voyage-ai-provider/dist/index.cjs.map

Voyage AI Provider for running Voyage AI models with Vercel AI SDK

{"version":3,"sources":["../src/index.ts","../src/voyage-provider.ts","../src/voyage-embedding-model.ts","../src/voyage-embedding-settings.ts","../src/voyage-error.ts","../src/voyage-multimodal-embedding-model.ts","../src/voyage-multimodal-embedding-settings.ts","../src/reranking/voyage-reranking-model.ts","../src/reranking/voyage-reranking-api.ts","../src/reranking/voyage-reranking-options.ts"],"sourcesContent":["export { createVoyage, voyage } from './voyage-provider';\nexport type { VoyageEmbeddingOptions } from './voyage-embedding-settings';\nexport type {\n  VoyageMultimodalEmbeddingOptions,\n  VoyageMultimodalContentPart,\n} from './voyage-multimodal-embedding-settings';\nexport type { VoyageRerankingOptions } from './reranking/voyage-reranking-options';\nexport type { VoyageProvider, VoyageProviderSettings } from './voyage-provider';\nexport type { MultimodalEmbeddingModel } from './voyage-multimodal-embedding-model';\n","import type {\n  EmbeddingModelV3,\n  ImageModelV3,\n  LanguageModelV3,\n  ProviderV3,\n  RerankingModelV3,\n} from '@ai-sdk/provider';\nimport {\n  type FetchFunction,\n  loadApiKey,\n  withoutTrailingSlash,\n} from '@ai-sdk/provider-utils';\nimport { VoyageEmbeddingModel } from './voyage-embedding-model';\nimport type { VoyageEmbeddingModelId } from './voyage-embedding-settings';\nimport type { VoyageMultimodalEmbeddingModelId } from './voyage-multimodal-embedding-settings';\nimport { MultimodalEmbeddingModel } from './voyage-multimodal-embedding-model';\nimport type { VoyageRerankingModelId } from './reranking/voyage-reranking-options';\nimport { VoyageRerankingModel } from './reranking/voyage-reranking-model';\n\nexport interface VoyageProvider extends ProviderV3 {\n  (modelId: VoyageEmbeddingModelId): EmbeddingModelV3;\n\n  embeddingModel: (modelId: VoyageEmbeddingModelId) => EmbeddingModelV3;\n\n  /**\n   * @deprecated Use `embeddingModel` instead. `textEmbeddingModel` is kept for\n   * backwards compatibility and points to the same implementation.\n   */\n  textEmbeddingModel: (modelId: VoyageEmbeddingModelId) => EmbeddingModelV3;\n\n  /**\n   * Multimodal (text and/or image) embeddings.\n   *\n   * Voyage exposes a single multimodal endpoint, so `imageEmbeddingModel` and\n   * `multimodalEmbeddingModel` are equivalent. Pass text in `values` and images\n   * via `providerOptions.voyage.content`.\n   */\n  multimodalEmbeddingModel: (\n    modelId: VoyageMultimodalEmbeddingModelId,\n  ) => EmbeddingModelV3;\n\n  /**\n   * Alias for {@link VoyageProvider.multimodalEmbeddingModel} — both use the\n   * `voyage-multimodal-3` model and the same multimodal endpoint.\n   */\n  imageEmbeddingModel: (\n    modelId: VoyageMultimodalEmbeddingModelId,\n  ) => EmbeddingModelV3;\n\n  reranking: (modelId: VoyageRerankingModelId) => RerankingModelV3;\n\n  rerankingModel: (modelId: VoyageRerankingModelId) => RerankingModelV3;\n}\n\nexport interface VoyageProviderSettings {\n  /**\n   * Use a different URL prefix for API calls, e.g. to use proxy servers.\n   * The default prefix is `https://api.voyageai.com/v1`.\n   *\n   * @see https://docs.voyageai.com/reference\n   */\n  baseURL?: string;\n\n  /**\n  API key that is being send using the `Authorization` header.\n  It defaults to the `VOYAGE_API_KEY` environment variable.\n     */\n  apiKey?: string;\n\n  /**\n  Custom headers to include in the requests.\n       */\n  headers?: Record<string, string>;\n\n  /**\n  Custom fetch implementation. You can use it as a middleware to intercept requests,\n  or to provide a custom fetch implementation for e.g. testing.\n      */\n  fetch?: FetchFunction;\n}\n\n/**\n  Create a Voyage AI provider instance.\n   */\nexport function createVoyage(\n  options: VoyageProviderSettings = {},\n): VoyageProvider {\n  const baseURL =\n    withoutTrailingSlash(options.baseURL) ?? 'https://api.voyageai.com/v1';\n\n  const getHeaders = () => ({\n    Authorization: `Bearer ${loadApiKey({\n      apiKey: options.apiKey,\n      environmentVariableName: 'VOYAGE_API_KEY',\n      description: 'Voyage',\n    })}`,\n    ...options.headers,\n  });\n\n  const createEmbeddingModel = (modelId: VoyageEmbeddingModelId) =>\n    new VoyageEmbeddingModel(modelId, {\n      provider: 'voyage.embedding',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createMultimodalEmbeddingModel = (\n    modelId: VoyageMultimodalEmbeddingModelId,\n  ) =>\n    new MultimodalEmbeddingModel(modelId, {\n      provider: 'voyage.multimodal.embedding',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const createRerankingModel = (modelId: VoyageRerankingModelId) =>\n    new VoyageRerankingModel(modelId, {\n      provider: 'voyage.reranking',\n      baseURL,\n      headers: getHeaders,\n      fetch: options.fetch,\n    });\n\n  const provider = function (modelId: VoyageEmbeddingModelId) {\n    if (new.target) {\n      throw new Error(\n        'The Voyage model function cannot be called with the new keyword.',\n      );\n    }\n\n    return createEmbeddingModel(modelId);\n  };\n\n  provider.embeddingModel = createEmbeddingModel;\n  provider.textEmbeddingModel = createEmbeddingModel;\n  provider.multimodalEmbeddingModel = createMultimodalEmbeddingModel;\n  provider.imageEmbeddingModel = createMultimodalEmbeddingModel;\n\n  provider.chat = provider.languageModel = (): LanguageModelV3 => {\n    throw new Error('languageModel method is not implemented.');\n  };\n  provider.imageModel = (): ImageModelV3 => {\n    throw new Error('imageModel method is not implemented.');\n  };\n\n  provider.reranking = createRerankingModel;\n  provider.rerankingModel = createRerankingModel;\n\n  return provider as unknown as VoyageProvider;\n}\n\n/**\n  Default Voyage provider instance.\n   */\nexport const voyage = createVoyage();\n","import {\n  type EmbeddingModelV3,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  type FetchFunction,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nimport {\n  voyageEmbeddingOptions,\n  type VoyageEmbeddingModelId,\n} from '@/voyage-embedding-settings';\nimport { voyageFailedResponseHandler } from '@/voyage-error';\n\ntype VoyageEmbeddingConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class VoyageEmbeddingModel implements EmbeddingModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly modelId: VoyageEmbeddingModelId;\n\n  private readonly config: VoyageEmbeddingConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get maxEmbeddingsPerCall(): number {\n    return 128;\n  }\n\n  get supportsParallelCalls(): boolean {\n    return false;\n  }\n\n  constructor(modelId: VoyageEmbeddingModelId, config: VoyageEmbeddingConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    abortSignal,\n    values,\n    headers,\n    providerOptions,\n  }: Parameters<EmbeddingModelV3['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV3['doEmbed']>>\n  > {\n    const embeddingOptions = await parseProviderOptions({\n      provider: 'voyage',\n      providerOptions,\n      schema: voyageEmbeddingOptions,\n    });\n\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        modelId: this.modelId,\n        provider: this.provider,\n        values,\n      });\n    }\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      abortSignal,\n      body: {\n        input: values,\n        model: this.modelId,\n        input_type: embeddingOptions?.inputType,\n        truncation: embeddingOptions?.truncation,\n        output_dimension: embeddingOptions?.outputDimension,\n        output_dtype: embeddingOptions?.outputDtype,\n      },\n      failedResponseHandler: voyageFailedResponseHandler,\n      fetch: this.config.fetch,\n      headers: combineHeaders(this.config.headers(), headers),\n      successfulResponseHandler: createJsonResponseHandler(\n        voyageTextEmbeddingResponseSchema,\n      ),\n      url: `${this.config.baseURL}/embeddings`,\n    });\n\n    return {\n      embeddings: response.data.map((item) => item.embedding),\n      usage: response.usage\n        ? { tokens: response.usage.total_tokens }\n        : undefined,\n      response: { headers: responseHeaders, body: rawValue },\n      warnings: [],\n    };\n  }\n}\n\n// minimal version of the schema, focussed on what is needed for the implementation\n// this approach limits breakages when the API changes and increases efficiency\nconst voyageTextEmbeddingResponseSchema = z.object({\n  data: z.array(z.object({ embedding: z.array(z.number()) })),\n  usage: z.object({ total_tokens: z.number() }).nullish(),\n});\n","import { z } from 'zod/v4';\n\nexport type VoyageEmbeddingModelId =\n  // Latest generation (recommended)\n  | 'voyage-4-large'\n  | 'voyage-4'\n  | 'voyage-4-lite'\n  | 'voyage-code-3'\n  | 'voyage-finance-2'\n  | 'voyage-law-2'\n  | 'voyage-code-2'\n\n  // Previous generation\n  | 'voyage-3-large'\n  | 'voyage-3.5'\n  | 'voyage-3.5-lite'\n  | 'voyage-3'\n  | 'voyage-3-lite'\n  | 'voyage-multilingual-2'\n\n  // Older models [Deprecated]\n  | 'voyage-large-2-instruct'\n  | 'voyage-large-2'\n  | 'voyage-2'\n  | 'voyage-02'\n  | 'voyage-01'\n  | 'voyage-lite-01'\n  | (string & NonNullable<unknown>);\n\nexport const voyageEmbeddingOptions = z.object({\n  /**\n   * The input type for the embeddings. Defaults to \"query\".\n   * For query, the prompt is \"Represent the query for retrieving supporting documents: \".\n   * For document, the prompt is \"Represent the document for retrieval: \".\n   */\n\n  inputType: z.enum(['query', 'document']).optional(),\n\n  // /**\n  //  * Format in which the embeddings are encoded. We support two options:\n  //  * If not specified (defaults to null): the embeddings are represented as lists of floating-point numbers;\n  //  * base64: the embeddings are compressed to base64 encodings.\n  //  */\n  // encodingFormat?: 'base64';\n\n  /**\n   * The number of dimensions for the resulting output embeddings.\n   *\n   * If not specified (defaults to null), the resulting output embeddings dimension is the default for the model.\n   * `voyage-code-3` supports the following `outputDimension` values: 2048, 1024 (default), 512, and 256.\n   * `voyage-3-large` supports the following `outputDimension` values: 2048, 1024 (default), 512, and 256.\n   *\n   * please refer to the model documentation for the supported values.\n   * https://docs.voyageai.com/docs/embeddings\n   */\n  outputDimension: z.number().optional(),\n\n  /**\n   * The data type for the resulting output embeddings.\n   *\n   * Defaults to 'float'.\n   *\n   * Other options: 'int8', 'uint8', 'binary', 'ubinary'.\n   * - 'float' is supported by all models.\n   * - 'float': Each returned embedding is a list of 32-bit (4-byte) single-precision floating-point numbers.\n   * - 'int8', 'uint8', 'binary', and 'ubinary' are supported by 'voyage-code-3'.\n   * - 'int8' and 'uint8': Each returned embedding is a list of 8-bit (1-byte) integers ranging from -128 to 127 and 0 to 255, respectively.\n   * - 'binary' and 'ubinary': Each returned embedding is a list of 8-bit integers that represent bit-packed, quantized single-bit embedding values:\n   *   'int8' for 'binary' and 'uint8' for 'ubinary'.\n   *   The length of the returned list of integers is 1/8 of outputDimension (which is the actual dimension of the embedding).\n   *   The 'binary' type uses the offset binary method.\n   *\n   * https://docs.voyageai.com/docs/faq#what-is-quantization-and-output-data-types\n   */\n  outputDtype: z\n    .enum(['float', 'int8', 'uint8', 'binary', 'ubinary'])\n    .optional(),\n\n  /**\n   *  Whether to truncate the input texts to fit within the context length.\n   */\n  truncation: z.boolean().optional(),\n});\n\nexport type VoyageEmbeddingOptions = z.infer<typeof voyageEmbeddingOptions>;\n","import { createJsonErrorResponseHandler } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\nconst voyageErrorDataSchema = z.object({\n  error: z.object({\n    code: z.string().nullable(),\n    message: z.string(),\n    param: z.any().nullable(),\n    type: z.string(),\n  }),\n});\n\nexport type VoyageErrorData = z.infer<typeof voyageErrorDataSchema>;\n\nexport const voyageFailedResponseHandler = createJsonErrorResponseHandler({\n  errorSchema: voyageErrorDataSchema,\n  errorToMessage: (data) => data.error.message,\n});\n","import {\n  type EmbeddingModelV3,\n  TooManyEmbeddingValuesForCallError,\n} from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  type FetchFunction,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\n\nimport {\n  voyageMultimodalEmbeddingOptions,\n  type VoyageMultimodalContentPart,\n  type VoyageMultimodalEmbeddingModelId,\n} from '@/voyage-multimodal-embedding-settings';\nimport { voyageFailedResponseHandler } from '@/voyage-error';\nimport { z } from 'zod/v4';\n\ntype VoyageEmbeddingConfig = {\n  baseURL: string;\n  fetch?: FetchFunction;\n  headers: () => Record<string, string | undefined>;\n  provider: string;\n};\n\n// API-compatible input for the Voyage multimodal embeddings endpoint.\n// https://docs.voyageai.com/reference/multimodal-embeddings-api\ntype VoyageMultimodalInput = {\n  content: VoyageMultimodalContentPart[];\n};\n\nexport class MultimodalEmbeddingModel implements EmbeddingModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly modelId: VoyageMultimodalEmbeddingModelId;\n\n  private readonly config: VoyageEmbeddingConfig;\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  get maxEmbeddingsPerCall(): number {\n    return 128;\n  }\n\n  get supportsParallelCalls(): boolean {\n    return false;\n  }\n\n  constructor(\n    modelId: VoyageMultimodalEmbeddingModelId,\n    config: VoyageEmbeddingConfig,\n  ) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  async doEmbed({\n    abortSignal,\n    values,\n    headers,\n    providerOptions,\n  }: Parameters<EmbeddingModelV3['doEmbed']>[0]): Promise<\n    Awaited<ReturnType<EmbeddingModelV3['doEmbed']>>\n  > {\n    const embeddingOptions = await parseProviderOptions({\n      provider: 'voyage',\n      providerOptions,\n      schema: voyageMultimodalEmbeddingOptions,\n    });\n\n    if (values.length > this.maxEmbeddingsPerCall) {\n      throw new TooManyEmbeddingValuesForCallError({\n        maxEmbeddingsPerCall: this.maxEmbeddingsPerCall,\n        modelId: this.modelId,\n        provider: this.provider,\n        values,\n      });\n    }\n\n    const multimodalContent = embeddingOptions?.content;\n\n    if (\n      multimodalContent != null &&\n      multimodalContent.length !== values.length\n    ) {\n      throw new Error(\n        `The number of multimodal content entries (${multimodalContent.length}) must match the number of values (${values.length}).`,\n      );\n    }\n\n    // Merge each text value with its per-index content parts (images), the\n    // same way the Google provider merges text with multimodal parts.\n    // https://docs.voyageai.com/reference/multimodal-embeddings-api\n    const inputs: VoyageMultimodalInput[] = values.map((value, index) => {\n      const valueParts = multimodalContent?.[index];\n      const textPart: VoyageMultimodalContentPart[] = value\n        ? [{ type: 'text', text: value }]\n        : [];\n      return {\n        content:\n          valueParts != null\n            ? [...textPart, ...valueParts]\n            : [{ type: 'text', text: value }],\n      };\n    });\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      abortSignal,\n      body: {\n        inputs,\n        model: this.modelId,\n        input_type: embeddingOptions?.inputType,\n        truncation: embeddingOptions?.truncation,\n        output_encoding: embeddingOptions?.outputEncoding,\n      },\n      failedResponseHandler: voyageFailedResponseHandler,\n      fetch: this.config.fetch,\n      headers: combineHeaders(this.config.headers(), headers),\n      successfulResponseHandler: createJsonResponseHandler(\n        voyageMultimodalEmbeddingResponseSchema,\n      ),\n      url: `${this.config.baseURL}/multimodalembeddings`,\n    });\n\n    return {\n      embeddings: response.data.map((item) => item.embedding),\n      usage: response.usage\n        ? { tokens: response.usage.total_tokens }\n        : undefined,\n      response: { headers: responseHeaders, body: rawValue },\n      warnings: [],\n    };\n  }\n}\n\nconst voyageMultimodalEmbeddingResponseSchema = z.object({\n  data: z.array(\n    z.object({\n      object: z.literal('embedding'),\n      embedding: z.array(z.number()),\n      index: z.number(),\n    }),\n  ),\n  usage: z.object({\n    text_tokens: z.number().nullish(),\n    image_pixels: z.number().nullish(),\n    total_tokens: z.number(),\n  }),\n  model: z.string(),\n});\n","import { z } from 'zod/v4';\n\nexport type VoyageMultimodalEmbeddingModelId =\n  | 'voyage-multimodal-3.5'\n  | 'voyage-multimodal-3'\n  | (string & {});\n\n/**\n * A single multimodal content part, mirroring the Voyage multimodal\n * embeddings API content items.\n *\n * https://docs.voyageai.com/reference/multimodal-embeddings-api\n */\nconst voyageMultimodalContentPart = z.union([\n  z.object({ type: z.literal('text'), text: z.string() }),\n  z.object({ type: z.literal('image_url'), image_url: z.string() }),\n  z.object({ type: z.literal('image_base64'), image_base64: z.string() }),\n]);\n\nexport type VoyageMultimodalContentPart = z.infer<\n  typeof voyageMultimodalContentPart\n>;\n\nexport const voyageMultimodalEmbeddingOptions = z.object({\n  /**\n   * Type of the input.\n   * Defaults to \"query\".\n   *\n   * When input_type is specified as \"query\" or \"document\", Voyage automatically prepends a prompt\n   * to your inputs before vectorize them, creating vectors more tailored for retrieval/search tasks.\n   *\n   * For retrieval/search purposes where a query is used to search through documents, we recommend\n   * specifying whether your inputs are queries or documents. Since inputs can be multimodal,\n   * \"queries\" and \"documents\" can be text, images, or an interleaving of both modalities.\n   *\n   * For transparency, the following prompts are prepended:\n   * - For \"query\": \"Represent the query for retrieving supporting documents: \"\n   * - For \"document\": \"Represent the document for retrieval: \"\n   */\n\n  inputType: z.enum(['query', 'document']).optional(),\n\n  /**\n   * The data type for the resulting output embeddings.\n   *\n   * Defaults to null.\n   *\n   * - If null, the embeddings are represented as a list of floating-point numbers.\n   * - If base64, the embeddings are represented as a Base64-encoded NumPy array of single-precision floats.\n   *\n   * https://docs.voyageai.com/docs/faq#what-is-quantization-and-output-data-types\n   */\n  outputEncoding: z.enum(['base64']).optional(),\n\n  /**\n   *  Whether to truncate the input texts to fit within the context length.\n   *\n   *  Defaults to true.\n   */\n  truncation: z.boolean().optional(),\n\n  /**\n   * Per-value multimodal content parts for embedding non-text content\n   * (images). Each entry corresponds to the embedding value at the same\n   * index and its parts are merged after the text value in the request.\n   * Use `null` for entries that are text-only.\n   *\n   * The array length must match the number of values being embedded.\n   *\n   * https://docs.voyageai.com/reference/multimodal-embeddings-api\n   */\n  content: z\n    .array(z.array(voyageMultimodalContentPart).min(1).nullable())\n    .optional(),\n});\n\nexport type VoyageMultimodalEmbeddingOptions = z.infer<\n  typeof voyageMultimodalEmbeddingOptions\n>;\n","import type { RerankingModelV3, SharedV3Warning } from '@ai-sdk/provider';\nimport {\n  combineHeaders,\n  createJsonResponseHandler,\n  type FetchFunction,\n  parseProviderOptions,\n  postJsonToApi,\n} from '@ai-sdk/provider-utils';\nimport { voyageFailedResponseHandler } from '@/voyage-error';\nimport {\n  type VoyageRerankingInput,\n  voyageRerankingResponseSchema,\n} from '@/reranking/voyage-reranking-api';\nimport {\n  type VoyageRerankingModelId,\n  voyageRerankingOptionsSchema,\n} from '@/reranking/voyage-reranking-options';\n\ntype VoyageRerankingConfig = {\n  provider: string;\n  baseURL: string;\n  headers: () => Record<string, string | undefined>;\n  fetch?: FetchFunction;\n};\n\nexport class VoyageRerankingModel implements RerankingModelV3 {\n  readonly specificationVersion = 'v3';\n  readonly modelId: VoyageRerankingModelId;\n\n  private readonly config: VoyageRerankingConfig;\n\n  constructor(modelId: VoyageRerankingModelId, config: VoyageRerankingConfig) {\n    this.modelId = modelId;\n    this.config = config;\n  }\n\n  get provider(): string {\n    return this.config.provider;\n  }\n\n  // current implementation is based on the API:https://docs.voyageai.com/reference/reranker-api\n  async doRerank({\n    documents,\n    headers,\n    query,\n    topN,\n    abortSignal,\n    providerOptions,\n  }: Parameters<RerankingModelV3['doRerank']>[0]): Promise<\n    Awaited<ReturnType<RerankingModelV3['doRerank']>>\n  > {\n    const rerankingOptions = await parseProviderOptions({\n      provider: 'voyage',\n      providerOptions,\n      schema: voyageRerankingOptionsSchema,\n    });\n\n    const warnings: SharedV3Warning[] = [];\n\n    const {\n      responseHeaders,\n      value: response,\n      rawValue,\n    } = await postJsonToApi({\n      url: `${this.config.baseURL}/rerank`,\n      headers: combineHeaders(this.config.headers(), headers),\n      body: {\n        model: this.modelId,\n        query,\n        documents:\n          documents.type === 'text'\n            ? documents.values\n            : documents.values.map((value) => JSON.stringify(value)),\n        top_k: topN,\n        return_documents: rerankingOptions?.returnDocuments ?? false,\n        truncation: rerankingOptions?.truncation ?? true,\n      } satisfies VoyageRerankingInput,\n      failedResponseHandler: voyageFailedResponseHandler,\n      successfulResponseHandler: createJsonResponseHandler(\n        voyageRerankingResponseSchema,\n      ),\n      abortSignal,\n      fetch: this.config.fetch,\n    });\n\n    return {\n      ranking: response.data.map((result) => ({\n        index: result.index,\n        relevanceScore: result.relevance_score,\n      })),\n      warnings: warnings.length > 0 ? warnings : undefined,\n      response: {\n        headers: responseHeaders,\n        body: rawValue,\n      },\n    };\n  }\n}\n","import { lazySchema, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://docs.voyageai.com/reference/reranker-api\nexport type VoyageRerankingInput = {\n  model: string;\n  query: string;\n  documents: string[];\n  top_k: number | undefined;\n  return_documents: boolean;\n  truncation: boolean;\n};\n\nexport const voyageRerankingResponseSchema = lazySchema(() =>\n  zodSchema(\n    z.object({\n      object: z.literal('list'),\n      data: z.array(\n        z.object({\n          relevance_score: z.number(),\n          index: z.number(),\n        }),\n      ),\n      model: z.string(),\n      usage: z.object({\n        total_tokens: z.number(),\n      }),\n    }),\n  ),\n);\n","import type { FlexibleSchema } from '@ai-sdk/provider-utils';\nimport { lazySchema, zodSchema } from '@ai-sdk/provider-utils';\nimport { z } from 'zod/v4';\n\n// https://docs.voyageai.com/docs/reranker\nexport type VoyageRerankingModelId =\n  | 'rerank-2.5'\n  | 'rerank-2.5-lite'\n  | 'rerank-2'\n  | 'rerank-2-lite'\n  | 'rerank-1'\n  | 'rerank-lite-1'\n  | (string & {});\n\nexport type VoyageRerankingOptions = {\n  /**\n   * Whether to return the documents in the response. Defaults to false.\n   *\n   * If false, the API will return a list of {\"index\", \"relevance_score\"} where \"index\" refers to\n   * the index of a document within the input list.\n   * If true, the API will return a list of {\"index\", \"document\", \"relevance_score\"} where \"document\"\n   * is the corresponding document from the input list.\n   *\n   * @default false\n   */\n  returnDocuments?: boolean;\n\n  /**\n   * Whether to truncate the input to satisfy the \"context length limit\" on the query\n   * and the documents. Defaults to true.\n   *\n   * If true, the query and documents will be truncated to fit within the context length limit,\n   * before processed by the reranker model.\n   *\n   * If false, an error will be raised when the query exceeds 8,000 tokens for rerank-2.5\n   * and rerank-2.5-lite; 4,000 tokens for rerank-2; 2,000 tokens for rerank-2-lite and rerank-1;\n   * and 1,000 tokens for rerank-lite-1, or the sum of the number of tokens in the query and the\n   * number of tokens in any single document exceeds 32,000 for rerank-2.5 and rerank-2.5-lite;\n   * 16,000 for rerank-2; 8,000 for rerank-2-lite and rerank-1; and 4,000 for rerank-lite-1.\n   *\n   * @default true\n   */\n  truncation?: boolean;\n};\n\nexport const voyageRerankingOptionsSchema: FlexibleSchema<VoyageRerankingOptions> =\n  lazySchema(() =>\n    zodSchema(\n      z.object({\n        returnDocuments: z.boolean().optional(),\n        truncation: z.boolean().optional(),\n      }),\n    ),\n  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