ave you ever wanted to ask questions about your own data and get quick, accurate answers? Well now you can, with the power of LlamaIndex 🦙 and LangChain 🦜️🔗! In this post, we’ll show you how to build a simple question answering over your data using Amazon Bedrock and Streamlit. Specifically, we’ll demonstrate Retrieval Augmented Generation (RAG) using LlamaIndex. LlamaIndex will index and retrieve relevant passages from your dataset, while LangChain provides LLM integration with Amazon Bedrock to generate human-like answers grounded on those passages. We’ll walk through a code sample using Streamlit to build a simple Web user interface. In just a few lines of Python code, you can stand up a Question Answering system tailored to your data. No deep learning expertise required!
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