LibreChat/api/app/clients/specs/OpenAIClient.test.js
Danny Avila 317a1bd8da
feat: ConversationSummaryBufferMemory (#973)
* refactor: pass model in message edit payload, use encoder in standalone util function

* feat: add summaryBuffer helper

* refactor(api/messages): use new countTokens helper and add auth middleware at top

* wip: ConversationSummaryBufferMemory

* refactor: move pre-generation helpers to prompts dir

* chore: remove console log

* chore: remove test as payload will no longer carry tokenCount

* chore: update getMessagesWithinTokenLimit JSDoc

* refactor: optimize getMessagesForConversation and also break on summary, feat(ci): getMessagesForConversation tests

* refactor(getMessagesForConvo): count '00000000-0000-0000-0000-000000000000' as root message

* chore: add newer model to token map

* fix: condition was point to prop of array instead of message prop

* refactor(BaseClient): use object for refineMessages param, rename 'summary' to 'summaryMessage', add previous_summary
refactor(getMessagesWithinTokenLimit): replace text and tokenCount if should summarize, summary, and summaryTokenCount are present
fix/refactor(handleContextStrategy): use the right comparison length for context diff, and replace payload first message when a summary is present

* chore: log previous_summary if debugging

* refactor(formatMessage): assume if role is defined that it's a valid value

* refactor(getMessagesWithinTokenLimit): remove summary logic
refactor(handleContextStrategy): add usePrevSummary logic in case only summary was pruned
refactor(loadHistory): initial message query will return all ordered messages but keep track of the latest summary
refactor(getMessagesForConversation): use object for single param, edit jsdoc, edit all files using the method
refactor(ChatGPTClient): order messages before buildPrompt is called, TODO: add convoSumBuffMemory logic

* fix: undefined handling and summarizing only when shouldRefineContext is true

* chore(BaseClient): fix test results omitting system role for summaries and test edge case

* chore: export summaryBuffer from index file

* refactor(OpenAIClient/BaseClient): move refineMessages to subclass, implement LLM initialization for summaryBuffer

* feat: add OPENAI_SUMMARIZE to enable summarizing, refactor: rename client prop 'shouldRefineContext' to 'shouldSummarize', change contextStrategy value to 'summarize' from 'refine'

* refactor: rename refineMessages method to summarizeMessages for clarity

* chore: clarify summary future intent in .env.example

* refactor(initializeLLM): handle case for either 'model' or 'modelName' being passed

* feat(gptPlugins): enable summarization for plugins

* refactor(gptPlugins): utilize new initializeLLM method and formatting methods for messages, use payload array for currentMessages and assign pastMessages sooner

* refactor(agents): use ConversationSummaryBufferMemory for both agent types

* refactor(formatMessage): optimize original method for langchain, add helper function for langchain messages, add JSDocs and tests

* refactor(summaryBuffer): add helper to createSummaryBufferMemory, and use new formatting helpers

* fix: forgot to spread formatMessages also took opportunity to pluralize filename

* refactor: pass memory to tools, namely openapi specs. not used and may never be used by new method but added for testing

* ci(formatMessages): add more exhaustive checks for langchain messages

* feat: add debug env var for OpenAI

* chore: delete unnecessary comments

* chore: add extra note about summary feature

* fix: remove tokenCount from payload instructions

* fix: test fail

* fix: only pass instructions to payload when defined or not empty object

* refactor: fromPromptMessages is deprecated, use renamed method fromMessages

* refactor: use 'includes' instead of 'startsWith' for extended OpenRouter compatibility

* fix(PluginsClient.buildPromptBody): handle undefined message strings

* chore: log langchain titling error

* feat: getModelMaxTokens helper

* feat: tokenSplit helper

* feat: summary prompts updated

* fix: optimize _CUT_OFF_SUMMARIZER prompt

* refactor(summaryBuffer): use custom summary prompt, allow prompt to be passed, pass humanPrefix and aiPrefix to memory, along with any future variables, rename messagesToRefine to context

* fix(summaryBuffer): handle edge case where messagesToRefine exceeds summary context,
refactor(BaseClient): allow custom maxContextTokens to be passed to getMessagesWithinTokenLimit, add defined check before unshifting summaryMessage, update shouldSummarize based on this
refactor(OpenAIClient): use getModelMaxTokens, use cut-off message method for summary if no messages were left after pruning

* fix(handleContextStrategy): handle case where incoming prompt is bigger than model context

* chore: rename refinedContent to splitText

* chore: remove unnecessary debug log
2023-09-26 21:02:28 -04:00

264 lines
8.9 KiB
JavaScript

const OpenAIClient = require('../OpenAIClient');
jest.mock('meilisearch');
describe('OpenAIClient', () => {
let client, client2;
const model = 'gpt-4';
const parentMessageId = '1';
const messages = [
{ role: 'user', sender: 'User', text: 'Hello', messageId: parentMessageId },
{ role: 'assistant', sender: 'Assistant', text: 'Hi', messageId: '2' },
];
beforeEach(() => {
const options = {
// debug: true,
openaiApiKey: 'new-api-key',
modelOptions: {
model,
temperature: 0.7,
},
};
client = new OpenAIClient('test-api-key', options);
client2 = new OpenAIClient('test-api-key', options);
client.summarizeMessages = jest.fn().mockResolvedValue({
role: 'assistant',
content: 'Refined answer',
tokenCount: 30,
});
client.buildPrompt = jest
.fn()
.mockResolvedValue({ prompt: messages.map((m) => m.text).join('\n') });
client.constructor.freeAndResetAllEncoders();
});
describe('setOptions', () => {
it('should set the options correctly', () => {
expect(client.apiKey).toBe('new-api-key');
expect(client.modelOptions.model).toBe(model);
expect(client.modelOptions.temperature).toBe(0.7);
});
});
describe('selectTokenizer', () => {
it('should get the correct tokenizer based on the instance state', () => {
const tokenizer = client.selectTokenizer();
expect(tokenizer).toBeDefined();
});
});
describe('freeAllTokenizers', () => {
it('should free all tokenizers', () => {
// Create a tokenizer
const tokenizer = client.selectTokenizer();
// Mock 'free' method on the tokenizer
tokenizer.free = jest.fn();
client.constructor.freeAndResetAllEncoders();
// Check if 'free' method has been called on the tokenizer
expect(tokenizer.free).toHaveBeenCalled();
});
});
describe('getTokenCount', () => {
it('should return the correct token count', () => {
const count = client.getTokenCount('Hello, world!');
expect(count).toBeGreaterThan(0);
});
it('should reset the encoder and count when count reaches 25', () => {
const freeAndResetEncoderSpy = jest.spyOn(client.constructor, 'freeAndResetAllEncoders');
// Call getTokenCount 25 times
for (let i = 0; i < 25; i++) {
client.getTokenCount('test text');
}
expect(freeAndResetEncoderSpy).toHaveBeenCalled();
});
it('should not reset the encoder and count when count is less than 25', () => {
const freeAndResetEncoderSpy = jest.spyOn(client.constructor, 'freeAndResetAllEncoders');
freeAndResetEncoderSpy.mockClear();
// Call getTokenCount 24 times
for (let i = 0; i < 24; i++) {
client.getTokenCount('test text');
}
expect(freeAndResetEncoderSpy).not.toHaveBeenCalled();
});
it('should handle errors and reset the encoder', () => {
const freeAndResetEncoderSpy = jest.spyOn(client.constructor, 'freeAndResetAllEncoders');
// Mock encode function to throw an error
client.selectTokenizer().encode = jest.fn().mockImplementation(() => {
throw new Error('Test error');
});
client.getTokenCount('test text');
expect(freeAndResetEncoderSpy).toHaveBeenCalled();
});
it('should not throw null pointer error when freeing the same encoder twice', () => {
client.constructor.freeAndResetAllEncoders();
client2.constructor.freeAndResetAllEncoders();
const count = client2.getTokenCount('test text');
expect(count).toBeGreaterThan(0);
});
});
describe('getSaveOptions', () => {
it('should return the correct save options', () => {
const options = client.getSaveOptions();
expect(options).toHaveProperty('chatGptLabel');
expect(options).toHaveProperty('promptPrefix');
});
});
describe('getBuildMessagesOptions', () => {
it('should return the correct build messages options', () => {
const options = client.getBuildMessagesOptions({ promptPrefix: 'Hello' });
expect(options).toHaveProperty('isChatCompletion');
expect(options).toHaveProperty('promptPrefix');
expect(options.promptPrefix).toBe('Hello');
});
});
describe('buildMessages', () => {
it('should build messages correctly for chat completion', async () => {
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
expect(result).toHaveProperty('prompt');
});
it('should build messages correctly for non-chat completion', async () => {
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: false,
});
expect(result).toHaveProperty('prompt');
});
it('should build messages correctly with a promptPrefix', async () => {
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
promptPrefix: 'Test Prefix',
});
expect(result).toHaveProperty('prompt');
const instructions = result.prompt.find((item) => item.name === 'instructions');
expect(instructions).toBeDefined();
expect(instructions.content).toContain('Test Prefix');
});
it('should handle context strategy correctly', async () => {
client.contextStrategy = 'summarize';
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
expect(result).toHaveProperty('prompt');
expect(result).toHaveProperty('tokenCountMap');
});
it('should assign name property for user messages when options.name is set', async () => {
client.options.name = 'Test User';
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
const hasUserWithName = result.prompt.some(
(item) => item.role === 'user' && item.name === 'Test User',
);
expect(hasUserWithName).toBe(true);
});
it('should handle promptPrefix from options when promptPrefix argument is not provided', async () => {
client.options.promptPrefix = 'Test Prefix from options';
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
const instructions = result.prompt.find((item) => item.name === 'instructions');
expect(instructions.content).toContain('Test Prefix from options');
});
it('should handle case when neither promptPrefix argument nor options.promptPrefix is set', async () => {
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
const instructions = result.prompt.find((item) => item.name === 'instructions');
expect(instructions).toBeUndefined();
});
it('should handle case when getMessagesForConversation returns null or an empty array', async () => {
const messages = [];
const result = await client.buildMessages(messages, parentMessageId, {
isChatCompletion: true,
});
expect(result.prompt).toEqual([]);
});
});
describe('getTokenCountForMessage', () => {
const example_messages = [
{
role: 'system',
content:
'You are a helpful, pattern-following assistant that translates corporate jargon into plain English.',
},
{
role: 'system',
name: 'example_user',
content: 'New synergies will help drive top-line growth.',
},
{
role: 'system',
name: 'example_assistant',
content: 'Things working well together will increase revenue.',
},
{
role: 'system',
name: 'example_user',
content:
'Let\'s circle back when we have more bandwidth to touch base on opportunities for increased leverage.',
},
{
role: 'system',
name: 'example_assistant',
content: 'Let\'s talk later when we\'re less busy about how to do better.',
},
{
role: 'user',
content:
'This late pivot means we don\'t have time to boil the ocean for the client deliverable.',
},
];
const testCases = [
{ model: 'gpt-3.5-turbo-0301', expected: 127 },
{ model: 'gpt-3.5-turbo-0613', expected: 129 },
{ model: 'gpt-3.5-turbo', expected: 129 },
{ model: 'gpt-4-0314', expected: 129 },
{ model: 'gpt-4-0613', expected: 129 },
{ model: 'gpt-4', expected: 129 },
{ model: 'unknown', expected: 129 },
];
testCases.forEach((testCase) => {
it(`should return ${testCase.expected} tokens for model ${testCase.model}`, () => {
client.modelOptions.model = testCase.model;
client.selectTokenizer();
// 3 tokens for assistant label
let totalTokens = 3;
for (let message of example_messages) {
totalTokens += client.getTokenCountForMessage(message);
}
expect(totalTokens).toBe(testCase.expected);
});
});
});
});