Requests – DB

The Ahnlich DB is an in-memory vector key–value store designed for storing embeddings or vectors alongside their metadata (key–value maps).

It provides AI/ML engineers with the ability to:

• Store and retrieve embeddings.

• Search for similar vectors using linear similarity algorithms (Cosine, Euclidean).

• Perform searches with non-linear similarity algorithms (such as KD-Tree).

• Filter results based on metadata values.

This makes it possible to build intelligent search and recommendation systems that combine vector similarity with metadata-based filtering.

Example

A query to retrieve the 2 most similar vectors to [0.2, 0.1] from the store my_store, using cosine similarity, while excluding any items where the metadata field page is equal to "hidden":

GETSIMN 2 WITH [0.2, 0.1] USING cosinesimilarity IN my_store WHERE (page != hidden)

Below is a breakdown of common DB request examples:

• Ping
• Info Server
• List Stores
• Create Store
• Set
• GetSimN
• Get Key
• Get By Predicate
• Create Predicate Index
• Drop Predicate Index
• Delete Key
• Drop Store
• List Connected Clients
• Create Non Linear Algorithm Index
• Drop Non Linear Algorithm Index
• Delete Predicate