> For the complete documentation index, see [llms.txt](https://calvin-lai.gitbook.io/calvin-lai-security/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://calvin-lai.gitbook.io/calvin-lai-security/ai-using-rag-based-enquiry-assistant-using-vector-database.md).

# AI: Using RAG-Based Enquiry Assistant using Vector Database

### 🧩 Enhanced Learning Objectives:

* Apply RAG principles using Amazon Bedrock’s agent and knowledge base
* Integrate real-time data sources (e.g., DynamoDB) with static knowledge (e.g., CSV in S3)
* Build a RESTful backend using AWS Lambda and API Gateway
* Create a responsive chatbot frontend using React and Chatbotify
* Deploy and secure the application using S3 and CloudFront