Episode
RAG using Semantic Kernel with Azure OpenAI and Azure Cosmos DB for MongoDB vCore | Python Data Science Day
with John Aziz
Learn about the new capabilities of Azure Cosmos DB for MongoDB vCore and Semantic Kernel that enables you to use vector search and integrate your AI-based applications with your data that's stored in Azure Cosmos DB. Vector search enables you to efficiently store, index, and query high-dimensional vector data that are stored directly in Azure Cosmos DB for MongoDB vCore. Semantic Kernel enables the integration with Azure OpenAI to perform retrieval augmented generation (RAG) and bring everything together.
John Aziz, a Microsoft AI MVP and a Gold Microsoft Learn Student Ambassador from Egypt.
In this session, you'll learn about vector search, RAG, and the steps you need to perform to enable it using the semantic kernel, discover how to set up an Azure Cosmos DB for MongoDB vCore, deploy the Azure OpenAI chat and embedding model, learn about how the semantic kernel works, and integrate it in a flask app.
Follow along as we provide clear instructions, make the process accessible to both beginners and experienced tech enthusiasts. By the end, you'll have a fully functional Flask application capable of generating responses using vector search and rag. Check it out here!
Chapters
- 00:00 - RAG using Semantic Kernel with Azure OpenAI and Azure Cosmos DB
- 00:33 - Agenda
- 01:06 - Prerequisites
- 01:43 - Microsoft Technologies used
- 12:05 - Concepts used
- 14:34 - Demo - RAG in Jupyter Notebooks
- 29:32 - Resources
Recommended resources
- Blog
- Access to Azure OpenAI
- Code
- Documentation - Azure OpenAI Service
- Documentation - Azure Cosmos DB for MongoDB vCore
- Documentation - Semantic Kernel
- Repository
- Vector Search on embeddings with Azure Cosmos DB for MongoDB vCore
Related episodes
Connect
- John Aziz | Twitter/X: @john00isaac
Learn about the new capabilities of Azure Cosmos DB for MongoDB vCore and Semantic Kernel that enables you to use vector search and integrate your AI-based applications with your data that's stored in Azure Cosmos DB. Vector search enables you to efficiently store, index, and query high-dimensional vector data that are stored directly in Azure Cosmos DB for MongoDB vCore. Semantic Kernel enables the integration with Azure OpenAI to perform retrieval augmented generation (RAG) and bring everything together.
John Aziz, a Microsoft AI MVP and a Gold Microsoft Learn Student Ambassador from Egypt.
In this session, you'll learn about vector search, RAG, and the steps you need to perform to enable it using the semantic kernel, discover how to set up an Azure Cosmos DB for MongoDB vCore, deploy the Azure OpenAI chat and embedding model, learn about how the semantic kernel works, and integrate it in a flask app.
Follow along as we provide clear instructions, make the process accessible to both beginners and experienced tech enthusiasts. By the end, you'll have a fully functional Flask application capable of generating responses using vector search and rag. Check it out here!
Chapters
- 00:00 - RAG using Semantic Kernel with Azure OpenAI and Azure Cosmos DB
- 00:33 - Agenda
- 01:06 - Prerequisites
- 01:43 - Microsoft Technologies used
- 12:05 - Concepts used
- 14:34 - Demo - RAG in Jupyter Notebooks
- 29:32 - Resources
Recommended resources
- Blog
- Access to Azure OpenAI
- Code
- Documentation - Azure OpenAI Service
- Documentation - Azure Cosmos DB for MongoDB vCore
- Documentation - Semantic Kernel
- Repository
- Vector Search on embeddings with Azure Cosmos DB for MongoDB vCore
Related episodes
Connect
- John Aziz | Twitter/X: @john00isaac
มีคำติชมหรือไม่? ส่งปัญหาที่เกิดขึ้นได้ที่นี่