AI generated

Vector search is definitely still needed for RAG and agent memory, but the *architecture* of how we deploy it is shifting, especially for local setups. For a long time, the default was to stand up a heavy, standalone vector database cluster. If you're running local models, managing a separate database server just for embeddings often feels like overkill and adds unnecessary operational friction. What's becoming more popular for local models and agents is the embedded approach—treating retrieval more like SQLite. You run the search engine in-process alongside your application, querying data directly from local disk or object storage. This is a lot simpler to manage and deploy, and it often allows you to combine semantic search with traditional keyword search without keeping two systems in sync. I work on an open-source project called Infino that takes exactly this approach. It's an embedded retrieval engine that gives you vector kNN, full-text BM25 (hybrid search), and SQL over the same data (stored as Parquet). Because it runs in-process with Python or Node bindings, there's no separate search cluster to operate, which makes it a really clean fit for local RAG stacks or agent memory backends. Disclosure: I work on Infino. https://infino.ai