생성형 AI
Last updated
Last updated
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.
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.
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.
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.
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.
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.
Question Answering Generative AI application with Large Language Models (LLMs) and Amazon OpenSearch Serverless Service
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.
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.
Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon MemoryDB for Redis.
Question Answering Generative AI application with Large Language Models (LLMs) deployed on Amazon SageMaker, and Amazon MemoryDB for Redis as a Vector Database.
Question Answering Generative AI application with Large Language Models (LLMs), Amazon Bedrock, and Amazon DocumentDB (with MongoDB Compatibility)
Question Answering Generative AI application with Large Language Models (LLMs) deployed on Amazon SageMaker, and Amazon DocumentDB (with MongoDB Compatibility) as a Vector Database.
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)
