sujee/mongodb-atlas-vector-search
Using MongDB Atlas with embedding models and LLMs to do vector search and RAG applications
This helps developers build applications that can understand and answer questions about large collections of documents. It takes raw text or PDF documents, processes them to create numerical representations (embeddings), and stores them in MongoDB Atlas. The output is a system that can intelligently retrieve relevant information and generate answers using large language models, useful for creating smart search or Q&A features.
No commits in the last 6 months.
Use this if you are a developer looking for practical examples and code to implement vector search and RAG capabilities using MongoDB Atlas.
Not ideal if you are an end-user without programming knowledge, as this project provides developer-focused code samples, not a ready-to-use application.
Stars
25
Forks
16
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Aug 23, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/embeddings/sujee/mongodb-atlas-vector-search"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
Praful932/Kitabe
Book Recommendation System built for Book Lovers📖. Simply Rate ⭐ some books and get immediate...
passadis/ai-assistant
Books recommendation AI engine
dvsander/mdb-search
Example application querying data in different ways
Arfazrll/OllamaLLM-RecomendationSystem
An AI book recommendation system built with Streamlit and Ollama. It uses 'nomic-embed-text' for...
ahmedshahriar/TwitterCelebrityMatcher
Match celebrity users with their respective tweets by making use of Semantic Textual Similarity...