Exploring the Advanced RAG (Retrieval Augmented Generation) Service
In the ever-evolving landscape of AI, LLM + RAG (Retrieval Augmented Generation) is a typical use scenario. Retrieving accurate related chunked data from complicated docs and then improving LLM response quality becomes challenge. There is no a silver bullet RAG can address all requirements so far. Developers need to verify different advanced RAG techs to find out which is a proper one for their scenarios considering accuracy, response speed, costs, etc. In order to solve this, with Azure Intelligent Document, Azure OpenAI, LlamaIndex, LangChain, Gradio..., I developed this AdvancedRAG service. This service is encapsulated in a Docker container, offers a streamlined way to experiment with different indexing techniques, evaluate their accuracy, and optimize performance for various RAG use cases. Whether you're building a quick MVP, a proof of concept, or simply exploring different indexing strategies, this service provides a versatile playground.
