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 🔥
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?
@hussein_r Lol thanks, we wanted our database to embody that chill vibe! We use Pinecone for vector search, and we've built an optimized data aggregation pipeline that traditionally would run on the client or application side. This setup delivers a faster end-to-end response time compared to a conventional in-house backend pipeline.
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.
@denisss thanks! If you have any questions or requests, please reach out to tomo@capybaradb.co. The great thing about using our early-stage product is that you can literally get involved and shape its future development.
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!
@gemanor Yes! Making the developer experience better is our main focus :)
Report
I absolutely love the name CAPYBARA! The product looks incredibly user-friendly and seems like a game-changer for AI app development. Congrats on the launch !🎉
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?
@alexanderwu The direct comparison between pure vector databases and CapybaraDB (muti-database + pipeline) isn’t as straightforward as a database-to-database comparison. Think of it this way: if you build your backend logic without CapybaraDB, you’d combine databases with your custom aggregation pipeline. But with CapybaraDB, you get Pinecone plus an optimized aggregation pipeline out of the box. By focusing our resources on building this efficient pipeline, we allow developers to skip that phase and concentrate on their core application logic. Also, CapybaraDB scales horizontally as your data grows.
Replies
OneNode
Hello, Product Hunt! I'm Tomo, and I'm the co-founder of CapybaraDB. I'm excited to share our product today!
🙋🏻What is CapybaraDB?
Built on Top of MongoDB and Pinecone: Leverages robust underlying technologies.
High-Level Data Management Abstraction: Simplifies complex data operations.
Multi-Modal Support: Natively handles text, images, videos, audio, websites, and more.
Robust Semantic Search Automation: Delivers precise, context-aware search capabilities.
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:
Simply wrapping the "pic" and "bio" fields makes them semantically searchable 🔥
Would love to have your feedback!
Happy building!
OneNode
@john_tans Thanks man!
Graphify
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?
OneNode
@hussein_r Lol thanks, we wanted our database to embody that chill vibe! We use Pinecone for vector search, and we've built an optimized data aggregation pipeline that traditionally would run on the client or application side. This setup delivers a faster end-to-end response time compared to a conventional in-house backend pipeline.
Graphify
Chance AI: Curiosity Lens
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.
Streak Hunter
OneNode
@denisss thanks! If you have any questions or requests, please reach out to tomo@capybaradb.co. The great thing about using our early-stage product is that you can literally get involved and shape its future development.
Shram
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!
Best wishes and sending wins to the team @new_user__2592022c1f0aa34ef1433a0
OneNode
@whatshivamdo Thank you for much! Stay tuned!
Permit.io
Seems like a huge step forward for AI app developers! The multi-modal support and asynchronous embedding sound particularly handy.
OneNode
@gemanor Yes! Making the developer experience better is our main focus :)
I absolutely love the name CAPYBARA! The product looks incredibly user-friendly and seems like a game-changer for AI app development. Congrats on the launch !🎉
OneNode
@kay_arkain Thank you so much! And yes, capybaras are the cutest!
MGX (Now Atoms)
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?
OneNode
@alexanderwu The direct comparison between pure vector databases and CapybaraDB (muti-database + pipeline) isn’t as straightforward as a database-to-database comparison. Think of it this way: if you build your backend logic without CapybaraDB, you’d combine databases with your custom aggregation pipeline. But with CapybaraDB, you get Pinecone plus an optimized aggregation pipeline out of the box. By focusing our resources on building this efficient pipeline, we allow developers to skip that phase and concentrate on their core application logic. Also, CapybaraDB scales horizontally as your data grows.
Really good!
OneNode
@prokop_polasek Appreciate it!
yes
OneNode
@libor_beran yes Capybara go!