rag-with-amazon-bedrock-and-opensearch and rag-with-amazon-bedrock-and-pgvector
These two tools are competitors, as both are opinionated sample implementations for building a RAG application with Amazon Bedrock, but they differ in their choice of vector database backend, with one using PGVector and the other OpenSearch.
About rag-with-amazon-bedrock-and-opensearch
aws-samples/rag-with-amazon-bedrock-and-opensearch
Opinionated sample on how to build and deploy a RAG application with Amazon Bedrock and OpenSearch
This project helps you build a custom question-answering system for your organization's documents. You provide a collection of PDF documents, and the system allows users to ask natural language questions and receive answers directly from those documents. It's designed for businesses or teams that need to quickly extract information from their internal knowledge base without manual searching.
About rag-with-amazon-bedrock-and-pgvector
aws-samples/rag-with-amazon-bedrock-and-pgvector
Opinionated sample on how to build/deploy a RAG web app on AWS powered by Amazon Bedrock and PGVector (on Amazon RDS)
This project helps developers build and deploy their own question-answering systems for internal documents. It takes a collection of PDF files as input, processes them, and then allows users to ask questions in natural language, retrieving relevant answers from the content. The target user is a software developer or cloud architect responsible for setting up internal knowledge bases or intelligent search applications.
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