {
  const supportedModels = {
    openai: ["summary", "embedding"],
    anthropic: ["summary"],
    // Anthropic doesn't support embeddings
    ollama: ["summary", "embedding"]
    // Ollama supports both
  };
  const supported = supportedModels[provider];
  if (!supported || !supported.includes(model)) {
    throw new Error(
      `Provider '${provider}' does not support model '${model}'. Supported models: ${supported?.join(", ") || "none"}`
    );
  }
}

// src/utils/vector-search.ts
async function detectVectorSearchSupport(model) {
  try {
    const collection = model.collection;
    const db = collection.db;
    if (!db) {
      return false;
    }
    try {
      const buildInfo = await db.admin().buildInfo();
      const version = buildInfo.version;
      const majorVersion = parseInt(version.split(".")[0]);
      if (majorVersion < 6) {
        return false;
      }
    } catch (adminError) {
    }
    await model.aggregate([
      {
        $vectorSearch: {
          index: "test_detection_index",
          path: "test_field",
          queryVector: [0.1],
          numCandidates: 1,
          limit: 1
        }
      },
      { $limit: 0 }
      // Don't return any results, just test if the operation is supported
    ]).exec();
    return true;
  } catch (error) {
    return false;
  }
}
async function createVectorIndex(model, embeddingField, dimensions, config) {
  try {
    const collection = model.collection;
    const indexName = config.indexName || "vector_index";
    if (typeof collection.listSearchIndexes !== "function") {
      console.warn(
        "MongoDB Atlas Search is not available. Vector search index creation skipped."
      );
      return;
    }
    const indexes = await collection.listSearchIndexes().toArray();
    const existingIndex = indexes.find((idx) => idx.name === indexName);
    if (existingIndex) {
      console.log(`Vector search index '${indexName}' already exists`);
      return;
    }
    if (!config.autoCreateIndex) {
      console.warn(
        `Vector search index '${indexName}' does not exist and auto-creation is disabled`
      );
      return;
    }
    const indexDefinition = {
      name: indexName,
      definition: {
        fields: [
          {
            type: "vector",
            path: `${embeddingField}.embedding`,
            numDimensions: dimensions,
            similarity: config.similarity || "cosine"
          }
        ]
      }
    };
    await collection.createSearchIndex(indexDefinition);
    console.log(
      `Created vector search index '${indexName}' for field '${embeddingField}.embedding'`
    );
    console.log(
      `Index '${indexName}' is being built. It may take 1-2 minutes to become available.`
    );
  } catch (error) {
    console.error(
      `Failed to create vector search index: ${error instanceof Error ? error.message : "Unknown error"}`
    );
  }
}
async function performVectorSearch(model, queryEmbedding, fieldName, options) {
  const {
    limit = 10,
    threshold = 0.7,
    filter = {},
    indexName = "vector_index",
    numCandidates = limit * 10
  } = options;
  try {
    const pipeline = [
      {
        $vectorSearch: {
          index: indexName,
          path: `${fieldName}.embedding`,
          queryVector: queryEmbedding,
          numCandidates: Math.max(numCandidates, limit),
          limit
        }
      }
    ];
    pipeline.push({
      $addFields: {
        similarity: { $meta: "vectorSearchScore" }
      }
    });
    if (threshold > 0) {
      pipeline.push({
        $match: {
          similarity: { $gte: threshold }
        }
      });
    }
    if (Object.keys(filter).length > 0) {
      pipeline.push({
        $match: filter
      });
    }
    pipeline.push({
      $sort: { similarity: -1 }
    });
    pipeline.push({
      $limit: limit
    });
    const results = await model.aggregate(pipeline).exec();
    return results.map((doc) => ({
      document: doc,
      similarity: doc.similarity || 0,
      metadata: {
        field: fieldName,
        distance: 1 - (doc.similarity || 0)
      }
    }));
  } catch (error) {
    console.error(
      `Vector search failed: ${error instanceof Error ? error.message : "Unknown error"}`
    );
    throw error;
  }
}
function cosineSimilarity(a, b) {
  if (!a || !b || a.length !== b.length) return 0;
  let dotProduct = 0;
  let normA = 0;
  let normB = 0;
  for (let i = 0; i < a.length; i++) {
    dotProduct += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }
  const denominator = Math.sqrt(normA) * Math.sqrt(normB);
  return denominator === 0 ? 0 : dotProduct / denominator;
}
async function performInMemorySearch(model, queryEmbedding, fieldName, options) {
  const { limit = 10, threshold = 0.7, filter = {} } = options;
  try {
    const searchFilter = {
      ...filter,
      [`${fieldName}.embedding`]: { $exists: true }
    };
    const docs = await model.find(searchFilter).exec();
    const results = [];
    for (const doc of docs) {
      const docEmbedding = doc[fieldName]?.embedding;
      if (!docEmbedding) continue;
      const similarity = cosineSimilarity(queryEmbedding, docEmbedding);
      if (similarity >= threshold) {
        results.push({
          document: doc,
          similarity,
          metadata: {
            field: fieldName,
            distance: 1 - similarity
          }
        });
      }
    }
    return results.sort((a, b) => b.similarity - a.similarity).slice(0, limit);
  } catch (error) {
    console.error(
      `In-memory search failed: ${error instanceof Error ? error.message : "Unknown error"}`
    );
    throw error;
  }
}

// src/plugin.ts
var vectorSearchCache = /* @__PURE__ */ new WeakMap();
function aiPlugin(schema, options) {
  const config = options.ai;
  if (!config.model || !["summary", "embedding"].includes(config.model)) {
    throw new Error("Valid model (summary|embedding) required");
  }
  if (!config.provider || !["openai", "anthropic", "ollama"].includes(config.provider)) {
    throw new Error("Valid provider (openai|anthropic|ollama) required");
  }
  if (!config.field || typeof config.field !== "string" || config.field.trim() === "") {
    throw new Error("Field name required");
  }
  if (!config.credentials?.apiKey || typeof config.credentials.apiKey !== "string" || config.credentials.apiKey.trim() === "") {
    throw new Error("API key required");
  }
  validateProviderModel(config.provider, config.model);
  const virtuals = schema.virtuals;
  if (schema.paths[config.field] || virtuals[config.field]) {
    throw new Error(`Field "${config.field}" already exists in schema`);
  }
  let provider;
  try {
    provider = createProvider(
      config.provider,
      config.credentials,
      config.modelConfig,
      config.advanced
    );
  } catch (error) {
    throw new Error(
      `Failed to initialize AI provider: ${error instanceof Error ? error.message : "Unknown error"}`
    );
  }
  const vectorSearchConfig = {
    enabled: true,
    indexName: "vector_index",
    autoCreateIndex: true,
    similarity: "cosine",
    ...config.vectorSearch
  };
  if (config.model === "summary") {
    schema.add({
      [config.field]: {
        type: {
          summary: { type: String },
          generatedAt: { type: Date },
          model: { type: String },
          tokenCount: { type: Number },
          processingTime: { type: Number },
          functionResults: [
            {
              name: { type: String },
              success: { type: Boolean },
              result: { type: import_mongoose.Schema.Types.Mixed },
              error: { type: String },
              executedAt: { type: Date }
            }
          ]
        },
        default: void 0
      }
    });
  } else if (config.model === "embedding") {
    schema.add({
      [config.field]: {
        type: {
          embedding: { type: [Number] },
          generatedAt: { type: Date },
          model: { type: String },
          dimensions: { type: Number },
          processingTime: { type: Number },
          functionResults: [
            {
              name: { type: String },
              success: { type: Boolean },
              result: { type: import_mongoose.Schema.Types.Mixed },
              error: { type: String },
              executedAt: { type: Date }
            }
          ]
        },
        default: void 0
      }
    });
  }
  schema.methods.getAIContent = function() {
    return this[config.field] || null;
  };
  schema.methods.regenerateAI = async function() {
    try {
      this[config.field] = void 0;
      await processAI(this, config, provider);
    } catch (error) {
      throw new Error(
        `Failed to regenerate AI content: ${error instanceof Error ? error.message : "Unknown error"}`
      );
    }
  };
  if (config.model === "embedding") {
    schema.methods.calculateSimilarity = function(other) {
      const thisEmbedding = this[config.field]?.embedding;
      const otherEmbedding = other[config.field]?.embedding;
      if (!thisEmbedding || !otherEmbedding) {
        return 0;
      }
      return cosineSimilarity(thisEmbedding, otherEmbedding);
    };
    schema.statics.semanticSearch = async function(query, options2 = {}) {
      const { limit = 10, threshold = 0.7, filter = {} } = options2;
      if (!query || typeof query !== "string") {
        throw new Error("Query string is required");
      }
      try {
        const queryResult = await provider.generateEmbedding(query);
        const queryEmbedding = queryResult.embedding;
        const shouldUseVectorSearch = await determineSearchMethod(
          this,
          options2,
          vectorSearchConfig
        );
        if (shouldUseVectorSearch) {
          await ensureVectorIndex(
            this,
            config.field,
            queryEmbedding.length,
            vectorSearchConfig
          );
          return await performVectorSearch(this, queryEmbedding, config.field, {
            ...options2,
            indexName: vectorSearchConfig.indexName,
            numCandidates: options2.numCandidates || limit * 10
          });
        } else {
          return await performInMemorySearch(
            this,
            queryEmbedding,
            config.field,
            options2
          );
        }
      } catch (error) {
        throw new Error(
          `Semantic search failed: ${error instanceof Error ? error.message : "Unknown error"}`
        );
      }
    };
    schema.statics.findSimilar = async function(document, options2 = {}) {
      if (!document) {
        throw new Error("Reference document is required");
      }
      const refEmbedding = document[config.field]?.embedding;
      if (!refEmbedding) {
        throw new Error("Document has no embedding");
      }
      const { limit = 10, threshold = 0.7, filter = {} } = options2;
      try {
        const shouldUseVectorSearch = await determineSearchMethod(
          this,
          options2,
          vectorSearchConfig
        );
        if (shouldUseVectorSearch) {
          await ensureVectorIndex(
            this,
            config.field,
            refEmbedding.length,
            vectorSearchConfig
          );
          return await performVectorSearch(this, refEmbedding, config.field, {
            ...options2,
            filter: { ...filter, _id: { $ne: document._id } },
            indexName: vectorSearchConfig.indexName,
            numCandidates: options2.numCandidates || limit * 10
          });
        } else {
          return await performInMemorySearch(this, refEmbedding, config.field, {
            ...options2,
            filter: { ...filter, _id: { $ne: document._id } }
          });
        }
      } catch (error) {
        throw new Error(
          `Find similar failed: ${error instanceof Error ? error.message : "Unknown error"}`
        );
      }
    };
  }
  schema.pre("save", async function(next) {
    try {
      if (config.advanced?.skipOnUpdate && !this.isNew) {
        return next();
      }
      if (this[config.field] && !config.advanced?.forceRegenerate) {
        return next();
      }
      if (!hasContent(this, config)) {
        return next();
      }
      await processAI(this, config, provider);
      next();
    } catch (error) {
      const errorMessage = error instanceof Error ? error.message : "Unknown AI processing error";
      if (config.advanced?.continueOnError !== false) {
        console.warn(`AI processing failed: ${errorMessage}`);
        next();
      } else {
        next(new Error(`AI processing failed: ${errorMessage}`));
      }
    }
  });
}
async function determineSearchMethod(model, options, vectorSearchConfig) {
  if (vectorSearchConfig.enabled === false) {
    return false;
  }
  if (options.useVectorSearch !== void 0) {
    return options.useVectorSearch;
  }
  if (vectorSearchCache.has(model)) {
    return vectorSearchCache.get(model);
  }
  try {
    const isSupported = await detectVectorSearchSupport(model);
    vectorSearchCache.set(model, isSupported);
    return isSupported;
  } catch (error) {
    vectorSearchCache.set(model, false);
    return false;
  }
}
async function ensureVectorIndex(model, fieldName, dimensions, vectorSearchConfig) {
  try {
    await createVectorIndex(model, fieldName, dimensions, vectorSearchConfig);
  } catch (error) {
    console.warn(
      `Failed to create vector index: ${error instanceof Error ? error.message : "Unknown error"}`
    );
  }
}
async function processAI(document, config, provider) {
  const docData = document.toObject();
  delete docData._id;
  delete docData.__v;
  delete docData.createdAt;
  delete docData.updatedAt;
  delete docData[config.field];
  let processedData = { ...docData };
  if (config.includeFields && config.includeFields.length > 0) {
    processedData = {};
    config.includeFields.forEach((field) => {
      if (docData[field] !== void 0) {
        processedData[field] = docData[field];
      }
    });
  }
  if (config.excludeFields && config.excludeFields.length > 0) {
    config.excludeFields.forEach((field) => {
      delete processedData[field];
    });
  }
  if (config.model === "summary") {
    const functionsWithDocument = config.functions?.map((func) => ({
      ...func,
      handler: (args, _) => func.handler(args, document)
    }));
    const result = await provider.summarize(
      processedData,
      config.prompt,
      functionsWithDocument
    );
    document[config.field] = result;
  } else if (config.model === "embedding") {
    const text = JSON.stringify(processedData);
    const result = await provider.generateEmbedding(text);
    document[config.field] = result;
  }
}
function hasContent(document, config) {
  const docObj = document.toObject();
  delete docObj._id;
  delete docObj.__v;
  delete docObj.createdAt;
  delete docObj.updatedAt;
  delete docObj[config.field];
  let processedData = { ...docObj };
  if (config.includeFields && config.includeFields.length > 0) {
    processedData = {};
    config.includeFields.forEach((field) => {
      if (docObj[field] !== void 0) {
        processedData[field] = docObj[field];
      }
    });
  }
  if (config.excludeFields && config.excludeFields.length > 0)