Vector Database for Large Language Models in Production (Sam Partee)

Large Language Models (LLMs) have lowered the bar for getting a product to market however they are "not (always) large enough" in that they don't always contain all the information they need to solve a certain task.
This is where Vector Databases come in!
In this talk Sam Partee, Principal Applied AI Engineer at Redis covers topics such as:
Vector Embeddings, Vector Similarity Search, what is a Vector Database, some common design patterns using Vector Databases and LLMs, what to consider when building a system using Vector Databases and some example uses cases for Vector Databases.
Speaker:
Sam Partee, Principal Applied AI Engineer, Redis
Host: Sam Reiswig, Han-chung Lee and the MLOps Discord community: / discord

[@trifforttv]

Storing embeddings is really great. I was doing serialization earlier and loading from disk on app startup so i can find cosine similarity easily. Another great thing is indexing which will make the search efficient. Great Presentaion.

[@srikanth-io9dv]

Thank You!!