In this month's community standup, we'll walk through how to use Azure Cache for Redis Enterprise to store and compare embeddings vectors produced by Azure OpenAI Service. We'll also use LangChain to make development even easier. Our end goal: a movie recommendation engine using semantic natural text queries!
Chapters:
00:00 Welcome to the Azure Cache Redis Community Standup
00:18 On today's stream" Azure Cache Redis and OpenAI
01:39 Today's demo:
AI-powered movie recommendation app
02:25 Embeddings
06:28 Our workflow
07:36 Dataset from Kaggle
08:44 Ingest and clean data with Pandas
09:52 Generate Embeddings through Azure OpenAI
11:23 Get a vector DB - Azure Cache for Redis
14:44 Run queries
16:32 Demo
19:52 Add a UI with Streamlit
21:00 Deploy to Azure Container Apps
22:10 Demo
25:59 Vector Similarity Search Use Cases
30:53 Additional Cache SKUs
31:35 Learn more and Get started
33:02 Thank you and Connect
Community Links: https://www.theurlist.com/redis-and-openai
Featuring: Kyle Teegarden
#Redis #Langchain #OpenAI