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https://github.com/gristlabs/grist-core.git
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add support for conversational state to assistance endpoint (#506)
* add support for conversational state to assistance endpoint This refactors the assistance code somewhat, to allow carrying along some conversational state. It extends the OpenAI-flavored assistant to make use of that state to have a conversation. The front-end is tweaked a little bit to allow for replies that don't have any code in them (though I didn't get into formatting such replies nicely). Currently tested primarily through the runCompletion script, which has been extended a bit to allow testing simulated conversations (where an error is pasted in follow-up, or an expected-vs-actual comparison). Co-authored-by: George Gevoian <85144792+georgegevoian@users.noreply.github.com>
This commit is contained in:
Paul Fitzpatrick
GitHub
@@ -14,7 +14,7 @@ import {
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} from 'app/common/ActionBundle';
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import {ActionGroup, MinimalActionGroup} from 'app/common/ActionGroup';
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import {ActionSummary} from "app/common/ActionSummary";
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import {Prompt, Suggestion} from "app/common/AssistancePrompts";
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import {AssistanceRequest, AssistanceResponse} from "app/common/AssistancePrompts";
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import {
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AclResources,
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AclTableDescription,
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@@ -85,7 +85,7 @@ import {Document} from 'app/gen-server/entity/Document';
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import {ParseOptions} from 'app/plugin/FileParserAPI';
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import {AccessTokenOptions, AccessTokenResult, GristDocAPI} from 'app/plugin/GristAPI';
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import {compileAclFormula} from 'app/server/lib/ACLFormula';
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import {sendForCompletion} from 'app/server/lib/Assistance';
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import {AssistanceDoc, AssistanceSchemaPromptV1Context, sendForCompletion} from 'app/server/lib/Assistance';
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import {Authorizer} from 'app/server/lib/Authorizer';
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import {checksumFile} from 'app/server/lib/checksumFile';
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import {Client} from 'app/server/lib/Client';
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@@ -180,7 +180,7 @@ interface UpdateUsageOptions {
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* either .loadDoc() or .createEmptyDoc() is called.
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* @param {String} docName - The document's filename, without the '.grist' extension.
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*/
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export class ActiveDoc extends EventEmitter {
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export class ActiveDoc extends EventEmitter implements AssistanceDoc {
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/**
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* Decorator for ActiveDoc methods that prevents shutdown while the method is running, i.e.
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* until the returned promise is resolved.
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@@ -1112,7 +1112,7 @@ export class ActiveDoc extends EventEmitter {
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* @param {Integer} rowId - Row number
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* @returns {Promise} Promise for a error message
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*/
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public async getFormulaError(docSession: DocSession, tableId: string, colId: string,
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public async getFormulaError(docSession: OptDocSession, tableId: string, colId: string,
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rowId: number): Promise<CellValue> {
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// Throw an error if the user doesn't have access to read this cell.
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await this._granularAccess.getCellValue(docSession, {tableId, colId, rowId});
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@@ -1260,22 +1260,28 @@ export class ActiveDoc extends EventEmitter {
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return this._pyCall('autocomplete', txt, tableId, columnId, rowId, user.toJSON());
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}
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public async getAssistance(docSession: DocSession, userPrompt: Prompt): Promise<Suggestion> {
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// Making a prompt can leak names of tables and columns.
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public async getAssistance(docSession: DocSession, request: AssistanceRequest): Promise<AssistanceResponse> {
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return this.getAssistanceWithOptions(docSession, request);
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}
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public async getAssistanceWithOptions(docSession: DocSession,
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request: AssistanceRequest): Promise<AssistanceResponse> {
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// Making a prompt leaks names of tables and columns etc.
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if (!await this._granularAccess.canScanData(docSession)) {
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throw new Error("Permission denied");
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}
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await this.waitForInitialization();
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const { tableId, colId, description } = userPrompt;
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const prompt = await this._pyCall('get_formula_prompt', tableId, colId, description);
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this._log.debug(docSession, 'getAssistance prompt', {prompt});
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const completion = await sendForCompletion(prompt);
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this._log.debug(docSession, 'getAssistance completion', {completion});
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const formula = await this._pyCall('convert_formula_completion', completion);
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const action: DocAction = ["ModifyColumn", tableId, colId, {formula}];
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return {
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suggestedActions: [action],
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};
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return sendForCompletion(this, request);
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}
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// Callback to make a data-engine formula tweak for assistance.
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public assistanceFormulaTweak(txt: string) {
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return this._pyCall('convert_formula_completion', txt);
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}
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// Callback to generate a prompt containing schema info for assistance.
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public assistanceSchemaPromptV1(options: AssistanceSchemaPromptV1Context): Promise<string> {
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return this._pyCall('get_formula_prompt', options.tableId, options.colId, options.docString);
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}
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public fetchURL(docSession: DocSession, url: string, options?: FetchUrlOptions): Promise<UploadResult> {
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@@ -2,116 +2,320 @@
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* Module with functions used for AI formula assistance.
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*/
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import {AssistanceRequest, AssistanceResponse} from 'app/common/AssistancePrompts';
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import {delay} from 'app/common/delay';
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import {DocAction} from 'app/common/DocActions';
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import log from 'app/server/lib/log';
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import fetch from 'node-fetch';
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export const DEPS = { fetch };
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export async function sendForCompletion(prompt: string): Promise<string> {
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let completion: string|null = null;
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let retries: number = 0;
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const openApiKey = process.env.OPENAI_API_KEY;
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const model = process.env.COMPLETION_MODEL || "text-davinci-002";
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/**
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* An assistant can help a user do things with their document,
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* by interfacing with an external LLM endpoint.
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*/
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export interface Assistant {
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apply(doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse>;
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}
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/**
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* Document-related methods for use in the implementation of assistants.
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* Somewhat ad-hoc currently.
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*/
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export interface AssistanceDoc {
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/**
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* Generate a particular prompt coded in the data engine for some reason.
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* It makes python code for some tables, and starts a function body with
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* the given docstring.
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* Marked "V1" to suggest that it is a particular prompt and it would
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* be great to try variants.
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*/
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assistanceSchemaPromptV1(options: AssistanceSchemaPromptV1Context): Promise<string>;
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/**
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* Some tweaks to a formula after it has been generated.
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*/
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assistanceFormulaTweak(txt: string): Promise<string>;
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}
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export interface AssistanceSchemaPromptV1Context {
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tableId: string,
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colId: string,
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docString: string,
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}
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/**
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* A flavor of assistant for use with the OpenAI API.
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* Tested primarily with text-davinci-002 and gpt-3.5-turbo.
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*/
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export class OpenAIAssistant implements Assistant {
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private _apiKey: string;
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private _model: string;
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private _chatMode: boolean;
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private _endpoint: string;
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public constructor() {
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const apiKey = process.env.OPENAI_API_KEY;
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if (!apiKey) {
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throw new Error('OPENAI_API_KEY not set');
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}
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this._apiKey = apiKey;
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this._model = process.env.COMPLETION_MODEL || "text-davinci-002";
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this._chatMode = this._model.includes('turbo');
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this._endpoint = `https://api.openai.com/v1/${this._chatMode ? 'chat/' : ''}completions`;
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}
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public async apply(doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse> {
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const messages = request.state?.messages || [];
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const chatMode = this._chatMode;
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if (chatMode) {
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if (messages.length === 0) {
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messages.push({
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role: 'system',
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content: 'The user gives you one or more Python classes, ' +
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'with one last method that needs completing. Write the ' +
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'method body as a single code block, ' +
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'including the docstring the user gave. ' +
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'Just give the Python code as a markdown block, ' +
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'do not give any introduction, that will just be ' +
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'awkward for the user when copying and pasting. ' +
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'You are working with Grist, an environment very like ' +
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'regular Python except `rec` (like record) is used ' +
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'instead of `self`. ' +
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'Include at least one `return` statement or the method ' +
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'will fail, disappointing the user. ' +
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'Your answer should be the body of a single method, ' +
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'not a class, and should not include `dataclass` or ' +
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'`class` since the user is counting on you to provide ' +
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'a single method. Thanks!'
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});
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messages.push({
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role: 'user', content: await makeSchemaPromptV1(doc, request),
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});
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} else {
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if (request.regenerate) {
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if (messages[messages.length - 1].role !== 'user') {
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messages.pop();
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}
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}
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messages.push({
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role: 'user', content: request.text,
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});
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}
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} else {
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messages.length = 0;
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messages.push({
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role: 'user', content: await makeSchemaPromptV1(doc, request),
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});
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}
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const apiResponse = await DEPS.fetch(
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this._endpoint,
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{
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method: "POST",
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headers: {
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"Authorization": `Bearer ${this._apiKey}`,
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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...(!this._chatMode ? {
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prompt: messages[messages.length - 1].content,
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} : { messages }),
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max_tokens: 1500,
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temperature: 0,
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model: this._model,
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stop: this._chatMode ? undefined : ["\n\n"],
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}),
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},
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);
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if (apiResponse.status !== 200) {
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log.error(`OpenAI API returned ${apiResponse.status}: ${await apiResponse.text()}`);
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throw new Error(`OpenAI API returned status ${apiResponse.status}`);
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}
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const result = await apiResponse.json();
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let completion: string = String(chatMode ? result.choices[0].message.content : result.choices[0].text);
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const reply = completion;
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const history = { messages };
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if (chatMode) {
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history.messages.push(result.choices[0].message);
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// This model likes returning markdown. Code will typically
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// be in a code block with ``` delimiters.
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let lines = completion.split('\n');
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if (lines[0].startsWith('```')) {
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lines.shift();
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completion = lines.join('\n');
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const parts = completion.split('```');
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if (parts.length > 1) {
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completion = parts[0];
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}
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lines = completion.split('\n');
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}
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// This model likes repeating the function signature and
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// docstring, so we try to strip that out.
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completion = lines.join('\n');
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while (completion.includes('"""')) {
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const parts = completion.split('"""');
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completion = parts[parts.length - 1];
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}
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// If there's no code block, don't treat the answer as a formula.
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if (!reply.includes('```')) {
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completion = '';
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}
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}
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const response = await completionToResponse(doc, request, completion, reply);
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if (chatMode) {
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response.state = history;
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}
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return response;
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}
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}
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export class HuggingFaceAssistant implements Assistant {
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private _apiKey: string;
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private _completionUrl: string;
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public constructor() {
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const apiKey = process.env.HUGGINGFACE_API_KEY;
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if (!apiKey) {
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throw new Error('HUGGINGFACE_API_KEY not set');
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}
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this._apiKey = apiKey;
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// COMPLETION_MODEL values I've tried:
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// - codeparrot/codeparrot
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// - NinedayWang/PolyCoder-2.7B
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// - NovelAI/genji-python-6B
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let completionUrl = process.env.COMPLETION_URL;
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if (!completionUrl) {
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if (process.env.COMPLETION_MODEL) {
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completionUrl = `https://api-inference.huggingface.co/models/${process.env.COMPLETION_MODEL}`;
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} else {
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completionUrl = 'https://api-inference.huggingface.co/models/NovelAI/genji-python-6B';
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}
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}
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this._completionUrl = completionUrl;
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}
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public async apply(doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse> {
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if (request.state) {
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throw new Error("HuggingFaceAssistant does not support state");
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}
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const prompt = await makeSchemaPromptV1(doc, request);
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const response = await DEPS.fetch(
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this._completionUrl,
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{
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method: "POST",
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headers: {
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"Authorization": `Bearer ${this._apiKey}`,
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"Content-Type": "application/json",
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},
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body: JSON.stringify({
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inputs: prompt,
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parameters: {
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return_full_text: false,
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max_new_tokens: 50,
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},
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}),
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},
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);
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if (response.status === 503) {
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log.error(`Sleeping for 10s - HuggingFace API returned ${response.status}: ${await response.text()}`);
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await delay(10000);
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}
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if (response.status !== 200) {
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const text = await response.text();
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log.error(`HuggingFace API returned ${response.status}: ${text}`);
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throw new Error(`HuggingFace API returned status ${response.status}: ${text}`);
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}
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const result = await response.json();
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let completion = result[0].generated_text;
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completion = completion.split('\n\n')[0];
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return completionToResponse(doc, request, completion);
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}
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}
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/**
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* Instantiate an assistant, based on environment variables.
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*/
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function getAssistant() {
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if (process.env.OPENAI_API_KEY) {
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return new OpenAIAssistant();
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}
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if (process.env.HUGGINGFACE_API_KEY) {
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return new HuggingFaceAssistant();
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}
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throw new Error('Please set OPENAI_API_KEY or HUGGINGFACE_API_KEY');
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}
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/**
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* Service a request for assistance, with a little retry logic
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* since these endpoints can be a bit flakey.
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*/
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export async function sendForCompletion(doc: AssistanceDoc,
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request: AssistanceRequest): Promise<AssistanceResponse> {
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const assistant = getAssistant();
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let retries: number = 0;
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let response: AssistanceResponse|null = null;
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while(retries++ < 3) {
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try {
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if (openApiKey) {
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completion = await sendForCompletionOpenAI(prompt, openApiKey, model);
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}
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if (process.env.HUGGINGFACE_API_KEY) {
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completion = await sendForCompletionHuggingFace(prompt);
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}
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response = await assistant.apply(doc, request);
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break;
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} catch(e) {
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log.error(`Completion error: ${e}`);
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await delay(1000);
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}
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}
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if (completion === null) {
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throw new Error("Please set OPENAI_API_KEY or HUGGINGFACE_API_KEY (and optionally COMPLETION_MODEL)");
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if (!response) {
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throw new Error('Failed to get response from assistant');
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}
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log.debug(`Received completion:`, {completion});
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completion = completion.split(/\n {4}[^ ]/)[0];
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return completion;
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return response;
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}
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async function sendForCompletionOpenAI(prompt: string, apiKey: string, model = "text-davinci-002") {
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if (!apiKey) {
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throw new Error("OPENAI_API_KEY not set");
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async function makeSchemaPromptV1(doc: AssistanceDoc, request: AssistanceRequest) {
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if (request.context.type !== 'formula') {
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throw new Error('makeSchemaPromptV1 only works for formulas');
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}
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const response = await DEPS.fetch(
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"https://api.openai.com/v1/completions",
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{
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method: "POST",
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headers: {
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"Authorization": `Bearer ${apiKey}`,
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"Content-Type": "application/json",
|
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},
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body: JSON.stringify({
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prompt,
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max_tokens: 150,
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temperature: 0,
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// COMPLETION_MODEL of `code-davinci-002` may be better if you have access to it.
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model,
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stop: ["\n\n"],
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}),
|
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},
|
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);
|
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if (response.status !== 200) {
|
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log.error(`OpenAI API returned ${response.status}: ${await response.text()}`);
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throw new Error(`OpenAI API returned status ${response.status}`);
|
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}
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const result = await response.json();
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const completion = result.choices[0].text;
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return completion;
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return doc.assistanceSchemaPromptV1({
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tableId: request.context.tableId,
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colId: request.context.colId,
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docString: request.text,
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});
|
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}
|
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|
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async function sendForCompletionHuggingFace(prompt: string) {
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const apiKey = process.env.HUGGINGFACE_API_KEY;
|
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if (!apiKey) {
|
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throw new Error("HUGGINGFACE_API_KEY not set");
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async function completionToResponse(doc: AssistanceDoc, request: AssistanceRequest,
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completion: string, reply?: string): Promise<AssistanceResponse> {
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if (request.context.type !== 'formula') {
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throw new Error('completionToResponse only works for formulas');
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}
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// COMPLETION_MODEL values I've tried:
|
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// - codeparrot/codeparrot
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// - NinedayWang/PolyCoder-2.7B
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// - NovelAI/genji-python-6B
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let completionUrl = process.env.COMPLETION_URL;
|
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if (!completionUrl) {
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if (process.env.COMPLETION_MODEL) {
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completionUrl = `https://api-inference.huggingface.co/models/${process.env.COMPLETION_MODEL}`;
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} else {
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completionUrl = 'https://api-inference.huggingface.co/models/NovelAI/genji-python-6B';
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completion = await doc.assistanceFormulaTweak(completion);
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// A leading newline is common.
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if (completion.charAt(0) === '\n') {
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completion = completion.slice(1);
|
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}
|
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// If all non-empty lines have four spaces, remove those spaces.
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// They are common for GPT-3.5, which matches the prompt carefully.
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const lines = completion.split('\n');
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const ok = lines.every(line => line === '\n' || line.startsWith(' '));
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if (ok) {
|
||||
completion = lines.map(line => line === '\n' ? line : line.slice(4)).join('\n');
|
||||
}
|
||||
|
||||
// Suggest an action only if the completion is non-empty (that is,
|
||||
// it actually looked like code).
|
||||
const suggestedActions: DocAction[] = completion ? [[
|
||||
"ModifyColumn",
|
||||
request.context.tableId,
|
||||
request.context.colId, {
|
||||
formula: completion,
|
||||
}
|
||||
}
|
||||
|
||||
const response = await DEPS.fetch(
|
||||
completionUrl,
|
||||
{
|
||||
method: "POST",
|
||||
headers: {
|
||||
"Authorization": `Bearer ${apiKey}`,
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
body: JSON.stringify({
|
||||
inputs: prompt,
|
||||
parameters: {
|
||||
return_full_text: false,
|
||||
max_new_tokens: 50,
|
||||
},
|
||||
}),
|
||||
},
|
||||
);
|
||||
if (response.status === 503) {
|
||||
log.error(`Sleeping for 10s - HuggingFace API returned ${response.status}: ${await response.text()}`);
|
||||
await delay(10000);
|
||||
}
|
||||
if (response.status !== 200) {
|
||||
const text = await response.text();
|
||||
log.error(`HuggingFace API returned ${response.status}: ${text}`);
|
||||
throw new Error(`HuggingFace API returned status ${response.status}: ${text}`);
|
||||
}
|
||||
const result = await response.json();
|
||||
const completion = result[0].generated_text;
|
||||
return completion.split('\n\n')[0];
|
||||
]] : [];
|
||||
return {
|
||||
suggestedActions,
|
||||
reply,
|
||||
};
|
||||
}
|
||||
|
||||
@@ -376,7 +376,8 @@ export class GranularAccess implements GranularAccessForBundle {
|
||||
function fail(): never {
|
||||
throw new ErrorWithCode('ACL_DENY', 'Cannot access cell');
|
||||
}
|
||||
if (!await this.hasTableAccess(docSession, cell.tableId)) { fail(); }
|
||||
const hasExceptionalAccess = this._hasExceptionalFullAccess(docSession);
|
||||
if (!hasExceptionalAccess && !await this.hasTableAccess(docSession, cell.tableId)) { fail(); }
|
||||
let rows: TableDataAction|null = null;
|
||||
if (docData) {
|
||||
const record = docData.getTable(cell.tableId)?.getRecord(cell.rowId);
|
||||
@@ -393,16 +394,18 @@ export class GranularAccess implements GranularAccessForBundle {
|
||||
return fail();
|
||||
}
|
||||
const rec = new RecordView(rows, 0);
|
||||
const input: AclMatchInput = {...await this.inputs(docSession), rec, newRec: rec};
|
||||
const rowPermInfo = new PermissionInfo(this._ruler.ruleCollection, input);
|
||||
const rowAccess = rowPermInfo.getTableAccess(cell.tableId).perms.read;
|
||||
if (rowAccess === 'deny') { fail(); }
|
||||
if (rowAccess !== 'allow') {
|
||||
const colAccess = rowPermInfo.getColumnAccess(cell.tableId, cell.colId).perms.read;
|
||||
if (colAccess === 'deny') { fail(); }
|
||||
if (!hasExceptionalAccess) {
|
||||
const input: AclMatchInput = {...await this.inputs(docSession), rec, newRec: rec};
|
||||
const rowPermInfo = new PermissionInfo(this._ruler.ruleCollection, input);
|
||||
const rowAccess = rowPermInfo.getTableAccess(cell.tableId).perms.read;
|
||||
if (rowAccess === 'deny') { fail(); }
|
||||
if (rowAccess !== 'allow') {
|
||||
const colAccess = rowPermInfo.getColumnAccess(cell.tableId, cell.colId).perms.read;
|
||||
if (colAccess === 'deny') { fail(); }
|
||||
}
|
||||
const colValues = rows[3];
|
||||
if (!(cell.colId in colValues)) { fail(); }
|
||||
}
|
||||
const colValues = rows[3];
|
||||
if (!(cell.colId in colValues)) { fail(); }
|
||||
return rec.get(cell.colId);
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user