Vector Databases in .NET: RAG, pgvector, HNSW and Production Design If you are building AI features in a .NET application, calling an LLM is often the easy part. The harder problem starts when the model needs information that is specific to your application. That information might be stored in an internal policy document, product catalog, support ticket, technical manual, compliance document, source repository, or company knowledge base. An LLM does not automatically know how to search those systems simply because your application can access them. Your application needs a retrieval layer that can identify useful information and provide it to the model. This is where embeddings, vector search, and vector databases become useful. A vector database stores numerical representations of information called embeddings and provides mechanisms for retrieving records according to vector similarity. This makes vector search an important building block for RAG applications, semantic ...