생성형 AI

LLM Observability Tools

Langfuse on AWS

This project is an AWS CDK Python project for deploying the Langfuse application using Amazon Elastic Container Registry (ECR) and Amazon Elastic Container Service (ECS). Langfuse is an open-source LLM engineering platform that helps teams collaboratively debug, analyze, and iterate on their LLM applications.

GitHub - aws-samples/deploy-langfuse-on-ecs-with-fargate: Hosting Langfuse on Amazon ECS with Fargate using CDK PythonGitHub

RAG(Retrieval Augmented Generation)

With Knowledge Bases for Amazon Bedrock

This project is an Question Answering application with Large Language Models (LLMs) and Knowledge Bases for Amazon Bedrock. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response. In this project, Amazon OpenSearch Serverless is used for a Knowledge Base for Amazon Bedrock.

aws-kr-startup-samples/gen-ai/rag-with-knowledge-bases-for-amazon-bedrock at main · aws-samples/aws-kr-startup-samplesGitHub

generative-ai-cdk constructs 사용 버전

aws-kr-startup-samples/gen-ai/rag-with-knowledge-bases-for-amazon-bedrock-using-L1-cdk-constructs at main · aws-samples/aws-kr-startup-samplesGitHub

AWS CDK L1 Constructs 사용 버전

With Amazon Aurora Postgresql used for a Knowledge Base for Amazon Bedrock

This project is a Question Answering application with Large Language Models (LLMs) and Amazon Aurora Postgresql using pgvector. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response. In this project, Amazon Aurora Postgresql with pgvector is used for a Knowledge Base for Amazon Bedrock.

aws-kr-startup-samples/gen-ai/rag-with-knowledge-bases-for-amazon-bedrock-using-aurora-postgresql at main · aws-samples/aws-kr-startup-samplesGitHub

With LLMs and Amazon Kendra

This project is a Question Answering application with Large Language Models (LLMs) and Amazon Kendra. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.

aws-kr-startup-samples/gen-ai/rag-with-amazon-kendra-and-sagemaker at main · aws-samples/aws-kr-startup-samplesGitHub

With Amazon Bedrock and Kendra

This project is a Question Answering application with Large Language Models (LLMs) and Amazon Kendra. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.

https://github.com/aws-samples/qa-app-with-rag-using-amazon-bedrock-and-kendragithub.com

With Amazon Bedrock and OpenSearch

aws-kr-startup-samples/gen-ai/rag-with-amazon-bedrock-and-opensearch at main · aws-samples/aws-kr-startup-samplesGitHub

With LLMs and Amazon OpenSearch

This project is an Question Answering application with Large Language Models (LLMs) and Amazon OpenSearch Service. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response.

GitHub - aws-samples/rag-with-amazon-opensearch-and-sagemaker: Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch ServiceGitHub

With LLMs and Amazon OpenSearch Serverless

Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Serverless Service

https://github.com/aws-samples/rag-with-amazon-opensearch-serverlessgithub.com

With Amazon Bedrock and Amazon Aurora Postgresql using pgvector

This project is a Question Answering application with Large Language Models (LLMs) and Amazon Aurora Postgresql using pgvector. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response. In this project, Amazon Aurora Postgresql with pgvector is used for knowledge base.

aws-kr-startup-samples/gen-ai/rag-with-amazon-bedrock-and-postgresql-using-pgvector at main · aws-samples/aws-kr-startup-samplesGitHub

With LLMs and Amazon Aurora Postgresql using pgvector

This project is a Question Answering application with Large Language Models (LLMs) and Amazon Aurora Postgresql using pgvector. An application using the RAG(Retrieval Augmented Generation) approach retrieves information most relevant to the user’s request from the enterprise knowledge base or content, bundles it as context along with the user’s request as a prompt, and then sends it to the LLM to get a GenAI response. In this project, Amazon Aurora Postgresql with pgvector is used for knowledge base.

GitHub - aws-samples/rag-with-amazon-postgresql-using-pgvector-and-sagemaker: Question Answering application with Large Language Models (LLMs) and Amazon Postgresql using pgvectorGitHub

With Amazon Bedrock and MemoryDB for Redis

Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon MemoryDB for Redis.

GitHub - aws-samples/rag-with-amazon-bedrock-and-memorydb: Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon MemoryDB for RedisGitHub

With Amazon MemoryDB for Redis and SageMaker

Question Answering Generative AI application with Large Language Models (LLMs) deployed on Amazon SageMaker, and Amazon MemoryDB for Redis as a Vector Database.

aws-kr-startup-samples/gen-ai/rag-with-amazon-memorydb-and-sagemaker at main · aws-samples/aws-kr-startup-samplesGitHub

With Amazon Bedrock and DocumentDB

Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon DocumentDB (with MongoDB Compatibility)

GitHub - aws-samples/rag-with-amazon-bedrock-and-documentdb: Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon DocumentDB (with MongoDB Compatibility)GitHub

With Amazon DocumentDB and SageMaker

Question Answering Generative AI application with Large Language Models (LLMs) deployed on Amazon SageMaker, and Amazon DocumentDB (with MongoDB Compatibility) as a Vector Database.

aws-kr-startup-samples/gen-ai/rag-with-amazon-documentdb-and-sagemaker at main · aws-samples/aws-kr-startup-samplesGitHub

Semantic Vector Search in PostgreSQL using Amazon SageMaker and pgvector

This project is a search solution using pgvector for an online retail store product catalog. We’ll build a search system that lets customers provide an item description to find similar items. For more information, check this blog post, Building AI-powered search in PostgreSQL using Amazon SageMaker and pgvector (on MAY 2023)

GitHub - ksmin23/semantic-vector-search-with-sagemaker-pgvector: A search application using Aurora Postgresql and pgvector for an online retail store product catalogGitHub