TL;DR – We are excited to introduce the rerank-2.5 series, which significantly improves upon rerank-2’s performance while also introducing instruction-following capabilities for the first time. On …| Voyage AI
TL;DR – We’re excited to introduce voyage-context-3, a contextualized chunk embedding model that produces vectors for chunks that capture the full document context without any manual metadata…| Voyage AI
TL;DR – We’re excited to introduce voyage-3.5 and voyage-3.5-lite, the latest generation of our embedding models. These models offer improved retrieval quality over voyage-3 and voyage-3-lite at th…| Voyage AI
It’s been two months since Voyage AI joined the MongoDB team, so I wanted to update everyone on how things are progressing. All of us here at Voyage AI remain focused on our core mission: developin…| Voyage AI
Today, we are excited to announce that we’ve joined MongoDB! When we founded Voyage AI in 2023, our mission was to help developers build the best retrieval systems to power intelligent AI applicati…| Voyage AI
Read time: 10 minutes Nearly all modern coding assistants and agents leverage some form of code retrieval — the task of retrieving relevant code snippets, docstrings, or documentation, etc., from c…| Voyage AI
TL;DR – Introducing voyage-3-large, a new state-of-the-art general-purpose and multilingual embedding model that ranks first across eight evaluated domains spanning 100 datasets, including law, fin…| Voyage AI
TL;DR – Introducing voyage-code-3, our next-generation embedding model optimized for code retrieval. It outperforms OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite …| Voyage AI
TL;DR — We are excited to announce voyage-multimodal-3, a new state-of-the-art for multimodal embeddings and a big step forward towards seamless RAG and semantic search for documents rich with both visuals and text. Unlike existing multimodal embedding models, voyage-multimodal-3 is capable of vectorizing interleaved texts + images and capturing key visual features from screenshots of […]| Voyage AI
Here at Voyage AI, we’re on a mission to help you build the very best RAG and semantic search applications. As such, we’ve released industry-leading embedding models & rerankers, partnered with preeminent AI companies such as Databricks, Snowflake, Anthropic, Harvey, & Xayn, and built integrations with leading vector database companies. To further this mission, we’re […]| Voyage AI
TL;DR — We’re excited to announce the Voyage 2 series of rerankers, rerank-2 and rerank-2-lite. When evaluated across 93 retrieval datasets spanning multiple domains, adding rerank-2 and rerank-2-l…| Voyage AI
TL;DR – We are excited to officially release voyage-multilingual-2, optimized for multilingual retrieval and retrieval-augmented generation (RAG). It outperforms alternatives, such as OpenAI …| Voyage AI
TL;DR – We are thrilled to launch our finance domain-specific embedding model voyage-finance-2, which demonstrates superior finance retrieval quality and outperformed competing models on fina…| Voyage AI
TL;DR – Voyage AI’s latest general-purpose text embedding model, voyage-large-2-instruct, now tops the overall MTEB leaderboard, outperforming OpenAI v3 large and Cohere English v…| Voyage AI
TL;DR – Domain-specific and custom embedding models have been shown to enhance the retrieval quality significantly. Hot on the heels of the state-of-the-art code embedding model (voyage-code-2), we…| Voyage AI
TL;DR – We are thrilled to introduce voyage-code-2, our latest embedding model specifically tailored for semantic retrieval of codes and related text data from both natural language and code querie…| Voyage AI
TL;DR – Voyage is a team of leading AI researchers, dedicated to enabling teams to build better RAG applications. Today, we’re releasing a new state-of-the-art embedding model and API, which alread…| Voyage AI
