How LeadSquared accelerated chatbot deployments with generative AI using Amazon Bedrock and Amazon Aurora PostgreSQL

This article discusses how LeadSquared, a customer relationship management (CRM) platform, used Amazon Bedrock and Amazon Aurora PostgreSQL to accelerate chatbot deployments with generative AI. It showcases how to build an intelligent chatbot that can retrieve relevant information from a knowledge base and provide contextually relevant responses using a technique called Retrieval Augmented Generation (RAG).

Specifically, the article covers:

• An overview of the RAG mechanism and its benefits in generating contextual responses
• The solution architecture involving Aurora PostgreSQL with the pgvector extension, Amazon Bedrock, and Amazon SageMaker
• Prerequisites and steps for setting up the solution
• Code walkthrough for loading data, generating embeddings, storing them in Aurora, and creating a conversational chain
• Examples of using different data sources like PDFs, videos, presentations, and documents
• Steps for deploying the sample Streamlit application
• Conclusion and benefits observed by LeadSquared