Build powerful RAG pipelines with LlamaIndex and Amazon Bedrock

This article discusses how to build advanced Retrieval Augmented Generation (RAG) pipelines for large language models (LLMs) using LlamaIndex and Amazon Bedrock.

Specifically, the article covers:

• Simple RAG pipeline setup with Amazon Bedrock models and vector search
• Router query that can automatically switch between semantic search and summarization
• Sub-question query that breaks down complex queries into simpler sub-queries
• Agentic RAG that uses an LLM agent to orchestrate tools like query decomposition and knowledge base retrieval
• LlamaCloud and LlamaParse services for enterprise-grade context augmentation
• Step-by-step integration of LlamaParse with Amazon Bedrock for an advanced RAG stack
• Conclusion highlighting resources to learn more about LlamaIndex and Amazon Bedrock integration