Machine Learning Mastery shows how to build RAG on scikit-learn without a vector database

On October 13, 2026, Machine Learning Mastery published a practical guide to building an end-to-end RAG pipeline on a minimal stack. scikit-learn handles the vector search, and open models provide the embeddings and text processing. At the prototype stage, the setup needs no vector database servers, no separate infrastructure and no paid APIs. The material is a tutorial, not a release, but it shows how little it takes to get a working "find context, then answer" loop running.

The announcement describes only the general outline of the setup, so the walkthrough below follows the typical RAG steps. It does not retell the specific parameters from the full text of the guide.

What the pipeline is made of

Classic RAG consists of four operations. Documents are split into chunks, the chunks are turned into vectors, the closest ones are retrieved for a query, and the retrieved text is added to the language model's prompt. All of this can fit in one file.

In the guide, open embedding models handle vectorization and scikit-learn handles nearest-neighbor search. The library has ready-made tools for this scenario: cosine similarity and the NearestNeighbors class. The vector matrix sits in memory, the query is converted into a vector of the same dimensionality, and the top-k results are then selected.

A rough profile of this stack:

• Search: scikit-learn, cosine similarity or NearestNeighbors, exact brute-force search with no approximation.
• Embeddings: an open model. The popular all-MiniLM-L6-v2, for example, produces 384-dimensional vectors.
• Storage: a NumPy array in RAM.
• Size: 100 thousand chunks at 384 dimensions in float32 take about 150 MB.
• Dependencies: Python, scikit-learn and a library for loading the embedding model.

The size figures are calculated from the vector dimensionality and are not taken from the article. They show the order of magnitude: 384 numbers at 4 bytes each come to 1.5 KB per chunk.

Why skip the vector database

The first advantage is transparency. When search comes down to matrix multiplication and sorting, you can see what happens at every step. You can print the similarity scores, check which chunk won and why, and immediately spot badly cut chunks. In specialized systems, part of this machinery is hidden behind an API.

The second advantage is the low cost of entry. A prototype needs no accounts, keys or network calls. That is enough for an internal knowledge base of a few thousand pages or for training a team. At these volumes, exact brute-force search finishes in fractions of a second, so approximate indexes such as HNSW add nothing.

The third advantage is reproducibility. Pin the library and model versions, and a colleague will get the same results you do.

Where this setup hits a ceiling

Brute-force search scales linearly. Once the corpus grows to millions of chunks, response time and memory will become a problem, and you will have to move to FAISS, pgvector, Qdrant or similar tools. Extra requirements also land on the developer: metadata filters, access rights, incremental index updates. In scikit-learn, you have to write all of this by hand.

Answer quality, meanwhile, depends not on the search library but on three things: how the text is chunked, which embedding model is chosen and how the prompt is designed. Swapping in a more powerful store will not fix these problems.

How to use it in practice

Start with a small corpus, such as your own documentation or FAQ, and build the pipeline in a single script. Write 20–30 test questions with known answers and measure how often the right chunk lands in the top 3. This metric, hit rate, shows retrieval quality before you connect generation.

Then change one variable at a time: chunk size, embedding model, the value of k. If the metric rises, the setup is working. When the corpus or the load outgrows the memory of a single machine, replace the search block with a production vector database. The rest of the code stays the same, because the interface, "query vector in, list of chunks out," is identical for both options.