lancedb/nodejs/examples/custom_embedding_function.ts

// --8<-- [start:imports]
import * as lancedb from "@lancedb/lancedb";
import {
  LanceSchema,
  TextEmbeddingFunction,
  getRegistry,
  register,
} from "@lancedb/lancedb/embedding";
import { pipeline } from "@xenova/transformers";
// --8<-- [end:imports]

// --8<-- [start:embedding_impl]
@register("sentence-transformers")
class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
  name = "Xenova/all-miniLM-L6-v2";
  #ndims!: number;
  extractor: any;

  async init() {
    this.extractor = await pipeline("feature-extraction", this.name);
    this.#ndims = await this.generateEmbeddings(["hello"]).then(
      (e) => e[0].length,
    );
  }

  ndims() {
    return this.#ndims;
  }

  toJSON() {
    return {
      name: this.name,
    };
  }
  async generateEmbeddings(texts: string[]) {
    const output = await this.extractor(texts, {
      pooling: "mean",
      normalize: true,
    });
    return output.tolist();
  }
}
// -8<-- [end:embedding_impl]

// --8<-- [start:call_custom_function]
const registry = getRegistry();

const sentenceTransformer = await registry
  .get<SentenceTransformersEmbeddings>("sentence-transformers")!
  .create();

const schema = LanceSchema({
  vector: sentenceTransformer.vectorField(),
  text: sentenceTransformer.sourceField(),
});

const db = await lancedb.connect("/tmp/db");
const table = await db.createEmptyTable("table", schema, { mode: "overwrite" });

await table.add([{ text: "hello" }, { text: "world" }]);

const results = await table.search("greeting").limit(1).toArray();
console.log(results[0].text);
// -8<-- [end:call_custom_function]