Atlas Vector Search

Atlas Vector Search Use Cases:
💬 Customer Chatbot
❓ Question-Answering (Q-A)
🛒 Ecommerce Search
👥 User Recommendations
✍️ Content Generation
📊 Analysis and Summary

Most Common Atlas Vector Search Use Cases:
🗄️ Internal Knowledge Bases
📄 Vectorized Documentation
🔍 JSON File to an Embedding Model
🌳 LangChain or LlamaIndex

Atlas Triggers:
👀 Watch for any data changes in a single view

Atlas Vector Search:
🔍 Matching documents by similarity search on indexed embeddings data
🗓️ Queries can use a vector's metadata (date created) to filter out older content

Atlas Search:
🔍 Matching keywords, chunked customer data
🤖 Fuzzy search to correct typos
🔮 Autocomplete (suggested search terms)
🔀 Index intersection (complex ad-hoc queries)

Infrastructure

Queryable Encryption:
🔒 Securing customer data
🔑 Encrypt most sensitive data uniquely identifying an individual (e.g., SSN)

Multi-document ACID Transactions:
🔄 Integrity of data

Atlas Global Clusters:
🌍 Define single or multi-region Zones
🌐 Each zone supports write and read operations from geographically local shards
⚙️ Configure zones to support global low-latency secondary reads

Atlas Online Archive:
🗄️ Data lifecycle management
🔽 Automatically send outdated data from active databases into lower-cost cloud object storage
💾 Keeping data accessible for querying
💯 9.995% uptime SLA

Distributed Architecture with Elastic Scale:
🚀 Dynamically adjust database capacity
🛒 Based on application demand (e.g., shopping seasonality, sales promotions)

Product Catalogs

MongoDB Product Catalogs:
📦 Diversity of different products
🤗 Benefit from flexible document data model

Challenges - Keyword Search:
🤔 Without extensive and laborious synonym mapping
🚲 e.g., mapping bikes to cycling or sneakers to trainers

Challenges - Recommendations:
🧠 Write complex rules-based engines to get specialized and scarce data

Solution - Product Catalog with Vector Embeddings:
🔍 Semantic meaning of products in the catalog
🤝 Understand similarities and relationships between products

Benefits:
🔍 Search experience more intelligent & predictive
📊 Track user click-through rates
💰 Sales conversions from search results

More MongoDB Features:
🕰️ Time Series Collections: Ingest and store high-velocity, click-streams
📊 Atlas Charts: Live visualizations of results, continuously tune and optimize business

Editor

Danny Chan, specialty of FSI and Serverless

Kenny Chan, specialty of FSI and Machine Learning