Thrilled to share that we've closed $28M in funding, led by CRV, with continued support from Wing VC and sarah guo // conviction. Also excited to onboard strategic partners SnowflakeDB and Databricks! voyage.ai Building the world’s best models for RAG and search 🧵🧵🧵:

Recently, I've shared how I believe that "native multimodality" is the future. voyage-multimodal-3, trained end-to-end on text, photos, figures, PDFs, PPTs, and more, is the first embedding model that fits this concept. No more unstructured data ETL. Screenshot is all you need.

I’m glad that there is more interest from embedding providers. Voyager was the first, next up Jina and Nomic? Cohere also.

Huge props to our partner Anthropic! OpenAI is the new Netscape. menlovc.com/2024-the-state…

Vector-based code retrieval is a critical building block in code assistants and agents. However, many people complained about the lack of diverse, high-quality evaluation datasets for it. We surveyed existing ones and proposed some methods to build better ones. 🧵🧵

voyage-code-3 is one of the first embedding models trained with both Matryoshka learning as well as quantization awareness. More in our blog post: blog.voyageai.com/2024/12/04/voy…

🚀 General vs. Domain-Specific: Which Embedding Model Should You Choose for Your RAG App? We tested OpenAI’s text-embedding-3-small vs. Voyage AI’s finance-2 on SEC filings using pgai Vectorizer (PostgreSQL-based): 🔸 Voyage AI by MongoDB: +15.5% accuracy overall, +23.75% on financial

hyped for the reranking event I'm throwing in sf next thursday: speakers from: Exa, which just purchased a supercluster of 144 h100s to revolutionize search Voyage AI by MongoDB, which is so good at reranking two fortune 500 companies (allegedly) discussed acquiring them 🤫

Had a really interesting discussion about agentic retrieval last night at a RAG event at Exa with Frank Liu from Voyage AI by MongoDB. The premise was basically whether there is sense in doing agentic retrieval or if regular RAG is enough. The two takeaways I got from the

Apparently, 67.4 is "#1 on CoIR Benchmark" It doesn't even beat Voyage AI by MongoDB's general-purpose embedding model...

Cosine similarities greater than 1 == "beautiful results" 🤣

Congrats Nomic AI! Great to see MoE coming to embedding models.

Jo Kristian Bergum Jina AI 's definition of deep research is shallow. openai's deep research is a trained system. stanfords storm is a trained system. google's Google Gemini App deep research is, no one knows. jina ai's definition is search on a for loop.

Among other things that make my day, one of them is a great podcast conversation with my guests. Thanks Frank Liu for a thought-provoking chat earlier... Stay tuned for the episode, coming up soon ..

Chad Metcalf wrote a deep dive on why your custom AI code assistant should include embeddings and a reranker from Voyage AI by MongoDB🥇

📢 Meet voyage-3.5 and voyage-3.5-lite! • flexible dim. and quantizations • voyage-3.5 & 3.5-lite (int8, 2048 dim.) are 8% & 6% more accurate than OpenAI-v3-large, and 2.2x & 6.5x cheaper, resp. Also 83% less vectorDB cost! • 3.5-lite ~ Cohere-v4 in quality, but 83% cheaper.

Our Multimodal Search Python Library is now in public preview. Giving developers a single interface to build applications that search across PDFs, images, and text, without the usual complexity. Built-in support for: 🧠 Voyage AI by MongoDB's voyage-multimodal-3 🗂️ Amazon Web Services S3 storage