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Refresh VoyageAI embedding model catalog #2

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0de1912
Refresh VoyageAI embedding model catalog
fzowl Aug 16, 2026
94e7e90
Address review: align voyage-context note, de-dupe model table
fzowl Aug 16, 2026
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10 changes: 10 additions & 0 deletions platform/integrations/voyage-ai.md
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Original file line number Diff line number Diff line change
Expand Up @@ -10,6 +10,12 @@ Epsilla integrates with Voyage AI with the following embedding models:

| Name | Dimensions |
|--------------------------------------|------------|
| **voyageai/voyage-4-large** | 1024 |
| **voyageai/voyage-4** | 1024 |
| **voyageai/voyage-4-lite** | 1024 |
| **voyageai/voyage-code-4** | 1024 |
| **voyageai/voyage-context-4** | 1024 |
| **voyageai/voyage-multimodal-3.5** | 1024 |
| **voyageai/voyage-multimodal-3** | 1024 |
| **voyageai/voyage-context-3** | 1024 |
| **voyageai/voyage-3.5** | 1024 |
Expand All @@ -26,6 +32,10 @@ Epsilla integrates with Voyage AI with the following embedding models:
| **voyageai/voyage-large-2** | 1536 |
| **voyageai/voyage-2** | 1024 |

{% hint style="info" %}
The `voyageai/voyage-context-*` models are contextualized embedding models that you use exactly like any other embedding model in Epsilla. Each input string is embedded independently as its own document, so you get one deterministic vector per input. Contextualization across the other texts in the same batch is not applied, because a batch may contain unrelated documents. Pick these models for their standalone embedding quality.
{% endhint %}

For Epsilla open source vector db, you just need to add a header in the data ingestion and semantic search queries [like this](../../vector-database/embeddings.md#voyage-ai-embedding).

Then you can start using the voyageai embedding models during vector table schema creation:
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18 changes: 14 additions & 4 deletions vector-database/embeddings.md
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Expand Up @@ -292,24 +292,34 @@ Epsilla supports these VoyageAI embedding models (learn more about Voyage AI emb

| Name | Dimensions |
| ------------------------------------ | ---------- |
| **voyageai/voyage-4-large** | 1024 |
| **voyageai/voyage-4** | 1024 |
| **voyageai/voyage-4-lite** | 1024 |
| **voyageai/voyage-code-4** | 1024 |
| **voyageai/voyage-context-4** | 1024 |
| **voyageai/voyage-context-3** | 1024 |
| **voyageai/voyage-multimodal-3.5** | 1024 |
| **voyageai/voyage-multimodal-3** | 1024 |
| **voyageai/voyage-3.5** | 1024 |
| **voyageai/voyage-3.5-lite** | 512 |
| **voyageai/voyage-large-2-instruct** | 1024 |
| **voyageai/voyage-finance-2** | 1024 |
| **voyageai/voyage-multilingual-2** | 1024 |
| **voyageai/voyage-law-2** | 1024 |
| **voyageai/voyage-code-2** | 1536 |
| **voyageai/voyage-large-2** | 1536 |
| **voyageai/voyage-code-2** | 1536 |
| **voyageai/voyage-2** | 1024 |
| **voyageai/voyage-02** | 1024 |
| **voyageai/voyage-law-2** | 1024 |
| **voyageai/voyage-finance-2** | 1024 |
| **voyageai/voyage-multilingual-2** | 1024 |
| **voyageai/voyage-lite-02-instruct** | 1024 |
| **voyageai/voyage-3-large** | 1024 |
| **voyageai/voyage-3** | 1024 |
| **voyageai/voyage-3-lite** | 512 |
| **voyageai/voyage-code-3** | 1024 |

{% hint style="info" %}
The `voyageai/voyage-context-*` models are contextualized embedding models that you use exactly like any other embedding model in Epsilla. Each input string is embedded independently as its own document, so you get one deterministic vector per input. Contextualization across the other texts in the same batch is not applied, because a batch may contain unrelated documents. Pick these models for their standalone embedding quality.
{% endhint %}

When using Voyage AI embedding on Docker, make sure provide the **X-VoyageAI-API-Key** header when connecting to the vector database:

{% tabs %}
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