gristlabs_grist-core/app/server/lib/Assistance.ts
2023-08-21 09:15:53 -04:00

508 lines
17 KiB
TypeScript

/**
* Module with functions used for AI formula assistance.
*/
import {
AssistanceContext,
AssistanceMessage,
AssistanceRequest,
AssistanceResponse
} from 'app/common/AssistancePrompts';
import {delay} from 'app/common/delay';
import {DocAction} from 'app/common/DocActions';
import {ActiveDoc} from 'app/server/lib/ActiveDoc';
import {getDocSessionUser, OptDocSession} from 'app/server/lib/DocSession';
import log from 'app/server/lib/log';
import fetch from 'node-fetch';
import {createHash} from "crypto";
import {getLogMetaFromDocSession} from "./serverUtils";
// These are mocked/replaced in tests.
// fetch is also replacing in the runCompletion script to add caching.
export const DEPS = { fetch, delayTime: 1000 };
/**
* An assistant can help a user do things with their document,
* by interfacing with an external LLM endpoint.
*/
interface Assistant {
apply(session: OptDocSession, doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse>;
}
/**
* Document-related methods for use in the implementation of assistants.
* Somewhat ad-hoc currently.
*/
interface AssistanceDoc extends ActiveDoc {
/**
* Generate a particular prompt coded in the data engine for some reason.
* It makes python code for some tables, and starts a function body with
* the given docstring.
* Marked "V1" to suggest that it is a particular prompt and it would
* be great to try variants.
*/
assistanceSchemaPromptV1(session: OptDocSession, options: AssistanceSchemaPromptV1Context): Promise<string>;
/**
* Some tweaks to a formula after it has been generated.
*/
assistanceFormulaTweak(txt: string): Promise<string>;
/**
* Compute the existing formula and return the result along with recorded values
* of (possibly nested) attributes of `rec`.
* Used by AI assistance to fix an incorrect formula.
*/
assistanceEvaluateFormula(options: AssistanceContext): Promise<AssistanceFormulaEvaluationResult>;
}
export interface AssistanceFormulaEvaluationResult {
error: boolean; // true if an exception was raised
result: string; // repr of the return value OR exception message
// Recorded attributes of `rec` at the time of evaluation.
// Keys may be e.g. "rec.foo.bar" for nested attributes.
attributes: Record<string, string>;
formula: string; // the code that was evaluated, without special grist syntax
}
export interface AssistanceSchemaPromptV1Context {
tableId: string,
colId: string,
docString: string,
}
class SwitchToLongerContext extends Error {
}
class NonRetryableError extends Error {
}
class TokensExceededFirstMessage extends NonRetryableError {
constructor() {
super(
"Sorry, there's too much information for the AI to process. " +
"You'll need to either shorten your message or delete some columns."
);
}
}
class TokensExceededLaterMessage extends NonRetryableError {
constructor() {
super(
"Sorry, there's too much information for the AI to process. " +
"You'll need to either shorten your message, restart the conversation, or delete some columns."
);
}
}
class QuotaExceededError extends NonRetryableError {
constructor() {
super(
"Sorry, the assistant is facing some long term capacity issues. " +
"Maybe try again tomorrow."
);
}
}
class RetryableError extends Error {
constructor(message: string) {
super(
"Sorry, the assistant is unavailable right now. " +
"Try again in a few minutes. \n" +
`(${message})`
);
}
}
/**
* A flavor of assistant for use with the OpenAI chat completion endpoint
* and tools with a compatible endpoint (e.g. llama-cpp-python).
* Tested primarily with gpt-3.5-turbo.
*
* Uses the ASSISTANT_CHAT_COMPLETION_ENDPOINT endpoint if set, else
* an OpenAI endpoint. Passes ASSISTANT_API_KEY or OPENAI_API_KEY in
* a header if set. An api key is required for the default OpenAI
* endpoint.
*
* If a model string is set in ASSISTANT_MODEL, this will be passed
* along. For the default OpenAI endpoint, a gpt-3.5-turbo variant
* will be set by default.
*
* If a request fails because of context length limitation, and the
* default OpenAI endpoint is in use, the request will be retried
* with ASSISTANT_LONGER_CONTEXT_MODEL (another gpt-3.5
* variant by default). Set this variable to "" if this behavior is
* not desired for the default OpenAI endpoint. If a custom endpoint was
* provided, this behavior will only happen if
* ASSISTANT_LONGER_CONTEXT_MODEL is explicitly set.
*
* An optional ASSISTANT_MAX_TOKENS can be specified.
*/
export class OpenAIAssistant implements Assistant {
public static DEFAULT_MODEL = "gpt-3.5-turbo-0613";
public static DEFAULT_LONGER_CONTEXT_MODEL = "gpt-3.5-turbo-16k-0613";
private _apiKey?: string;
private _model?: string;
private _longerContextModel?: string;
private _endpoint: string;
private _maxTokens = process.env.ASSISTANT_MAX_TOKENS ?
parseInt(process.env.ASSISTANT_MAX_TOKENS, 10) : undefined;
public constructor() {
const apiKey = process.env.ASSISTANT_API_KEY || process.env.OPENAI_API_KEY;
const endpoint = process.env.ASSISTANT_CHAT_COMPLETION_ENDPOINT;
if (!apiKey && !endpoint) {
throw new Error('Please set either OPENAI_API_KEY or ASSISTANT_CHAT_COMPLETION_ENDPOINT');
}
this._apiKey = apiKey;
this._model = process.env.ASSISTANT_MODEL;
this._longerContextModel = process.env.ASSISTANT_LONGER_CONTEXT_MODEL;
if (!endpoint) {
this._model = this._model ?? OpenAIAssistant.DEFAULT_MODEL;
this._longerContextModel = this._longerContextModel ?? OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL;
}
this._endpoint = endpoint || `https://api.openai.com/v1/chat/completions`;
}
public async apply(
optSession: OptDocSession, doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse> {
const messages = request.state?.messages || [];
const newMessages = [];
if (messages.length === 0) {
newMessages.push({
role: 'system',
content: 'You are a helpful assistant for a user of software called Grist. ' +
"Below are one or more fake Python classes representing the structure of the user's data. " +
'The function at the end needs completing. ' +
"The user will probably give a description of what they want the function (a 'formula') to return. " +
'If so, your response should include the function BODY as Python code in a markdown block. ' +
"Your response will be automatically concatenated to the code below, so you mustn't repeat any of it. " +
'You cannot change the function signature or define additional functions or classes. ' +
'It should be a pure function that performs some computation and returns a result. ' +
'It CANNOT perform any side effects such as adding/removing/modifying rows/columns/cells/tables/etc. ' +
'It CANNOT interact with files/databases/networks/etc. ' +
'It CANNOT display images/charts/graphs/maps/etc. ' +
'If the user asks for these things, tell them that you cannot help. ' +
"\n\n" +
'```python\n' +
await makeSchemaPromptV1(optSession, doc, request) +
'\n```',
});
}
if (request.context.evaluateCurrentFormula) {
const result = await doc.assistanceEvaluateFormula(request.context);
let message = "Evaluating this code:\n\n```python\n" + result.formula + "\n```\n\n";
if (Object.keys(result.attributes).length > 0) {
const attributes = Object.entries(result.attributes).map(([k, v]) => `${k} = ${v}`).join('\n');
message += `where:\n\n${attributes}\n\n`;
}
message += `${result.error ? 'raises an exception' : 'returns'}: ${result.result}`;
newMessages.push({
role: 'system',
content: message,
});
}
newMessages.push({
role: 'user', content: request.text,
});
messages.push(...newMessages);
const newMessagesStartIndex = messages.length - newMessages.length;
for (const [index, {role, content}] of newMessages.entries()) {
doc.logTelemetryEvent(optSession, 'assistantSend', {
full: {
conversationId: request.conversationId,
context: request.context,
prompt: {
index: newMessagesStartIndex + index,
role,
content,
},
},
});
}
const userIdHash = getUserHash(optSession);
const completion: string = await this._getCompletion(messages, userIdHash);
// It's nice to have this ready to uncomment for debugging.
// console.log(completion);
const response = await completionToResponse(doc, request, completion);
if (response.suggestedFormula) {
// Show the tweaked version of the suggested formula to the user (i.e. the one that's
// copied when the Apply button is clicked).
response.reply = replaceMarkdownCode(completion, response.suggestedFormula);
} else {
response.reply = completion;
}
response.state = {messages};
doc.logTelemetryEvent(optSession, 'assistantReceive', {
full: {
conversationId: request.conversationId,
context: request.context,
message: {
index: messages.length - 1,
content: completion,
},
suggestedFormula: response.suggestedFormula,
},
});
return response;
}
private async _fetchCompletion(messages: AssistanceMessage[], userIdHash: string, longerContext: boolean) {
const model = longerContext ? this._longerContextModel : this._model;
const apiResponse = await DEPS.fetch(
this._endpoint,
{
method: "POST",
headers: {
...(this._apiKey ? {
"Authorization": `Bearer ${this._apiKey}`,
} : undefined),
"Content-Type": "application/json",
},
body: JSON.stringify({
messages,
temperature: 0,
...(model ? { model } : undefined),
user: userIdHash,
...(this._maxTokens ? {
max_tokens: this._maxTokens,
} : undefined),
}),
},
);
const resultText = await apiResponse.text();
const result = JSON.parse(resultText);
const errorCode = result.error?.code;
if (errorCode === "context_length_exceeded" || result.choices?.[0].finish_reason === "length") {
if (!longerContext && this._longerContextModel) {
log.info("Switching to longer context model...");
throw new SwitchToLongerContext();
} else if (messages.length <= 2) {
throw new TokensExceededFirstMessage();
} else {
throw new TokensExceededLaterMessage();
}
}
if (errorCode === "insufficient_quota") {
log.error("OpenAI billing quota exceeded!!!");
throw new QuotaExceededError();
}
if (apiResponse.status !== 200) {
throw new Error(`OpenAI API returned status ${apiResponse.status}: ${resultText}`);
}
return result;
}
private async _fetchCompletionWithRetries(
messages: AssistanceMessage[], userIdHash: string, longerContext: boolean
): Promise<any> {
const maxAttempts = 3;
for (let attempt = 1; ; attempt++) {
try {
return await this._fetchCompletion(messages, userIdHash, longerContext);
} catch (e) {
if (e instanceof SwitchToLongerContext) {
return await this._fetchCompletionWithRetries(messages, userIdHash, true);
} else if (e instanceof NonRetryableError) {
throw e;
} else if (attempt === maxAttempts) {
throw new RetryableError(e.toString());
}
log.warn(`Waiting and then retrying after error: ${e}`);
await delay(DEPS.delayTime);
}
}
}
private async _getCompletion(messages: AssistanceMessage[], userIdHash: string) {
const result = await this._fetchCompletionWithRetries(messages, userIdHash, false);
const {message} = result.choices[0];
messages.push(message);
return message.content;
}
}
export class HuggingFaceAssistant implements Assistant {
private _apiKey: string;
private _completionUrl: string;
public constructor() {
const apiKey = process.env.HUGGINGFACE_API_KEY;
if (!apiKey) {
throw new Error('HUGGINGFACE_API_KEY not set');
}
this._apiKey = apiKey;
// COMPLETION_MODEL values I've tried:
// - codeparrot/codeparrot
// - NinedayWang/PolyCoder-2.7B
// - NovelAI/genji-python-6B
let completionUrl = process.env.COMPLETION_URL;
if (!completionUrl) {
if (process.env.COMPLETION_MODEL) {
completionUrl = `https://api-inference.huggingface.co/models/${process.env.COMPLETION_MODEL}`;
} else {
completionUrl = 'https://api-inference.huggingface.co/models/NovelAI/genji-python-6B';
}
}
this._completionUrl = completionUrl;
}
public async apply(
optSession: OptDocSession, doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse> {
if (request.state) {
throw new Error("HuggingFaceAssistant does not support state");
}
const prompt = await makeSchemaPromptV1(optSession, doc, request);
const response = await DEPS.fetch(
this._completionUrl,
{
method: "POST",
headers: {
"Authorization": `Bearer ${this._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();
let completion = result[0].generated_text;
completion = completion.split('\n\n')[0];
return completionToResponse(doc, request, completion);
}
}
/**
* Test assistant that mimics ChatGPT and just returns the input.
*/
class EchoAssistant implements Assistant {
public async apply(sess: OptDocSession, doc: AssistanceDoc, request: AssistanceRequest): Promise<AssistanceResponse> {
if (request.text === "ERROR") {
throw new Error(`ERROR`);
}
const messages = request.state?.messages || [];
if (messages.length === 0) {
messages.push({
role: 'system',
content: ''
});
}
messages.push({
role: 'user', content: request.text,
});
const completion = request.text;
const history = { messages };
history.messages.push({
role: 'assistant',
content: completion,
});
const response = await completionToResponse(doc, request, completion, completion);
response.state = history;
return response;
}
}
/**
* Instantiate an assistant, based on environment variables.
*/
export function getAssistant() {
if (process.env.OPENAI_API_KEY === 'test') {
return new EchoAssistant();
}
if (process.env.OPENAI_API_KEY || process.env.ASSISTANT_CHAT_COMPLETION_ENDPOINT) {
return new OpenAIAssistant();
}
throw new Error('Please set OPENAI_API_KEY or ASSISTANT_CHAT_COMPLETION_ENDPOINT');
}
/**
* Service a request for assistance.
*/
export async function sendForCompletion(
optSession: OptDocSession,
doc: AssistanceDoc,
request: AssistanceRequest,
): Promise<AssistanceResponse> {
const assistant = getAssistant();
return await assistant.apply(optSession, doc, request);
}
/**
* Returns a new Markdown string with the contents of its first multi-line code block
* replaced with `replaceValue`.
*/
export function replaceMarkdownCode(markdown: string, replaceValue: string) {
return markdown.replace(/```\w*\n(.*)```/s, '```python\n' + replaceValue + '\n```');
}
async function makeSchemaPromptV1(session: OptDocSession, doc: AssistanceDoc, request: AssistanceRequest) {
if (request.context.type !== 'formula') {
throw new Error('makeSchemaPromptV1 only works for formulas');
}
return doc.assistanceSchemaPromptV1(session, {
tableId: request.context.tableId,
colId: request.context.colId,
docString: request.text,
});
}
async function completionToResponse(
doc: AssistanceDoc,
request: AssistanceRequest,
completion: string,
reply?: string
): Promise<AssistanceResponse> {
if (request.context.type !== 'formula') {
throw new Error('completionToResponse only works for formulas');
}
const suggestedFormula = await doc.assistanceFormulaTweak(completion) || undefined;
// Suggest an action only if the completion is non-empty (that is,
// it actually looked like code).
const suggestedActions: DocAction[] = suggestedFormula ? [[
"ModifyColumn",
request.context.tableId,
request.context.colId, {
formula: suggestedFormula,
}
]] : [];
return {
suggestedActions,
suggestedFormula,
reply,
};
}
function getUserHash(session: OptDocSession): string {
const user = getDocSessionUser(session);
// Make it a bit harder to guess the user ID.
const salt = "7a8sb6987asdb678asd687sad6boas7f8b6aso7fd";
const hashSource = `${user?.id} ${user?.ref} ${salt}`;
const hash = createHash('sha256').update(hashSource).digest('base64');
// So that if we get feedback about a user ID hash, we can
// search for the hash in the logs to find the original user ID.
log.rawInfo("getUserHash", {...getLogMetaFromDocSession(session), userRef: user?.ref, hash});
return hash;
}