forked from sheetjs/docs.sheetjs.com
54 lines
1.9 KiB
JavaScript
54 lines
1.9 KiB
JavaScript
/* NOTE: hnswlib-node@3.0.0 does not install on a fresh Windows 11 setup */
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// import { existsSync } from 'fs';
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import { ChatOllama } from "@langchain/community/chat_models/ollama";
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import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama"
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// import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
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import { MemoryVectorStore } from "langchain/vectorstores/memory";
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import { SelfQueryRetriever } from "langchain/retrievers/self_query";
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import { FunctionalTranslator } from "@langchain/core/structured_query";
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import LoadOfSheet from "./loadofsheet.mjs";
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const modelName = "llama3-chatqa:8b-v1.5-q8_0";
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console.log(`Using model ${modelName}`);
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const model = new ChatOllama({ baseUrl: "http://localhost:11434", model: modelName });
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const embeddings = new OllamaEmbeddings({model: modelName});
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console.time("load of sheet");
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const loader = new LoadOfSheet("./cd.xls");
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const docs = await loader.load();
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console.timeEnd("load of sheet");
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console.time("vector store");
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const vectorstore = await MemoryVectorStore.fromDocuments(docs, embeddings);
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/*
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const vectorstore = await (async() => {
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if(!existsSync("store/hnswlib.index")) {
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const vectorstore = await HNSWLib.fromDocuments(docs, embeddings);
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await vectorstore.save("store");
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return vectorstore;
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}
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return await HNSWLib.load("store", embeddings);
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})();
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*/
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console.timeEnd("vector store");
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console.time("query");
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const selfQueryRetriever = SelfQueryRetriever.fromLLM({
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llm: model,
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vectorStore: vectorstore,
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documentContents: "Data rows from a worksheet",
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attributeInfo: loader.attributes,
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structuredQueryTranslator: new FunctionalTranslator(),
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searchParams: { k: 1024 } // default is 4
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});
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const res = await selfQueryRetriever.invoke(
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"Which rows have over 40 miles per gallon?"
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);
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console.timeEnd("query");
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res.forEach(({metadata}) => { console.log({ Name: metadata.Name, MPG: metadata.Miles_per_Gallon }); });
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