AWS Machine Learning Blog
In this post, we show you how to use LlamaIndex with Amazon Bedrock to build robust and sophisticated RAG pipelines that unlock the full potential of LLMs for knowledge-intensive tasks. We tested Llamaparse on a real-world, challenging example of asking questions about a document containing Bank of America Q3 2023 financial results. An example slide from the full slide deck (48 complex slides!) is shown below. Using the procedure outlined above, we asked “What is the trend in digital households/relationships from 3Q20 to 3Q23?”; take a look at the answer generated using Llamaindex tools vs. the reference answer from human annotation. LlamaIndex + LlamaParse answer Reference answer The trend in digital households/relationships shows a steady increase from 3Q20 to 3Q23. In 3Q20, the number of digital households/relationships was 550K, which increased to 645K in 3Q21, then to 672K in 3Q22, and further to 716K in 3Q23. This indicates consistent growth in the adoption of digital services among households and relationships over the reported quarters. The trend shows a steady increase in digital households/relationships from 645,000 in 3Q20 to 716,000 in 3Q23. The digital adoption percentage also increased from 76% to 83% over the same period. The following are example notebooks to try out these steps on your own examples. Note the prerequisite steps and cleanup resources after testing them. Ingest with LlamaParse into S3 for KB Agentic RAG with Bedrock KB and LlamaIndex SubQuestionQueryEngine Conclusion In this post, we explored various advanced RAG patterns with LlamaIndex and Amazon Bedrock. To delve deeper into the capabilities of LlamaIndex and its integration with Amazon Bedrock, check out the following resources: LlamaIndex documentation Amazon Bedrock User Guide LlamaIndex examples GitHub repo By combining the power of LlamaIndex and Amazon Bedrock, you can build robust and sophisticated RAG pipelines that unlock the full potential of LLMs for knowledge-intensive tasks. About the Author Shreyas Subramanian is a Principal data scientist and helps customers by using Machine Learning to solve their business challenges using the AWS platform. Shreyas has a background in large scale optimization and Machine Learning, and in use of Machine Learning and Reinforcement Learning for accelerating optimization tasks. Jerry Liu is the co-founder/CEO of LlamaIndex, a data framework for building LLM applications. Before this, he has spent his career at the intersection of ML, research, and startups. He led the ML monitoring team at Robust Intelligence, did self-driving AI research at Uber ATG, and worked on recommendation systems at Quora.
Published: 2024-09-05T21:53:23
