Voyage 2 is a general-purpose text embedding model from Voyage AI, the second generation of their core retrieval-focused lineup. It was trained specifically to optimize semantic similarity and retrieval performance across a wide range of domains, and it remains a strong choice for production retrieval-augmented generation (RAG) pipelines where cost-efficiency matters.
Voyage AI positions Voyage 2 as a well-rounded embedding model that balances retrieval quality with inference speed. It has been benchmarked favorably against models of similar vintage on standard retrieval tasks and is often the recommended starting point for teams evaluating Voyage's embedding stack before considering their larger or more specialized variants.
Key Features
Strong out-of-the-box retrieval performance on diverse document sets
Optimized for semantic search and RAG pipeline integration
Competitive on MTEB retrieval benchmarks for its generation
Efficient inference suitable for large-scale corpus indexing
Versatile across technical, legal, and general-domain text
Ideal Use Cases
Powering semantic search over document knowledge bases
Retrieval-augmented generation (RAG) context fetching
Clustering customer support tickets or feedback by topic
Building recommendation systems based on content similarity
Duplicate document detection in content moderation pipelines
Example Prompts for Voyage 2
Technical Specifications
| Provider | Voyage |
| Category | Embedding |
| Modality | Text -> Vector |
Frequently Asked Questions
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