#44 Supercharged Search with Semantic Search and Vector Embeddings

Semantic search or search based on the meaning analyzes the context and intent behind the query term to provide relevant results. Using Vector embeddings, the data structure behind semantic search, you can supercharge your search to include text, images, and other types of data. With vector databases you can store and index vector embeddings and provide similarity search over these embeddings. In this session, we will delve into the building blocks of semantic search and explore vector embeddings and similarity metrics. You will learn how to generate embeddings with large language models using OpenAI or HuggingFace API. I will also explain how to store, index, and query vector embeddings in PostgreSQL using the pgvector extension. Finally, you will see how to save and query these embeddings from .NET with Entity Framework Core and pgvector-dotnet library. Join me for a demo-rich session and learn how to implement semantic search in .NET with PostgreSQL and Entity Framework Core.