Key features:

• Use vector embeddings with ML models (OpenAI, Hugging Face) 🤖
• Store data, metadata, and vector embeddings on Atlas 💾
• Leverage Atlas Vector Search for:
  • Retrieval Augmented Generation (RAG) 🧠
  • Semantic search 🔍
  • Recommendation engines 📊
  • Dynamic personalization 👤

Build faster and easier:

• No need to copy or transfer data 🚀
• Store vector embeddings alongside source data and metadata 📚
• Vector embeddings within application data, create vector index 🔗

Hassle-free database management:

• Auto provisioning, patching, upgrades, scaling, security, disaster recovery 🤖

Vectors:

• Numeric representation of data and related context 📊
• Measure semantic similarity by vector distance 🌐
• Use cases: RAG, semantic search 🔍

Atlas Vector Search:

• Search vector embeddings alongside operational data 🔍
• Avoid data sync, save money 💰
• Support LlamaIndex, OpenAI, Hugging Face, LangChain 🤖

Hybrid search:

• Combine full-text and vector search 🔍
• Accuracy of full-text, semantic capabilities of vector search 🎯

Infrastructure:

• Independently scalable, eliminate risk 🔒
• High resource contention, low downtime 💪

Atlas Search Nodes:

• Auto scale search workloads 🚀
• Isolate search and database workloads 🔍🗄️
• Synchronized search cluster data, no ETL 🔄

RAGs minimize hallucinations:

• Ground model's responses in factual information 🧠
• Use up-to-date sources 📚

Key use cases:

• Semantic search 🔍
• Retrieval Augmented Generation (RAG) for business productivity 👨‍💻

Compute-heavy search nodes:

• Memory-optimized, low CPU option 💻
• Optimal for Vector Search 🔍

Retrieve similar vectors:

• Approximate Nearest Neighbor (ANN) algorithm 🔍

Retrieve most similar vectors:

• K Nearest Neighbor (KNN) search 🔍
• Hierarchical Navigable Small Worlds' (HNSW) algorithm 🔍

Start your MongoDB Atlas Vector Search journey today! 🚀💻

Reference:

Gradio: Build Machine Learning Web Apps — in Python
https://www.gradio.app/
https://github.com/gradio-app/gradio

https://www.mongodb.com/blog/post/retool-state-of-ai-report-mongodb-vector-search-most-loved-vector-database
Atlas Vector Search Once Again Voted Most Loved Vector Database

https://www.mongodb.com/products/platform/atlas-vector-search
Vector Search

https://www.mongodb.com/resources/basics/databases/document-databases
What is a Document Database?

https://www.mongodb.com/resources/solutions/use-cases/generative-ai-shaping-the-future-of-search
How Generative AI is Shaping The Future of Search

https://www.mongodb.com/products/tools/mongodb-query-api
Query API

Editor

Danny Chan, specialty of FSI and Serverless

Kenny Chan, specialty of FSI and Machine Learning