Cost-Effective Vector Search with MRL and KDB.AI

Matryoshka Representation Learning (MRL) enables training embedding models that "nest" higher-dimensional representations (e.g., 1024D) inside lower-dimensional subsets (e.g., 64D), allowing truncation without significant performance loss, as validated by its use in Jina Embeddings v3 and some OpenAI embeddings. According to additional sources, Jina Embeddings v3 uses LoRA adapters for task-specific embeddings and supports sequence lengths up to 8192 tokens using RoPE.
Using MRL to reduce embeddings to 64D and storing them in an on-disk index like `qHnsw` in KDB.AI allows fitting millions of vectors into low-cost cloud tiers, achieving ~200ms query latency for 5 million 64D vectors, compared to 600ms+ for 1.5 million 1024D vectors.
A two-stage Retrieval-Augmented Generation (RAG) pipeline can mitigate precision loss from aggressive dimensionality reduction: (1) retrieve top-k candidates using a compressed index and (2) rerank with a cross-encoder or Reciprocal Rank Fusion (RRF), combining dense vectors with keyword matches (e.g., BM25).
KDB.AI offers a free tier suitable for sub-5M vector datasets, and the `qHnsw` index type persists mostly on disk, reducing RAM footprint; the article demonstrates creating a table schema with `id` (int32) and `embeddings` (float32s) columns and a `qHnsw` index with specified dimensions and cosine similarity metric.
The article provides a Python code example using `kdbai_client` to connect to KDB.AI Cloud, create a table with a `qHnsw` index, insert 5 million 64D random vectors, and perform similarity search, but it lacks specifics on API endpoints and interfaces. According to additional sources, the KDB.AI developer samples provide core functionality for interacting with KDB.AI, but details on the technical stack, installation steps, and API usage are needed.
While MRL and dimension reduction might slightly reduce recall, the impact is often negligible in RAG pipelines due to the reranking stage, enabling cost-effective retrieval with high-precision final answers.

Source:

https://medium.com/kx-systems/why-your-vector-database-should-be-free-if-you-have-fewer-than-5m-vectors-215613373b47