mirror of
https://github.com/gristlabs/grist-core.git
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360 lines
12 KiB
TypeScript
360 lines
12 KiB
TypeScript
/**
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* This module holds an evaluation scripts for AI assistance. It tests ai assistance on the formula
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* dataset. The formula dataset is made of an index file (formula-dataset-index.csv) and a list of
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* grist documents hosted on S3. A row in the index file, reference one column (doc_id, table_id,
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* col_id) amongst theses documents and a free-text description.
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*
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* For each entries of the data set, the scripts load the document, requests assistance based on the
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* description, and applies the suggested actions to the document. Then it compares the col values
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* before and after. Finally it reverts the modification.
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*
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* The list of grist documents for the formula dataset is a screenshot of all templates document
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* taken somewhere in the beginning of Feb 2023.
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*
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* The script maintains a simple cache of all request to AI to save on the ai requests.
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*
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* USAGE:
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* OPENAI_API_KEY=<my_openai_api_key> node core/test/formula-dataset/runCompletion.js
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* or
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* ASSISTANT_CHAT_COMPLETION_ENDPOINT=http.... node core/test/formula-dataset/runCompletion.js
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* (see Assistance.ts for more options).
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*
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* # WITH VERBOSE:
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* VERBOSE=1 OPENAI_API_KEY=<my_openai_api_key> node core/test/formula-dataset/runCompletion.js
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*
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* # to reset cache
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* rm core/test/formula-dataset/data/cache.json
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*/
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import { ActiveDoc, Deps as ActiveDocDeps } from "app/server/lib/ActiveDoc";
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import { DEPS, sendForCompletion } from "app/server/lib/Assistance";
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import log from 'app/server/lib/log';
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import crypto from 'crypto';
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import { parse } from 'csv-parse/sync';
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import fetch, {RequestInfo, RequestInit, Response} from 'node-fetch';
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import * as fs from "fs";
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import JSZip from "jszip";
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import { isEqual, MapCache } from "lodash";
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import path from 'path';
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import * as os from 'os';
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import { pipeline } from 'stream';
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import { createDocTools } from "test/server/docTools";
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import { promisify } from 'util';
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import { AssistanceResponse, AssistanceState } from "app/common/AssistancePrompts";
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import { CellValue } from "app/plugin/GristData";
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const streamPipeline = promisify(pipeline);
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const DATA_PATH = process.env.DATA_PATH || path.join(__dirname, 'data');
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const PATH_TO_DOC = path.join(DATA_PATH, 'templates');
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const PATH_TO_RESULTS = path.join(DATA_PATH, 'results');
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const PATH_TO_CSV = path.join(DATA_PATH, 'formula-dataset-index.csv');
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const PATH_TO_CACHE = path.join(DATA_PATH, 'cache');
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const TEMPLATE_URL = "https://grist-static.com/datasets/grist_dataset_formulai_2023_02_20.zip";
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const oldFetch = DEPS.fetch;
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interface FormulaRec {
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no_formula: string;
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table_id: string;
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col_id: string;
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doc_id: string;
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Description: string;
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}
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const _stats = {
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callCount: 0,
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};
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const SIMULATE_CONVERSATION = true;
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const FOLLOWUP_EVALUATE = false;
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export async function runCompletion() {
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// This could take a long time for LLMs running on underpowered hardware >:)
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ActiveDocDeps.ACTIVEDOC_TIMEOUT = 500000;
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// if template directory not exists, make it
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if (!fs.existsSync(path.join(PATH_TO_DOC))) {
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fs.mkdirSync(path.join(PATH_TO_DOC), {recursive: true});
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// create tempdir
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const dir = fs.mkdtempSync(path.join(os.tmpdir(), 'grist-templates-'));
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const destPath = path.join(dir, 'template.zip');
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// start downloading
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console.log(
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`source url: ${TEMPLATE_URL}\n` +
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`destination: ${destPath}\n` +
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`download...`
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);
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const response = await fetch(TEMPLATE_URL);
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if (!response.ok) { throw new Error(`unexpected response ${response.statusText}`); }
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await streamPipeline(response.body, fs.createWriteStream(destPath));
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console.log('done!\n\n' +
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'start extraction...');
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// unzip to directory
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const data = fs.readFileSync(destPath);
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const zip = await JSZip.loadAsync(data);
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let count = 0;
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for (const filename of Object.keys(zip.files)) {
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if (filename.includes('/')) { continue; }
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const fileBuffer = await zip.files[filename].async('nodebuffer');
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fs.writeFileSync(path.join(PATH_TO_DOC, filename), fileBuffer);
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count++;
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}
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console.log(
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`Successfully extracted ${count} template files to ${PATH_TO_DOC}`
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);
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}
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const content = fs.readFileSync(PATH_TO_CSV, {encoding: 'utf8'});
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const records = parse(content, {columns: true}) as FormulaRec[];
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// let's group by doc id to save on document loading time
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records.sort((a, b) => a.doc_id.localeCompare(b.doc_id));
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if (!process.env.VERBOSE) {
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log.transports.file.level = 'error'; // Suppress most of log output.
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}
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const docTools = createDocTools();
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const session = docTools.createFakeSession('owners');
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await docTools.before();
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let successCount = 0;
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let caseCount = 0;
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fs.mkdirSync(path.join(PATH_TO_RESULTS), {recursive: true});
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console.log('Testing AI assistance: ');
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try {
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DEPS.fetch = fetchWithCache;
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let activeDoc: ActiveDoc|undefined;
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for (const rec of records) {
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let success: boolean = false;
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let suggestedActions: AssistanceResponse['suggestedActions'] | undefined;
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let newValues: CellValue[] | undefined;
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let formula: string | undefined;
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let history: AssistanceState = {messages: []};
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let lastFollowUp: string | undefined;
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// load new document
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if (!activeDoc || activeDoc.docName !== rec.doc_id) {
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const docPath = path.join(PATH_TO_DOC, rec.doc_id + '.grist');
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activeDoc = await docTools.loadLocalDoc(docPath);
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await activeDoc.waitForInitialization();
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}
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// get values
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await activeDoc.docData!.fetchTable(rec.table_id);
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const expected = activeDoc.docData!.getTable(rec.table_id)!.getColValues(rec.col_id)!.slice();
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async function sendMessage(followUp?: string, rowId?: number) {
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if (!activeDoc) {
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throw new Error("No doc");
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}
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// send prompt
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const tableId = rec.table_id;
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const colId = rec.col_id;
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const description = rec.Description;
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const colInfo = await activeDoc.docStorage.get(`
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select *
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from _grist_Tables_column as c
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left join _grist_Tables as t on t.id = c.parentId
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where c.colId = ?
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and t.tableId = ?
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`, rec.col_id, rec.table_id);
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formula = colInfo?.formula;
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const result = await sendForCompletion(session, activeDoc, {
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conversationId: 'conversationId',
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context: {
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type: 'formula',
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tableId,
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colId,
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evaluateCurrentFormula: Boolean(followUp) && FOLLOWUP_EVALUATE,
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rowId,
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},
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state: history,
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text: followUp || description,
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});
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if (result.state) {
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history = result.state;
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}
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if (rec.no_formula == "1") {
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success = result.suggestedActions.length === 0;
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return;
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}
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suggestedActions = result.suggestedActions;
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if (!suggestedActions.length) {
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success = false;
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return;
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}
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// apply modification
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const {actionNum} = await activeDoc.applyUserActions(session, suggestedActions);
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// get new values
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newValues = activeDoc.docData!.getTable(rec.table_id)!.getColValues(rec.col_id)!.slice();
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// compare values
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success = isEqual(expected, newValues);
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if (!success && SIMULATE_CONVERSATION) {
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for (let i = 0; i < expected.length; i++) {
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const e = expected[i];
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const v = newValues[i];
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if (String(e) !== String(v)) {
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const txt = `I got \`${v}\` where I expected \`${e}\`\n` +
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'Please answer with the code block you (the assistant) just gave, ' +
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'revised based on this information. Your answer must include a code ' +
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'block. If you have to explain anything, do it after.\n';
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const rowIds = activeDoc.docData!.getTable(rec.table_id)!.getRowIds();
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const rowId = rowIds[i];
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if (followUp) {
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lastFollowUp = txt;
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} else {
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await sendMessage(txt, rowId);
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}
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break;
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}
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}
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}
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// revert modification
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const [bundle] = await activeDoc.getActions([actionNum]);
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await activeDoc.applyUserActionsById(session, [bundle!.actionNum], [bundle!.actionHash!], true);
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}
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try {
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await sendMessage();
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} catch (e) {
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console.error(e);
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}
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console.log(` ${success ? 'Successfully' : 'Failed to'} complete formula ` +
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`for column ${rec.table_id}.${rec.col_id} (doc=${rec.doc_id})`);
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if (success) {
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successCount++;
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} else {
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// TODO: log the difference between expected and actual, similar to what mocha does on
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// failure.
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// console.log('expected=', expected);
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// console.log('actual=', newValues);
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}
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const suggestedFormula = suggestedActions?.length === 1 &&
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suggestedActions[0][0] === 'ModifyColumn' &&
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suggestedActions[0][3].formula || suggestedActions;
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fs.writeFileSync(
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path.join(
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PATH_TO_RESULTS,
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`${rec.table_id}_${rec.col_id}_` +
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caseCount.toLocaleString('en', {minimumIntegerDigits: 8, useGrouping: false}) + '.json'),
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JSON.stringify({
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formula,
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suggestedFormula, success,
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expectedValues: expected,
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suggestedValues: newValues,
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history,
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lastFollowUp,
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}, null, 2));
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caseCount++;
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}
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} finally {
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await docTools.after();
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log.transports.file.level = 'debug';
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printStats();
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DEPS.fetch = oldFetch;
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console.log(
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`AI Assistance completed ${successCount} successful prompt on a total of ${records.length};`
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);
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console.log(JSON.stringify(
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{
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hit: successCount,
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total: records.length,
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percentage: (100.0 * successCount) / Math.max(records.length, 1),
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}
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));
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}
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}
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export function main() {
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runCompletion().catch(console.error);
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}
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function printStats() {
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console.log(`Ai assistance requests stats: ${_stats.callCount} calls`);
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}
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/**
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* Implements a simple cache that read/write from filesystem.
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*/
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class JsonCache implements MapCache {
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constructor() {
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if (!fs.existsSync(PATH_TO_CACHE)) {
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fs.mkdirSync(path.join(PATH_TO_CACHE), {recursive: true});
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}
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}
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public get(key: string): any {
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if (!this.has(key)) { return undefined; }
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const content = JSON.parse(fs.readFileSync(this._path(key), 'utf8'));
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return JSON.stringify(content.responseBody);
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}
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public has(key: string): boolean {
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return fs.existsSync(this._path(key));
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}
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public set(key: string, value: any): JsonCache {
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const content = {
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requestBody: key,
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responseBody: JSON.parse(value),
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};
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fs.writeFileSync(this._path(key), JSON.stringify(content));
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return this;
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}
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public clear(): void {
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throw new Error('not implemented');
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}
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public delete(_key: string): boolean {
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throw new Error('not implemented');
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}
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private _path(key: string) {
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return path.join(PATH_TO_CACHE, this._hash(key) + '.json');
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}
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private _hash(key: string) {
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return crypto.createHash('md5').update(key).digest('hex');
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}
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}
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/**
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* Calls fetch and uses caching.
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*/
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const _cache = new JsonCache();
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const _queue = new Map<string, any>();
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async function fetchWithCache(rinfo: RequestInfo, init?: RequestInit): Promise<Response>
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async function fetchWithCache(rinfo: any, init?: RequestInit): Promise<Response> {
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const url: string = rinfo.url || rinfo.href || rinfo;
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const hash = JSON.stringify({url, body: init?.body});
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if (_cache.has(hash)) { return new Response(_cache.get(hash), {status: 200}); }
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if (_queue.has(hash)) { return new Response(await _queue.get(hash), {status: 200}); }
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_queue.set(hash, fetch(url, init));
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const response = await _queue.get(hash);
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_stats.callCount++;
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if (response.status === 200) {
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_cache.set(hash, await response.clone().text()); // response cannot be read twice, hence clone
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}
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return response;
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}
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// ts expect this function
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fetchWithCache.isRedirect = fetch.isRedirect;
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