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Create Non-Linear Algorithm Index

Schema​

This request accepts an optional schema field. When it is omitted, the server uses the public schema. Set schema to target a store in another schema.

Creating non-linear algorithm indexes allows you to optimize query execution based on spatial or high-dimensional data structures.

Ahnlich supports the following non-linear algorithm indexes:

  • HNSW (Hierarchical Navigable Small World) — approximate nearest-neighbor search with configurable accuracy/speed tradeoff.

Non-linear algorithm indexes improve query performance by pre-structuring the data, but depending on the algorithm, there may be tradeoffs between query time and memory consumption.

In the Ahnlich client, you can create a non-linear algorithm index by calling the create_non_linear_algorithm_index RPC via the DbServiceStub. Each index type is specified using a NonLinearIndex message with a HnswConfig.

Define a Client and Call the API​

The following example shows how to initialize a client, request index creation, and inspect the server’s response.

Click to expand code
Python
import asyncio
from grpclib.client import Channel
from ahnlich_client_py.grpc.services.db_service import DbServiceStub
from ahnlich_client_py.grpc.db import query as db_query
from ahnlich_client_py.grpc.algorithm.nonlinear import NonLinearIndex, HnswConfig


async def create_non_linear_algo_index():
  async with Channel(host="127.0.0.1", port=1369) as channel:
      client = DbServiceStub(channel)

      # Create an HNSW index (with optional config)
      response = await client.create_non_linear_algorithm_index(
          db_query.CreateNonLinearAlgorithmIndex(
              store="test store 003",
              schema="analytics",
              non_linear_indices=[NonLinearIndex(hnsw=HnswConfig())]
          )
      )
      # response.created_indexes shows how many indexes were created
  print(response)


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