We are excited to announce that Voyage AI is officially joining @MongoDB !. Joining @MongoDB enables us to bring our cutting-edge AI retrieval technology to a broader audience and seamlessly integrate it into mission-critical applications. Learn more:.

RT @ElevateHQ_: Huge thanks to @TengyuMa for coming on the Elevate Podcast! Check out the podcast episode here: htt….

voyage-3.5 and voyage-3.5-lite are available today! The first 200 million tokens are free. Get all the details in our latest blog:

Across evaluated domains & multilingual tasks, voyage-3.5 outperforms OpenAI-v3-large, voyage-3, and Cohere-v4 by an average of 8.26%, 2.66%, and 1.63%, respectively; and voyage-3.5-lite outperforms OpenAI-v3-large and voyage-3-lite by an average of 6.34% and 4.28%, respectively.

voyage-3.5 improves retrieval quality over voyage-3 by 2.66%, and voyage-3.5-lite improves over voyage-3-lite by 4.28%—both maintaining a 32K context length and their respective price points of $0.06 and $0.02 per 1M tokens.

📢 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.

RT @llama_index: Learn how to do multi-modal retrieval using @VoyageAI's multi-modal embeddings and @MongoDB's multi-modal indexes!. In thi….

Voyage AI joined @MongoDB just 2 months ago, and we’re accelerating our mission of building the best embedding models for all developers!. Here’s what’s already in motion:.• 100x API scaling.• New, advanced models.• Built-in embeddings in Atlas Vector Search.• Direct API

🧑‍💻Better code search means a better developer experience. Our partner @continuedev builds the leading open-source AI code assistant and voyage-code-3 and rerank-2 are two models helping them do it! . Learn more in their blog:

RT @MongoDB: The performance of AI-driven apps hinges on vector efficiency. Join us tomorrow to learn how MongoDB Atlas Vector Search + @Vo….

@pinecone Seamless setup, optimized performance. Come check it out:

@pinecone With the Pinecone Model Gallery, you can create an index tailored for the latest Voyage models in just a few clicks. Simply search for the model, hit “Create Index”, and Pinecone auto-fills key settings like dimensionality & similarity metric.

We just realized that many people don’t realize how easy it is to use @VoyageAI embeddings with @pinecone !.

@SnowflakeDB Stay tuned for more guides on using Voyage embeddings in Snowflake!.

@SnowflakeDB This guide covers data ingestion and indexing using a sample dataset, showing how to build a blazing-fast, highly accurate semantic search system directly in Snowflake. Come check it out:

@SnowflakeDB Voyage AI + Snowflake Cortex =.🔹Voyage AI delivers state-of-the-art embeddings optimized for search and retrieval.🔹Snowflake Cortex provides scalability, security, and seamless integration for vector search—all within your data warehouse.

🚀 Supercharge your semantic search with Snowflake Cortex & Voyage AI embeddings!. Here’s how to build high-accuracy, high-performance semantic search directly in @SnowflakeDB using Voyage embeddings. 🧵

Voyage text embeddings and rerankers are now available in @Haystack_AI, an open-source AI framework by @deepset_AI !🚀.To get started, check out the Voyage integration page on Haystack: Shout out to @awinml for laying the foundation for this integration!

@lancedb Using this new integration makes it incredibly easy to build, experiment, and scale your AI applications to billions of vectors and beyond. Check out our sample notebook to get started: