Vector DB

Vector DB is a specialized database that stores vector representations of texts (embeddings) and can quickly find vectors that are closest in meaning. Classic SQL searches by exact match; the vector database looks for “similar in meaning” - even if the words are completely different.

In my projects, I use vector databases in RAG (Retrieval-Augmented Generation) systems: I load client documentation, a knowledge base, a request history → for each user question, the system finds relevant chunks → transfers them to LLM as context. It's cheaper than retraining the model, and it updates in real time.

Among specific tools: pgvector - if PostgreSQL is already used and there is no need for huge volumes (conveniently, one stack); Qdrant - when you need performance and Rust under the hood; Pinecone is a managed service that starts quickly, but becomes more expensive as it grows. For prototypes, I usually start with Chroma - locally, no registration, five lines of code.