RagXO
Export and Version E2E RAG pipelines as a single artifact 🚀
Listed in categories:
Developer ToolsArtificial IntelligenceOpen Source
Description
RagXO extends the capabilities of traditional RAG (Retrieval-Augmented Generation) systems by providing a unified way to package, version, and deploy your entire RAG pipeline with LLM (Large Language Model) integration. It allows users to export their complete system, including embedding functions, preprocessing steps, vector store, and LLM configurations, into a single portable artifact.
How to use RagXO?
To use RagXO, install it via pip, set your OpenAI API key, and import the RagXO client. You can then define your preprocessing steps, embedding functions, and LLM configurations before exporting your RAG pipeline as a versioned artifact.
Core features of RagXO:
1️⃣
Complete RAG Pipeline Packaging
2️⃣
LLM Integration with OpenAI models
3️⃣
Flexible Embedding Compatibility
4️⃣
Custom Preprocessing Steps
5️⃣
Vector Store Integration with Milvus support
Why could be used RagXO?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Exporting and reusing E2E RAG pipelines | ✅ | |
| # 2 | Integrating with OpenAI models for enhanced data retrieval | ✅ | |
| # 3 | Customizing preprocessing steps for specific data needs | ✅ | |
Who developed RagXO?
RagXO is developed by Mohamed Fawzy, who focuses on enhancing the capabilities of RAG systems and providing tools for efficient data retrieval and processing.