# 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


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://calvin-lai.gitbook.io/calvin-lai-security/ai-using-rag-based-enquiry-assistant-using-vector-database.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.