Embeddings
6 guides on Embeddings.
Vectors, similarity and the stores that hold them. Including the failures that raise no exception at all: two embedding models mixed in one index, chunks too broad to match anything, and queries that were never semantic to begin with.
Choosing a Vector Database for .NET: pgvector vs Qdrant vs Azure AI Search vs SQL Server
A practical comparison of the vector stores .NET can talk to — pgvector, Qdrant, Azure AI Search, SQL Server 2025, Redis, Cosmos DB and SQLite — and why the choice matters less than you think.
Embeddings in .NET, Explained
What are embeddings, and how do you generate and use them in C#? A clear, practical explanation of vector embeddings for .NET developers — with code and use cases.
Vector Search in .NET with pgvector and PostgreSQL
You do not need a new database for vector search. Use pgvector in PostgreSQL from .NET to store embeddings and run similarity queries — with the database you already run.
Chunking Strategies for RAG (with .NET Examples)
Bad chunking is the #1 reason RAG underperforms. A practical guide to chunking strategies — size, overlap, and structure-aware splitting — for .NET RAG apps.
Building Semantic Search in C# with Embeddings
Keyword search misses meaning. Build semantic search in C# using embeddings and vector similarity, so queries match by intent — not just matching words.
Giving Your .NET Agent a Knowledge Base: RAG in C#
Your agent does not know your documents — until you add retrieval. A practical guide to RAG in .NET: embeddings, a vector store, and grounding an agent in your own data.