gristlabs_grist-core/test/server/lib/Assistance.ts

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import {createDocTools} from "test/server/docTools";
import {ActiveDoc} from "app/server/lib/ActiveDoc";
import {DEPS, OpenAIAssistant, sendForCompletion} from "app/server/lib/Assistance";
import {assert} from 'chai';
import * as sinon from 'sinon';
import {Response} from 'node-fetch';
import {DocSession} from "app/server/lib/DocSession";
import {AssistanceState} from "app/common/AssistancePrompts";
// For some reason, assert.isRejected is not getting defined,
// though test/chai-as-promised.js should be taking care of this.
// So test/chai-as-promised.js is just repeated here.
const chai = require('chai');
const chaiAsPromised = require('chai-as-promised');
chai.use(chaiAsPromised);
describe('Assistance', function () {
this.timeout(10000);
const docTools = createDocTools({persistAcrossCases: true});
const table1Id = "Table1";
const table2Id = "Table2";
let session: DocSession;
let doc: ActiveDoc;
before(async () => {
process.env.OPENAI_API_KEY = "fake";
session = docTools.createFakeSession();
doc = await docTools.createDoc('test.grist');
await doc.applyUserActions(session, [
["AddTable", table1Id, [{id: "A"}, {id: "B"}, {id: "C"}]],
["AddTable", table2Id, [{id: "A"}, {id: "B"}, {id: "C"}]],
]);
});
const colId = "C";
const userMessageContent = "Sum of A and B";
function checkSendForCompletion(state?: AssistanceState) {
return sendForCompletion(session, doc, {
conversationId: 'conversationId',
context: {type: 'formula', tableId: table1Id, colId},
state,
text: userMessageContent,
});
}
let fakeResponse: () => any;
let fakeFetch: sinon.SinonSpy;
beforeEach(() => {
fakeFetch = sinon.fake(() => {
const body = fakeResponse();
return new Response(
JSON.stringify(body),
{status: body.status},
);
});
sinon.replace(DEPS, 'fetch', fakeFetch as any);
sinon.replace(DEPS, 'delayTime', 1);
});
afterEach(function () {
sinon.restore();
});
function checkModels(expectedModels: string[]) {
assert.deepEqual(
fakeFetch.getCalls().map(call => JSON.parse(call.args[1].body).model),
expectedModels,
);
}
it('can suggest a formula', async function () {
const reply = "Here's a formula that adds columns A and B:\n\n"
+ "```python\na = int(rec.A)\nb=int(rec.B)\n\nreturn str(a + b)\n```"
+ "\n\nLet me know if there's anything else I can help with.";
const replyMessage = {"role": "assistant", "content": reply};
fakeResponse = () => ({
"choices": [{
"index": 0,
"message": replyMessage,
"finish_reason": "stop"
}],
status: 200,
});
const result = await checkSendForCompletion();
checkModels([OpenAIAssistant.DEFAULT_MODEL]);
const callInfo = fakeFetch.getCall(0);
const [url, request] = callInfo.args;
assert.equal(url, 'https://api.openai.com/v1/chat/completions');
assert.equal(request.method, 'POST');
const {messages: requestMessages} = JSON.parse(request.body);
const systemMessageContent = requestMessages[0].content;
assert.match(systemMessageContent, /def C\(rec: Table1\)/);
assert.deepEqual(requestMessages, [
{
role: "system",
content: systemMessageContent,
},
{
role: "user",
content: userMessageContent,
}
]
);
const suggestedFormula = "a = int($A)\nb=int($B)\n\nstr(a + b)";
const replyWithSuggestedFormula = "Here's a formula that adds columns A and B:\n\n"
+ "```python\na = int($A)\nb=int($B)\n\nstr(a + b)\n```"
+ "\n\nLet me know if there's anything else I can help with.";
assert.deepEqual(result, {
suggestedActions: [
["ModifyColumn", table1Id, colId, {formula: suggestedFormula}]
],
suggestedFormula,
reply: replyWithSuggestedFormula,
state: {
messages: [...requestMessages, replyMessage]
}
}
);
});
it('does not suggest anything if formula is invalid', async function () {
const reply = "This isn't valid Python code:\n```python\nclass = 'foo'\n```";
const replyMessage = {
"role": "assistant",
"content": reply,
};
fakeResponse = () => ({
"choices": [{
"index": 0,
"message": replyMessage,
"finish_reason": "stop"
}],
status: 200,
});
const result = await checkSendForCompletion();
const callInfo = fakeFetch.getCall(0);
const [, request] = callInfo.args;
const {messages: requestMessages} = JSON.parse(request.body);
const suggestedFormula = undefined;
assert.deepEqual(result, {
suggestedActions: [],
suggestedFormula,
reply,
state: {
messages: [...requestMessages, replyMessage],
},
}
);
});
it('tries 3 times in case of network errors', async function () {
fakeResponse = () => {
throw new Error("Network error");
};
await assert.isRejected(
checkSendForCompletion(),
"Sorry, the assistant is unavailable right now. " +
"Try again in a few minutes. \n" +
"(Error: Network error)",
);
assert.equal(fakeFetch.callCount, 3);
});
it('tries 3 times in case of bad status code', async function () {
fakeResponse = () => ({status: 500});
await assert.isRejected(
checkSendForCompletion(),
"Sorry, the assistant is unavailable right now. " +
"Try again in a few minutes. \n" +
'(Error: OpenAI API returned status 500: {"status":500})',
);
assert.equal(fakeFetch.callCount, 3);
});
it('handles exceeded billing quota', async function () {
fakeResponse = () => ({
error: {
code: "insufficient_quota",
},
status: 429,
});
await assert.isRejected(
checkSendForCompletion(),
"Sorry, the assistant is facing some long term capacity issues. " +
"Maybe try again tomorrow.",
);
assert.equal(fakeFetch.callCount, 1);
});
it('switches to a longer model with no retries if the prompt is too long', async function () {
fakeResponse = () => ({
error: {
code: "context_length_exceeded",
},
status: 400,
});
await assert.isRejected(
checkSendForCompletion(),
/You'll need to either shorten your message or delete some columns/
);
checkModels([
OpenAIAssistant.DEFAULT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
]);
});
it('switches to a shorter prompt if the longer model exceeds its token limit', async function () {
fakeResponse = () => ({
error: {
code: "context_length_exceeded",
},
status: 400,
});
await assert.isRejected(
checkSendForCompletion(),
/You'll need to either shorten your message or delete some columns/
);
fakeFetch.getCalls().map((callInfo, i) => {
const [, request] = callInfo.args;
const {messages} = JSON.parse(request.body);
const systemMessageContent = messages[0].content;
const shortCallIndex = 2;
if (i === shortCallIndex) {
assert.match(systemMessageContent, /class Table1/);
assert.notMatch(systemMessageContent, /class Table2/);
assert.notMatch(systemMessageContent, /def lookupOne/);
assert.lengthOf(systemMessageContent, 1001);
} else {
assert.match(systemMessageContent, /class Table1/);
assert.match(systemMessageContent, /class Table2/);
assert.match(systemMessageContent, /def lookupOne/);
assert.lengthOf(systemMessageContent, 1982);
}
});
});
it('switches to a longer model with no retries if the model runs out of tokens while responding', async function () {
fakeResponse = () => ({
"choices": [{
"index": 0,
"message": {},
"finish_reason": "length"
}],
status: 200,
});
await assert.isRejected(
checkSendForCompletion(),
/You'll need to either shorten your message or delete some columns/
);
checkModels([
OpenAIAssistant.DEFAULT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
]);
});
it('suggests restarting conversation if the prompt is too long and there are past messages', async function () {
fakeResponse = () => ({
error: {
code: "context_length_exceeded",
},
status: 400,
});
await assert.isRejected(
checkSendForCompletion({
messages: [
{role: "system", content: "Be good."},
{role: "user", content: "Hi."},
{role: "assistant", content: "Hi!"},
]
}),
/You'll need to either shorten your message, restart the conversation, or delete some columns/
);
checkModels([
OpenAIAssistant.DEFAULT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
]);
});
it('can switch to a longer model, retry, and succeed', async function () {
fakeResponse = () => {
if (fakeFetch.callCount === 1) {
return {
error: {
code: "context_length_exceeded",
},
status: 400,
};
} else if (fakeFetch.callCount === 2) {
return {
status: 500,
};
} else {
return {
"choices": [{
"index": 0,
"message": {role: "assistant", content: "123"},
"finish_reason": "stop"
}],
status: 200,
};
}
};
const result = await checkSendForCompletion();
checkModels([
OpenAIAssistant.DEFAULT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
OpenAIAssistant.DEFAULT_LONGER_CONTEXT_MODEL,
]);
assert.deepEqual(result.suggestedActions, [
["ModifyColumn", table1Id, colId, {formula: "123"}]
]);
});
});