Abstracted database that streamlines your semantic search, by orchestrating MongoDB, Pinecone, AWS 3S, Redis, etc.
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Abstracted database that streamlines your semantic search, by orchestrating MongoDB, Pinecone, AWS 3S, Redis, etc.
If you’re working with semantic search or media-heavy data, CapybaraDB is worth checking out. It combines the flexibility of Mongo with the AI-friendliness of vector databases like Pinecone. I used it on a small NLP project and the performance was super smooth—even with a decent amount of data
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!
@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?
@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
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.