rag-with-amazon-opensearch-and-sagemaker and rag-from-scratch

Maintenance 0/25
Adoption 7/25
Maturity 16/25
Community 9/25
Maintenance 10/25
Adoption 0/25
Maturity 11/25
Community 0/25
Stars: 29
Forks: 3
Downloads:
Commits (30d): 0
Language: Python
License: MIT-0
Stars:
Forks:
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stale 6m No Package No Dependents
No Package No Dependents

About rag-with-amazon-opensearch-and-sagemaker

aws-samples/rag-with-amazon-opensearch-and-sagemaker

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

This project helps you build an internal question-answering system for your business. You provide your company's documents, and it allows users to ask questions and receive accurate answers generated by an AI, drawing only from your provided information. This is ideal for knowledge managers, HR professionals, or anyone responsible for making large volumes of internal documentation easily searchable and digestible.

enterprise-search knowledge-management internal-documentation information-retrieval business-intelligence

About rag-from-scratch

noaman680/rag-from-scratch

Production-ready RAG (Retrieval Augmented Generation) system built from scratch using LangChain, OpenAI, and FAISS. Features document indexing, semantic search, and AI-powered Q&A with source attribution.

Scores updated daily from GitHub, PyPI, and npm data. How scores work