@langchain/community/dist/vectorstores/tests/closevector_node.test.js

Third-party integrations for LangChain.js

import { test, expect } from "@jest/globals";
import { Document } from "@langchain/core/documents";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { CloseVectorNode } from "../closevector/node.js";
test("Test CloseVectorNode.fromTexts + addVectors", async () => {
    const vectorStore = await CloseVectorNode.fromTexts(["Hello world"], [{ id: 2 }], new FakeEmbeddings());
    expect(vectorStore.instance.index?.getMaxElements()).toBe(1);
    expect(vectorStore.instance.index?.getCurrentCount()).toBe(1);
    await vectorStore.addVectors([
        [0, 1, 0, 0],
        [1, 0, 0, 0],
        [0.5, 0.5, 0.5, 0.5],
    ], [
        new Document({
            pageContent: "hello bye",
            metadata: { id: 5 },
        }),
        new Document({
            pageContent: "hello worlddwkldnsk",
            metadata: { id: 4 },
        }),
        new Document({
            pageContent: "hello you",
            metadata: { id: 6 },
        }),
    ]);
    expect(vectorStore.instance.index?.getMaxElements()).toBe(4);
    const resultTwo = await vectorStore.similaritySearchVectorWithScore([1, 0, 0, 0], 3);
    const resultTwoMetadatas = resultTwo.map(([{ metadata }]) => metadata);
    expect(resultTwoMetadatas).toEqual([{ id: 4 }, { id: 6 }, { id: 2 }]);
});
test("Test CloseVectorNode metadata filtering", async () => {
    const pageContent = "Hello world";
    const vectorStore = await CloseVectorNode.fromTexts([pageContent, pageContent, pageContent], [{ id: 2 }, { id: 3 }, { id: 4 }], new FakeEmbeddings());
    // If the filter wasn't working, we'd get all 3 documents back
    const results = await vectorStore.similaritySearch(pageContent, 3, (document) => document.metadata.id === 3);
    expect(results).toEqual([new Document({ metadata: { id: 3 }, pageContent })]);
});