Generative AI models have the potential to revolutionize enterprise operations, but businesses must address challenges like data protection and content quality. The Retrieval-Augmented Generation (RAG) framework combines external data sources with prompts to enhance domain-specific tasks. MongoDB Atlas with Vector Search and Amazon SageMaker JumpStart support this transformative potential.
Revolutionize Enterprise Operations with Generative AI Models
Generative AI models have the potential to transform enterprise operations, but businesses need to address challenges like data security and content quality. The Retrieval-Augmented Generation (RAG) framework combines external data sources with foundation models to enhance domain-specific tasks. MongoDB Atlas with Vector Search is a powerful tool that complements the RAG model.
MongoDB Atlas: Accelerate Data-Driven Applications
MongoDB Atlas is an integrated suite of data services that simplifies the development of data-driven applications. With its Vector Search feature, it seamlessly integrates with operational data storage, eliminating the need for a separate database. This integration enables powerful semantic search capabilities and AI-powered applications.
Amazon SageMaker: Build, Train, and Deploy ML Models
Amazon SageMaker empowers enterprises to build, train, and deploy machine learning models. SageMaker JumpStart provides pre-trained models and data to jumpstart your ML projects. With just a few clicks, you can access, customize, and deploy these models through Amazon SageMaker Studio.
Amazon Lex: Create Conversational Interfaces
Amazon Lex is a conversational interface that helps businesses create chatbots and voice bots for natural, lifelike interactions. By integrating Amazon Lex with generative AI, businesses can create a holistic ecosystem where user input seamlessly transitions into coherent and contextually relevant responses.
Solution Overview
The solution architecture consists of several components that work together to deliver powerful AI capabilities.
Set up a MongoDB Cluster
Follow the instructions to create a free tier MongoDB Atlas cluster. Set up database and network access to ensure security.
Deploy the SageMaker Embedding Model
Choose the ALL MiniLM L6 v2 embedding model from SageMaker JumpStart and deploy it. Verify the successful deployment and create an endpoint.
Vector Embedding
Generate vector embeddings using SageMaker JumpStart and update the collection with the created vector for each document. This allows for efficient semantic search and analysis.
MongoDB Vector Data Store
MongoDB Atlas Vector Search is a feature that enables the storage and search of vector data in MongoDB. This type of data is commonly used in ML and AI applications. Storing vector data alongside operational data improves performance and real-time access.
Create a Vector Search Index
Create a MongoDB Vector Search index on the vector field to enable semantic search on the vector data store. Ensure the vector field is represented as an array of numbers.
Query the Vector Data Store
Use the Vector Search aggregation pipeline to query the vector data store. This pipeline leverages the Vector Search index and performs semantic searches on the vector data.
Deploy the SageMaker Large Language Model
Choose the Hugging Face FLAN-T5-XL model from SageMaker JumpStart and deploy it. Verify the successful deployment and activate the endpoint.
Create an Amazon Lex Bot
Follow the steps to create an Amazon Lex bot for natural language interactions. Specify the AWS Lambda function that interacts with MongoDB Atlas and the large language model to provide responses.
Clean Up
To clean up resources, delete the Amazon Lex bot, Lambda function, SageMaker endpoint, embeddings model endpoint, and MongoDB Atlas cluster.
Conclusion
This solution demonstrates how to create a bot that utilizes MongoDB Atlas semantic search and integrates with a model from SageMaker JumpStart. By combining these technologies, businesses can prototype user interactions with different models and leverage the context stored in MongoDB Atlas.
About the Authors
Igor Alekseev is a Senior Partner Solution Architect at AWS, specializing in Data and Analytics. Babu Srinivasan is a Senior Partner Solutions Architect at MongoDB, working on technical integrations and reference architectures. They both have extensive experience in their respective domains.
Discover the Power of AI for Your Company
Evolve your company with AI and stay competitive by leveraging Retrieval-Augmented Generation, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search. Identify automation opportunities, define measurable KPIs, select customizable AI solutions, and implement gradually. For AI KPI management advice, contact us at hello@itinai.com. Stay updated on AI insights through our Telegram channel t.me/itinainews or Twitter @itinaicom.
Spotlight on a Practical AI Solution: AI Sales Bot
Visit itinai.com/aisalesbot to explore the AI Sales Bot, designed to automate customer engagement and manage interactions across all stages of the customer journey. Redefine your sales processes and customer engagement with AI. Discover solutions at itinai.com.
