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Voyage AI

Voyage AI builds state-of-the-art embedding and reranker models for retrieval-augmented generation (RAG) and semantic search. The platform exposes an OpenAI-style REST API at api.voyageai.com/v1 for text embeddings, multimodal embeddings, contextualized embeddings, and reranking, with Python and TypeScript SDKs. Model families include voyage-3.x and voyage-4.x text embeddings, voyage-code-3, domain-specialised models (voyage-finance-2, voyage-law-2), voyage-multimodal-3, and the voyage-rerank-2 reranker family. Voyage AI was acquired by MongoDB in February 2024 and is integrated into MongoDB Atlas Vector Search; models are also distributed via AWS Marketplace, Azure Marketplace, and Snowflake.

6 APIs 0 Features
EmbeddingsRerankersRAGSemantic SearchAI ModelsVector SearchMultimodal

Voyage AI publishes 1 API on the APIs.io network: Embeddings API. Tagged areas include Embeddings, Rerankers, RAG, Semantic Search, and AI Models.

Voyage AI’s developer surface includes documentation, GitHub presence, pricing, and 6 more developer resources.

APIs

Voyage AI Embeddings API

OpenAI-compatible REST endpoint that returns dense vector embeddings for input text. Supports model selection (voyage-3.5, voyage-3-large, voyage-code-3, voyage-finance-2, voyag...

Voyage AI Rerank API

Reranking endpoint that scores a list of candidate documents against a query and returns relevance scores. Powered by the voyage-rerank-2 model family, used downstream of vector...

Voyage AI Multimodal Embeddings API

Multimodal embeddings endpoint backed by voyage-multimodal-3 that accepts interleaved text and images in a single request and returns embeddings in a shared vector space, enabli...

Voyage AI Contextualized Embeddings API

Endpoint that embeds chunks while conditioning on surrounding document context, improving recall for long-document RAG workflows where chunk embeddings would otherwise lose docu...

Voyage AI Python SDK

Official Python client (voyageai) wrapping the embeddings, multimodal, contextualized, and reranking endpoints with batching, retries, and async support.

Voyage AI TypeScript SDK

Official TypeScript / JavaScript client for the Voyage AI REST API.

Resources

🔗
LinkedIn
LinkedIn
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Website
Website
🔗
Documentation
Documentation
👥
GitHub
GitHub
💰
Pricing
Pricing
🔗
Parent
Parent
🔗
Plans
Plans
🔗
RateLimits
RateLimits
🔗
FinOps
FinOps

Sources

Raw ↑
aid: voyage-ai
url: https://raw.githubusercontent.com/api-evangelist/voyage-ai/refs/heads/main/apis.yml
name: Voyage AI
kind: company
description: >-
  Voyage AI builds state-of-the-art embedding and reranker models for
  retrieval-augmented generation (RAG) and semantic search. The platform
  exposes an OpenAI-style REST API at api.voyageai.com/v1 for text embeddings,
  multimodal embeddings, contextualized embeddings, and reranking, with
  Python and TypeScript SDKs. Model families include voyage-3.x and voyage-4.x
  text embeddings, voyage-code-3, domain-specialised models (voyage-finance-2,
  voyage-law-2), voyage-multimodal-3, and the voyage-rerank-2 reranker family.
  Voyage AI was acquired by MongoDB in February 2024 and is integrated into
  MongoDB Atlas Vector Search; models are also distributed via AWS Marketplace,
  Azure Marketplace, and Snowflake.
image: https://kinlane-productions.s3.amazonaws.com/apis-json/apis-json-logo.jpg
tags:
  - Embeddings
  - Rerankers
  - RAG
  - Semantic Search
  - AI Models
  - Vector Search
  - Multimodal
created: '2026-05-23'
modified: '2026-05-23'
specificationVersion: '0.19'
apis:
  - aid: voyage-ai:embeddings
    name: Voyage AI Embeddings API
    description: >-
      OpenAI-compatible REST endpoint that returns dense vector embeddings for
      input text. Supports model selection (voyage-3.5, voyage-3-large,
      voyage-code-3, voyage-finance-2, voyage-law-2, voyage-4 family),
      configurable output dimensions (256, 512, 1024, 2048), output dtype
      (float, int8, uint8, binary, ubinary), input_type hints (query or
      document), and batch sizes up to 1000 inputs per request.
    humanURL: https://docs.voyageai.com/reference/embeddings-api
    baseURL: https://api.voyageai.com/v1
    tags:
      - Embeddings
      - Text
      - REST
    properties:
      - type: Documentation
        url: https://docs.voyageai.com/reference/embeddings-api
      - type: OpenAPI
        url: https://docs.voyageai.com/llms.txt
  - aid: voyage-ai:rerank
    name: Voyage AI Rerank API
    description: >-
      Reranking endpoint that scores a list of candidate documents against a
      query and returns relevance scores. Powered by the voyage-rerank-2 model
      family, used downstream of vector search to improve retrieval precision
      in RAG pipelines.
    humanURL: https://docs.voyageai.com/reference/reranker-api
    baseURL: https://api.voyageai.com/v1
    tags:
      - Rerank
      - Retrieval
      - RAG
    properties:
      - type: Documentation
        url: https://docs.voyageai.com/reference/reranker-api
  - aid: voyage-ai:multimodal-embeddings
    name: Voyage AI Multimodal Embeddings API
    description: >-
      Multimodal embeddings endpoint backed by voyage-multimodal-3 that accepts
      interleaved text and images in a single request and returns embeddings in
      a shared vector space, enabling cross-modal retrieval for documents that
      mix text, screenshots, charts, and figures.
    humanURL: https://docs.voyageai.com/reference/multimodal-embeddings-api
    baseURL: https://api.voyageai.com/v1
    tags:
      - Embeddings
      - Multimodal
      - Vision
    properties:
      - type: Documentation
        url: https://docs.voyageai.com/reference/multimodal-embeddings-api
  - aid: voyage-ai:contextualized-embeddings
    name: Voyage AI Contextualized Embeddings API
    description: >-
      Endpoint that embeds chunks while conditioning on surrounding document
      context, improving recall for long-document RAG workflows where chunk
      embeddings would otherwise lose document-level signal.
    humanURL: https://docs.voyageai.com/reference/contextualized-embeddings-api
    baseURL: https://api.voyageai.com/v1
    tags:
      - Embeddings
      - Contextualized
      - RAG
    properties:
      - type: Documentation
        url: https://docs.voyageai.com/reference/contextualized-embeddings-api
  - aid: voyage-ai:python-sdk
    name: Voyage AI Python SDK
    description: >-
      Official Python client (voyageai) wrapping the embeddings, multimodal,
      contextualized, and reranking endpoints with batching, retries, and
      async support.
    humanURL: https://github.com/voyage-ai/voyageai-python
    baseURL: https://github.com/voyage-ai/voyageai-python
    tags:
      - SDK
      - Python
    properties:
      - type: Repository
        url: https://github.com/voyage-ai/voyageai-python
      - type: Package
        url: https://pypi.org/project/voyageai/
  - aid: voyage-ai:typescript-sdk
    name: Voyage AI TypeScript SDK
    description: >-
      Official TypeScript / JavaScript client for the Voyage AI REST API.
    humanURL: https://github.com/voyage-ai/typescript-sdk
    baseURL: https://github.com/voyage-ai/typescript-sdk
    tags:
      - SDK
      - TypeScript
      - JavaScript
    properties:
      - type: Repository
        url: https://github.com/voyage-ai/typescript-sdk
common:
  - type: LinkedIn
    url: https://www.linkedin.com/company/voyage-ai
  - type: Website
    url: https://www.voyageai.com/
  - type: Documentation
    url: https://docs.voyageai.com/
  - type: GitHub
    url: https://github.com/voyage-ai
  - type: Pricing
    url: https://docs.voyageai.com/docs/pricing
  - type: Parent
    url: https://www.mongodb.com/
  - type: Plans
    url: plans/voyage-ai-plans-pricing.yml
  - type: RateLimits
    url: rate-limits/voyage-ai-rate-limits.yml
  - type: FinOps
    url: finops/voyage-ai-finops.yml
integrations:
  - name: MongoDB Atlas Vector Search
  - name: AWS Marketplace
  - name: Azure Marketplace
  - name: Snowflake
  - name: LangChain
  - name: LlamaIndex
  - name: Haystack
maintainers:
  - FN: Kin Lane
    email: [email protected]