Vecman

Vecman

EMAV : Encoder Model As Vector Database

5.0•1 review•

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VECMAN (Vector Manager) - A VQ-VAE based vector database for efficient text embeddings and retrieval. This package provides a memory-efficient way to store and retrieve text embeddings using Vector Quantized Variational Autoencoder (VQ-VAE). - Vec1man/vecman
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Free
Launch tags:Developer Tools•Artificial Intelligence•GitHub
Launch Team
Auth0
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What do you think? …

Loaii abdalslam
Maker
📌
Why did you build this? We built this to be a faster, more efficient, and sustainable alternative to traditional vector databases when building Retrieval-Augmented Generation (RAG) systems. Today’s RAG stacks are often over-engineered, requiring unnecessary complexity and configuration. Our goal was to simplify the entire process: with just one click, anyone can launch a fully functioning RAG system—no need for technical expertise or dealing with useless details. What’s new and unique about your launch compared to alternatives? What makes our launch unique is the ability to build and run a RAG system “in the air”—without needing heavy infrastructure or traditional databases. Our tool abstracts away all the technical friction and lets you instantly deploy an intelligent system that's ready to use. It’s like going from assembling parts to flying a drone—you just press a button, and it works. What are you most proud of in this launch? We’re proud to say we’ve built something truly transformative. This isn’t just another tool—it’s a shift in how people think about AI infrastructure. We're introducing a new paradigm where AI systems are lightweight, real-time, and infrastructure-free. We believe this will redefine how developers and teams approach building smart assistants, and we're just getting started.
THE DAYMN

an: Finally, a Dev-Friendly Vector Search You Can Actually Enjoy Using

I’ve been exploring semantic search and RAG pipelines for a while, and honestly, setting up tools like FAISS or Milvus always felt like a tradeoff between performance and sanity. Vecman changes that.

From the first interaction, it’s clear that Vecman is built for developers—with a minimal setup, clean API design, and fast local performance. Whether you're building AI-powered apps, chatbots, recommendation engines, or retrieval-based systems, Vecman delivers a surprisingly smooth experience without needing to wrangle infrastructure.

Why I love it:

🧠 Simple and intuitive: No databases, no servers, no YAML jungle.

⚡️ Fast local search: Perfect for prototyping or production with minimal overhead.

🛠️ Flexible embedding support: Easily integrates with OpenAI, HuggingFace, or custom embeddings.

🪶 Lightweight: Ideal for microservices and serverless functions.

📦 Open source and actively evolving.

In a world crowded with over-engineered vector DBs, Vecman feels refreshingly practical. It’s what I wish existed when I first started building semantic search into my apps.

Huge shoutout to the team for making vector search feel... fun again. 🙌

Highly recommended for developers who want power without the pain.

Amr Eldesouky

Wonderful Loaii

Hussein Ali

Amazing work loaii I love the idea and will be using it very soon

Lakshya Singh

Congrats on the launch @loaii_abdalslam