Create Non-Linear algorithm Index

The Create Non Linear Algorithm Index operation builds an index structure for non-linear search algorithms, such as KD-Tree. These index enable faster query performance in high-dimensional vector spaces by avoiding brute-force scans.

This operation is typically used when:

You want to optimize search performance for similarity lookups.
You are initializing a new store and need efficient query structures.

If an index for the specified algorithm already exists, the call will fail when error_if_exists=True is set.

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.algorithm.nonlinear import NonLinearAlgorithm


async def create_non_linear_algorithm_index():
  async with Channel(host="127.0.0.1", port=1370) as channel:
      client = AiServiceStub(channel)
      response = await client.create_non_linear_algorithm_index(
          ai_query.CreateNonLinearAlgorithmIndex(
              store="test store",
              non_linear_indices=[NonLinearAlgorithm.KDTree],
              # error_if_exists=True
          )
      )
      print(response) # CreateIndex(created_indexes=1)


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

Behavior

Index does not exist - The index for the given algorithm(s) is created successfully.
Index already exists -
- If error_if_exists=True, the request fails with an error.
- If error_if_exists=False, the request completes without creating a duplicate index.

Notes

Non-linear index are designed to improve query performance but may require additional memory.
You can create indices for multiple algorithms by listing them under algorithms=[...].
This operation only creates the index; it does not insert or modify store data.

Behavior​

Notes​

Behavior

Notes