Create Non Linear Algorithm Index

The CreateNonLinearAlgorithmIndex request creates a non-linear index (KDTree or HNSW) to speed up similarity searches.

• Input: Store name and list of non-linear indices to create.

• Behavior: Builds the specified non-linear index structure for faster similarity queries.

• Response: Confirmation of index creation.

Create a KDTree Index

Click to expand source code

import { createDbClient } from "ahnlich-client-node";
import { CreateNonLinearAlgorithmIndex } from "ahnlich-client-node/grpc/db/query_pb";
import { NonLinearIndex, KDTreeConfig } from "ahnlich-client-node/grpc/algorithm/nonlinear_pb";

async function createKDTreeIndex() {
  const client = createDbClient("127.0.0.1:1369");

  await client.createNonLinearAlgorithmIndex(
    new CreateNonLinearAlgorithmIndex({
      store: "my_store",
      nonLinearIndices: [
        new NonLinearIndex({
          index: { case: "kdtree", value: new KDTreeConfig() },
        }),
      ],
    })
  );

  console.log("KDTree index created successfully");
}

createKDTreeIndex();

Create an HNSW Index

Click to expand source code

import { createDbClient } from "ahnlich-client-node";
import { CreateNonLinearAlgorithmIndex } from "ahnlich-client-node/grpc/db/query_pb";
import { NonLinearIndex, HNSWConfig } from "ahnlich-client-node/grpc/algorithm/nonlinear_pb";

async function createHNSWIndex() {
  const client = createDbClient("127.0.0.1:1369");

  await client.createNonLinearAlgorithmIndex(
    new CreateNonLinearAlgorithmIndex({
      store: "my_store",
      nonLinearIndices: [
        new NonLinearIndex({
          index: { case: "hnsw", value: new HNSWConfig() },
        }),
      ],
    })
  );

  console.log("HNSW index created successfully");
}

createHNSWIndex();

Parameters

Parameter	Type	Required	Description
store	string	Yes	The name of the store
nonLinearIndices	NonLinearIndex[]	Yes	List of indices to create

Index Types

Type	Description	Best For
KDTree	K-dimensional tree	Lower dimensions (less than 20), exact searches
HNSW	Hierarchical Navigable Small World	High dimensions, approximate but fast

Notes

• Non-linear indices dramatically improve GetSimN performance on large stores
• HNSW is generally recommended for high-dimensional embeddings (128+)
• Building indices takes time and memory proportional to store size
• You can have multiple index types on the same store