Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

Objective:
Build a powerful question answering bot that leverages Amazon SageMaker, Amazon OpenSearch Service, Streamlit, LangChain, and FastAPI to provide users with accurate answers to their questions using a knowledge corpus.
AWS Services Used:
1. Amazon SageMaker: Host two Language Model Models (LLMs) - gpt-j-6b and flan-t5-xxl, and use SageMaker JumpStart for model hosting.
2. Amazon OpenSearch Service: Store embeddings of the enterprise knowledge corpus and perform similarity searches with user questions.
3. AWS Lambda: Implement the Retrieval-Augmented Generation (RAG) functionality and expose it as a REST endpoint via Amazon API Gateway.
4. Amazon API Gateway: Handle routing all requests to the Lambda function.
5. Amazon SageMaker Processing Jobs: Used for large-scale data ingestion into OpenSearch.
6. Amazon SageMaker Studio: Host the Streamlit application.
7. AWS Identity and Access Management (IAM): Roles and policies for access management.
8. AWS CloudFormation: Create the entire solution stack through infrastructure as code.
Open-Source Packages:
1. LangChain: Utilized for interfacing with OpenSearch Service and SageMaker.
2. FastAPI: Implemented for developing the REST API interface in the Lambda.
Step-by-Step Implementation Plan:
Data Preprocessing:
Convert HTML pages from the SageMaker docs into smaller overlapping chunks for better context continuity.
Use LangChain to convert these chunks into embeddings.
Store the embeddings in Amazon OpenSearch Service.
Model Hosting on SageMaker:
Host the gpt-j-6b and flan-t5-xxl models using Amazon SageMaker.
Utilize SageMaker JumpStart for efficient model hosting.
Lambda Function Implementation:
Implement an AWS Lambda function to handle the RAG functionality.
Expose the Lambda function as a REST endpoint using FastAPI and Amazon API Gateway.
Configure the Lambda function to perform similarity searches in the OpenSearch Service index for user questions.
Streamlit Application:
Develop a Streamlit chatbot application.
Configure the application to invoke the Lambda function via the API Gateway for question answering.
IAM Roles and Policies:
Define AWS Identity and Access Management (IAM) roles and policies to manage access to AWS services securely.
Large-Scale Data Ingestion:
Use Amazon SageMaker Processing jobs for efficient data ingestion into Amazon OpenSearch.
Infrastructure as Code (IaC):
Define the entire solution stack using AWS CloudFormation for infrastructure as code to automate deployment and scaling.
credit: AWS