Set

The Set request is used to insert or update entries inside an AI-powered store.

Unlike the DB client (which expects raw vectors), the AI client allows you to store raw strings or other inputs directly. The AI service will automatically embed these inputs using the store’s configured 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 import keyval, metadata
from ahnlich_client_py.grpc.ai import preprocess


async def sets():
  async with Channel(host="127.0.0.1", port=1370) as channel:
      client = AiServiceStub(channel)
      response = await client.set(
          ai_query.Set(
              store="test store",
              inputs=[
                  keyval.AiStoreEntry(
                      key=keyval.StoreInput(raw_string="Jordan One"),
                      value=keyval.StoreValue(
                          value={"brand": metadata.MetadataValue(raw_string="Nike")}
                      ),
                  ),
                  keyval.AiStoreEntry(
                      key=keyval.StoreInput(raw_string="Yeezey"),
                      value=keyval.StoreValue(
                          value={"brand": metadata.MetadataValue(raw_string="Adidas")}
                      ),
                  )
              ],
              preprocess_action=preprocess.PreprocessAction.NoPreprocessing
          )
      )
      print(response) #Set(upsert=StoreUpsert(inserted=2))


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

Key Notes

• inputs - list of entries to be stored.

  • Each entry has:

    • key - the raw input (e.g., "Jordan One") that gets embedded by the AI model.

    • value - metadata associated with the key (e.g., "brand": Nike).

• preprocess_action - defines how inputs are preprocessed before embedding.

  • NoPreprocessing - raw text is passed as-is to the embedding model.

  • Other preprocessing options (like normalization or tokenization) can be applied depending on the use case.

• Response → returns counts of inserted vs. updated items (upsert counts).