bigint/rag.computer
📦 Open Source Projectbigint
A self-hostable RAG platform providing document ingestion, embedding, and vector search via a simple REST API.
rag.computer simplifies the RAG pipeline by consolidating essential components into a single, deployable service. Built with Python, the platform handles the end-to-end lifecycle of document processing: from raw ingestion and chunking to vectorization and storage. It leverages modern vector database principles to ensure high-performance similarity searches, making it an ideal choice for developers looking to implement private, secure, and scalable AI knowledge bases. The architecture is designed for ease of integration, allowing users to interact with their data using standard HTTP requests. By abstracting away the complexities of embedding models and vector indexing, rag.computer enables teams to focus on building AI-driven features rather than maintaining infrastructure. Its modular approach supports various document formats, ensuring flexibility for diverse enterprise or personal knowledge management use cases.
💡Highlights
- ├─Self-hosted RAG infrastructure
- ├─Simple REST API integration
- └─Automated document embedding
🎯For
- ├─Backend Developers
- └─AI Engineers