At a glance
- Availability: Experimental (how to enable).
- Auth: API key.
- Connection: The key from
VOYAGE_API_KEYis sent as theBearerprefixedAuthorizationheader. - Docs: https://docs.voyageai.com/docs/api-key-and-installation
Credentials
Set these per environment. See Connect an integration.| Variable | Required | Description |
|---|---|---|
VOYAGE_API_KEY | Yes | Voyage AI API key from the Voyage dashboard (starts with pa-) Docs. |
Setup
- Create a Voyage AI account: Sign up at https://dashboard.voyageai.com. New accounts include free trial tokens for every model, which is enough for evaluation without billing.
- Create an API key: In the dashboard, open the API Keys section and create a new key. Copy it immediately.
- Store the key: Add VOYAGE_API_KEY=… to your .env file. Requests authenticate with an Authorization: Bearer header.
- Verify access: Run the Create Embeddings tool with model voyage-3.5 and a single short input string. A 401 means the key is wrong or revoked.
- Use the same embedding model (and input_type convention) for indexing and querying - vectors from different models are not comparable
- Contextualized chunk embeddings (voyage-context-3) expect each inner list to be one document’s chunks in order; regular embeddings treat every array item independently
- Usage beyond the free trial tokens is billed per million tokens and requires adding a payment method in the dashboard
Tools
| Tool | Access | Description |
|---|---|---|
| Create Embeddings | Read | Create embedding vectors for one or more texts - use input_type to optimize for retrieval queries vs documents |
| Rerank Documents | Read | Rank candidate documents by relevance to a query with a Voyage reranker - use as a precision pass after vector search |
| Create Contextualized Embeddings | Read | Embed document chunks with document-level context (voyage-context-3) so each chunk vector also captures the surrounding document |
| Create Multimodal Embeddings | Read | Embed interleaved text and images into one vector space (voyage-multimodal models) for visual RAG over screenshots, slides, and PDFs |
Example prompts
- Create Voyage AI embeddings for the texts I provide and report the model, dimensions, and token usage.
- Use Voyage AI rerank-2.5 to order a list of documents I provide by relevance to my query and show the relevance scores.