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Reference

Ahnlich AI (ahnlich-ai) is the proxy layer that connects raw input (text, images, etc.) to Ahnlich DB, automatically generating embeddings and managing them in vector stores.

Below are the supported commands for Ahnlich AI, with examples in CLI, Rust, Python, and Go.

The supported model in your setup is:

  • all-minilm-l6-v2 (used for both indexing and querying).

1. Ping​

Description​

Check whether the Ahnlich AI service is up and running.

Command​

PING

2. Info Server​

Description​

Returns runtime information such as version, uptime, and models available.

Command​

INFOSERVER

3. List Stores​

Description​

Displays all stores currently created in Ahnlich AI.

Command​

LIST STORES

4. Create Store​

Description​

Creates a new store with specified index and query models.

Command​

CREATESTORE my_store INDEXMODEL all-minilm-l6-v2 QUERYMODEL all-minilm-l6-v2

  • Rust

    client.create_store("my_store", "all-minilm-l6-v2", "all-minilm-l6-v2")?;

  • Python

    client.create_store("my_store", index_model="all-minilm-l6-v2", query_model="all-minilm-l6-v2")

  • Go

    client.CreateStore(ctx, "my_store", "all-minilm-l6-v2", "all-minilm-l6-v2")

5. Set​

Description​

Insert raw input into a store. Metadata can be added as key–value pairs.

Command​

SET doc1 "The future of AI in healthcare" WITH {"category":"news"} IN article_store

6. Drop Store​

Description​

Removes a store and its contents permanently.

Command​

DROPSTORE article_store

7. Get Sim N​

Description​

Retrieve the top N most similar vectors to a given raw input.

Command​

GETSIMN 3 WITH "renewable energy storage" USING cosinesimilarity IN article_store WHERE (category != "sports")

8. Get By Predicate​

Description​

Retrieve all items in a store that satisfy a metadata condition.

Command​

GETPRED (category = "news") IN article_store

9. Create Predicate Index​

Description​

Create an index on metadata to optimize predicate queries.

Command​

CREATEPREDICATEINDEX category IN article_store

10. Drop Predicate Index​

Description​

Remove a previously created metadata index.

Command​

DROPPREDICATEINDEX category IN article_store

11. Create Non Linear Algorithm Index​

Description​

Create an advanced index (e.g., k-d tree) for faster similarity searches.

Command​

CREATENONLINEARALGORITHMINDEX kdtree IN geo_store

12. Drop Non Linear Algorithm Index​

Description​

Drop a previously created non-linear index.

Command​

DROPNONLINEARALGORITHMINDEX kdtree IN geo_store

13. Delete Key​

Description​

Remove a specific key from a store.

Command​

DELETEKEY doc1 IN article_store

Ahnlich AI Commands​

AI CommandRust API EquivalentPython API EquivalentGo API Equivalent
PINGclient.ping()?;client.ping()client.Ping(ctx)
INFO SERVERclient.info_server()?;client.info_server()client.InfoServer(ctx)
LIST STORESclient.list_stores()?;client.list_stores()client.ListStores(ctx)
CREATE STORE my_store INDEXMODEL all-minilm-l6-v2 QUERYMODEL all-minilm-l6-v2client.create_store("my_store", "all-minilm-l6-v2", "all-minilm-l6-v2")?;client.create_store("my_store", index_model="all-minilm-l6-v2", query_model="all-minilm-l6-v2")client.CreateStore(ctx, "my_store", "all-minilm-l6-v2", "all-minilm-l6-v2")
SET doc1 "The future of AI in healthcare" WITH {"category":"news"} IN article_storeclient.set("article_store", "doc1", "The future of AI in healthcare", hashmap!{"category"=>"news"})?;client.set("article_store", "doc1", "The future of AI in healthcare", {"category":"news"})client.Set(ctx, "article_store", "doc1", "The future of AI in healthcare", map[string]string{"category":"news"})
DROP STORE article_storeclient.drop_store("article_store")?;client.drop_store("article_store")client.DropStore(ctx, "article_store")
GET SIM N 3 WITH "renewable energy storage" USING cosinesimilarity IN article_store WHERE (category != "sports")client.get_sim_n("article_store", "renewable energy storage", 3, "cosine", Some("category!='sports'"))?;client.get_sim_n("article_store", "renewable energy storage", 3, "cosine", predicate="category!='sports'")client.GetSimN(ctx, "article_store", "renewable energy storage", 3, "cosine", "category!='sports'")
GET BY PREDICATE (category = "news") IN article_storeclient.get_by_predicate("article_store", "category='news'")?;client.get_by_predicate("article_store", "category='news'")client.GetByPredicate(ctx, "article_store", "category='news'")
CREATE PREDICATE INDEX category IN article_storeclient.create_predicate_index("article_store", "category")?;client.create_predicate_index("article_store", "category")client.CreatePredicateIndex(ctx, "article_store", "category")
DROP PREDICATE INDEX category IN article_storeclient.drop_predicate_index("article_store", "category")?;client.drop_predicate_index("article_store", "category")client.DropPredicateIndex(ctx, "article_store", "category")
CREATE NON LINEAR ALGORITHM INDEX kdtree IN geo_storeclient.create_non_linear_index("geo_store", "kdtree")?;client.create_non_linear_index("geo_store", "kdtree")client.CreateNonLinearIndex(ctx, "geo_store", "kdtree")
DROP NON LINEAR ALGORITHM INDEX kdtree IN geo_storeclient.drop_non_linear_index("geo_store", "kdtree")?;client.drop_non_linear_index("geo_store", "kdtree")client.DropNonLinearIndex(ctx, "geo_store", "kdtree")
DELETE KEY doc1 IN article_storeclient.delete_key("article_store", "doc1")?;client.delete_key("article_store", "doc1")client.DeleteKey(ctx, "article_store", "doc1")