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List Stores

How to request a list of available vector stores from the Ahnlich AI Service using the Python client.

In Ahnlich, vector stores are the fundamental units that organize data for semantic search, embeddings, and AI-driven retrieval. The List Stores request allows developers to discover which stores are currently registered and available to query.

Source Code Example​

In the context of the rest of the application code:

Click to expand source code
import asyncio
from grpclib.client import Channel
from ahnlich_client_py.grpc.services.ai_service import AiServiceStub
from ahnlich_client_py.grpc.ai import query as ai_query


async def list_stores():
  async with Channel(host="127.0.0.1", port=1370) as channel:
      client = AiServiceStub(channel)
      response = await client.list_stores(ai_query.ListStores())
      print(response) #StoreList(stores=[AiStoreInfo(name='test store', embedding_size=384)])


if __name__ == "__main__":
  asyncio.run(list_stores())

Define Request Parameters​

The ListStores request does not take any required parameters. It queries the AI service registry and returns metadata about all accessible stores.

Define Response Handling​

The response provides a structured list of stores, where each entry typically contains:

  • Store name (unique identifier)

  • Configuration details (embedding dimensions, indexing strategy, etc.)

  • Associated algorithms (if applicable)

This allows developers to dynamically discover stores at runtime without hardcoding store names.

Customize Usage​

ListStores is useful for:

  • Dynamic discovery: Applications can adapt to whatever stores exist at runtime.

  • Debugging: Confirming that a store was successfully created and registered.

  • Observability: Displaying available stores in admin dashboards.

The List Stores request is often used as a precursor to querying embeddings or similarity search, since it ensures the target store exists before making downstream calls.