Ahnlich DB

Ahnlich DB is a database for meaning. A normal database is great at exact matches — "find the user whose email is x". Ahnlich DB is built for a different question: "find the things that are most similar to this one." Similar photos, similar products, documents about the same topic, songs with the same vibe.

It does this by storing your data as vectors (lists of numbers that capture meaning) and finding the ones that sit closest together. It keeps everything in memory, so answers come back in milliseconds.

You store each item as a vector plus metadata. To search, you send another vector and Ahnlich DB returns the closest matches, ranked.

The two things you store

Every item you put in Ahnlich DB has two parts:

1. A vector — a list of numbers, e.g. [0.12, 0.98, 0.41, …]. A machine-learning model produces this from your raw data (a sentence, an image, a product). Items that mean similar things end up with vectors that are close together. New to this idea? Start with Vectors & embeddings.
2. Metadata — plain key–value labels attached to the vector, like {"genre": "sci-fi", "year": 1968}. You use these to filter results.

If you don't want to generate vectors yourself, the Ahnlich AI proxy does it for you — you send text, images, or audio and it handles the numbers.

What you can do with it

Find the nearest matches (similarity search)

Give Ahnlich DB a vector and ask for the N closest items. This powers semantic search, recommendations, and "more like this" features.

$GETSIMN 3 WITH [0.23, 0.91, -0.44] USING cosinesimilarity IN article_store

Returns the 3 most similar articles. cosinesimilarity is one way to measure "closeness" — see Similarity metrics.

Filter by metadata at the same time

Combine "closest" with "matches these labels", so results are relevant and correct.

$GETSIMN 5 WITH [0.11, 0.75, -0.32] USING euclideandistance IN music_store WHERE (genre = "jazz")

The 5 most similar songs — but only jazz ones.

Stay fast as you grow

Because everything lives in memory, lookups are quick enough for live features like chat assistants or real-time recommendations. When a store gets large, you can add a vector index (HNSW) to keep searches fast.

A first end-to-end example

$CREATESTORE books DIMENSION 3          # 1. make a store for 3-number vectors$SET book1 [0.12, 0.98, 0.41] WITH {"genre": "sci-fi"}   # 2. add an item$GETSIMN 2 WITH [0.10, 0.95, 0.44] USING cosinesimilarity IN books   # 3. search

That's the whole loop: create a store → add vectors → search for the nearest ones.

Where to go next

• Quickstart: Ahnlich DB — a hands-on walkthrough with a relatable example.
• Command reference — every command, explained.
• Concepts — the ideas (vectors, metadata, similarity, indexes) in plain language.
• Advanced — algorithms, tuning, and performance.