Learn how to implement Retrieval Augmented Generation (RAG) using Cohere Command model via Amazon Bedrock & domain data in Elasticsearch.

Limitations of LLMs

Approaches to enhance LLMs

RAG using Elasticsearch and Cohere Command model through Amazon Bedrock

Why Cohere?

Why Elasticsearch?

Why Amazon Bedrock?

Solution overview

Step by step explanation

Conclusion

Generative AI is a type of Artificial Intelligence (AI) that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by Machine Learning (ML) models—very large models that are pre-trained on vast corpora of data and commonly referred to as Foundation Models (FMs).

What is generative AI?

LLMs have certain limitations. One notable constraint is that they are trained on general domain corpora, making them less effective on domain-specific tasks. Also, LLMs are trained offline until a knowledge cutoff date. It will be agnostic of any developments that have happened after the knowledge cutoff date. This may lead to inaccurate interpretations.

What are the limitations of Large Language Models (LLMs)?

RAG using Elasticsearch & Cohere through Amazon Bedrock

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch

Learn how to build a generative artificial intelligence (GAI) solution with Amazon SageMaker JumpStart, Elastic, and Hugging Face open source LLMs using the sample implementation provided in this post and a data set relevant to your business.

Implementing RAG using Elasticsearch and open source LLM available in Amazon SageMaker JumpStart

Step-by-step explanation

GAI represents a significant leap forward in AI capabilities, enabling machines to generate original and creative content across various domains including conversations, stories, images, videos, and music.

What is generative artificial intelligence (GAI)?

How to implement RAG using Elasticsearch and open source LLM available in Amazon SageMaker JumpStart

Choose your LLM by logging in to Amazon SageMaker Studio, opening Amazon SageMaker JumpStart, and searching for your preferred Foundation model.

Deploy your LLM and then copy the Endpoint Name.

Download and set up the RAG Application. Launch an EC2 instance and clone the code from this GitHub link. Set up a virtual environment. Install the required Python libraries. Update the config.sh file.

Run the application using the command streamlit run rag_elastic_aws.py. This will start a web browser and the URL will be printed to the command line.

Get a response from LLM with context derived from Elasticsearch.

Author

Uday Theepireddy

Articles

Retrieval Augmented Generation (RAG) using Cohere Command model through Amazon Bedrock and domain data in Elasticsearch

Generative AI using Elastic and Amazon SageMaker JumpStart