@matthew.ngo/ai-toolkit/dist/chunk-6OKKVZHN.cjs.map

A comprehensive AI toolkit with multi-provider support

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{\n  AIProvider,\n  Classification,\n  CodeGenerationOptions,\n  CodeResult,\n  GenerateOptions,\n  ImageAnalysis,\n  ImageAnalysisOptions,\n  ImageGenerationOptions,\n  ImageResult,\n  SentimentAnalysis,\n  SpeechOptions,\n  SummarizeOptions,\n  TranscriptionOptions,\n  TranscriptionResult,\n} from \"../types\";\n\nexport interface ProviderConfig {\n  name: string;\n  apiKey?: string;\n  baseUrl?: string;\n  model?: string;\n  headers?: Record<string, string>;\n  maxRetries?: number;\n  timeout?: number;\n}\n\nexport abstract class BaseProvider implements AIProvider {\n  public name: string;\n  protected config: ProviderConfig;\n  protected defaultModel: string;\n\n  constructor(config: ProviderConfig) {\n    this.name = config.name;\n    this.config = config;\n    this.defaultModel = config.model || this.getDefaultModel();\n  }\n\n  /**\n   * Get default model for provider\n   */\n  protected abstract getDefaultModel(): string;\n\n  /**\n   * Validate API key format\n   */\n  protected validateApiKey(): void {\n    if (!this.config.apiKey) {\n      throw new Error(`API key required for ${this.name} provider`);\n    }\n  }\n\n  /**\n   * Get headers for API requests\n   */\n  protected getHeaders(\n    additionalHeaders?: Record<string, string>\n  ): Record<string, string> {\n    return {\n      \"Content-Type\": \"application/json\",\n      ...this.config.headers,\n      ...additionalHeaders,\n    };\n  }\n\n  /**\n   * Make API request with error handling\n   */\n  protected async makeRequest<T>(\n    url: string,\n    options: RequestInit\n  ): Promise<T> {\n    const timeout = this.config.timeout || 30000;\n    const controller = new AbortController();\n    const timeoutId = setTimeout(() => controller.abort(), timeout);\n\n    try {\n      const response = await fetch(url, {\n        ...options,\n        signal: controller.signal,\n      });\n\n      if (!response.ok) {\n        const error = await this.parseError(response);\n        throw error;\n      }\n\n      return await response.json();\n    } catch (error: any) {\n      if (error.name === \"AbortError\") {\n        throw new Error(`Request timeout after ${timeout}ms`);\n      }\n      throw error;\n    } finally {\n      clearTimeout(timeoutId);\n    }\n  }\n\n  /**\n   * Parse API error response\n   */\n  protected async parseError(response: Response): Promise<Error> {\n    let errorMessage = `${this.name} API error: ${response.status} ${response.statusText}`;\n\n    try {\n      const errorData = await response.json();\n      if (errorData.error?.message) {\n        errorMessage = errorData.error.message;\n      } else if (errorData.message) {\n        errorMessage = errorData.message;\n      }\n    } catch {\n      // Use default error message if parsing fails\n    }\n\n    const error = new Error(errorMessage) as any;\n    error.status = response.status;\n    error.provider = this.name;\n\n    return error;\n  }\n\n  /**\n   * Get model to use for request\n   */\n  protected getModel(options?: { model?: string }): string {\n    return options?.model || this.config.model || this.defaultModel;\n  }\n\n  // Default implementations that throw not implemented errors\n  async generateText(\n    _prompt: string,\n    _options?: GenerateOptions\n  ): Promise<string> {\n    throw new Error(\n      `Text generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async *generateStream(\n    _prompt: string,\n    _options?: GenerateOptions\n  ): AsyncGenerator<string> {\n    throw new Error(\n      `Stream generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async generateEmbedding(_text: string): Promise<number[]> {\n    throw new Error(\n      `Embedding generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async classifyText(\n    _text: string,\n    _labels: string[]\n  ): Promise<Classification> {\n    throw new Error(\n      `Text classification not implemented for ${this.name} provider`\n    );\n  }\n\n  async summarize(_text: string, _options?: SummarizeOptions): Promise<string> {\n    throw new Error(\n      `Text summarization not implemented for ${this.name} provider`\n    );\n  }\n\n  async analyzeSentiment(_text: string): Promise<SentimentAnalysis> {\n    throw new Error(\n      `Sentiment analysis not implemented for ${this.name} provider`\n    );\n  }\n\n  async generateImage(\n    _prompt: string,\n    _options?: ImageGenerationOptions\n  ): Promise<ImageResult> {\n    throw new Error(\n      `Image generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async analyzeImage(\n    _image: string | Blob,\n    _options?: ImageAnalysisOptions\n  ): Promise<ImageAnalysis> {\n    throw new Error(`Image analysis not implemented for ${this.name} provider`);\n  }\n\n  async transcribeAudio(\n    _audio: Blob,\n    _options?: TranscriptionOptions\n  ): Promise<TranscriptionResult> {\n    throw new Error(\n      `Audio transcription not implemented for ${this.name} provider`\n    );\n  }\n\n  async generateSpeech(_text: string, _options?: SpeechOptions): Promise<Blob> {\n    throw new Error(\n      `Speech generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async generateCode(\n    _prompt: string,\n    _options?: CodeGenerationOptions\n  ): Promise<CodeResult> {\n    throw new Error(\n      `Code generation not implemented for ${this.name} provider`\n    );\n  }\n\n  async explainCode(_code: string, _language?: string): Promise<string> {\n    throw new Error(\n      `Code explanation not implemented for ${this.name} provider`\n    );\n  }\n}\n","import Anthropic from \"@anthropic-ai/sdk\";\nimport { Classification, GenerateOptions, SummarizeOptions } from \"../types\";\nimport { BaseProvider, ProviderConfig } from \"./base-provider\";\n\nexport class AnthropicProvider extends BaseProvider {\n  private client: Anthropic;\n\n  constructor(config: ProviderConfig) {\n    super(config);\n    this.validateApiKey();\n\n    this.client = new Anthropic({\n      apiKey: this.config.apiKey!,\n      baseURL: this.config.baseUrl,\n      timeout: this.config.timeout,\n      maxRetries: this.config.maxRetries || 2,\n    });\n  }\n\n  protected getDefaultModel(): string {\n    return \"claude-3-sonnet-20240229\";\n  }\n\n  /**\n   * Generate text completion\n   */\n  override async generateText(\n    prompt: string,\n    options?: GenerateOptions\n  ): Promise<string> {\n    const model = this.getModel(options as { model?: string });\n\n    const response = await this.client.messages.create({\n      model,\n      messages: [\n        {\n          role: \"user\",\n          content: prompt,\n        },\n      ],\n      system: options?.systemPrompt,\n      max_tokens: options?.maxTokens || 1000,\n      temperature: options?.temperature,\n      top_p: options?.topP,\n      top_k: options?.topK,\n      stop_sequences: options?.stopSequences,\n      stream: false,\n    });\n\n    // Extract text from content blocks\n    const content = response.content\n      .filter((block) => block.type === \"text\")\n      .map((block) => (block as any).text)\n      .join(\"\\n\");\n\n    return content;\n  }\n\n  /**\n   * Generate streaming text completion\n   */\n  override async *generateStream(\n    prompt: string,\n    options?: GenerateOptions\n  ): AsyncGenerator<string> {\n    const model = this.getModel(options as { model?: string });\n\n    const stream = await this.client.messages.create({\n      model,\n      messages: [\n        {\n          role: \"user\",\n          content: prompt,\n        },\n      ],\n      system: options?.systemPrompt,\n      max_tokens: options?.maxTokens || 1000,\n      temperature: options?.temperature,\n      top_p: options?.topP,\n      top_k: options?.topK,\n      stop_sequences: options?.stopSequences,\n      stream: true,\n    });\n\n    for await (const event of stream) {\n      if (\n        event.type === \"content_block_delta\" &&\n        event.delta.type === \"text_delta\"\n      ) {\n        yield event.delta.text;\n      }\n    }\n  }\n\n  /**\n   * Classify text\n   */\n  override async classifyText(\n    text: string,\n    labels: string[]\n  ): Promise<Classification> {\n    const prompt = `Classify this text into exactly one category from: ${labels.join(\", \")}.\n\nText: \"${text}\"\n\nRespond with a JSON object:\n{\n  \"label\": \"chosen category\",\n  \"confidence\": 0.0-1.0,\n  \"scores\": { \"category1\": 0.0-1.0, ... }\n}`;\n\n    const response = await this.generateText(prompt, {\n      temperature: 0,\n      maxTokens: 200,\n    });\n\n    try {\n      // Extract JSON from response\n      const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback parsing\n    }\n\n    // Fallback response\n    const label = labels[0] ?? \"\";\n    return {\n      label,\n      confidence: 0.5,\n      scores: labels.reduce(\n        (acc, l) => ({\n          ...acc,\n          [l]: l === label ? 0.5 : 0.5 / (labels.length - 1),\n        }),\n        {}\n      ),\n    };\n  }\n\n  /**\n   * Summarize text\n   */\n  override async summarize(\n    text: string,\n    options?: SummarizeOptions\n  ): Promise<string> {\n    const instructions = {\n      bullet: \"Create a bullet-point summary with key points\",\n      paragraph: \"Write a concise paragraph summary\",\n      tldr: \"Write a one-sentence TL;DR\",\n      \"key-points\": \"Extract the most important key points\",\n      executive: \"Write an executive summary for business leaders\",\n    };\n\n    const prompt = `${instructions[options?.style || \"paragraph\"]} of the following text.\n${options?.maxLength ? `Keep it under ${options.maxLength} characters.` : \"\"}\n\nText:\n\"${text}\"\n\nSummary:`;\n\n    return this.generateText(prompt, {\n      maxTokens: options?.maxLength ? Math.ceil(options.maxLength / 4) : 200,\n      temperature: 0.3,\n    });\n  }\n\n  /**\n   * Analyze sentiment\n   */\n  override async analyzeSentiment(text: string): Promise<any> {\n    const prompt = `Analyze the sentiment of the following text. Respond with JSON:\n{\n  \"sentiment\": \"positive\" | \"negative\" | \"neutral\" | \"mixed\",\n  \"score\": 0.0-1.0,\n  \"aspects\": [\n    { \"aspect\": \"...\", \"sentiment\": \"...\", \"score\": 0.0-1.0 }\n  ]\n}\n\nText: \"${text}\"`;\n\n    const response = await this.generateText(prompt, {\n      temperature: 0,\n      maxTokens: 300,\n    });\n\n    try {\n      const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback\n    }\n\n    return {\n      sentiment: \"neutral\",\n      score: 0.5,\n      aspects: [],\n    };\n  }\n\n  /**\n   * Generate embeddings (not supported by Anthropic)\n   */\n  override async generateEmbedding(_text: string): Promise<number[]> {\n    throw new Error(\n      \"Embeddings not supported by Anthropic. Use a different provider.\"\n    );\n  }\n\n  /**\n   * Generate code\n   */\n  override async generateCode(prompt: string, options?: any): Promise<any> {\n    const systemPrompt = `You are Claude, an expert programmer. Generate clean, efficient code.\n${options?.includeComments ? \"Include helpful comments.\" : \"\"}\n${options?.includeTests ? \"Include unit tests.\" : \"\"}\nAlways respond with valid JSON containing: code, language, explanation, and dependencies array.`;\n\n    const fullPrompt = `Language: ${options?.language || \"auto-detect\"}\nFramework: ${options?.framework || \"none\"}\n\nTask: ${prompt}\n\nRespond with JSON:\n{\n  \"code\": \"the generated code\",\n  \"language\": \"detected or specified language\",\n  \"explanation\": \"brief explanation\",\n  ${options?.includeTests ? '\"tests\": \"unit test code\",' : \"\"}\n  \"dependencies\": [\"required\", \"packages\"]\n}`;\n\n    const response = await this.generateText(fullPrompt, {\n      systemPrompt,\n      temperature: 0.2,\n      maxTokens: 2000,\n    });\n\n    try {\n      const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback\n    }\n\n    return {\n      code: response,\n      language: options?.language || \"unknown\",\n      explanation: \"Generated code\",\n      dependencies: [],\n    };\n  }\n\n  /**\n   * Explain code\n   */\n  override async explainCode(code: string, language?: string): Promise<string> {\n    const prompt = `Explain this ${language || \"code\"} in detail:\n\n\\`\\`\\`${language || \"\"}\n${code}\n\\`\\`\\`\n\nExplain:\n1. What it does\n2. How it works\n3. Key concepts/patterns\n4. Potential improvements`;\n\n    return this.generateText(prompt, {\n      temperature: 0.3,\n      maxTokens: 1000,\n    });\n  }\n\n  /**\n  //  * Extract token usage from response\n  //  */\n  // private _extractTokenUsage(usage: any): TokenUsage {\n  //   return {\n  //     promptTokens: usage?.input_tokens || 0,\n  //     completionTokens: usage?.output_tokens || 0,\n  //     totalTokens: (usage?.input_tokens || 0) + (usage?.output_tokens || 0),\n  //   };\n  // }\n}\n","import { GoogleGenerativeAI } from \"@google/generative-ai\";\nimport {\n  Classification,\n  GenerateOptions,\n  ImageAnalysis,\n  ImageAnalysisOptions,\n  SummarizeOptions,\n} from \"../types\";\nimport { BaseProvider, ProviderConfig } from \"./base-provider\";\n\nexport class GoogleProvider extends BaseProvider {\n  private client: GoogleGenerativeAI;\n\n  constructor(config: ProviderConfig) {\n    super(config);\n    this.validateApiKey();\n\n    this.client = new GoogleGenerativeAI(this.config.apiKey!);\n  }\n\n  protected getDefaultModel(): string {\n    return \"gemini-pro\";\n  }\n\n  /**\n   * Generate text completion\n   */\n  override async generateText(\n    prompt: string,\n    options?: GenerateOptions\n  ): Promise<string> {\n    const modelName = this.getModel(options as { model?: string });\n    const model = this.client.getGenerativeModel({ model: modelName });\n\n    // Configure generation settings\n    const generationConfig = {\n      maxOutputTokens: options?.maxTokens || 1000,\n      temperature: options?.temperature || 0.7,\n      topP: options?.topP || 0.95,\n      topK: options?.topK || 40,\n      stopSequences: options?.stopSequences,\n    };\n\n    // Build prompt with system message if provided\n    const fullPrompt = options?.systemPrompt\n      ? `${options.systemPrompt}\\n\\n${prompt}`\n      : prompt;\n\n    const result = await model.generateContent({\n      contents: [{ role: \"user\", parts: [{ text: fullPrompt }] }],\n      generationConfig,\n    });\n\n    const response = await result.response;\n    return response.text();\n  }\n\n  /**\n   * Generate streaming text completion\n   */\n  override async *generateStream(\n    prompt: string,\n    options?: GenerateOptions\n  ): AsyncGenerator<string> {\n    const modelName = this.getModel(options as { model?: string });\n    const model = this.client.getGenerativeModel({ model: modelName });\n\n    const generationConfig = {\n      maxOutputTokens: options?.maxTokens || 1000,\n      temperature: options?.temperature || 0.7,\n      topP: options?.topP || 0.95,\n      topK: options?.topK || 40,\n      stopSequences: options?.stopSequences,\n    };\n\n    const fullPrompt = options?.systemPrompt\n      ? `${options.systemPrompt}\\n\\n${prompt}`\n      : prompt;\n\n    const result = await model.generateContentStream({\n      contents: [{ role: \"user\", parts: [{ text: fullPrompt }] }],\n      generationConfig,\n    });\n\n    for await (const chunk of result.stream) {\n      const text = chunk.text();\n      if (text) {\n        yield text;\n      }\n    }\n  }\n\n  /**\n   * Generate embeddings\n   */\n  override async generateEmbedding(text: string): Promise<number[]> {\n    const model = this.client.getGenerativeModel({ model: \"embedding-001\" });\n\n    const result = await model.embedContent(text);\n    return result.embedding.values;\n  }\n\n  /**\n   * Classify text\n   */\n  override async classifyText(\n    text: string,\n    labels: string[]\n  ): Promise<Classification> {\n    const prompt = `Classify the following text into exactly one of these categories: ${labels.join(\", \")}.\n\nText: \"${text}\"\n\nRespond with only JSON in this format:\n{\n  \"label\": \"chosen category\",\n  \"confidence\": 0.95,\n  \"scores\": {\n    \"category1\": 0.95,\n    \"category2\": 0.03,\n    \"category3\": 0.02\n  }\n}`;\n\n    const response = await this.generateText(prompt, {\n      temperature: 0,\n      maxTokens: 200,\n    });\n\n    try {\n      // Extract JSON from response\n      const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback\n    }\n\n    // Default response\n    const label = labels[0] || \"unknown\";\n    return {\n      label,\n      confidence: 0.5,\n      scores: labels.reduce(\n        (acc, l) => ({\n          ...acc,\n          [l]: l === label ? 0.5 : 0.5 / (labels.length - 1),\n        }),\n        {}\n      ),\n    };\n  }\n\n  /**\n   * Summarize text\n   */\n  override async summarize(\n    text: string,\n    options?: SummarizeOptions\n  ): Promise<string> {\n    const styleInstructions = {\n      bullet: \"Create a concise bullet-point summary\",\n      paragraph: \"Write a well-structured paragraph summary\",\n      tldr: \"Provide a single sentence TL;DR\",\n      \"key-points\": \"List only the most critical key points\",\n      executive: \"Write a professional executive summary\",\n    };\n\n    const prompt = `${styleInstructions[options?.style || \"paragraph\"]} of this text:\n\n\"${text}\"\n\n${options?.maxLength ? `Limit to ${options.maxLength} characters.` : \"\"}`;\n\n    return this.generateText(prompt, {\n      maxTokens: options?.maxLength ? Math.ceil(options.maxLength / 4) : 200,\n      temperature: 0.3,\n    });\n  }\n\n  /**\n   * Analyze image (Gemini Pro Vision)\n   */\n  override async analyzeImage(\n    image: string | Blob,\n    options?: ImageAnalysisOptions\n  ): Promise<ImageAnalysis> {\n    const model = this.client.getGenerativeModel({\n      model: \"gemini-pro-vision\",\n    });\n\n    // Convert image to base64 if needed\n    let base64Image: string;\n    if (image instanceof Blob) {\n      const buffer = await image.arrayBuffer();\n      base64Image = btoa(String.fromCharCode(...new Uint8Array(buffer)));\n    } else {\n      base64Image = image.replace(/^data:image\\/\\w+;base64,/, \"\");\n    }\n\n    const features = options?.features || [\"description\", \"objects\", \"text\"];\n    const prompt = `Analyze this image and provide a detailed response in JSON format with these features: ${features.join(\", \")}.\n\nExpected format:\n{\n  ${features.includes(\"description\") ? '\"description\": \"detailed description\",' : \"\"}\n  ${features.includes(\"objects\") ? '\"objects\": [{\"label\": \"object\", \"confidence\": 0.9}],' : \"\"}\n  ${features.includes(\"text\") ? '\"text\": [{\"text\": \"extracted text\", \"confidence\": 0.9}],' : \"\"}\n  ${features.includes(\"colors\") ? '\"colors\": [{\"hex\": \"#000000\", \"name\": \"black\", \"percentage\": 30}],' : \"\"}\n  ${features.includes(\"tags\") ? '\"tags\": [\"tag1\", \"tag2\"]' : \"\"}\n}`;\n\n    const result = await model.generateContent({\n      contents: [\n        {\n          role: \"user\",\n          parts: [\n            { text: prompt },\n            {\n              inlineData: {\n                mimeType: \"image/jpeg\",\n                data: base64Image,\n              },\n            },\n          ],\n        },\n      ],\n    });\n\n    const response = await result.response;\n    const responseText = response.text();\n\n    try {\n      const jsonMatch = responseText.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback\n    }\n\n    return {\n      description: responseText,\n      objects: [],\n      text: [],\n      colors: [],\n      tags: [],\n    };\n  }\n\n  /**\n   * Generate code\n   */\n  override async generateCode(prompt: string, options?: any): Promise<any> {\n    const systemContext = `You are an expert programmer. Generate high-quality code.\n${options?.includeComments ? \"Include clear comments.\" : \"\"}\n${options?.includeTests ? \"Include comprehensive tests.\" : \"\"}\nStyle: ${options?.style || \"clean and efficient\"}`;\n\n    const fullPrompt = `${systemContext}\n\nLanguage: ${options?.language || \"auto-detect\"}\nFramework: ${options?.framework || \"none\"}\nTask: ${prompt}\n\nProvide response as JSON:\n{\n  \"code\": \"complete code\",\n  \"language\": \"programming language\",\n  \"explanation\": \"what the code does\",\n  ${options?.includeTests ? '\"tests\": \"test code\",' : \"\"}\n  \"dependencies\": [\"list\", \"of\", \"dependencies\"]\n}`;\n\n    const response = await this.generateText(fullPrompt, {\n      temperature: 0.2,\n      maxTokens: 2000,\n    });\n\n    try {\n      const jsonMatch = response.match(/\\{[\\s\\S]*\\}/);\n      if (jsonMatch) {\n        return JSON.parse(jsonMatch[0]);\n      }\n    } catch {\n      // Fallback\n    }\n\n    return {\n      code: response,\n      language: options?.language || \"unknown\",\n      explanation: \"Generated code\",\n    };\n  }\n\n  /**\n   * Explain code\n   */\n  override async explainCode(code: string, language?: string): Promise<string> {\n    const prompt = `Analyze and explain this ${language || \"code\"}:\n\n\\`\\`\\`${language