RAG with AWS S3

Learn how to create an intelligent chatbot that can understand and respond to questions using documents from your Amazon S3 bucket. This tutorial leverages Lamatic.ai and Retrieval-Augmented Generation (RAG) technology.

What You'll Build

• Utilize Lamatic.ai Studio for building flow.
• Create a Chat Widget powered by LLMs.
• Implement RAG with AWS S3 for document-based knowledge retrieval.

Workflow Overview

We'll implement the following steps:

1. S3 Node: Collect files from an S3 bucket.
2. Extract from File Node: Parse and extract content from files.
3. Logic Node: Transform the extracted data.
4. Vectorize Node: Convert data into vectors for AI processing.
5. Index Node: Manage and store vectorized data.

Getting Started

1. Create an Account and New Workflow

1. Sign up at Lamatic.ai (opens in a new tab) and log in.
2. Navigate to Flows, select New Flow, and choose Create from Scratch as your starting point.
3. Click on new flow.
4. Choose S3 Node as the trigger. Refer to S3 Node setup instructions (opens in a new tab).

2. Extract Data from Files

The Extract from File Node processes and extracts content from various file formats.

1. Add the Extract from File Node to your flow.
2. Configure the node:
   • Click the + icon to add data.
   • Provide the document_url as the File URL(s).

3. Transform the Data

1. Use a Logic Node to extract chunked text:
   • Add the Extract Chunked Text Node and define transformation logic.

4. Vectorize the Data

Transform textual data into vectors for AI processing.

1. Add a Vector Node and configure it:

   • Click the + icon to input text for vectorization.
   • Choose an embedding model, e.g., OpenAI’s text-embedding-3-large.

   

5. Store Vector Data in a Database

Organize and store vectorized data for efficient retrieval.

1. Add a Index Node
2. Configure the node:
   • Select the desired vector database.
   • Add vectors, metadata, and a primary key.

6. Test and Deploy Workflow

1. Test the flow using the Test button.
2. Deploy the flow to make it accessible for chatbot integration.
   Logs can be monitored in the Logs section.

7. Build the Chatbot Using RAG

RAG (Retrieval-Augmented Generation) combines a knowledge base with language understanding to provide accurate, context-aware responses.

1. Create a new flow named RAG Chatbot, or use the Document Chatbot Template (opens in a new tab).
2. Add a Chat Interface Node.
3. Add a RAG Node:
   • Select the vector database.
   • Pass the query and choose the respective LLM.

8. Test and Deploy Chatbot

1. Test the chatbot to ensure accurate responses.
2. Deploy the chatbot by clicking the Deploy button.
3. Integrate the chat widget into your website:
   • Click Setup to access the integration code.
   • Paste the code into your website.

Congratulations!

You’ve successfully built an intelligent document-based chatbot using AWS S3 and RAG technology with Lamatic.ai. 🎉