Build Vector Search in .NET with Postgres and pgvector

Summary

The transcript discusses leveraging Postgres with the PG vector extension to enable advanced vector database capabilities in .NET applications, focusing on implementing vector similarity search and embeddings using technologies like Ollama and the Qwen 3 embedding model. Key technologies mentioned include Aspire, Npgsql, Dapper, and EF Core for integrating vector data types and performing operations like recommendations and retrieval augmented generation. The practical takeaway is that developers can now perform sophisticated vector-based searches and AI-related tasks directly within Postgres without requiring a specialized vector database, using straightforward .NET libraries and extensions.