You Probably Don't Need a Vector Database
Introduction Conversations about RAG almost always start with a vector database. This article suggests you dig deeper before implementing one.
2026-06-19
1,340 reads
Vector Database
Vector Search in SQL Server 2025: Storing Embeddings, Querying Them, and What to Watch Out For
This article shows how you can generate embeddings in SQL Server 2025, store them, and use them in your queries.
2026-06-08
3,159 reads
Introducing the Relational Embedding Retrieval Pattern: Storing and Querying Vector Embeddings in SQL Server
Learn about using SQL Server to support AI-enhanced search queries with the Relational Embedding Retrieval Pattern (RERP).
2026-04-24
2,042 reads
Vectors in SQL Server 2025
Introduction SQL Server 2025 introduced new features, including vectors. The main purpose of vectors is to create a new semantic search with the help of AI. Modern AI models represent text as vectors (embeddings) that capture semantic meaning. Similar meanings produce vectors that are close to each other in this vector space, allowing AI systems to […]
2026-03-23
10,339 reads
Vector DB implementation using FAISS
Searching for relevant information in vast repositories of unstructured text can be a challenge. This article explains a Python-based approach to implementing an efficient document search system using FAISS (Facebook AI Similarity Search) for Vector DB and sentence embeddings, which can be useful in applications like chatbots, document retrieval, and natural language understanding. In this […]
2025-01-17
4,179 reads
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