build-on-aws/llm-rag-vectordb-python
Explore sample applications and tutorials demonstrating the prowess of Amazon Bedrock with Python. Learn to integrate Bedrock with databases, use RAG techniques, and showcase experiments with langchain and streamlit.
This project offers sample applications to help Python developers build generative AI tools using Amazon Bedrock. It provides blueprints for creating various applications, from Q&A bots that search your own data to resume screeners and data analysis tools. Developers can learn to integrate Bedrock with databases and use techniques like RAG (Retrieval-augmented generation) to build custom AI solutions.
153 stars. No commits in the last 6 months.
Use this if you are a Python developer looking for practical examples and tutorials to build generative AI applications on Amazon Bedrock, integrating with vector databases and other AWS services.
Not ideal if you are an end-user seeking a ready-to-use application, as this repository provides code examples and tutorials for developers.
Stars
153
Forks
31
Language
Jupyter Notebook
License
MIT-0
Category
Last pushed
Feb 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/rag/build-on-aws/llm-rag-vectordb-python"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
aws-samples/generative-ai-use-cases
Application implementation with business use cases for safely utilizing generative AI in...
aws-samples/serverless-rag-demo
Amazon Bedrock Foundation models with Amazon Opensearch Serverless as a Vector DB
aws-samples/amazon-bedrock-rag
Fully managed RAG solution implemented using Knowledge Bases for Amazon Bedrock
IBM/granite-workshop
Source code for the IBM Granite AI Model Workshop
aws-samples/rag-with-amazon-bedrock-and-opensearch
Opinionated sample on how to build and deploy a RAG application with Amazon Bedrock and OpenSearch