litellm-js/dist/litellm.mjs.map

Universal JavaScript client for LLM APIs

{"version":3,"file":"litellm.mjs","sources":["../src/types.js","../src/client.js","../src/provider.js","../src/providers/openai.js","../src/providers/anthropic.js","../src/litellm.js"],"sourcesContent":["/**\n * @typedef {Object} LLMMessage\n * @property {string} role - The role of the message sender (system, user, assistant)\n * @property {string} content - The content of the message\n */\n\n/**\n * @typedef {Object} CompletionOptions\n * @property {string} model - The name of the model to use\n * @property {Array<LLMMessage>} messages - Array of messages to generate completions for\n * @property {number} [temperature=0.7] - Sampling temperature\n * @property {number} [max_tokens] - Maximum number of tokens to generate\n * @property {boolean} [stream=false] - Whether to stream the response\n * @property {Object} [additional_params] - Any additional provider-specific parameters\n */\n\n/**\n * @typedef {Object} LLMProvider\n * @property {string} name - Provider name\n * @property {string} baseUrl - Base URL for the provider's API\n * @property {Array<string>} models - List of supported models\n */\n\n/**\n * @typedef {Object} ProxyConfig\n * @property {string} name - The name of the proxy configuration\n * @property {Array<string>} models - List of models to route through this proxy\n * @property {string} url - The proxy URL\n * @property {Object} headers - Headers to include with requests to this proxy\n */\n\nexport const PROVIDER_TYPES = {\n  OPENAI: 'openai',\n  ANTHROPIC: 'anthropic',\n  AZURE: 'azure',\n  GOOGLE: 'google',\n  COHERE: 'cohere',\n  HUGGINGFACE: 'huggingface'\n};\n\nexport const MODEL_PREFIXES = {\n  'gpt': PROVIDER_TYPES.OPENAI,\n  'claude': PROVIDER_TYPES.ANTHROPIC,\n  'azure': PROVIDER_TYPES.AZURE,\n  'gemini': PROVIDER_TYPES.GOOGLE,\n  'palm': PROVIDER_TYPES.GOOGLE,\n  'command': PROVIDER_TYPES.COHERE\n};","import fetch from 'cross-fetch';\n\n/**\n * Universal HTTP client for making requests to LLM APIs\n */\nclass LiteLLMClient {\n  /**\n   * Make a request to an LLM API\n   * \n   * @param {string} url - The API endpoint\n   * @param {Object} options - Request options\n   * @param {Object} options.headers - HTTP headers\n   * @param {string} options.method - HTTP method (GET, POST, etc.)\n   * @param {Object|null} options.body - Request body (for POST, PUT, etc.)\n   * @param {AbortSignal|null} options.signal - AbortController signal\n   * @returns {Promise<Object>} - The API response\n   */\n  async request(url, options = {}) {\n    const { headers = {}, method = 'GET', body = null, signal = null, stream = false } = options;\n    \n    const requestOptions = {\n      method,\n      headers: {\n        'Content-Type': 'application/json',\n        ...headers\n      },\n      signal\n    };\n\n    if (body) {\n      requestOptions.body = JSON.stringify(body);\n    }\n\n    try {\n      const response = await fetch(url, requestOptions);\n      \n      if (!response.ok) {\n        const errorData = await response.json().catch(() => ({}));\n        throw new LiteLLMError(\n          `API request failed with status ${response.status}`,\n          response.status,\n          errorData\n        );\n      }\n\n      // If streaming is requested, return the raw response\n      if (stream) {\n        return response;\n      }\n\n      return await response.json();\n    } catch (error) {\n      if (error instanceof LiteLLMError) {\n        throw error;\n      }\n      \n      throw new LiteLLMError(\n        `Request failed: ${error.message}`,\n        500,\n        { originalError: error }\n      );\n    }\n  }\n}\n\n/**\n * Custom error class for LiteLLM errors\n */\nclass LiteLLMError extends Error {\n  constructor(message, status, data = {}) {\n    super(message);\n    this.name = 'LiteLLMError';\n    this.status = status;\n    this.data = data;\n  }\n}\n\nexport default new LiteLLMClient();\nexport { LiteLLMClient, LiteLLMError };","import client from './client.js';\n\n/**\n * Base provider class for all LLM providers\n */\nclass Provider {\n  /**\n   * Initialize a new provider\n   * \n   * @param {Object} options - Provider options\n   * @param {string} options.apiKey - API key for the provider\n   * @param {string} [options.baseUrl] - Base URL for the provider's API\n   * @param {Object} [options.defaultParams={}] - Default parameters for all requests\n   */\n  constructor(options = {}) {\n    this.apiKey = options.apiKey;\n    this.baseUrl = options.baseUrl || this.constructor.defaultBaseUrl;\n    this.defaultParams = options.defaultParams || {};\n  }\n\n  /**\n   * Generate a completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {Promise<Object>} - The completion response\n   */\n  async completion(options) {\n    throw new Error('Not implemented');\n  }\n\n  /**\n   * Generate a streaming completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {AsyncGenerator} - An async generator that yields completion chunks\n   */\n  async *streamCompletion(options) {\n    throw new Error('Not implemented');\n  }\n\n  /**\n   * Make a request to the provider's API\n   * \n   * @param {string} path - API path\n   * @param {Object} options - Request options\n   * @returns {Promise<Object>} - The API response\n   */\n  async makeRequest(path, options = {}) {\n    const url = `${this.baseUrl}${path}`;\n    const headers = {\n      ...this._getAuthHeaders(),\n      ...options.headers\n    };\n\n    return await client.request(url, {\n      ...options,\n      headers\n    });\n  }\n\n  /**\n   * Get authentication headers for the provider\n   * \n   * @returns {Object} - Authentication headers\n   */\n  _getAuthHeaders() {\n    throw new Error('Not implemented');\n  }\n\n  /**\n   * Transform messages to provider-specific format\n   * \n   * @param {Array<LLMMessage>} messages - Messages to transform\n   * @returns {Array<Object>} - Transformed messages\n   */\n  _transformMessages(messages) {\n    return messages;\n  }\n\n  /**\n   * Transform options to provider-specific format\n   * \n   * @param {CompletionOptions} options - Options to transform\n   * @returns {Object} - Transformed options\n   */\n  _transformOptions(options) {\n    return {\n      ...this.defaultParams,\n      ...options,\n      messages: this._transformMessages(options.messages)\n    };\n  }\n\n  /**\n   * Check if the provider supports the given model\n   * \n   * @param {string} model - Model name to check\n   * @returns {boolean} - True if the provider supports the model\n   */\n  supportsModel(model) {\n    return false;\n  }\n}\n\nexport default Provider;","import Provider from '../provider.js';\nimport { PROVIDER_TYPES } from '../types.js';\n\nclass OpenAIProvider extends Provider {\n  static defaultBaseUrl = 'https://api.openai.com/v1';\n  static providerType = PROVIDER_TYPES.OPENAI;\n  \n  /**\n   * Initialize a new OpenAI provider\n   * \n   * @param {Object} options - Provider options\n   * @param {string} options.apiKey - OpenAI API key\n   * @param {string} [options.baseUrl] - Base URL for the OpenAI API\n   * @param {Object} [options.defaultParams={}] - Default parameters for all requests\n   */\n  constructor(options = {}) {\n    super(options);\n  }\n\n  /**\n   * Generate a completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {Promise<Object>} - The completion response\n   */\n  async completion(options) {\n    const transformedOptions = this._transformOptions(options);\n    \n    return await this.makeRequest('/chat/completions', {\n      method: 'POST',\n      body: transformedOptions\n    });\n  }\n\n  /**\n   * Generate a streaming completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {AsyncGenerator} - An async generator that yields completion chunks\n   */\n  async *streamCompletion(options) {\n    const transformedOptions = this._transformOptions({\n      ...options,\n      stream: true\n    });\n    \n    const response = await this.makeRequest('/chat/completions', {\n      method: 'POST',\n      body: transformedOptions,\n      stream: true\n    });\n\n    // Handle streaming in a way that works in both Node.js and browser environments\n    if (typeof response.body === 'object' && response.body !== null) {\n      // Browser environment or Node.js with fetch that supports ReadableStream\n      if (typeof response.body.getReader === 'function') {\n        const reader = response.body.getReader();\n        const decoder = new TextDecoder('utf-8');\n        \n        try {\n          while (true) {\n            const { done, value } = await reader.read();\n            \n            if (done) {\n              break;\n            }\n            \n            const chunk = decoder.decode(value);\n            yield* this._processChunk(chunk);\n          }\n        } finally {\n          reader.releaseLock();\n        }\n      } \n      // Node.js environment with response.body as a Node.js Readable stream\n      else if (typeof response.body.on === 'function') {\n        const chunks = [];\n        for await (const chunk of response.body) {\n          const strChunk = new TextDecoder('utf-8').decode(chunk);\n          yield* this._processChunk(strChunk);\n        }\n      }\n    } else if (typeof response.text === 'function') {\n      // Fallback for environments where we can't directly access the stream\n      const text = await response.text();\n      yield* this._processChunk(text);\n    }\n  }\n\n  /**\n   * Process a text chunk from a stream\n   * \n   * @private\n   * @param {string} chunk - The text chunk to process\n   * @returns {Array} - Array of parsed JSON objects from the chunk\n   */\n  *_processChunk(chunk) {\n    const lines = chunk\n      .split('\\n')\n      .filter(line => line.trim().startsWith('data:'))\n      .map(line => line.replace(/^data: /, '').trim());\n    \n    for (const line of lines) {\n      if (line === '[DONE]') {\n        return;\n      }\n      \n      try {\n        if (line) {\n          const parsed = JSON.parse(line);\n          yield parsed;\n        }\n      } catch (e) {\n        console.error('Error parsing SSE line:', line, e);\n      }\n    }\n  }\n\n  /**\n   * Get authentication headers for OpenAI\n   * \n   * @returns {Object} - OpenAI authentication headers\n   */\n  _getAuthHeaders() {\n    return {\n      'Authorization': `Bearer ${this.apiKey}`\n    };\n  }\n\n  /**\n   * Check if OpenAI supports the given model\n   * \n   * @param {string} model - Model name to check\n   * @returns {boolean} - True if OpenAI supports the model\n   */\n  supportsModel(model) {\n    return model.startsWith('gpt-') || \n           model.startsWith('text-') ||\n           model === 'dall-e-3';\n  }\n}\n\nexport default OpenAIProvider;","import Provider from '../provider.js';\nimport { PROVIDER_TYPES } from '../types.js';\n\nclass AnthropicProvider extends Provider {\n  static defaultBaseUrl = 'https://api.anthropic.com/v1';\n  static providerType = PROVIDER_TYPES.ANTHROPIC;\n  \n  /**\n   * Initialize a new Anthropic provider\n   * \n   * @param {Object} options - Provider options\n   * @param {string} options.apiKey - Anthropic API key\n   * @param {string} [options.baseUrl] - Base URL for the Anthropic API\n   * @param {Object} [options.defaultParams={}] - Default parameters for all requests\n   */\n  constructor(options = {}) {\n    super(options);\n    this.defaultVersion = options.version || '2023-06-01';\n  }\n\n  /**\n   * Generate a completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {Promise<Object>} - The completion response\n   */\n  async completion(options) {\n    const transformedOptions = this._transformOptions(options);\n    \n    const response = await this.makeRequest('/messages', {\n      method: 'POST',\n      body: transformedOptions\n    });\n    \n    // Convert Anthropic response format to OpenAI format\n    return this._convertResponseToOpenAIFormat(response, options);\n  }\n\n  /**\n   * Generate a streaming completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {AsyncGenerator} - An async generator that yields completion chunks\n   */\n  async *streamCompletion(options) {\n    const transformedOptions = this._transformOptions({\n      ...options,\n      stream: true\n    });\n    \n    const response = await this.makeRequest('/messages', {\n      method: 'POST',\n      body: transformedOptions,\n      stream: true\n    });\n\n    // Handle streaming in a way that works in both Node.js and browser environments\n    if (typeof response.body === 'object' && response.body !== null) {\n      // Browser environment or Node.js with fetch that supports ReadableStream\n      if (typeof response.body.getReader === 'function') {\n        const reader = response.body.getReader();\n        const decoder = new TextDecoder('utf-8');\n        \n        try {\n          while (true) {\n            const { done, value } = await reader.read();\n            \n            if (done) {\n              break;\n            }\n            \n            const chunk = decoder.decode(value);\n            const processedChunks = this._processChunk(chunk);\n            \n            for (const processedChunk of processedChunks) {\n              // Convert each Anthropic chunk to OpenAI format\n              yield this._convertStreamChunkToOpenAIFormat(processedChunk, options);\n            }\n          }\n        } finally {\n          reader.releaseLock();\n        }\n      } \n      // Node.js environment with response.body as a Node.js Readable stream\n      else if (typeof response.body.on === 'function') {\n        for await (const chunk of response.body) {\n          const strChunk = new TextDecoder('utf-8').decode(chunk);\n          const processedChunks = this._processChunk(strChunk);\n          \n          for (const processedChunk of processedChunks) {\n            // Convert each Anthropic chunk to OpenAI format\n            yield this._convertStreamChunkToOpenAIFormat(processedChunk, options);\n          }\n        }\n      }\n    } else if (typeof response.text === 'function') {\n      // Fallback for environments where we can't directly access the stream\n      const text = await response.text();\n      const processedChunks = this._processChunk(text);\n      \n      for (const processedChunk of processedChunks) {\n        // Convert each Anthropic chunk to OpenAI format\n        yield this._convertStreamChunkToOpenAIFormat(processedChunk, options);\n      }\n    }\n  }\n\n  /**\n   * Process a text chunk from a stream\n   * \n   * @private\n   * @param {string} chunk - The text chunk to process\n   * @returns {Array} - Array of parsed JSON objects from the chunk\n   */\n  _processChunk(chunk) {\n    const result = [];\n    const lines = chunk\n      .split('\\n')\n      .filter(line => line.trim().startsWith('data:'))\n      .map(line => line.replace(/^data: /, '').trim());\n    \n    for (const line of lines) {\n      if (line === '[DONE]') {\n        continue;\n      }\n      \n      try {\n        if (line) {\n          const parsed = JSON.parse(line);\n          result.push(parsed);\n        }\n      } catch (e) {\n        console.error('Error parsing SSE line:', line, e);\n      }\n    }\n    \n    return result;\n  }\n\n  /**\n   * Convert Anthropic stream chunk to OpenAI format\n   * \n   * @private\n   * @param {Object} chunk - Anthropic format chunk\n   * @param {Object} options - Original request options\n   * @returns {Object} - OpenAI format chunk\n   */\n  _convertStreamChunkToOpenAIFormat(chunk, options) {\n    // Generate a unique ID if needed\n    const id = `chatcmpl-${Date.now().toString(36)}${Math.random().toString(36).substr(2, 5)}`;\n    \n    // Default structure for OpenAI format\n    const openAIFormat = {\n      id: id,\n      object: 'chat.completion.chunk',\n      created: Math.floor(Date.now() / 1000),\n      model: options.model,\n      choices: [\n        {\n          index: 0,\n          delta: {},\n          finish_reason: null\n        }\n      ]\n    };\n\n    // Handle different Anthropic SSE event types\n    if (chunk.type === 'content_block_start') {\n      // Content block start doesn't contain actual content yet\n      return openAIFormat;\n    } \n    else if (chunk.type === 'content_block_delta') {\n      if (chunk.delta.type === 'text_delta' && chunk.delta.text) {\n        openAIFormat.choices[0].delta.content = chunk.delta.text;\n      }\n      // Handle tool calls in streaming mode\n      else if (chunk.delta.type === 'tool_use') {\n        // For tool_use, we need to convert to function_call format\n        openAIFormat.choices[0].delta.function_call = {\n          name: chunk.delta.name || '',\n          arguments: chunk.delta.input ? JSON.stringify(chunk.delta.input) : ''\n        };\n        openAIFormat.choices[0].finish_reason = 'function_call';\n      }\n    } \n    else if (chunk.type === 'content_block_stop') {\n      // End of a content block\n      openAIFormat.choices[0].finish_reason = 'stop';\n    }\n    else if (chunk.type === 'message_stop') {\n      // End of the entire message\n      openAIFormat.choices[0].finish_reason = 'stop';\n    }\n    \n    return openAIFormat;\n  }\n\n  /**\n   * Convert complete Anthropic response to OpenAI format\n   * \n   * @private\n   * @param {Object} response - Anthropic format response\n   * @param {Object} options - Original request options\n   * @returns {Object} - OpenAI format response\n   */\n  _convertResponseToOpenAIFormat(response, options) {\n    // Generate a unique ID if needed\n    const id = `chatcmpl-${Date.now().toString(36)}${Math.random().toString(36).substr(2, 5)}`;\n    \n    // Extract content and handle different content types\n    let content = null;\n    let functionCall = null;\n    \n    if (response.content && Array.isArray(response.content)) {\n      // Process different types of content blocks\n      for (const block of response.content) {\n        if (block.type === 'text') {\n          content = block.text;\n        } \n        else if (block.type === 'tool_use') {\n          // Convert tool_use to function_call\n          functionCall = {\n            name: block.name,\n            arguments: JSON.stringify(block.input)\n          };\n        }\n      }\n    }\n    \n    // Map Anthropic stop_reason to OpenAI finish_reason\n    let finishReason = 'stop';\n    if (response.stop_reason === 'tool_use') {\n      finishReason = 'function_call';\n    } else if (response.stop_reason === 'max_tokens') {\n      finishReason = 'length';\n    }\n    \n    // Build message object\n    const message = {\n      role: 'assistant',\n      content: content\n    };\n    \n    // Add function_call if present\n    if (functionCall) {\n      message.function_call = functionCall;\n      message.content = null; // OpenAI sets content to null when there's a function call\n    }\n    \n    // Create OpenAI-format response\n    return {\n      id: id,\n      object: 'chat.completion',\n      created: Math.floor(Date.now() / 1000),\n      model: options.model,\n      choices: [\n        {\n          index: 0,\n          message: message,\n          finish_reason: finishReason\n        }\n      ],\n      usage: {\n        prompt_tokens: response.usage?.input_tokens || 0,\n        completion_tokens: response.usage?.output_tokens || 0,\n        total_tokens: (response.usage?.input_tokens || 0) + (response.usage?.output_tokens || 0)\n      }\n    };\n  }\n\n  /**\n   * Get authentication headers for Anthropic\n   * \n   * @returns {Object} - Anthropic authentication headers\n   */\n  _getAuthHeaders() {\n    return {\n      'X-API-Key': this.apiKey,\n      'anthropic-version': this.defaultVersion\n    };\n  }\n\n  /**\n   * Transform messages to Anthropic-specific format\n   * \n   * @param {Array<LLMMessage>} messages - Messages to transform\n   * @returns {Array<Object>} - Transformed messages\n   */\n  _transformMessages(messages) {\n    if (!messages || !Array.isArray(messages)) {\n      return [];\n    }\n    \n    // Anthropic uses a different format for messages\n    // Extract system message if present\n    let systemMessage = '';\n    const formattedMessages = [];\n    \n    for (const message of messages) {\n      if (message.role === 'system') {\n        systemMessage = message.content;\n      } else if (message.role === 'user' || message.role === 'assistant') {\n        formattedMessages.push({\n          role: message.role,\n          content: message.content\n        });\n        \n        // Handle function calls from user (tool results)\n        if (message.role === 'user' && message.function_call_result) {\n          formattedMessages.push({\n            role: 'tool',\n            name: message.function_call_result.name,\n            content: message.function_call_result.content\n          });\n        }\n        \n        // Handle function calls from assistant\n        if (message.role === 'assistant' && message.function_call) {\n          // Anthropic expects tool_use inside the content array\n          formattedMessages.push({\n            role: 'assistant',\n            content: [{\n              type: 'tool_use',\n              name: message.function_call.name,\n              input: JSON.parse(message.function_call.arguments)\n            }]\n          });\n        }\n      }\n    }\n    \n    return formattedMessages;\n  }\n\n  /**\n   * Transform options to Anthropic-specific format\n   * \n   * @param {CompletionOptions} options - Options to transform\n   * @returns {Object} - Transformed options for Anthropic\n   */\n  _transformOptions(options) {\n    const messages = this._transformMessages(options.messages);\n    \n    const transformed = {\n      ...this.defaultParams,\n      model: options.model,\n      messages: messages,\n      stream: options.stream || false,\n    };\n    \n    // Extract all system messages and combine them if there are multiple\n    const systemMessages = options.messages?.filter(m => m.role === 'system') || [];\n    if (systemMessages.length > 1) {\n      // Multiple system messages, combine them into a single system message\n      const combinedContent = systemMessages.map(m => m.content).join('\\n');\n      transformed.system = combinedContent;\n\n      // Remove all system messages and add a single combined one\n      options.messages = options.messages.filter(m => m.role !== 'system');\n    }\n\n    // Add completion parameters\n    // max_tokens is required for Anthropic\n    transformed.max_tokens = options.max_tokens || 2048;\n    transformed.stop = options.stop || ['stop', 'max_tokens'];\n    if (options.temperature !== undefined) {\n      transformed.temperature = options.temperature;\n    }\n\n    if (options.tools || options.functions) {\n      // Convert OpenAI functions/tools to Anthropic tools\n      const tools = options.tools || \n        (options.functions ? [{ type: 'function', functions: options.functions }] : []);\n        \n      transformed.tools = tools.map(tool => {\n        if (tool.type === 'function') {\n          return {\n            name: tool.function.name,\n            description: tool.function.description,\n            input_schema: tool.function.parameters\n          };\n        }\n        return tool;\n      });\n    }\n\n    if (options.additional_params) {\n      Object.assign(transformed, options.additional_params);\n    }\n\n    return transformed;\n  }\n\n  /**\n   * Check if Anthropic supports the given model\n   * \n   * @param {string} model - Model name to check\n   * @returns {boolean} - True if Anthropic supports the model\n   */\n  supportsModel(model) {\n    return model.startsWith('claude-');\n  }\n}\n\nexport default AnthropicProvider;","import { MODEL_PREFIXES, PROVIDER_TYPES } from './types.js';\nimport OpenAIProvider from './providers/openai.js';\nimport AnthropicProvider from './providers/anthropic.js';\nimport { LiteLLMError } from './client.js';\n\n/**\n * LiteLLM class for unified access to various LLM providers\n */\nclass LiteLLM {\n  /**\n   * Initialize a new LiteLLM instance\n   */\n  constructor() {\n    this.providers = {};\n    this.proxies = [];\n  }\n\n  /**\n   * Parse model string to extract provider and actual model name\n   * Supports formats: \"provider/model\" and \"model\"\n   * \n   * @param {string} modelString - The model string to parse\n   * @returns {Object} - Object with provider and model properties\n   */\n  parseModelString(modelString) {\n    if (!modelString || typeof modelString !== 'string') {\n      return { provider: null, model: modelString };\n    }\n\n    const parts = modelString.split('/');\n    if (parts.length === 2 && parts[0] && parts[1]) {\n      return {\n        provider: parts[0].toLowerCase(),\n        model: parts[1]\n      };\n    }\n\n    return {\n      provider: null,\n      model: modelString\n    };\n  }\n\n  /**\n   * Register a provider for use with LiteLLM\n   * \n   * @param {string} type - Provider type (e.g., 'openai', 'anthropic')\n   * @param {Object} options - Provider options\n   * @returns {Provider} - The registered provider\n   */\n  registerProvider(type, options) {\n    const providerType = type.toLowerCase();\n    let provider;\n    \n    switch (providerType) {\n      case PROVIDER_TYPES.OPENAI:\n        provider = new OpenAIProvider(options);\n        break;\n      case PROVIDER_TYPES.ANTHROPIC:\n        provider = new AnthropicProvider(options);\n        break;\n      // Add other providers here\n      default:\n        throw new LiteLLMError(`Unsupported provider type: ${type}`, 400);\n    }\n    \n    this.providers[providerType] = provider;\n    return provider;\n  }\n\n  /**\n   * Register a proxy configuration\n   * \n   * @param {ProxyConfig} proxyConfig - Proxy configuration\n   */\n  registerProxy(proxyConfig) {\n    this.proxies.push(proxyConfig);\n  }\n\n  /**\n   * Determine the provider type from a model name\n   * \n   * @param {string} model - Model name\n   * @returns {string|null} - Provider type or null if unknown\n   */\n  getProviderTypeForModel(model) {\n    for (const [prefix, providerType] of Object.entries(MODEL_PREFIXES)) {\n      if (model.startsWith(prefix)) {\n        return providerType;\n      }\n    }\n    return null;\n  }\n\n  /**\n   * Get the appropriate proxy for a model\n   * \n   * @param {string} modelString - Model string (can be \"provider/model\" or just \"model\")\n   * @returns {Object|null} - Proxy provider and model name or null if not found\n   */\n  getProxyForModel(modelString) {\n    const { provider, model } = this.parseModelString(modelString);\n    \n    // Check if any proxy handles this model\n    for (const proxy of this.proxies) {\n      if (proxy.models.includes(model) || \n          proxy.models.includes(modelString) || \n          proxy.models.includes('*')) {\n        return { \n          provider: proxy.provider,\n          actualModel: proxy.proxyModel || model\n        };\n      }\n    }\n    return null;\n  }\n\n  /**\n   * Get the appropriate provider for a model\n   * \n   * @param {string} modelString - Model string (can be \"provider/model\" or just \"model\")\n   * @returns {Object} - Object with provider and actualModel properties\n   */\n  getProviderForModel(modelString) {\n    const { provider: explicitProvider, model: actualModel } = this.parseModelString(modelString);\n    \n    // Check if there's a proxy for this model\n    const proxyResult = this.getProxyForModel(modelString);\n    if (proxyResult) {\n      return proxyResult;\n    }\n\n    // If explicit provider is specified, try to use it\n    if (explicitProvider && this.providers[explicitProvider]) {\n      return { \n        provider: this.providers[explicitProvider],\n        actualModel\n      };\n    }\n\n    // Find provider by model prefix\n    const providerType = this.getProviderTypeForModel(actualModel || modelString);\n    if (providerType && this.providers[providerType]) {\n      return {\n        provider: this.providers[providerType],\n        actualModel: actualModel || modelString\n      };\n    }\n\n    // Check all providers to see if any explicitly support this model\n    for (const [name, provider] of Object.entries(this.providers)) {\n      if (provider.supportsModel && provider.supportsModel(actualModel || modelString)) {\n        return {\n          provider,\n          actualModel: actualModel || modelString\n        };\n      }\n    }\n\n    return { provider: null, actualModel: actualModel || modelString };\n  }\n\n  /**\n   * Generate a completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {Promise<Object>} - The completion response\n   */\n  async completion(options) {\n    const { model: modelString } = options;\n    const { provider, actualModel } = this.getProviderForModel(modelString);\n    \n    if (!provider) {\n      throw new LiteLLMError(`No provider found for model: ${modelString}`, 400);\n    }\n\n    // Create a new options object with the actual model name\n    const completionOptions = {\n      ...options,\n      model: actualModel\n    };\n    \n    return await provider.completion(completionOptions);\n  }\n\n  /**\n   * Generate a streaming completion for the given messages\n   * \n   * @param {CompletionOptions} options - Completion options\n   * @returns {AsyncGenerator} - An async generator that yields completion chunks\n   */\n  async *streamCompletion(options) {\n    const { model: modelString } = options;\n    const { provider, actualModel } = this.getProviderForModel(modelString);\n    \n    if (!provider) {\n      throw new LiteLLMError(`No provider found for model: ${modelString}`, 400);\n    }\n\n    // Create a new options object with the actual model name\n    const completionOptions = {\n      ...options,\n      model: actualModel\n    };\n    \n    yield* provider.streamCompletion(completionOptions);\n  }\n\n  /**\n   * Process a text chunk from a stream\n   * \n   * @private\n   * @param {string} chunk - The text chunk to process\n   * @returns {Array} - Array of parsed JSON objects from the chunk\n   */\n  *_processChunk(chunk) {\n    const lines = chunk\n      .split('\\n')\n      .filter(line => line.trim().startsWith('data:'))\n      .map(line => line.replace(/^data: /, '').trim());\n    \n    for (const line of lines) {\n      if (line === '[DONE]') {\n        return;\n      }\n      \n      try {\n        if (line) {\n          const parsed = JSON.parse(line);\n          yield parsed;\n        }\n      } catch (e) {\n        console.error('Error parsing SSE line:', line, e);\n      }\n    }\n  }\n\n  /**\n   * Create a proxy provider\n   * \n   * @param {Object} options - Proxy options\n   * @param {string} options.url - The proxy URL\n   * @param {Object} options.headers - Headers to include with proxy requests\n   * @param {Array<string>} options.models - List of models to route through this proxy\n   * @param {string} options.name - The name of the proxy\n   * @param {string} [options.proxyModel] - Optional model to use when making requests through the proxy\n   * @returns {void}\n   */\n  createProxy(options) {\n    const { url, headers = {}, models = ['*'], name, proxyModel = null } = options;\n    const self = this;\n    \n    // Create a custom provider for this proxy\n    const proxyProvider = {\n      // Add properties to help identify this as a proxy provider\n      isProxy: true, \n      proxyName: name,\n      providerType: 'proxy',\n      \n      completion: async (completionOptions) => {\n        // If proxyModel is specified, use it instead of the requested model\n        const finalOptions = {\n          ...completionOptions,\n          model: proxyModel || completionOptions.model\n        };\n\n        const response = await fetch(`${url}/chat/completions`, {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n            ...headers\n          },\n          body: JSON.stringify(finalOptions)\n        });\n        \n        if (!response.ok) {\n          const errorData = await response.json().catch(() => ({}));\n          throw new LiteLLMError(\n            `Proxy request failed with status ${response.status}`,\n            response.status,\n            errorData\n          );\n        }\n        \n        return await response.json();\n      },\n      \n      streamCompletion: async function* (completionOptions) {\n        // If proxyModel is specified, use it instead of the requested model\n        const finalOptions = {\n          ...completionOptions,\n          model: proxyModel || completionOptions.model,\n          stream: true\n        };\n\n        const response = await fetch(`${url}/chat/completions`, {\n          method: 'POST',\n          headers: {\n            'Content-Type': 'application/json',\n            ...headers\n          },\n          body: JSON.stringify(finalOptions)\n        });\n        \n        if (!response.ok) {\n          const errorData = await response.json().catch(() => ({}));\n          throw new LiteLLMError(\n            `Proxy request failed with status ${response.status}`,\n            response.status,\n            errorData\n          );\n        }\n        \n        // Handle streaming in a way that works in both Node.js and browser environments\n        if (typeof response.body === 'object' && response.body !== null) {\n          // Browser environment or Node.js with fetch that supports ReadableStream\n          if (typeof response.body.getReader === 'function') {\n            const reader = response.body.getReader();\n            const decoder = new TextDecoder('utf-8');\n            \n            try {\n              while (true) {\n                const { done, value } = await reader.read();\n                \n                if (done) {\n                  break;\n                }\n                \n                const chunk = decoder.decode(value);\n                yield* self._processChunk(chunk);\n              }\n            } finally {\n              reader.releaseLock();\n            }\n          } \n          // Node.js environment with response.body as a Node.js Readable stream\n          else if (typeof response.body.on === 'function') {\n            for await (const chunk of response.body) {\n              const strChunk = new TextDecoder('utf-8').decode(chunk);\n              yield* self._processChunk(strChunk);\n            }\n          }\n        } else if (typeof response.text === 'function') {\n          // Fallback for environments where we can't directly access the stream\n          const text = await response.text();\n          yield* self._processChunk(text);\n        }\n      }\n    };\n    \n    // Register this proxy\n    this.registerProxy({\n      name,\n      models,\n      url,\n      headers,\n      provider: proxyProvider,\n      proxyModel // Store the proxyModel with the proxy configuration\n    });\n\n    console.log(`Proxy '${name}' registered for models: ${models.join(', ')}${proxyModel ? ` (using proxyModel: ${proxyModel})` : ''}`);\n  }\n}\n\n// Create and export a singleton instance\nconst liteLLM = new LiteLLM();\n\nexport default liteLLM;\nexport { LiteLLM 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