GetSimN

The GetSimN request performs semantic similarity search, finding entries most similar to a query input.

• Input: Store name, query input, number of results, and similarity algorithm.

• Behavior: Embeds the query using the store's query model and finds the N most similar entries.

• Response: A list of entries with their similarity scores.

Click to expand source code

import { createAiClient } from "ahnlich-client-node";
import { GetSimN } from "ahnlich-client-node/grpc/ai/query_pb";
import { StoreInput } from "ahnlich-client-node/grpc/keyval_pb";
import { Algorithm } from "ahnlich-client-node/grpc/algorithm/algorithm_pb";

async function getSimN() {
  const client = createAiClient("127.0.0.1:1370");

  const response = await client.getSimN(
    new GetSimN({
      store: "ai_store",
      searchInput: new StoreInput({ value: { case: "rawString", value: "Jordan" } }),
      closestN: 3,
      algorithm: Algorithm.COSINE_SIMILARITY,
    })
  );

  console.log(response.entries);

  for (const entry of response.entries) {
    console.log(`Input: ${entry.input?.value}`);
    console.log(`Similarity: ${entry.similarity}`);
    console.log(`Metadata: ${JSON.stringify(entry.value?.value)}`);
  }
}

getSimN();

Parameters

Parameter	Type	Required	Description
store	string	Yes	The name of the AI store
searchInput	StoreInput	Yes	The query input (text or image)
closestN	number	Yes	Number of similar entries to return
algorithm	Algorithm	Yes	Similarity algorithm to use
condition	PredicateCondition	No	Optional filter condition

Example with Text Search

Click to expand source code

import { createAiClient } from "ahnlich-client-node";
import { GetSimN } from "ahnlich-client-node/grpc/ai/query_pb";
import { StoreInput } from "ahnlich-client-node/grpc/keyval_pb";
import { Algorithm } from "ahnlich-client-node/grpc/algorithm/algorithm_pb";

async function semanticSearch() {
  const client = createAiClient("127.0.0.1:1370");

  // Search for shoes similar to "comfortable running shoes"
  const response = await client.getSimN(
    new GetSimN({
      store: "products",
      searchInput: new StoreInput({
        value: { case: "rawString", value: "comfortable running shoes" },
      }),
      closestN: 5,
      algorithm: Algorithm.COSINE_SIMILARITY,
    })
  );

  console.log("Top 5 similar products:");
  for (const entry of response.entries) {
    console.log(`- ${entry.input?.value} (similarity: ${entry.similarity})`);
  }
}

semanticSearch();

Example with Predicate Filter

Click to expand source code

import { createAiClient } from "ahnlich-client-node";
import { GetSimN } from "ahnlich-client-node/grpc/ai/query_pb";
import { StoreInput } from "ahnlich-client-node/grpc/keyval_pb";
import { Algorithm } from "ahnlich-client-node/grpc/algorithm/algorithm_pb";
import { PredicateCondition, Predicate, Equals } from "ahnlich-client-node/grpc/predicate_pb";
import { MetadataValue } from "ahnlich-client-node/grpc/metadata_pb";

async function searchWithFilter() {
  const client = createAiClient("127.0.0.1:1370");

  // Search for Nike products similar to "basketball shoes"
  const response = await client.getSimN(
    new GetSimN({
      store: "products",
      searchInput: new StoreInput({
        value: { case: "rawString", value: "basketball shoes" },
      }),
      closestN: 5,
      algorithm: Algorithm.COSINE_SIMILARITY,
      condition: new PredicateCondition({
        kind: {
          case: "value",
          value: new Predicate({
            kind: {
              case: "equals",
              value: new Equals({
                key: "brand",
                value: new MetadataValue({ value: { case: "rawString", value: "Nike" } }),
              }),
            },
          }),
        },
      }),
    })
  );

  console.log("Top Nike basketball-related products:");
  for (const entry of response.entries) {
    console.log(`- ${entry.input?.value}`);
  }
}

searchWithFilter();

Notes

• The query input is automatically embedded using the store's query model
• Semantic search finds conceptually similar results, not just keyword matches
• Use predicate filters to narrow results by metadata