docs.sheetjs.com/docz/static/loadofsheet/query.mjs

/* NOTE: hnswlib-node@3.0.0 does not install on a fresh Windows 11 setup */
// import { existsSync } from 'fs';
import { ChatOllama } from "@langchain/community/chat_models/ollama";
import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama"
// import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { SelfQueryRetriever } from "langchain/retrievers/self_query";
import { FunctionalTranslator } from "@langchain/core/structured_query";

import LoadOfSheet from "./loadofsheet.mjs";

const modelName = "llama3-chatqa:8b-v1.5-q8_0";

console.log(`Using model ${modelName}`);

const model = new ChatOllama({ baseUrl: "http://localhost:11434", model: modelName });
const embeddings = new OllamaEmbeddings({model: modelName});

console.time("load of sheet");
const loader = new LoadOfSheet("./cd.xls");
const docs = await loader.load();
console.timeEnd("load of sheet");

console.time("vector store");
const vectorstore = await MemoryVectorStore.fromDocuments(docs, embeddings);
/*
const vectorstore = await (async() => {
  if(!existsSync("store/hnswlib.index")) {
    const vectorstore = await HNSWLib.fromDocuments(docs, embeddings);
    await vectorstore.save("store");
    return vectorstore;
  }
  return await HNSWLib.load("store", embeddings);
})();
*/
console.timeEnd("vector store");

console.time("query");
const selfQueryRetriever = SelfQueryRetriever.fromLLM({
  llm: model,
  vectorStore: vectorstore,
  documentContents: "Data rows from a worksheet",
  attributeInfo: loader.attributes,
  structuredQueryTranslator: new FunctionalTranslator(),
  searchParams: { k: 1024 } // default is 4
});

const res = await selfQueryRetriever.invoke(
  "Which rows have over 40 miles per gallon?"
);
console.timeEnd("query");

res.forEach(({metadata}) => { console.log({ Name: metadata.Name, MPG: metadata.Miles_per_Gallon }); });