Build a Multimodal RAG Pipeline in Python with VelociRAG and NexaAPI
This article introduces VelociRAG, a new ONNX-based RAG framework for fast retrieval, and shows how to integrate it with NexaAPI for multimodal AI generation (images, text-to-speech) in Python.
Why it matters
This article showcases a powerful combination of open-source and commercial AI tools that can significantly enhance data processing and content generation workflows.
Key Points
- 1VelociRAG is an open-source ONNX RAG framework with high performance and easy integration
- 2NexaAPI provides 50+ AI models for image generation, text-to-speech, and more at low cost
- 3The article demonstrates a combined VelociRAG and NexaAPI workflow for AI-enhanced data processing
Details
VelociRAG is a new ONNX-based Retrieval Augmented Generation (RAG) framework that has gained significant attention from the developer community. It offers seamless integration with existing tools, high-performance processing, and open-source support. To enhance the VelociRAG pipeline, the article introduces NexaAPI, an AI inference API that provides access to 50+ models for tasks like image generation and text-to-speech at low cost (starting at $0.003 per image). The tutorial demonstrates how to combine VelociRAG and NexaAPI to build a multimodal AI-powered workflow, where the VelociRAG framework processes input data and NexaAPI is used to generate relevant images and audio.
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