voyage-context-4 is a contextualized chunk embedding model that produces vectors for chunks that capture the full document context without any manual metadata and context augmentation. This leads to higher retrieval accuracies than with or without augmentation. The model is also simpler, faster, and cheaper. It serves as a drop-in replacement for standard embeddings without downstream workflow changes and reduces chunking strategy sensitivity.
To learn more, see the blog post.
Available Models
Model | Context Length | Dimensions | Description |
|---|---|---|---|
| 120,000 tokens * | 1024 (default), 256, 512, 2048 | Contextualized chunk embeddings optimized for general-purpose and multilingual retrieval quality. |
Note
* The total number of tokens across all inputs must not exceed 120K if enable_auto_chunk = true; otherwise they must not exceed 32K.
The latest models perform better than the legacy models in all aspects, such as quality, context length, latency, and throughput.
Model | Context Length | Dimensions | Description |
|---|---|---|---|
| 120,000 tokens * | 1024 (default), 256, 512, 2048 | Contextualized chunk embeddings optimized for general-purpose and multilingual retrieval quality. To learn more, see the blog post. |
Tutorial
For a tutorial on using contextualized chunk embeddings, see Semantic Search with Voyage AI Embeddings.