Remove APIs Remove Chatbots Remove Workshop
article thumbnail

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 101
article thumbnail

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 101
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Trending Sources

article thumbnail

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?

article thumbnail

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 94
article thumbnail

Unlock the potential of generative AI in industrial operations

AWS Machine Learning

The user can use the Amazon Recognition DetectText API to extract text data from these images. Because the Python example codes were saved as a JSON file, they were indexed in OpenSearch Service as vectors via an OpenSearchVevtorSearch.fromtexts API call. About the authors Julia Hu is a Sr.

article thumbnail

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.

article thumbnail

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 97