GetSimN

How to retrieve the top N most similar entries from an AI store using the Ahnlich Python SDK.

GetSimN returns an array of tuples (store_key, store_value, similarity_score) of the maximum specified N. This allows you to perform similarity searches against the stored AI embeddings.

• store – Name of the AI store to query.

• search_input – Query input (string or vector).

• closest_n – Maximum number of similar results to return (must be > 0).

• algorithm – Similarity algorithm to use (e.g., Cosine Similarity).

• condition – Optional predicate condition to filter results. See Predicates documentation.

• preprocess_action – Controls input preprocessing:

  • PreprocessAction.ModelPreprocessing – Apply model's built-in preprocessing (recommended)
  • PreprocessAction.NoPreprocessing – Skip preprocessing (use if you've already preprocessed the input)

• execution_provider – Optional hardware acceleration (e.g., CUDA, TensorRT, CoreML). Set to None to use default CPU execution.

The result contains a list of entries with similarity scores.

Source code in the context of the rest of the application code.

Click to expand

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
from ahnlich_client_py.grpc.algorithm import algorithms
from ahnlich_client_py.grpc.ai.preprocess import PreprocessAction


async def get_sim_n():
  async with Channel(host="127.0.0.1", port=1370) as channel:
      client = AiServiceStub(channel)
      
      response = await client.get_sim_n(
          ai_query.GetSimN(
              store="test store 1",
              search_input=keyval.StoreInput(raw_string="Jordan"),
              condition=None,  # Optional predicate condition
              closest_n=3,
              algorithm=algorithms.Algorithm.CosineSimilarity,
              preprocess_action=PreprocessAction.ModelPreprocessing,  # Apply model's preprocessing
              execution_provider=None  # Optional execution provider
          )
      )
      
      # Response contains entries with similarity scores
      for entry in response.entries:
          print(f"Key: {entry.key.raw_string}")
          print(f"Score: {entry.similarity}")
          print(f"Value: {entry.value}")


      # Key: Jordan One
      # Score: Similarity(value=0.858908474445343)
      # Value: StoreValue(value={'brand': MetadataValue(raw_string='Nike')})
      # Key: Yeezey
      # Score: Similarity(value=0.21911849081516266)
      # Value: StoreValue(value={'brand': MetadataValue(raw_string='Adidas')})


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

• closest_n must always be a non-zero integer.

• This request is designed specifically for AI store queries.