https://github.com/dmickelson/knowledgebaseproject

This project outlines the development of a cloud-based, serverless, scalable AI-driven knowledge base platform.
https://github.com/dmickelson/knowledgebaseproject

aws aws-bedrock aws-lambda aws-s3 chatbot microservices python retrieval-augmented-generation serverless

Last synced: 7 months ago
JSON representation

This project outlines the development of a cloud-based, serverless, scalable AI-driven knowledge base platform.

• Host: GitHub
• URL: https://github.com/dmickelson/knowledgebaseproject
• Owner: dmickelson
• Created: 2024-08-13T14:45:50.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-08-14T16:23:20.000Z (about 1 year ago)
• Last Synced: 2025-01-12T10:09:16.652Z (9 months ago)
• Topics: aws, aws-bedrock, aws-lambda, aws-s3, chatbot, microservices, python, retrieval-augmented-generation, serverless
• Homepage:
• Size: 116 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# AI-Driven Knowledge Base Platform

![Alt text here](knowledgebase.svg)

## Overview

This project outlines the development of a cloud-based, serverless, microservice, scalable architected AI-driven knowledge base platform. The platform enables team members to ask questions that span multiple documents and leverage the context of prompt/chat history for extended conversations. The architectural design focuses on being interactive, serverless, modular, and scalable.

## Goal

The primary goal was to create a platform that is:

- Interactive: Remembers previous sessions to facilitate extended conversations.

- Serverless: No infrastructure engineering is required.

- Microserviced: Components are modular and replaceable.

- Scalable: Capable of scaling with the growing demand of the team.

Initially, the plan was to use a separate third-party embedding vector and store them in DynamoDB. However, Amazon Bedrock provided a fully managed solution that better aligned with our needs, leading us to pivot in that direction.

## Architecture Overview

### File Uploading Web Interface

- S3 Buckets: Used to host the static webpage, which serves as a simple UI for uploading documents into S3.

- REST API Endpoints: Protected behind Amazon API Gateway to facilitate secure document uploads.

- Amazon CloudFront: Employed to cache the website, ensuring faster load times and content delivery.

- AWS Certification Manager: Used to create an SSL certificate for secure communications.

- Route 53: For domain registration, pointing to CloudFront endpoints, and integrating with SSL.

- Security: Handled through a combination of Amazon Cognito, AWS IAM, and Amazon KMS.

### Gather Information and Import Content

- S3 Buckets: Serve as the repository for unstructured documents used within the Knowledge Base.

- Upload Process:

  - Users can upload PDF, DOC, or TXT files using the web interface to the S3 upload bucket.

  - Upon upload, an AWS Lambda function is triggered to validate the uploaded documents.

  - If validation is successful, the file is moved to the designated S3 bucket; otherwise, the file is deleted.

### Index Source and Create Knowledge Base

- Amazon Bedrock RAG Knowledge Base:

  - Securely syncs Foundation Models (FMs) to S3 data sources.

  - Ingests, retrieves, and augments queries and retrieval.

  - Offers built-in session content management for multi-turn conversations.

  - Provides automatic citations with retrievals.

  - Embedding Model: Amazon Titan Embedding G1 - Text.

  - Vector Store: Amazon OpenSearch Serverless and Pinecone.

- AWS Kendra:

  - Considered for future implementation.

  - Offers better out-of-the-box integration with various other unstructured data sources.

  - Creates an enterprise search index off of S3 content sources and metadata.

### Chat Interface

The chat interface serves as a Q&A GenAI knowledge base-powered conversation platform, built on top of imported content.

- AWS Lex: Acts as the chat interface for the Q&A knowledge base.

- Model Utilization: Leverages Anthropic Claude V2 models.

- Content Integration: Uses AWS Bedrock as the RAG content foundation.

## Conclusion

This project showcases the power of combining various AWS services to create a highly scalable, interactive, and serverless knowledge base platform. By leveraging the capabilities of Amazon Bedrock, AWS Lambda, and other cloud services, we have established a robust framework for future enhancements and integrations.