diff --git a/platform/integrations/voyage-ai.md b/platform/integrations/voyage-ai.md
index fcb8ab6..03033e9 100644
--- a/platform/integrations/voyage-ai.md
+++ b/platform/integrations/voyage-ai.md
@@ -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       |
@@ -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:
diff --git a/vector-database/embeddings.md b/vector-database/embeddings.md
index fcac3bb..10b870c 100644
--- a/vector-database/embeddings.md
+++ b/vector-database/embeddings.md
@@ -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 %}