Learn to Build Your Own AI Apps with Azure Cosmos DB! Part 2
TLDRIn this informative session, event planner Danny introduces Jasine Greenway, who leads a developer guide on building AI applications with Azure Cosmos DB. The session covers the importance of speed, handling diverse data types, natural language interaction, scalability, security, and cost-effectiveness for AI applications. Jasine demonstrates using Azure Open AI for creating embeddings and implementing retrieval augmented generation. The guide walks through setting up a web application's backend, deploying APIs, and integrating with Azure Cosmos DB. It also explores the concept of vector representation for data, enabling efficient searching and similarity detection. The session concludes with a Q&A, addressing the difference between vector embeddings and completion models, and encouraging participation in an ongoing hackathon to apply these learnings.
Takeaways
- ๐ The session is a continuation of a developer guide on building AI applications with Azure Cosmos DB, focusing on embeddings and retrieval augmented generation.
- ๐ AI applications require speed, the ability to handle various data types, natural language interaction, scalability, security, compliance, and cost-effectiveness.
- ๐ The use of embeddings involves converting text, images, or other data into vectors that can be used for efficient searching and understanding relationships between data points.
- ๐ Azure Cosmos DB can store and manage these vectors, allowing for the creation of a searchable product base accessible through chat interfaces.
- ๐ The backend of the application interacts with Azure Open AI service and Azure Cosmos DB, leveraging the power of embeddings for product recommendations and searches.
- ๐ป The session includes a live demonstration of using embeddings to vectorize and store data in Cosmos DB, and how to perform vector searches to find related products.
- ๐ The use of Lang chain, an LLM orchestrator, is highlighted for streamlining AI app processes and managing the complexity of different AI packages and libraries.
- ๐ The backend API is deployed as a container app, emphasizing the benefits of containerization for consistency and ease of deployment across different environments.
- ๐ A Dockerfile is used to manage the containerization process, ensuring that the application's deployment is reproducible and consistent.
- ๐ The Azure CLI and Cloud Shell are mentioned as tools for deploying and managing Azure resources, including the container app.
- ๐ค A chat-based UI is demonstrated, showing how user inputs are processed using both completion models like GPT-3.5 and vector embedding models like Ada 002 for generating tailored responses.
- ๐ The session concludes with information on additional resources, including a developer guide, hackathon opportunities, and upcoming office hours on Discord for further questions and support.
Q & A
What is the topic of the session presented by Jasine Greenway?
-The session presented by Jasine Greenway is about building AI applications with Azure Cosmos DB, focusing on part two of the Azure Cosmos DB developer guide.
What is the purpose of the code of conduct mentioned at the beginning of the session?
-The code of conduct is intended to provide a respectful environment for both the audience and presenters, encouraging engagement in the chat while maintaining professionalism and staying on topic.
How can participants find teammates for the ongoing hackathon?
-Participants can find teammates by engaging in the chat, where some individuals may have expressed their need for teammates.
What is the significance of embeddings in the context of AI applications?
-Embeddings are used to convert categorical variables or text into a form that can be input into a model, which is particularly useful for creating searchable product bases that are accessible through chat interfaces.
What are some modern requirements for AI applications?
-Modern AI applications require speed, the ability to work with different types of data under one application, natural language interaction, scalability, security and compliance, and cost-effectiveness.
What is the role of Azure Cosmos DB in the application being built?
-Azure Cosmos DB is used to store the vector representations (embeddings) of the products, which are then used for creating a searchable product base.
How does the vector representation of data, such as text, help in AI applications?
-Vector representation transforms data into a form that can be processed by AI models, making it easier to search and find similarities between different pieces of data.
What is the Lang chain API used for in the context of the session?
-The Lang chain API is used as an LLM (Large Language Model) orchestrator, which helps manage and chain different AI packages and libraries to streamline the AI app process.
How can participants access Azure OpenAI during the hackathon?
-Participants can access Azure OpenAI through the hackathon proxy, which provides an Azure OpenAI key and an endpoint that can be used in their applications.
What is the benefit of deploying the backend API in a container?
-Deploying the backend API in a container ensures consistency across different environments, as it abstracts away the underlying infrastructure and allows the application to run the same way regardless of where it is deployed.
How can participants get help or ask questions related to the session?
-Participants can get help or ask questions by joining the Discord server mentioned in the session, where the presenter, Jasine Greenway, will also be available for office hours.
Outlines
๐ Welcome and Introduction to Azure Cosmos DB Developer Guide
Danny, the event planner, opens the session by asking participants to review the code of conduct for a respectful environment. He introduces Jasine Greenway, who will guide the audience through part two of the Azure Cosmos DB developer guide. The session is recorded and will be available on the Microsoft Reactor YouTube channel. Jasine discusses the importance of speed, data handling, cloud solutions, natural language interaction, accuracy, scalability, security, and cost-effectiveness for AI applications. She recaps the progress made in the previous session, where they worked on the front end and experimented with Azure AI Open API endpoints.
๐ Deep Dive into Embeddings and Azure OpenAI
Jasine explains the concept of embeddings in machine learning, which is a method to convert text, images, or any data into a form that can be input into a model. She discusses how embeddings can be used to create a searchable product base through chat. The session covers the use of embeddings to understand the relationship between different data points and how Azure Cosmos DB stores these vectors for the application. Jasine also touches on the idea of using natural language to interact with databases and the importance of providing meaningful and fast responses to users.
๐ Exploring Azure OpenAI and Embeddings in Notebook Labs
The presentation moves on to the existing notebook labs, where the participants are reminded that they have access to Azure OpenAI through a proxy for the hackathon. The lab involves setting up an endpoint, creating clients, connecting to Azure OpenAI, and creating vector representations of products stored in Cosmos DB. Jasine demonstrates how to use the embeddings deployment to vectorize and store embeddings in documents. She also differentiates between the GPT model used for chat completions and the model used for creating embeddings, which is text embedding Ada 002.
๐ Backend API and Vector Index Creation
Jasine guides the audience through creating a vector index, which is essential for vector search in Azure Cosmos DB. She uses the term 'IVF' (Inverted File) to describe the type of index being used. The backend API is then explored, with a focus on how to run it locally and the necessary steps for deployment. The session also covers how to use the Azure Cosmos DB SDK to perform vector searches and how to use the completion model GPT-3.5 for tailored responses to user queries.
๐ Working with Lang Chain and Backend API
The session continues with a focus on Lang Chain, an LLM (Large Language Model) orchestrator that simplifies the process of working with multiple AI packages and libraries. Jasine demonstrates how to use Lang Chain to create clients for completion and embeddings, and how to format documents into JSON for API use. She also discusses the process of running the backend API locally and the steps for deploying it in a container.
๐๏ธ Deploying Backend API in a Container
Jasine explains the benefits of deploying a backend application as a container, emphasizing consistency and the ease of deployment across different environments. She walks through the Docker steps, showing how to install requirements and serve the API package. The session also covers how to run the application locally and the commands needed to deploy it to Azure Container Apps in the cloud.
๐ Final Deployment and Troubleshooting
The presentation concludes with the final deployment of the application using Bicep, a deployment tool. Jasine encounters some issues with her application and invites participants to share their experiences and solutions. She also mentions an additional demo, which seems to be a local application with a chat interface, and discusses the use of embeddings and completion models in the application. The session ends with a Q&A, where Jasine answers questions about vector embeddings versus completion models and provides a link for the ongoing hackathon.
โ Q&A and Closing Remarks
Jasine addresses the final questions from the audience, clarifying the difference between vector embeddings and completion models. She emphasizes that completion models are for text generation, while vector embeddings are for creating similarity between data points. Jasine also provides information about the hackathon and encourages participants to join the Discord channel for further interaction and support. She wraps up the session by thanking everyone for their time and patience and looks forward to the next interaction on Discord.
Mindmap
Keywords
๐กAzure Cosmos DB
๐กEmbeddings
๐กRetrieval Augmented Generation (RAG)
๐กNatural Language Interaction
๐กScalability
๐กAzure Open AI
๐กVector Database
๐กHackathon
๐กDocker
๐กAPI
๐กLang Chain
Highlights
Session focuses on building AI applications with Azure Cosmos DB, emphasizing modern AI application requirements such as speed, data diversity, and natural language interaction.
Speaker Jasine Greenway introduces the concept of embeddings, a method to convert text into vectors for AI model input, enhancing search capabilities.
The guide demonstrates using Azure Open AI's endpoints for creating embeddings and applying retrieval augmented generation in AI applications.
Azure Cosmos DB is highlighted for its ability to store and manage vector representations of data, facilitating efficient similarity searches.
The session covers the importance of scalability in AI applications, especially when dealing with services like Azure Open AI.
Security and compliance are discussed as critical factors in responsible AI development.
Cost-effectiveness in AI application development is addressed, emphasizing the management of resources and monetary costs.
A walkthrough of deploying a front-end as a single page application (SPA) to Azure App Service is provided.
The backend development process is explored, including the use of a container app for API deployment and interaction with Azure Open AI service.
Participants are shown how to use the Lang chain API for AI package orchestration, simplifying the management of different AI libraries.
The session includes a live demo of a chat application utilizing Azure Open AI and Cosmos DB Vector store for product recommendations.
Jasine Greenway discusses the process of deploying backend APIs in containers for consistency and ease of management.
The benefits of using Docker for local application development and Azure Container Apps for cloud deployment are explained.
An overview of the hackathon and the opportunity for participants to showcase their AI skills using Azure Open AI is provided.
The session concludes with an invitation to join Discord for further interaction, support, and office hours with the speaker.
The difference between vector embeddings and completion models is clarified, with embeddings focusing on data similarity for searches and completion models on text generation.
The importance of engaging with the community through forums and Discord for troubleshooting and knowledge sharing is emphasized.
A reminder is given for participants to check out the developer guide and hackathon links shared in the chat for further learning and competition opportunities.