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href=\"https://deepwiki.com/patelvivekdev/voyageai-ai-provider\"><img src=\"https://deepwiki.com/badge.svg\" alt=\"Ask DeepWiki\"></a>\n</div>\n<br>\n\n## Introduction\n\nThe Voyage AI Provider is a provider for the AI SDK. It provides a simple interface to the Voyage AI API.\n\nVersion 5 targets AI SDK 7 and requires Node.js 22 or newer. It is distributed\nas an ESM-only package.\n\n## Installation\n\n```bash\nnpm install voyage-ai-provider\n\n# or\n\nyarn add voyage-ai-provider\n\n# or\n\npnpm add voyage-ai-provider\n\n# or\n\nbun add voyage-ai-provider\n```\n\n## Configuration\n\nThe Voyage AI Provider requires an API key to be configured. You can obtain an API key by signing up at [Voyage AI](https://www.voyageai.com).\n\nadd the following to your `.env` file:\n\n```bash\nVOYAGE_API_KEY=your-api-key\n```\n\n## Usage\n\n### Text Embedding\n\n```typescript\nimport { voyage } from 'voyage-ai-provider';\nimport { embedMany } from 'ai';\n\nconst embeddingModel = voyage.embeddingModel('voyage-3-lite');\n\nexport const generateEmbeddings = async (\n  value: string,\n): Promise<Array<{ embedding: number[]; content: string }>> => {\n  // Generate chunks from the input value\n  const chunks = value.split('\\n');\n\n  // Optional: You can also split the input value by comma\n  // const chunks = value.split('.');\n\n  // Or you can use LLM to generate chunks(summarize) from the input value\n\n  const { embeddings } = await embedMany({\n    model: embeddingModel,\n    values: chunks,\n  });\n  return embeddings.map((e, i) => ({ content: chunks[i], embedding: e }));\n};\n```\n\n### How to pass additional settings to the model\n\nThe settings object should contain the settings you want to add to the model. You can find the available settings for the model in the Voyage API documentation: https://docs.voyageai.com/reference/embeddings-api\n\n```typescript\nimport { embedMany } from 'ai';\nimport { createVoyage } from 'voyage-ai-provider';\n\nconst voyage = createVoyage({\n  apiKey: process.env.VOYAGE_API_KEY,\n});\n\nconst embeddingModel = voyage.embeddingModel('voyage-3-lite');\n\nconst { embeddings } = await embedMany({\n  model: embeddingModel,\n  values: ['sunny day at the beach', 'rainy day in the city'],\n  providerOptions: {\n    voyage: {\n      inputType: 'document',\n      outputDimension: 1024,\n      outputDtype: 'float',\n    },\n  },\n});\n```\n\n### Image & Multi-modal Embedding\n\nMultimodal and image embeddings both use the `voyage-multimodal-3` model and the\nsame `/multimodalembeddings` endpoint. Following the AI SDK convention (and the\nofficial providers such as Google), the `embed`/`embedMany` `values` array holds\nthe **text** for each embedding, and any non-text content (images) is passed via\n`providerOptions.voyage.content`.\n\n`content` is an array aligned to `values` by index: `content[i]` are the extra\nparts merged with the text in `values[i]`. Its length must equal `values.length`.\nUse `null` for entries that are text-only. For an image-only embedding, pass an\nempty string (`''`) for that value.\n\nEach content part is one of:\n\n- `{ type: 'text', text: string }`\n- `{ type: 'image_url', image_url: string }`\n- `{ type: 'image_base64', image_base64: string }`\n\n#### Example 1: A single image per embedding (image-only)\n\n```typescript\nimport {\n  voyage,\n  type VoyageMultimodalEmbeddingOptions,\n} from 'voyage-ai-provider';\nimport { embedMany } from 'ai';\n\nconst imageModel = voyage.imageEmbeddingModel('voyage-multimodal-3');\n\nconst { embeddings } = await embedMany({\n  model: imageModel,\n  values: ['', ''], // one empty string per image-only embedding\n  providerOptions: {\n    voyage: {\n      content: [\n        [\n          {\n            type: 'image_url',\n            image_url:\n              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',\n          },\n        ],\n        [\n          {\n            type: 'image_base64',\n            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',\n          },\n        ],\n      ],\n    } satisfies VoyageMultimodalEmbeddingOptions,\n  },\n});\n```\n\n#### Example 2: Multiple images in a single embedding\n\n```typescript\nimport {\n  voyage,\n  type VoyageMultimodalEmbeddingOptions,\n} from 'voyage-ai-provider';\nimport { embedMany } from 'ai';\n\nconst imageModel = voyage.imageEmbeddingModel('voyage-multimodal-3');\n\nconst { embeddings } = await embedMany({\n  model: imageModel,\n  values: [''],\n  providerOptions: {\n    voyage: {\n      content: [\n        [\n          {\n            type: 'image_url',\n            image_url:\n              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',\n          },\n          {\n            type: 'image_base64',\n            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',\n          },\n        ],\n      ],\n    } satisfies VoyageMultimodalEmbeddingOptions,\n  },\n});\n```\n\n#### Example 3: Text and images combined per embedding\n\n```typescript\nimport {\n  voyage,\n  type VoyageMultimodalEmbeddingOptions,\n} from 'voyage-ai-provider';\nimport { embedMany } from 'ai';\n\nconst multimodalModel = voyage.multimodalEmbeddingModel('voyage-multimodal-3');\n\nconst { embeddings } = await embedMany({\n  model: multimodalModel,\n  values: ['This is a banana', 'This is a coding test'],\n  providerOptions: {\n    voyage: {\n      content: [\n        [\n          {\n            type: 'image_url',\n            image_url:\n              'https://raw.githubusercontent.com/voyage-ai/voyage-multimodal-3/refs/heads/main/images/banana_200_x_200.jpg',\n          },\n        ],\n        [\n          {\n            type: 'image_base64',\n            image_base64: 'data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAA...',\n          },\n        ],\n      ],\n    } satisfies VoyageMultimodalEmbeddingOptions,\n  },\n});\n```\n\n> [!TIP]\n> If you are getting an error for an image URL not found, convert the image to base64 and pass it as an `image_base64` part instead.\n> The value should be a Base64-encoded image in the data URL format `data:[<mediatype>];base64,<data>`.\n> Currently supported mediatypes are: image/png, image/jpeg, image/webp, and image/gif.\n\n> [!NOTE]\n> The following constraints apply to the values list:\n> The list must not contain more than 1,000 values.\n> Each image must not contain more than 16 million pixels or be larger than 20 MB in size.\n> With every 560 pixels of an image being counted as a token, each input in the list must not exceed 32,000 tokens, and the total number of tokens across all inputs must not exceed 320,000.\n\n## Voyage embedding models:\n\n| Model                 | Context Length (tokens) | Embedding Dimension            |\n| --------------------- | ----------------------- | ------------------------------ |\n| voyage-4-large        | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-4              | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-4-lite         | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-code-3         | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-finance-2      | 32,000                  | 1024                           |\n| voyage-law-2          | 16,000                  | 1024                           |\n| voyage-code-2         | 16,000                  | 1536                           |\n| voyage-3-large        | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-3.5            | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-3.5-lite       | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-3              | 32,000                  | 1024                           |\n| voyage-3-lite         | 32,000                  | 512                            |\n| voyage-multilingual-2 | 32,000                  | 1024                           |\n\n> [!WARNING]\n> The older models are deprecated and will be removed in the future.\n> Use the latest models instead.\n> https://docs.voyageai.com/docs/embeddings\n\n## Multi-modal Embedding Models\n\n| Model                 | Context Length (tokens) | Embedding Dimension            |\n| --------------------- | ----------------------- | ------------------------------ |\n| voyage-multimodal-3.5 | 32,000                  | 1024 (default), 256, 512, 2048 |\n| voyage-multimodal-3   | 32,000                  | 1024                           |\n\n### Reranking\n\nReranking helps improve search results by reordering documents based on their relevance to a query.\n\n```typescript\nimport { voyage } from 'voyage-ai-provider';\nimport { rerank } from 'ai';\n\nconst rerankingModel = voyage.reranking('rerank-2.5');\n\nconst result = await rerank({\n  model: rerankingModel,\n  query: 'talk about rain',\n  documents: [\n    'sunny day at the beach',\n    'rainy day in the city',\n    'snowy mountain peak',\n  ],\n  topN: 2,\n});\n```\n\n#### How to pass additional settings to the reranking model\n\nThe settings object should contain the settings you want to add to the model. You can find the available settings for the model in the Voyage API documentation: https://docs.voyageai.com/reference/reranker-api\n\n```typescript\nimport { voyage, type VoyageRerankingOptions } from 'voyage-ai-provider';\nimport { rerank } from 'ai';\n\nconst rerankingModel = voyage.reranking('rerank-2.5');\n\nconst result = await rerank({\n  model: rerankingModel,\n  query: 'talk about rain',\n  documents: [\n    'sunny day at the beach',\n    'rainy day in the city',\n    'snowy mountain peak',\n  ],\n  topN: 2,\n  providerOptions: {\n    voyage: {\n      returnDocuments: true, // Return documents in the response\n      truncation: true, // Truncate inputs to fit context length\n    } satisfies VoyageRerankingOptions,\n  },\n});\n```\n\n> [!NOTE]\n> The following constraints apply to reranking:\n>\n> - Query token limits: rerank-2.5 and rerank-2.5-lite (8,000), rerank-2 (4,000), rerank-2-lite and rerank-1 (2,000), rerank-lite-1 (1,000)\n> - Query + document token limits: rerank-2.5 and rerank-2.5-lite (32,000), rerank-2 (16,000), rerank-2-lite and rerank-1 (8,000), rerank-lite-1 (4,000)\n> - If `truncation` is set to `false`, an error will be raised when these limits are exceeded\n\n## Voyage Reranking Models\n\n| Model           | Query Token Limit | Query + Document Token Limit |\n| --------------- | ----------------- | ---------------------------- |\n| rerank-2.5      | 8,000             | 32,000                       |\n| rerank-2.5-lite | 8,000             | 32,000                       |\n| rerank-2        | 4,000             | 16,000                       |\n| rerank-2-lite   | 2,000             | 8,000                        |\n| rerank-1        | 2,000             | 8,000                        |\n| rerank-lite-1   | 1,000             | 4,000                        |\n\n> [!TIP]\n> Use `rerank-2.5` or `rerank-2.5-lite` for the best performance and accuracy.\n> Older models (rerank-2, rerank-2-lite, rerank-1, rerank-lite-1) are available but may have lower performance.\n> https://docs.voyageai.com/docs/reranker\n\n## Authors\n\n- [patelvivekdev](https://patelvivek.dev)\n","readmeFilename":"readme.md"}