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Implement RAG while meeting data residency requirements using AWS hybrid and edge services

AWS Machine Learning

Chatbot application On a second EC2 instance (C5 family), deploy the following two components: a backend service responsible for ingesting prompts and proxying the requests back to the LLM running on the Outpost, and a simple React application that allows users to prompt a local generative AI chatbot with questions.

APIs 88
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Your guide to generative AI and ML at AWS re:Invent 2024

AWS Machine Learning

Workshops – In these hands-on learning opportunities, in 2 hours, you’ll be able to build a solution to a problem, and understand the inner workings of the resulting infrastructure and cross-service interaction. Builders’ sessions – These highly interactive 60-minute mini-workshops are conducted in small groups of fewer than 10 attendees.

APIs 88
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Build your gen AI–based text-to-SQL application using RAG, powered by Amazon Bedrock (Claude 3 Sonnet and Amazon Titan for embedding)

AWS Machine Learning

Start learning with these interactive workshops. Solution overview This solution is primarily based on the following services: Foundational model We use Anthropics Claude 3.5 streamlit run app.py To visit the application using your browser, navigate to the localhost. Ready to get started with Amazon Bedrock?

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Build RAG-based generative AI applications in AWS using Amazon FSx for NetApp ONTAP with Amazon Bedrock

AWS Machine Learning

This enables a RAG scenario with Amazon Bedrock by enriching the generative AI prompt using Amazon Bedrock APIs with your company-specific data retrieved from the OpenSearch Serverless vector database. The chatbot application container is built using Streamli t and fronted by an AWS Application Load Balancer (ALB).

APIs 86
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Choosing the Right Chatbot Development Services: Why Vietnamese Partners Stand Out

CSM Magazine

In today’s digital landscape, chatbots have become an invaluable customer engagement and support tool for many businesses. According to Statista, the chatbot market is forecast to reach around 1.25 Moreover, the cost of developing a sophisticated chatbot with local teams can be prohibitively high for many companies. billion U.S.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro has used the input filter and join functionality of SageMaker batch transformation API. The response is returned to Lambda and sent back to the application through API Gateway. Use QuickSight refresh dataset APIs to automate the spice data refresh. It helped enrich the scoring data for better decision making.

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

AWS Machine Learning

Amazon Lex provides the framework for building AI based chatbots. We implement the RAG functionality inside an AWS Lambda function with Amazon API Gateway to handle routing all requests to the Lambda. The Streamlit application invokes the API Gateway endpoint REST API. The API Gateway invokes the Lambda function.

APIs 88