Traditional databases are optimized for keyword matches, but AI applications require semantic search—finding data based on meaning. This is where vector databases and embeddings come in.
What are Vector Embeddings?
Embeddings convert text, images, or code into high-dimensional numerical arrays (vectors) representing their semantic meaning. Vector databases store these arrays and perform fast similarity search using cosine distance.
Implementing Semantic Search in .NET
Using PostgreSQL with the pgvector extension or specialized databases like Pinecone, .NET developers can easily run semantic queries using Entity Framework Core, providing highly relevant search results to users.