RAG based Generative AI using Amazon Bedrock Knowledge Base

Overview

Generative AI chatbots, powered by advanced language models, offer natural, contextual, and versatile conversations by dynamically generating responses.

Unlike traditional chatbots, they utilize techniques like transformers, attention mechanisms, and reinforcement learning to enhance coherence and relevance.

These capabilities make them ideal for customer service, virtual assistance, and creative tasks like content generation.

Introduction

Retrieval Augmented Generation (RAG) enhances language models by integrating external knowledge retrieval with their generation process.

Using vector embeddings to find relevant information from a knowledge base, RAG combines this data with the model's outputs to produce more accurate, context-aware, and informed responses.

This approach excels in tasks like question answering, dialog systems, and content generation, improving text quality and coherence.

Scenario

1. The user makes a request to the GenAI app.
2. The app passes the query to the Bedrock agent.
3. If the agent finds it relevant, it sends a request to the Knowledge base to get context based on user input.
4. The question is converted into embeddings using Bedrock via the Titan embeddings v1.2 model.
5. The embedding is used to find similar documents from an OpenSearch Service Serverless index.
6. OpenSearch output is returned to the Knowledge base.
7. The Knowledge base returns the context.
8. The Bedrock agent sends the user’s request, along with the data retrieved from the index as context in the prompt, to the LLM.
9. The LLM returns a succinct response to the user request based on the retrieved data.
10. The response from the LLM is sent back to the app.
11. The app displays the Agent/LLM output to the users.

Practice

In this video, you can learn how to build a RAG-based Generative AI Chatbot in 20 minutes using Amazon Bedrock Knowledge Base.

In this video, you'll learn:

• What is Amazon Bedrock Knowledge Base and how to set it up?
• How to set up a managed Amazon OpenSearch Serverless vector database with Amazon Bedrock Knowledge Base.
• How to sync data and test Amazon Bedrock Knowledge Base with a managed chatbot test feature using Amazon Bedrock LLMs.

Links/Sources

• Build a RAG-based Generative AI Chatbot in 20 mins using Amazon Bedrock Knowledge Base
• Foundation Models
• Amazon Bedrock Agents
• Retrieval Augmented Generation (RAG) Technique

Comments

[Winzod AI]

Hey folks, came across this post and thought it might be helpful for you! Check out this blog about evaluating RAG performance: metrics and benchmarks - Rag Evaluation Metrics