Create Store

The CreateStore request is used to initialize a new AI-powered store. Unlike the DB client (which deals with raw vector dimensions), the AI client lets you specify pretrained AI models to handle embedding generation and indexing.

This means you don’t have to manage vectors manually — the AI service will automatically embed inputs using the selected models.

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
from ahnlich_client_py.grpc.ai.models import AiModel


async def create_store():
  async with Channel(host="127.0.0.1", port=1370) as channel:
      client = AiServiceStub(channel)
      response = await client.create_store(
          ai_query.CreateStore(
              store="test store",
              query_model=AiModel.ALL_MINI_LM_L6_V2,
              index_model=AiModel.ALL_MINI_LM_L6_V2,
              predicates=["job"],
              error_if_exists=True,
              # Store original controls if we choose to store the raw inputs
              # within the DB in order to be able to retrieve the originals again
              # during query, else only store values are returned
              store_original=True
          )
      )
      print(response) # Unit()


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

Key Notes

• query_model - model used for encoding query inputs during searches.

• index_model - model used for encoding stored data vectors.

• predicates - metadata fields that can be filtered against (e.g., "job").

• store_original - if True, the original raw input is stored alongside embeddings for later retrieval.

• Response - returns Unit() on success.

This request is critical in AI workflows because it allows you to:

• Configure semantic stores with specialized embedding models.

• Decide whether to preserve raw input text/images for retrieval.

• Build intelligent, AI-driven search and recommendation systems without managing embeddings manually.