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Accelerating Path to Production for Generative AI-powered Applications

"In this session, we will discuss some recent developments in Generative AI and how those can be leveraged to build intelligent applications. Learn how to bring the power of large language models (LLMs) to your private, real-time operational data across multiple data types. We will talk about improving the accuracy of LLMs in your applications by leveraging Retrieval Augmented Generation, which provides proprietary knowledge to the LLM. From real-time responses to sophisticated interactions, learn how you can easily build a range of AI-driven experiences that leverage your operational data with minimal complexity. MongoDB Atlas provides native vector search capabilities and a flexible document model all within an enterprise-ready developer data platform empowering teams to iterate quickly on applications enriched with generative AI. Coupling Atlas with Confluent makes it easier to leverage streaming data when informing LLMs with proprietary data."

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MongoDB for Generative
AI-powered Applications
Welcome
Prakul Agarwal
Senior Product Manager
Machine Learning
David Macias
Lead Product Marketer
Agenda
Intro to Generative AI-Powered Apps
Use Cases and RAG w/MongoDB Atlas
Demo: Confluent Cloud + MongoDB Atlas
MongoDB Atlas Vector Search
What is a Generative AI-powered App
Generative AI (Gen AI) is software that can generate, or
create, something new when asked through a prompt.
Adobe Firefly
What we’re seeing in the industry
Chatbots Mind Blowing Ideas
360 View + LLM
Demo at the end!
: Global auto manufacturer
Gen AI
Diagnostics
Car
sounds
What is an LLM ?
-Gen AI uses “Large Language Models” or
LLMs that provide a general-purpose AI
“brain,” no custom building needed for each
project.
-The LLM performs the “generative” functions,
for example, creating text, image, write code
or video.
-LLMs “studied” enormous amounts of public
data to learn patterns between words, images,
videos, or other data.
- OpenAI’s GPT is the most popular LLM
- ChatGPT is a Gen AI app built using the
popular GPT as it’s brain
- Meta’s LLaMa is an example of an open
source LLM

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Accelerating Path to Production for Generative AI-powered Applications

  • 1. MongoDB for Generative AI-powered Applications Welcome Prakul Agarwal Senior Product Manager Machine Learning David Macias Lead Product Marketer
  • 2. Agenda Intro to Generative AI-Powered Apps Use Cases and RAG w/MongoDB Atlas Demo: Confluent Cloud + MongoDB Atlas MongoDB Atlas Vector Search
  • 3. What is a Generative AI-powered App Generative AI (Gen AI) is software that can generate, or create, something new when asked through a prompt. Adobe Firefly
  • 4. What we’re seeing in the industry Chatbots Mind Blowing Ideas 360 View + LLM Demo at the end!
  • 5. : Global auto manufacturer Gen AI Diagnostics Car sounds
  • 6. What is an LLM ? -Gen AI uses “Large Language Models” or LLMs that provide a general-purpose AI “brain,” no custom building needed for each project. -The LLM performs the “generative” functions, for example, creating text, image, write code or video. -LLMs “studied” enormous amounts of public data to learn patterns between words, images, videos, or other data. - OpenAI’s GPT is the most popular LLM - ChatGPT is a Gen AI app built using the popular GPT as it’s brain - Meta’s LLaMa is an example of an open source LLM
  • 7. Non-specific answer LLM No context prompt “How do I sell new MongoDB and Kafka to my accounts based on their current and upcoming priorities?” “MongoDB is chosen for its flexibility and scalability, performance and availability, and its ease of use and security. Tailor these points to the needs of the engineering leader you're pitching to.” Not specific. Doesn’t mention Atlas or Confluent Cloud. Giant “brain” of general knowledge An LLM that hasnʼt been made useful
  • 8. Making an LLM useful General intelligence (generic AI/ML models) Streams Serverless Edge Hybrid Search Access to proprietary data A well trained LLM refined with multimodal data From this To This How Company specific data Order history Product info
  • 9. Specific, well informed answer LLM Augmenting an LLM with proprietary data Retrieval-augmented generation (RAG) Proprietary Data Context data Vector Embeddings Metadata and app data Audio files Customer Data Images [0.234, 0.351 …] [0.531, 0.276 …] [0.713, 0.453 …] [0.124, 0.321 …] With context prompt Embedding model No context prompt “How do I sell MongoDB and Kafka to my accounts based on their current and upcoming priorities?” Giant “brain” of general knowledge “Consider pitching that MongoDB Atlas and Confluent Cloud work great together for any of their real-time app needs …” RAG “How do I sell MongoDB and Kafka based on their current and upcoming priorities? Take into account their Atlas usage, these call transcripts, etc.” [0.424, 0.365 …] Vector search retrieves contextual data fast
  • 10. What are Vectors Vectors (or vector embedding or just embeddings) A vector is a list of numbers in an N-dimensional space that represents the “semantic” (or underlying) meaning of something - text, image, video, etc. How are they created? For each data record (often just a chunk of text), an embedding model generates a vector to represent the data record
  • 11. Prototype Enterprise-Ready Flexibility to iterate with speed Foundational modern app requirements: Highly reliable, scalable, secure, multi-cloud Teams building Gen AI powered apps need… To go from innovative Gen AI idea to production application Minimal time, cost, and complexity when augmenting LLMs with proprietary data
  • 12. Multi-Cloud Scale, Resilience, Performance, & Security A well trained LLM refined with multimodal data Developer data platform + = An incredibly sophisticated AI powered app 1 Developer data platform 1 3 Document Model & Unified API Multi-Cloud Scale, Resilience, Performance, & Security with Atlas Vector Search Unifying operational and Gen AI data services
  • 15. Application User Profiles, E-commerce Inventory Vector Indexes Collections Atlas Vector Search Atlas Triggers Embedding Model Large Language Model ( 1 ) User Query ( 2 ) Make embedding from query ( 3 ) Look up related facts from Atlas Vector Search ( 4 ) Create final response MongoDB Kafka Connector Internal Data Systems Customer 360 View Asynchronously update views with real-time data
  • 16. MongoDB Atlas Vector Search Searching across Vector embeddings and metadata Achieving low latency for search AI Ecosystem Multimodality
  • 18. Vectors in a Document _id: ObjectId('62f13a3fe7321ca47aecb216') symbol: "ABMD" quarter: 4 year: 2021 date: 2021-04-29T20:10:40.000+00:00 content: "Operator: Ladies and gentlemen, thank you for standing by, and welcome..." content_embeddings: Array 0.03898080065846443 -0.05879044905304909 0.04323238879442215 -0.021337900310754776 -0.036346953362226486 0.028689613565802574 -0.03514527902007103 -0.07414846867322922 -0.00993054173886776 0.007234036456793547 -0.03197460621595383 embeddings are stored as an array of floats
  • 19. { "mappings": { "fields": { "content_embedding": { "type": "knnVector", "dimensions": 1536, "similarity": "<euclidean | dotProduct | cosine>" }, "field1": { "type": "date" }, // optional "field2": { "type": "double" } // optional } } } MongoDB Vector Search - Index definition
  • 20. [{ "$vectorsearch": { "queryVector": [ 0.03898080065846443, ... ], "path": "content_embedding", "limit": 5 "filter": { // traditional point & range queries }, } } }] MongoDB Vector Search Query
  • 21. query = "Houses in new Jersey with a front yard which are less than 500k" results = collection.aggregate([{ '$vectorSearch': { "index": semantic_index_description, "queryVector": generate_embedding(query), "path": content_embedding, "limit": 5 }}}]) Semantic Search Example
  • 23. HNSW Hierarchical Navigable Small World graphs - Yury Malkov et al (2016) ● Atlas Vector Search is powered by a graph based algorithm called HNSW ● These queries are called ANN (Approximate K nearest neighbors) ● This provides low-latency search and high-recall results ● Atlas Vector Search keeps updating this graph async as your underlying data changes
  • 27. Connecting Text and Images - Using models like OpenAI CLIP you can obtain embeddings that can work across text and images - With Atlas Vector Search you can build powerful applications like “Find similar images” easily - CLIP stands for Contrastive Language-Image Pre-training
  • 28. query = "Houses with swimming pool" results = collection.aggregate([{ '$vectorSearch': { "index": CLIP_image_index, "queryVector": generate_embedding(query), "limit": 3, "path": clip_embedding }}}]) Text to Image Search Example
  • 29. image_url = "https://cdn.com/pictures/34569517.jpg" results = collection.aggregate([{ '$vectorSearch': { "index": CLIP_image_index, "queryVector": generate_embedding_url(image_url), "limit": 1, "path": clip_embedding }}}]) Image Similarity Search Find Similar Property