donadviser/advanced-rag-mongodb-vector-search
Advanced Retrieval-Augmented Generation (RAG) pipeline using MongoDB Atlas Vector Search, LangChain & OpenAI. PDF ingestion, embeddings, vector index creation, semantic search & grounded LLM responses.
Stars
—
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 19, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/donadviser/advanced-rag-mongodb-vector-search"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
aws-samples/amazon-bedrock-samples
This repository contains examples for customers to get started using the Amazon Bedrock Service....
debnsuma/fcc-ai-engineering-aws
A Practical Course on Embeddings, RAG, Multimodal Models, and Agents with Amazon Nova.
aws-samples/news-clustering-and-summarization
This repository contains code for a near real-time news clustering and summarization solution...
arnobt78/Embeddable-RAG-Chatbot-Widget--JavaScript-Cloudflare-Workers-FullStack
A production-ready, embeddable AI chatbot widget built with Cloudflare Workers that can be...
f2daz/openclaw-knowledgebase
Self-hosted RAG system with Ollama embeddings and Supabase/pgvector. 100% local, 100% free.