CapybaraDB is a high-level database for AI applications that automates data management asynchronously. It is built on robust, proven technologies, including MongoDB, Pinecone, and AWS S3.
Asynchronous Processing: Embedding processes run in the background so the client isn’t left waiting.
💻Introducing EmbJSON – CapybaraDB Extended JSON: EmbJSON lets you perform semantic searches on ANY field in your JSON document without needing a semantic index. No embedding, chunking, or media-to-text processing is required.
🧑🏻💻Example EmbJSON Usage:
# Semantically retrievable user profiles
users = [{
"firstName": "Alice",
"pic": EmbImage(base64_image_data), # Raw image data
"bio": EmbText("Explorer of curious places. And she ...") # long text data
}]
collection.insert(users)
Simply wrapping the "pic" and "bio" fields makes them semantically searchable 🔥
Seems like a huge step forward for AI app developers! The multi-modal support and asynchronous embedding sound particularly handy.
Am I the only one thinking it's a black box when we all need explaination in AI ?
It's a nice engineering project however, I won't use it personnaly.
CapybaraDB Beta is taking an innovative approach to simplifying semantic search implementation. Since its initial launch on January 25th, 2025, the platform has gained impressive traction, with its second launch ranking #2 for the day and #22 for the week, earning 307 upvotes in just three weeks.
Its core value proposition is clear: eliminating the complexity of AI-powered search by leveraging established technologies like MongoDB, Pinecone, and AWS S3 instead of reinventing the wheel. This makes it particularly appealing for developers who want to integrate semantic search without managing multiple services.
Key highlights:
✅ Built on proven technologies for reliability and scalability ✅ Automated asynchronous data management for seamless performance ✅ Free tier available to encourage adoption ✅ High-level abstraction to simplify AI-powered applications
The team—Tomo Kanazawa and Hardik—is iterating rapidly, with two launches in less than a month. For developers seeking an efficient, hassle-free way to integrate semantic search, CapybaraDB could be a major time-saver.
With its SaaS, AI, and Database focus, the platform is positioned at the crossroads of several fast-growing markets, making it one to watch. 🚀
Congrats on launching CapybaraDB, Tomo! The blend of MongoDB and Pinecone, combined with EmbJSON, sounds like a game-changer for AI app development. Loving the seamless semantic search capability without the usual indexing hassle. Can't wait to see how this evolves!
CapybaraDB Beta is taking an interesting approach to simplifying semantic search implementation. Launched just about 3 weeks ago (first launch on January 25th, 2025), they're already showing strong traction with their second launch ranking #2 for the day and #22 for the week with 307 upvotes.
The core value proposition is compelling: they're abstracting away the complexity of managing AI-powered search by building on established technologies (MongoDB, Pinecone, AWS S3). This is particularly valuable for developers who want to implement semantic search without dealing with the intricacies of multiple services.
Key highlights:
Built on proven technologies rather than reinventing the wheel
Asynchronous data management automation
Free tier available
Focus on high-level abstraction for AI applications
The team (Tomo Kanazawa and Hardik) seems to be moving fast with iterations, as evidenced by this being their second launch in less than a month. For developers looking to implement semantic search without the overhead of managing multiple services, this could be a significant time-saver.
The combination of SaaS, AI, and Database tags positions them well at the intersection of several growing markets.
Sounds great! Signed up but the web UI seems to be non-functional. I got no project ID, create new collection gives an error - nothing else to do. Will check out the CLI next time
Congrats on the launch!
Saw the demo video, very innovative product! It's easy to use and nice implementation of semantic search queries.
Good work!
This api looks outstanding. Are there any actual comparisons with other vector databases? In terms of effectiveness, could it be even better? What scale of data can it support?
First off, love the name—Capybaras are the chillest animals, and if your DB is anything like them, I’m sold. 😂
Jokes aside, does EmbJSON really let you run semantic search on raw images without pre-processing? If so, that’s a game-changer. How does it compare to traditional vector databases in terms of speed?
@new_user__2592022c1f0aa34ef1433a0 @CapybaraDB sounds like a game-changer for AI app development—combining MongoDB and Pinecone to simplify multi-modal data management is a huge time-saver. The EmbJSON feature is especially intriguing! how does it compare to traditional vector search methods in terms of speed and scalability?
