Talk to Your Documents
Instantly.
A developer-first document analysis tool using Django, Next.js, and pgvector. High-performance RAG implementation for your complex PDF datasets.
The project uses pgvector with a custom Django model. Chunks are stored with an embedding column indexed using HNSW for sub-10ms retrieval.
Built for Developers
Extensible Python-based backend with a type-safe Next.js frontend. Fully containerized for rapid deployment and local testing.
Vector Search
Native PostgreSQL integration via pgvector. No proprietary vector databases required—keep your stack simple and reliable.
Open Source
Released under MIT License. Contribute to the core engine or fork it to build your own custom document intelligence platform.
Exploring the intersection of Django, Next.js, and Vector Databases. Open-source RAG engine built for high-performance retrieval.
- Django / Python
- Next.js / TypeScript
- pgvector