@mastra/core/dist/chunk-XWGHD4C7.cjs.map

Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.

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{ MastraError } from '../../../error/index.js';\nimport type { MastraModelGateway } from './base.js';\nexport { MastraModelGateway, type ProviderConfig } from './base.js';\nexport { ModelsDevGateway } from './models-dev.js';\nexport { NetlifyGateway } from './netlify.js';\n\n/**\n * Find the gateway that handles a specific model ID based on gateway ID\n * Gateway ID is used as the prefix (e.g., \"netlify\" for netlify gateway)\n * Exception: models.dev is a provider registry and doesn't use a prefix\n */\nexport function findGatewayForModel(gatewayId: string, gateways: MastraModelGateway[]): MastraModelGateway {\n  // First, check for gateways whose ID matches the prefix (true gateways like netlify, openrouter, vercel)\n  const prefixedGateway = gateways.find(\n    (g: MastraModelGateway) => g.id !== 'models.dev' && (g.id === gatewayId || gatewayId.startsWith(`${g.id}/`)),\n  );\n  if (prefixedGateway) {\n    return prefixedGateway;\n  }\n\n  // Then check models.dev (provider registry without prefix)\n  const modelsDevGateway = gateways.find((g: MastraModelGateway) => g.id === 'models.dev');\n  if (modelsDevGateway) {\n    return modelsDevGateway;\n  }\n\n  throw new MastraError({\n    id: 'MODEL_ROUTER_NO_GATEWAY_FOUND',\n    category: 'USER',\n    domain: 'MODEL_ROUTER',\n    text: `No Mastra model router gateway found for model id ${gatewayId}`,\n  });\n}\n","import { randomUUID } from 'crypto';\nimport type { LanguageModelV2, LanguageModelV2CallOptions } from '@ai-sdk/provider-v5';\nimport type { MastraLanguageModelV2 } from '../../shared.types';\n\ntype StreamResult = Awaited<ReturnType<LanguageModelV2['doStream']>>;\n\nexport class AISDKV5LanguageModel implements MastraLanguageModelV2 {\n  /**\n   * The language model must specify which language model interface version it implements.\n   */\n  readonly specificationVersion: 'v2' = 'v2';\n  /**\n   * Name of the provider for logging purposes.\n   */\n  readonly provider: string;\n  /**\n   * Provider-specific model ID for logging purposes.\n   */\n  readonly modelId: string;\n  /**\n   * Supported URL patterns by media type for the provider.\n   *\n   * The keys are media type patterns or full media types (e.g. `*\\/*` for everything, `audio/*`, `video/*`, or `application/pdf`).\n   * and the values are arrays of regular expressions that match the URL paths.\n   * The matching should be against lower-case URLs.\n   * Matched URLs are supported natively by the model and are not downloaded.\n   * @returns A map of supported URL patterns by media type (as a promise or a plain object).\n   */\n  supportedUrls: PromiseLike<Record<string, RegExp[]>> | Record<string, RegExp[]>;\n\n  #model: LanguageModelV2;\n\n  constructor(config: LanguageModelV2) {\n    this.#model = config;\n    this.provider = this.#model.provider;\n    this.modelId = this.#model.modelId;\n    this.supportedUrls = this.#model.supportedUrls;\n  }\n\n  async doGenerate(options: LanguageModelV2CallOptions) {\n    const result = await this.#model.doGenerate(options);\n\n    return {\n      request: result.request!,\n      response: result.response as unknown as StreamResult['response'],\n      stream: new ReadableStream({\n        start(controller) {\n          controller.enqueue({ type: 'stream-start', warnings: result.warnings });\n          controller.enqueue({\n            type: 'response-metadata',\n            id: result.response?.id,\n            modelId: result.response?.modelId,\n            timestamp: result.response?.timestamp,\n          });\n\n          for (const message of result.content) {\n            if (message.type === 'tool-call') {\n              const toolCall = message;\n              controller.enqueue({\n                type: 'tool-input-start',\n                id: toolCall.toolCallId,\n                toolName: toolCall.toolName,\n              });\n              controller.enqueue({\n                type: 'tool-input-delta',\n                id: toolCall.toolCallId,\n               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           const id = `reasoning_${randomUUID()}`;\n\n              const reasoning = message;\n              controller.enqueue({\n                type: 'reasoning-start',\n                id,\n                providerMetadata: reasoning.providerMetadata,\n              });\n              controller.enqueue({\n                type: 'reasoning-delta',\n                id,\n                delta: reasoning.text,\n                providerMetadata: reasoning.providerMetadata,\n              });\n              controller.enqueue({\n                type: 'reasoning-end',\n                id,\n                providerMetadata: reasoning.providerMetadata,\n              });\n            } else if (message.type === 'file') {\n              const file = message;\n              controller.enqueue({\n                type: 'file',\n                mediaType: file.mediaType,\n                data: file.data,\n              });\n            } else if (message.type === 'source') {\n              const source = message;\n              if (source.sourceType === 'url') {\n                controller.enqueue({\n                  type: 'source',\n                  id: source.id,\n                  sourceType: 'url',\n                  url: source.url,\n                  title: source.title,\n                  providerMetadata: source.providerMetadata,\n                });\n              } else {\n                controller.enqueue({\n                  type: 'source',\n                  id: source.id,\n                  sourceType: 'document',\n                  mediaType: source.mediaType,\n                  filename: source.filename,\n                  title: source.title,\n                  providerMetadata: source.providerMetadata,\n                });\n              }\n            }\n          }\n\n          controller.enqueue({\n            type: 'finish',\n            finishReason: result.finishReason,\n            usage: result.usage,\n            providerMetadata: result.providerMetadata,\n          });\n\n          controller.close();\n        },\n      }),\n    };\n  }\n\n  async doStream(options: LanguageModelV2CallOptions) {\n    return await this.#model.doStream(options);\n  }\n}\n","import { createHash } from 'node:crypto';\nimport { createOpenAICompatible } from '@ai-sdk/openai-compatible-v5';\nimport type { LanguageModelV2, LanguageModelV2CallOptions, LanguageModelV2StreamPart } from '@ai-sdk/provider-v5';\nimport { AISDKV5LanguageModel } from './aisdk/v5/model';\nimport { parseModelRouterId } from './gateway-resolver.js';\nimport type { MastraModelGateway } from './gateways/base.js';\nimport { findGatewayForModel } from './gateways/index.js';\n\nimport { ModelsDevGateway } from './gateways/models-dev.js';\nimport { NetlifyGateway } from './gateways/netlify.js';\nimport type { ModelRouterModelId } from './provider-registry.js';\nimport { PROVIDER_REGISTRY } from './provider-registry.js';\nimport type { MastraLanguageModelV2, OpenAICompatibleConfig } from './shared.types';\n\ntype StreamResult = Awaited<ReturnType<LanguageModelV2['doStream']>>;\n\nfunction getStaticProvidersByGateway(name: string) {\n  return Object.fromEntries(Object.entries(PROVIDER_REGISTRY).filter(([_provider, config]) => config.gateway === name));\n}\n\nexport const defaultGateways = [new NetlifyGateway(), new ModelsDevGateway(getStaticProvidersByGateway(`models.dev`))];\n\n/**\n * @deprecated Use defaultGateways instead. This export will be removed in a future version.\n */\nexport const gateways = defaultGateways;\n\nexport class ModelRouterLanguageModel implements MastraLanguageModelV2 {\n  readonly specificationVersion = 'v2' as const;\n  readonly defaultObjectGenerationMode = 'json' as const;\n  readonly supportsStructuredOutputs = true;\n  readonly supportsImageUrls = true;\n  readonly supportedUrls = {} as Record<string, RegExp[]>;\n\n  readonly modelId: string;\n  readonly provider: string;\n\n  private config: OpenAICompatibleConfig & { routerId: string };\n  private gateway: MastraModelGateway;\n\n  constructor(config: ModelRouterModelId | OpenAICompatibleConfig, customGateways?: MastraModelGateway[]) {\n    // Normalize config to always have an 'id' field for routing\n    let normalizedConfig: {\n      id: `${string}/${string}`;\n      url?: string;\n      apiKey?: string;\n      headers?: Record<string, string>;\n    };\n\n    if (typeof config === 'string') {\n      normalizedConfig = { id: config as `${string}/${string}` };\n    } else if ('providerId' in config && 'modelId' in config) {\n      // Convert providerId/modelId to id format\n      normalizedConfig = {\n        id: `${config.providerId}/${config.modelId}` as `${string}/${string}`,\n        url: config.url,\n        apiKey: config.apiKey,\n        headers: config.headers,\n      };\n    } else {\n      // config has 'id' field\n      normalizedConfig = {\n        id: config.id,\n        url: config.url,\n        apiKey: config.apiKey,\n        headers: config.headers,\n      };\n    }\n\n    const parsedConfig: {\n      id: `${string}/${string}`;\n      routerId: string;\n      url?: string;\n      apiKey?: string;\n      headers?: Record<string, string>;\n    } = {\n      ...normalizedConfig,\n      routerId: normalizedConfig.id,\n    };\n\n    // Resolve gateway once using the normalized ID\n    this.gateway = findGatewayForModel(normalizedConfig.id, [...(customGateways || []), ...defaultGateways]);\n    // Extract provider from id if present\n    // Gateway ID is used as prefix (except for models.dev which is a provider registry)\n    const gatewayPrefix = this.gateway.id === 'models.dev' ? undefined : this.gateway.id;\n    const parsed = parseModelRouterId(normalizedConfig.id, gatewayPrefix);\n\n    this.provider = parsed.providerId || 'openai-compatible';\n\n    if (parsed.providerId && parsed.modelId !== normalizedConfig.id) {\n      parsedConfig.id = parsed.modelId as `${string}/${string}`;\n    }\n\n    this.modelId = parsedConfig.id;\n    this.config = parsedConfig;\n  }\n\n  async doGenerate(options: LanguageModelV2CallOptions): Promise<StreamResult> {\n    let apiKey: string;\n    try {\n      // If custom URL is provided, skip gateway API key resolution\n      // The provider might not be in the registry (e.g., custom providers like ollama)\n      if (this.config.url) {\n        apiKey = this.config.apiKey || '';\n      } else {\n        apiKey = this.config.apiKey || (await this.gateway.getApiKey(this.config.routerId));\n      }\n    } catch (error) {\n      // Return an error stream instead of throwing\n      return {\n        stream: new ReadableStream({\n          start(controller) {\n            controller.enqueue({\n              type: 'error',\n              error: error,\n            } as LanguageModelV2StreamPart);\n            controller.close();\n          },\n        }),\n      };\n    }\n\n    const gatewayPrefix = this.gateway.id === 'models.dev' ? undefined : this.gateway.id;\n    const model = await this.resolveLanguageModel({\n      apiKey,\n      headers: this.config.headers,\n      ...parseModelRouterId(this.config.routerId, gatewayPrefix),\n    });\n\n    const aiSDKV5Model = new AISDKV5LanguageModel(model);\n    return aiSDKV5Model.doGenerate(options);\n  }\n\n  async doStream(options: LanguageModelV2CallOptions): Promise<StreamResult> {\n    // Validate API key and return error stream if validation fails\n    let apiKey: string;\n    try {\n      // If custom URL is provided, skip gateway API key resolution\n      // The provider might not be in the registry (e.g., custom providers like ollama)\n      if (this.config.url) {\n        apiKey = this.config.apiKey || '';\n      } else {\n        apiKey = this.config.apiKey || (await this.gateway.getApiKey(this.config.routerId));\n      }\n    } catch (error) {\n      // Return an error stream instead of throwing\n      return {\n        stream: new ReadableStream({\n          start(controller) {\n            controller.enqueue({\n              type: 'error',\n              error: error,\n            } as LanguageModelV2StreamPart);\n            controller.close();\n          },\n        }),\n      };\n    }\n\n    const gatewayPrefix = this.gateway.id === 'models.dev' ? undefined : this.gateway.id;\n    const model = await this.resolveLanguageModel({\n      apiKey,\n      headers: this.config.headers,\n      ...parseModelRouterId(this.config.routerId, gatewayPrefix),\n    });\n\n    const aiSDKV5Model = new AISDKV5LanguageModel(model);\n    return aiSDKV5Model.doStream(options);\n  }\n\n  private async resolveLanguageModel({\n    modelId,\n    providerId,\n    apiKey,\n    headers,\n  }: {\n    modelId: string;\n    providerId: string;\n    apiKey: string;\n    headers?: Record<string, string>;\n  }): Promise<LanguageModelV2> {\n    const key = createHash('sha256')\n      .update(\n        this.gateway.id +\n          modelId +\n          providerId +\n          apiKey +\n          (this.config.url || '') +\n          (headers ? JSON.stringify(headers) : ''),\n      )\n      .digest('hex');\n    if (ModelRouterLanguageModel.modelInstances.has(key)) return ModelRouterLanguageModel.modelInstances.get(key)!;\n\n    // If custom URL is provided, use it directly with openai-compatible\n    if (this.config.url) {\n      const modelInstance = createOpenAICompatible({\n        name: providerId,\n        apiKey,\n        baseURL: this.config.url,\n        headers: this.config.headers,\n        supportsStructuredOutputs: true,\n      }).chatModel(modelId);\n      ModelRouterLanguageModel.modelInstances.set(key, modelInstance);\n      return modelInstance;\n    }\n\n    const modelInstance = await this.gateway.resolveLanguageModel({ modelId, providerId, apiKey, headers });\n    ModelRouterLanguageModel.modelInstances.set(key, modelInstance);\n    return modelInstance;\n  }\n  private static modelInstances = new Map<string, LanguageModelV2>();\n}\n","import type { LanguageModelV2 } from '@ai-sdk/provider-v5';\nimport type { Mastra } from '../../mastra';\nimport { RuntimeContext } from '../../runtime-context';\nimport { AISDKV5LanguageModel } from './aisdk/v5/model';\nimport { ModelRouterLanguageModel } from './router';\nimport type { MastraModelConfig, OpenAICompatibleConfig, MastraLanguageModel } from './shared.types';\n\n/**\n * Type guard to check if a model config is an OpenAICompatibleConfig object\n * @internal\n */\nexport function isOpenAICompatibleObjectConfig(\n  modelConfig:\n    | MastraModelConfig\n    | (({\n        runtimeContext,\n        mastra,\n      }: {\n        runtimeContext: RuntimeContext;\n        mastra?: Mastra;\n      }) => MastraModelConfig | Promise<MastraModelConfig>),\n): modelConfig is OpenAICompatibleConfig {\n  if (typeof modelConfig === 'object' && 'specificationVersion' in modelConfig) return false;\n  // Check for OpenAICompatibleConfig - it should have either:\n  // 1. 'id' field (but NOT 'model' - that's ModelWithRetries)\n  // 2. Both 'providerId' and 'modelId' fields\n  if (typeof modelConfig === 'object' && !('model' in modelConfig)) {\n    if ('id' in modelConfig) return true;\n    if ('providerId' in modelConfig && 'modelId' in modelConfig) return true;\n  }\n  return false;\n}\n\n/**\n * Resolves a model configuration to a LanguageModel instance.\n * Supports:\n * - Magic strings like \"openai/gpt-4o\"\n * - Config objects like { id: \"openai/gpt-4o\", apiKey: \"...\" }\n * - Direct LanguageModel instances\n * - Dynamic functions that return any of the above\n *\n * @param modelConfig The model configuration\n * @param runtimeContext Optional runtime context for dynamic resolution\n * @param mastra Optional Mastra instance for dynamic resolution\n * @returns A resolved LanguageModel instance\n *\n * @example\n * ```typescript\n * // String resolution\n * const model = await resolveModelConfig(\"openai/gpt-4o\");\n *\n * // Config object resolution\n * const model = await resolveModelConfig({\n *   id: \"openai/gpt-4o\",\n *   apiKey: \"sk-...\"\n * });\n *\n * // Dynamic resolution\n * const model = await resolveModelConfig(\n *   ({ runtimeContext }) => runtimeContext.get(\"preferredModel\")\n * );\n * ```\n */\nexport async function resolveModelConfig(\n  modelConfig:\n    | MastraModelConfig\n    | (({\n        runtimeContext,\n        mastra,\n      }: {\n        runtimeContext: RuntimeContext;\n        mastra?: Mastra;\n      }) => MastraModelConfig | Promise<MastraModelConfig>),\n  runtimeContext: RuntimeContext = new RuntimeContext(),\n  mastra?: Mastra,\n): Promise<MastraLanguageModel> {\n  // If it's a function, resolve it first\n  if (typeof modelConfig === 'function') {\n    modelConfig = await modelConfig({ runtimeContext, mastra });\n  }\n\n  // Filter out custom language model instances\n  // TODO need a better trick, maybme symbol\n  if (modelConfig instanceof ModelRouterLanguageModel || modelConfig instanceof AISDKV5LanguageModel) {\n    return modelConfig;\n  }\n\n  // If it's already a LanguageModel, return it\n  if (typeof modelConfig === 'object' && 'specificationVersion' in modelConfig) {\n    if (modelConfig.specificationVersion === 'v2') {\n      return new AISDKV5LanguageModel(modelConfig as LanguageModelV2);\n    }\n\n    return modelConfig;\n  }\n\n  const gatewayRecord = mastra?.listGateways();\n  const customGateways = gatewayRecord ? Object.values(gatewayRecord) : undefined;\n\n  // If it's a string (magic string like \"openai/gpt-4o\") or OpenAICompatibleConfig, create ModelRouterLanguageModel\n  if (typeof modelConfig === 'string' || isOpenAICompatibleObjectConfig(modelConfig)) {\n    return new ModelRouterLanguageModel(modelConfig, customGateways);\n  }\n\n  throw new Error('Invalid model configuration provided');\n}\n","import { createGoogleGenerativeAI } from '@ai-sdk/google-v5';\nimport { createOpenAICompatible } from '@ai-sdk/openai-compatible-v5';\nimport { createOpenAI } from '@ai-sdk/openai-v5';\nimport type { EmbeddingModelV2 } from '@ai-sdk/provider-v5';\n\nimport { GatewayRegistry } from './provider-registry.js';\nimport type { OpenAICompatibleConfig } from './shared.types.js';\n\n/**\n * Information about a known embedding model\n */\nexport interface EmbeddingModelInfo {\n  id: string;\n  provider: string;\n  dimensions: number;\n  maxInputTokens: number;\n  description?: string;\n}\n\n/**\n * Hardcoded list of known embedding models\n * This is a curated list that provides autocomplete support\n */\nexport const EMBEDDING_MODELS: EmbeddingModelInfo[] = [\n  // OpenAI\n  {\n    id: 'text-embedding-3-small',\n    provider: 'openai',\n    dimensions: 1536,\n    maxInputTokens: 8191,\n    description: 'OpenAI text-embedding-3-small model',\n  },\n  {\n    id: 'text-embedding-3-large',\n    provider: 'openai',\n    dimensions: 3072,\n    maxInputTokens: 8191,\n    description: 'OpenAI text-embedding-3-large model',\n  },\n  {\n    id: 'text-embedding-ada-002',\n    provider: 'openai',\n    dimensions: 1536,\n    maxInputTokens: 8191,\n    description: 'OpenAI text-embedding-ada-002 model',\n  },\n  // Google\n  {\n    id: 'gemini-embedding-001',\n    provider: 'google',\n    dimensions: 768,\n    maxInputTokens: 2048,\n    description: 'Google gemini-embedding-001 model',\n  },\n  {\n    id: 'text-embedding-004',\n    provider: 'google',\n    dimensions: 768,\n    maxInputTokens: 3072,\n    description: 'Google text-embedding-004 model',\n  },\n];\n\n/**\n * Type for embedding model IDs in the format \"provider/model\"\n */\nexport type EmbeddingModelId =\n  | 'openai/text-embedding-3-small'\n  | 'openai/text-embedding-3-large'\n  | 'openai/text-embedding-ada-002'\n  | 'google/gemini-embedding-001'\n  | 'google/text-embedding-004';\n\n/**\n * Check if a model ID is a known embedding model\n */\nexport function isKnownEmbeddingModel(modelId: string): boolean {\n  return EMBEDDING_MODELS.some(m => m.id === modelId);\n}\n\n/**\n * Get information about a known embedding model\n */\nexport function getEmbeddingModelInfo(modelId: string): EmbeddingModelInfo | undefined {\n  return EMBEDDING_MODELS.find(m => m.id === modelId);\n}\n\n/**\n * Model router for embedding models that uses the provider/model string format.\n * Automatically resolves the correct AI SDK provider and initializes the embedding model.\n *\n * @example\n * ```ts\n * const embedder = new ModelRouterEmbeddingModel('openai/text-embedding-3-small');\n * const result = await embedder.doEmbed({ values: ['hello world'] });\n * ```\n */\nexport class ModelRouterEmbeddingModel<VALUE extends string = string> implements EmbeddingModelV2<VALUE> {\n  readonly specificationVersion = 'v2' as const;\n  readonly modelId: string;\n  readonly provider: string;\n  maxEmbeddingsPerCall: number | PromiseLike<number | undefined> = 2048;\n  supportsParallelCalls: boolean | PromiseLike<boolean> = true;\n\n  private providerModel: EmbeddingModelV2<VALUE>;\n\n  constructor(config: string | OpenAICompatibleConfig) {\n    // Normalize config to always have provider and model IDs\n    let normalizedConfig: {\n      providerId: string;\n      modelId: string;\n      url?: string;\n      apiKey?: string;\n      headers?: Record<string, string>;\n    };\n\n    if (typeof config === 'string') {\n      // Parse provider/model from string (e.g., \"openai/text-embedding-3-small\")\n      const parts = config.split('/');\n      if (parts.length !== 2) {\n        throw new Error(`Invalid model string format: \"${config}\". Expected format: \"provider/model\"`);\n      }\n      const [providerId, modelId] = parts as [string, string];\n      normalizedConfig = { providerId, modelId };\n    } else if ('providerId' in config && 'modelId' in config) {\n      normalizedConfig = {\n        providerId: config.providerId,\n        modelId: config.modelId,\n        url: config.url,\n        apiKey: config.apiKey,\n        headers: config.headers,\n      };\n    } else {\n      // config has 'id' field\n      const parts = config.id.split('/');\n      if (parts.length !== 2) {\n        throw new Error(`Invalid model string format: \"${config.id}\". Expected format: \"provider/model\"`);\n      }\n      const [providerId, modelId] = parts as [string, string];\n      normalizedConfig = {\n        providerId,\n        modelId,\n        url: config.url,\n        apiKey: config.apiKey,\n        headers: config.headers,\n      };\n    }\n\n    this.provider = normalizedConfig.providerId;\n    this.modelId = normalizedConfig.modelId;\n\n    // If custom URL is provided, skip provider registry validation\n    // and use the provided API key (or empty string if not provided)\n    if (normalizedConfig.url) {\n      const apiKey = normalizedConfig.apiKey || '';\n      this.providerModel = createOpenAICompatible({\n        name: normalizedConfig.providerId,\n        apiKey,\n        baseURL: normalizedConfig.url,\n        headers: normalizedConfig.headers,\n      }).textEmbeddingModel(normalizedConfig.modelId) as EmbeddingModelV2<VALUE>;\n    } else {\n      // Get provider config from registry\n      const registry = GatewayRegistry.getInstance();\n      const providerConfig = registry.getProviderConfig(normalizedConfig.providerId);\n\n      if (!providerConfig) {\n        throw new Error(`Unknown provider: ${normalizedConfig.providerId}`);\n      }\n\n      // Get API key from config or environment\n      let apiKey = normalizedConfig.apiKey;\n      if (!apiKey) {\n        const apiKeyEnvVar = providerConfig.apiKeyEnvVar;\n        if (Array.isArray(apiKeyEnvVar)) {\n          // Try each possible environment variable\n          for (const envVar of apiKeyEnvVar) {\n            apiKey = process.env[envVar];\n            if (apiKey) break;\n          }\n        } else {\n          apiKey = process.env[apiKeyEnvVar];\n        }\n      }\n\n      if (!apiKey) {\n        const envVarDisplay = Array.isArray(providerConfig.apiKeyEnvVar)\n          ? providerConfig.apiKeyEnvVar.join(' or ')\n          : providerConfig.apiKeyEnvVar;\n        throw new Error(`API key not found for provider ${normalizedConfig.providerId}. Set ${envVarDisplay}`);\n      }\n\n      // Initialize the provider model directly in constructor\n      if (normalizedConfig.providerId === 'openai') {\n        this.providerModel = createOpenAI({ apiKey }).textEmbeddingModel(\n          normalizedConfig.modelId,\n        ) as EmbeddingModelV2<VALUE>;\n      } else if (normalizedConfig.providerId === 'google') {\n        this.providerModel = createGoogleGenerativeAI({ apiKey }).textEmbedding(\n          normalizedConfig.modelId,\n        ) as EmbeddingModelV2<VALUE>;\n      } else {\n        // Use OpenAI-compatible provider for other providers\n        if (!providerConfig.url) {\n          throw new Error(`Provider ${normalizedConfig.providerId} does not have a URL configured`);\n        }\n        this.providerModel = createOpenAICompatible({\n          name: normalizedConfig.providerId,\n          apiKey,\n          baseURL: providerConfig.url,\n        }).textEmbeddingModel(normalizedConfig.modelId) as EmbeddingModelV2<VALUE>;\n      }\n    }\n\n    // Copy properties from the provider model if available\n    if (this.providerModel.maxEmbeddingsPerCall !== undefined) {\n      this.maxEmbeddingsPerCall = this.providerModel.maxEmbeddingsPerCall;\n    }\n    if (this.providerModel.supportsParallelCalls !== undefined) {\n      this.supportsParallelCalls = this.providerModel.supportsParallelCalls;\n    }\n  }\n\n  async doEmbed(\n    args: Parameters<EmbeddingModelV2<VALUE>['doEmbed']>[0],\n  ): Promise<Awaited<ReturnType<EmbeddingModelV2<VALUE>['doEmbed']>>> {\n    return this.providerModel.doEmbed(args);\n  }\n}\n"]}