Using the CLI

ahnlich-cli is an interactive shell for talking to Ahnlich without writing any code. It's the fastest way to explore your data, run admin tasks, and sanity-check queries.

When to use what

• Building an app? Use a client SDK — see the Quickstart.
• Need to install or run the servers? See Installation.
• Exploring or administering? You're in the right place.

Open the CLI

Make sure a server is running first (see Installation), then connect the CLI to it. The --agent flag picks which service you're talking to:

DB Engine

Talk directly to the vector store — you provide raw vectors yourself.

$ahnlich-cli ahnlich --agent db --host 127.0.0.1 --port 1369

AI Proxy

Talk to the AI proxy — send raw text, images, or audio and it embeds them for you.

$ahnlich-cli ahnlich --agent ai --host 127.0.0.1 --port 1370

The command language

Ahnlich uses a declarative, SQL-like command style. Most commands follow this shape:

$<COMMAND> <ARGS> IN <STORE> [WHERE (<predicate>)]

Commands are case-insensitive and can be chained with ;. Both agents share the same grammar — the only difference is that the DB engine takes raw vectors while the AI proxy takes raw text, images, or audio. Pick an agent below; the tabs stay in sync with the connection command above.

DB Engine

Create a store

$CREATESTORE test_store DIMENSION 2 PREDICATES (author, country)

Insert data

$SET (([1.0, 2.1], {name: Haks, category: dev}), ([3.1, 4.8], {name: Deven, category: dev})) IN test_store

Retrieve by key

$GETKEY ([1.0, 2.0], [3.0, 4.0]) IN test_store

Search by similarity

$GETSIMN 2 WITH [1.0, 2.0] USING cosinesimilarity IN test_store WHERE (category = dev)

Chain commands

$GETKEY ([1.0, 2.0]) IN test_store; CREATEPREDINDEX (name, category) IN test_store

AI Proxy

Create a store

$CREATESTORE my_store QUERYMODEL all-minilm-l6-v2 INDEXMODEL all-minilm-l6-v2 PREDICATES (author, category)

Insert data

$SET (([This is the life of Haks], {name: Haks, category: dev}), ([This is the life of Deven], {name: Deven, category: dev})) IN my_store PREPROCESSACTION nopreprocessing

Search by meaning

$GETSIMN 2 WITH [life of deven] USING cosinesimilarity IN my_store WHERE (category = dev)

info

You pass raw text — the AI proxy embeds it automatically before comparing it against the stored vectors.

Command reference

DB Engine

Command	Description
PING	Check if the server is responsive
LISTCLIENTS	List active connections
LISTSTORES [SCHEMA <schema>]	List stores in a schema (defaults to public)
INFOSERVER	Get server metadata / version
CREATESTORE <name> DIMENSION <n> ...	Create a vector store
CREATEPREDINDEX (k1, k2) IN <store>	Create a predicate (metadata) index
SET (...) IN <store>	Insert one or more vectors
UPSERT NEWKEY [<vector>] NEWVALUE {k:v} WHERE (<predicate>) IN <store>	Update a single entry matching predicate
GETKEY (<vector>) IN <store>	Retrieve entries by exact key
GETSIMN <n> WITH [<vector>] USING <metric> IN <store> WHERE (<predicate>)	Query nearest neighbors
DROPSTORE <name> IF EXISTS	Delete a store

AI Proxy

Command	Description
PING	Check if the server is responsive
LISTSTORES	List all stores
INFOSERVER	Get server metadata / version
CREATESTORE <name> QUERYMODEL <m> INDEXMODEL <m> ...	Create an AI store bound to embedding models
CREATEPREDINDEX (k1, k2) IN <store>	Create a predicate (metadata) index
SET (...) IN <store> PREPROCESSACTION <action>	Insert raw text, images, or audio (embedded automatically)
UPSERT NEWINPUT [<text>] NEWVALUE {k:v} WHERE (<predicate>) IN <store>	Update a single entry matching predicate
GETSIMN <n> WITH [<raw input>] USING <metric> IN <store> WHERE (<predicate>)	Query nearest neighbors by meaning
DROPSTORE <name> IF EXISTS	Delete a store

Supported metrics for GETSIMN: cosinesimilarity, euclideandistance, and dotproductsimilarity.