Practice project: Implementing RAG with LlamaIndex using Python
For our practice project, we will shift from LangChain to exploring another library that facilitates the RAG approach. LlamaIndex is an open source library that is specifically designed for RAG-based applications. LlamaIndex simplifies ingestion and indexing across various data sources. However, before we dive into implementation, we will explain the underlying methods and approach behind RAG.
As discussed, the key premise of RAG is to enhance LLM outputs by supplying relevant context from external data sources. These sources should provide specific and verified information to ground model outputs. Moreover, RAG can optionally leverage the few-shot approach by retrieving few-shot examples at inference time to guide generation. This approach alleviates the need to store examples in the prompt chain and only retrieves relevant examples when needed. In essence, the RAG approach is a culmination of many of the prompt engineering...
