Open-Source RAG Framework for Deep Learning Pipelines and large datasets – Faster Retrieval, Lower Latency, Smarter Integrations

Been exploring ways to optimize Retrieval-Augmented Generation (RAG) lately, and it’s clear that there’s always more ground to cover when it comes to balancing performance, speed, and resource efficiency in dynamic environments.

So, we decided to build an open-source framework designed to push those boundaries, handling retrieval tasks faster, scaling efficiently, and integrating with key tools in the ecosystem.

We’re still in early development, but initial benchmarks are already showing some promising results. In certain cases, it’s matching or even surpassing well-known solutions like LangChain and LlamaIndex in performance.

Comparisson for pdf extraction and chunking

It integrates seamlessly with tools like TensorRT, FAISS, vLLM and more integrations are on the way. And our roadmap is packed with further optimizations and updates we’re excited to roll out.

If that sounds like something you’d like to explore, check out the GitHub repo:👉 https://github.com/pureai-ecosystem/purecpp. Contributions are welcome, whether through ideas, code, or simply sharing feedback. And if you find it useful, dropping a star on GitHub would mean a lot!