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@Ahmath-Gadji Ahmath-Gadji released this 08 Jul 15:59
· 556 commits to main since this release
v0.1.0
cb36f62

RAGondin v0.1.0 Release Notes

RAGondin is a lightweight, modular, and fully open-source Retrieval-Augmented Generation (RAG) framework designed for advanced RAG experimentation with a focus on sovereignty, scalability, and extensibility.

Key Features

Advanced Document Processing

  • Supports a wide range of document formats including text, PDF, Office files, audio/video (via Whisper), and images with VLM-generated captions.
  • Extracts and processes text, tables, images, and charts to provide deeper insights and richer contextual understanding.
  • Flexible chunking strategies: recursive, semantic, and markdown chunkers with optimized default settings.

Scalable & Modular Architecture

  • Designed for scalable deployment in distributed environments using Ray clusters for high-throughput parallel processing.
  • Modular and flexible architecture enables easy customization and extension to meet diverse use cases.

Optimized Data Management & Security

  • Vector indexing powered by Milvus with state-of-the-art multilingual embeddings (default: Qwen3-Embedding).
  • Hybrid semantic and keyword search combining BM25 with Reciprocal Rank Fusion for superior retrieval.
  • Data partitioning and secure access control designed for large organizations and sensitive environments.
  • Token-based authentication for secure API access.

Seamless Integration & User Interfaces

  • Provides RESTful APIs for indexing, searching, and RAG pipelines.
  • OpenAI-compatible API and chat interface for easy adoption and integration with existing OpenAI-based workflows.
  • Chainlit UI: Interactive chat interface with optional authentication and chat history persistence.
  • IndexerUI: Web interface for intuitive document ingestion, indexing, and management.

Deployment

  • Containerized deployment with Docker Compose, supporting both GPU and CPU environments for maximum flexibility.

Getting Started

  • Requires Python 3.12+, Docker, and NVIDIA Container Toolkit for GPU acceleration.
  • Configurable via Hydra and environment variables for embedding, retrieval, reranking, and transcription models.
  • Ready-to-use APIs and interfaces allow quick setup for both development and production environments.
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