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How to Set Up a RAG Chatbot in AWS Bedrock: A Step-by-Step Guide

How to Set Up a RAG Chatbot in AWS Bedrock: A Step-by-Step Guide

Retrieval-Augmented Generation (RAG) chatbots enhance AI-generated responses by retrieving relevant information from structured data sources. In this step-by-step guide, learn how to build a powerful RAG chatbot using AWS Bedrock, Amazon S3, and Amazon OpenSearch Serverless.

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Sevim Durmus
Published Mar 3, 2025
Retrieval-Augmented Generation (RAG) chatbots combine generative AI with knowledge retrieval, making them incredibly powerful for answering questions based on stored data. In this guide, you'll learn how to build a RAG chatbot using AWS Bedrock, Amazon S3, and Amazon OpenSearch.

Prerequisites

Before we start, ensure you have:
  • An AWS account with access to Bedrock
  • Basic understanding of AWS services like S3 and OpenSearch
  • Some knowledge of AI and vector databases

Step 1: Set Up an S3 Bucket for Documents

AWS S3 will store documents that the chatbot will reference.
  1. Log in to your AWS Console.
  2. Navigate to Amazon S3 and create a new bucket.
  3. Name your bucket (e.g., rag-chatbot-docs).
  4. Disable bucket key and create the bucket.
  5. Upload the documents that will serve as the chatbot's knowledge base.

Step 2: Create an S3 Bucket for Output Data

  1. Go back to Amazon S3.
  2. Create another bucket (e.g., rag-chatbot-output).
  3. This bucket will store chatbot-generated responses and logs.

Step 3: Set Up a Knowledge Base in Amazon Bedrock

  1. Navigate to Amazon Bedrock.
  2. Go to Knowledge Bases > Create Knowledge Base.
  3. Choose Vector Store as the storage method.
  4. Add details like name and description.
  5. Assign a service role with access to S3.

Step 4: Configure Data Sources

AWS Bedrock supports multiple data sources, including:
  1. Amazon S3 (for uploaded documents)
  2. Web crawlers (for real-time data retrieval)
  3. Salesforce, SharePoint, Confluence (for enterprise data)
  4. Select Amazon S3 and link it to the previously created bucket.

Step 5: Select Parsing and Chunking Strategy

  1. Choose a parsing model. The Bedrock default parser ignores images, but you can select Foundational Models to process them.
  2. Select a chunking strategy. The default setting will break documents into smaller segments for better indexing.

Step 6: Choose Embedding Model & Vector Store

  1. Select Amazon Titan Embeddings for vectorization.
  2. Configure the vector database:
    • Select Amazon OpenSearch Serverless (recommended).
    • Enable automatic chunking and embedding storage.
  3. Bedrock will now embed and store the document data in the vector database.

Step 7: Enable AI Models for Querying

  1. Navigate to Model Access Settings in Bedrock.
  2. Enable Amazon Titan Embeddings.
  3. Optionally, enable models like Llama 3 for additional AI capabilities.

Step 8: Test Your RAG Chatbot

  1. Go to Bedrock Knowledge Base and select your chatbot.
  2. Click on Test Chatbot.
  3. Ask a question related to the uploaded documents.
  4. Bedrock will retrieve relevant chunks from the vector database and generate a response.

Conclusion

You’ve successfully built a RAG-powered chatbot in AWS Bedrock! This chatbot can now retrieve and generate responses based on structured document knowledge. You can further refine it by:
  • Adding more data sources (SharePoint, databases, web crawlers).
  • Experimenting with different vector stores.
  • Fine-tuning parsing and chunking strategies for better retrieval.
Stay tuned for more AWS AI tutorials! 🚀
 

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