mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-18 01:10:14 +01:00
* refactor(Chains/llms): allow passing callbacks * refactor(BaseClient): accurately count completion tokens as generation only * refactor(OpenAIClient): remove unused getTokenCountForResponse, pass streaming var and callbacks in initializeLLM * wip: summary prompt tokens * refactor(summarizeMessages): new cut-off strategy that generates a better summary by adding context from beginning, truncating the middle, and providing the end wip: draft out relevant providers and variables for token tracing * refactor(createLLM): make streaming prop false by default * chore: remove use of getTokenCountForResponse * refactor(agents): use BufferMemory as ConversationSummaryBufferMemory token usage not easy to trace * chore: remove passing of streaming prop, also console log useful vars for tracing * feat: formatFromLangChain helper function to count tokens for ChatModelStart * refactor(initializeLLM): add role for LLM tracing * chore(formatFromLangChain): update JSDoc * feat(formatMessages): formats langChain messages into OpenAI payload format * chore: install openai-chat-tokens * refactor(formatMessage): optimize conditional langChain logic fix(formatFromLangChain): fix destructuring * feat: accurate prompt tokens for ChatModelStart before generation * refactor(handleChatModelStart): move to callbacks dir, use factory function * refactor(initializeLLM): rename 'role' to 'context' * feat(Balance/Transaction): new schema/models for tracking token spend refactor(Key): factor out model export to separate file * refactor(initializeClient): add req,res objects to client options * feat: add-balance script to add to an existing users' token balance refactor(Transaction): use multiplier map/function, return balance update * refactor(Tx): update enum for tokenType, return 1 for multiplier if no map match * refactor(Tx): add fair fallback value multiplier incase the config result is undefined * refactor(Balance): rename 'tokens' to 'tokenCredits' * feat: balance check, add tx.js for new tx-related methods and tests * chore(summaryPrompts): update prompt token count * refactor(callbacks): pass req, res wip: check balance * refactor(Tx): make convoId a String type, fix(calculateTokenValue) * refactor(BaseClient): add conversationId as client prop when assigned * feat(RunManager): track LLM runs with manager, track token spend from LLM, refactor(OpenAIClient): use RunManager to create callbacks, pass user prop to langchain api calls * feat(spendTokens): helper to spend prompt/completion tokens * feat(checkBalance): add helper to check, log, deny request if balance doesn't have enough funds refactor(Balance): static check method to return object instead of boolean now wip(OpenAIClient): implement use of checkBalance * refactor(initializeLLM): add token buffer to assure summary isn't generated when subsequent payload is too large refactor(OpenAIClient): add checkBalance refactor(createStartHandler): add checkBalance * chore: remove prompt and completion token logging from route handler * chore(spendTokens): add JSDoc * feat(logTokenCost): record transactions for basic api calls * chore(ask/edit): invoke getResponseSender only once per API call * refactor(ask/edit): pass promptTokens to getIds and include in abort data * refactor(getIds -> getReqData): rename function * refactor(Tx): increase value if incomplete message * feat: record tokenUsage when message is aborted * refactor: subtract tokens when payload includes function_call * refactor: add namespace for token_balance * fix(spendTokens): only execute if corresponding token type amounts are defined * refactor(checkBalance): throws Error if not enough token credits * refactor(runTitleChain): pass and use signal, spread object props in create helpers, and use 'call' instead of 'run' * fix(abortMiddleware): circular dependency, and default to empty string for completionTokens * fix: properly cancel title requests when there isn't enough tokens to generate * feat(predictNewSummary): custom chain for summaries to allow signal passing refactor(summaryBuffer): use new custom chain * feat(RunManager): add getRunByConversationId method, refactor: remove run and throw llm error on handleLLMError * refactor(createStartHandler): if summary, add error details to runs * fix(OpenAIClient): support aborting from summarization & showing error to user refactor(summarizeMessages): remove unnecessary operations counting summaryPromptTokens and note for alternative, pass signal to summaryBuffer * refactor(logTokenCost -> recordTokenUsage): rename * refactor(checkBalance): include promptTokens in errorMessage * refactor(checkBalance/spendTokens): move to models dir * fix(createLanguageChain): correctly pass config * refactor(initializeLLM/title): add tokenBuffer of 150 for balance check * refactor(openAPIPlugin): pass signal and memory, filter functions by the one being called * refactor(createStartHandler): add error to run if context is plugins as well * refactor(RunManager/handleLLMError): throw error immediately if plugins, don't remove run * refactor(PluginsClient): pass memory and signal to tools, cleanup error handling logic * chore: use absolute equality for addTitle condition * refactor(checkBalance): move checkBalance to execute after userMessage and tokenCounts are saved, also make conditional * style: icon changes to match official * fix(BaseClient): getTokenCountForResponse -> getTokenCount * fix(formatLangChainMessages): add kwargs as fallback prop from lc_kwargs, update JSDoc * refactor(Tx.create): does not update balance if CHECK_BALANCE is not enabled * fix(e2e/cleanUp): cleanup new collections, import all model methods from index * fix(config/add-balance): add uncaughtException listener * fix: circular dependency * refactor(initializeLLM/checkBalance): append new generations to errorMessage if cost exceeds balance * fix(handleResponseMessage): only record token usage in this method if not error and completion is not skipped * fix(createStartHandler): correct condition for generations * chore: bump postcss due to moderate severity vulnerability * chore: bump zod due to low severity vulnerability * chore: bump openai & data-provider version * feat(types): OpenAI Message types * chore: update bun lockfile * refactor(CodeBlock): add error block formatting * refactor(utils/Plugin): factor out formatJSON and cn to separate files (json.ts and cn.ts), add extractJSON * chore(logViolation): delete user_id after error is logged * refactor(getMessageError -> Error): change to React.FC, add token_balance handling, use extractJSON to determine JSON instead of regex * fix(DALL-E): use latest openai SDK * chore: reorganize imports, fix type issue * feat(server): add balance route * fix(api/models): add auth * feat(data-provider): /api/balance query * feat: show balance if checking is enabled, refetch on final message or error * chore: update docs, .env.example with token_usage info, add balance script command * fix(Balance): fallback to empty obj for balance query * style: slight adjustment of balance element * docs(token_usage): add PR notes
247 lines
6.2 KiB
JavaScript
247 lines
6.2 KiB
JavaScript
const { formatMessage, formatLangChainMessages, formatFromLangChain } = require('./formatMessages');
|
|
const { HumanMessage, AIMessage, SystemMessage } = require('langchain/schema');
|
|
|
|
describe('formatMessage', () => {
|
|
it('formats user message', () => {
|
|
const input = {
|
|
message: {
|
|
sender: 'user',
|
|
text: 'Hello',
|
|
},
|
|
userName: 'John',
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toEqual({
|
|
role: 'user',
|
|
content: 'Hello',
|
|
name: 'John',
|
|
});
|
|
});
|
|
|
|
it('formats a realistic user message', () => {
|
|
const input = {
|
|
message: {
|
|
_id: '6512cdfb92cbf69fea615331',
|
|
messageId: 'b620bf73-c5c3-4a38-b724-76886aac24c4',
|
|
__v: 0,
|
|
cancelled: false,
|
|
conversationId: '5c23d24f-941f-4aab-85df-127b596c8aa5',
|
|
createdAt: Date.now(),
|
|
error: false,
|
|
finish_reason: null,
|
|
isCreatedByUser: true,
|
|
isEdited: false,
|
|
model: null,
|
|
parentMessageId: '00000000-0000-0000-0000-000000000000',
|
|
sender: 'User',
|
|
text: 'hi',
|
|
tokenCount: 5,
|
|
unfinished: false,
|
|
updatedAt: Date.now(),
|
|
user: '6512cdf475f05c86d44c31d2',
|
|
},
|
|
userName: 'John',
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toEqual({
|
|
role: 'user',
|
|
content: 'hi',
|
|
name: 'John',
|
|
});
|
|
});
|
|
|
|
it('formats assistant message', () => {
|
|
const input = {
|
|
message: {
|
|
sender: 'assistant',
|
|
text: 'Hi there',
|
|
},
|
|
assistantName: 'Assistant',
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toEqual({
|
|
role: 'assistant',
|
|
content: 'Hi there',
|
|
name: 'Assistant',
|
|
});
|
|
});
|
|
|
|
it('formats system message', () => {
|
|
const input = {
|
|
message: {
|
|
role: 'system',
|
|
text: 'Hi there',
|
|
},
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toEqual({
|
|
role: 'system',
|
|
content: 'Hi there',
|
|
});
|
|
});
|
|
|
|
it('formats user message with langChain', () => {
|
|
const input = {
|
|
message: {
|
|
sender: 'user',
|
|
text: 'Hello',
|
|
},
|
|
userName: 'John',
|
|
langChain: true,
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toBeInstanceOf(HumanMessage);
|
|
expect(result.lc_kwargs.content).toEqual(input.message.text);
|
|
expect(result.lc_kwargs.name).toEqual(input.userName);
|
|
});
|
|
|
|
it('formats assistant message with langChain', () => {
|
|
const input = {
|
|
message: {
|
|
sender: 'assistant',
|
|
text: 'Hi there',
|
|
},
|
|
assistantName: 'Assistant',
|
|
langChain: true,
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toBeInstanceOf(AIMessage);
|
|
expect(result.lc_kwargs.content).toEqual(input.message.text);
|
|
expect(result.lc_kwargs.name).toEqual(input.assistantName);
|
|
});
|
|
|
|
it('formats system message with langChain', () => {
|
|
const input = {
|
|
message: {
|
|
role: 'system',
|
|
text: 'This is a system message.',
|
|
},
|
|
langChain: true,
|
|
};
|
|
const result = formatMessage(input);
|
|
expect(result).toBeInstanceOf(SystemMessage);
|
|
expect(result.lc_kwargs.content).toEqual(input.message.text);
|
|
});
|
|
|
|
it('formats langChain messages into OpenAI payload format', () => {
|
|
const human = {
|
|
message: new HumanMessage({
|
|
content: 'Hello',
|
|
}),
|
|
};
|
|
const system = {
|
|
message: new SystemMessage({
|
|
content: 'Hello',
|
|
}),
|
|
};
|
|
const ai = {
|
|
message: new AIMessage({
|
|
content: 'Hello',
|
|
}),
|
|
};
|
|
const humanResult = formatMessage(human);
|
|
const systemResult = formatMessage(system);
|
|
const aiResult = formatMessage(ai);
|
|
expect(humanResult).toEqual({
|
|
role: 'user',
|
|
content: 'Hello',
|
|
});
|
|
expect(systemResult).toEqual({
|
|
role: 'system',
|
|
content: 'Hello',
|
|
});
|
|
expect(aiResult).toEqual({
|
|
role: 'assistant',
|
|
content: 'Hello',
|
|
});
|
|
});
|
|
});
|
|
|
|
describe('formatLangChainMessages', () => {
|
|
it('formats an array of messages for LangChain', () => {
|
|
const messages = [
|
|
{
|
|
role: 'system',
|
|
content: 'This is a system message',
|
|
},
|
|
{
|
|
sender: 'user',
|
|
text: 'Hello',
|
|
},
|
|
{
|
|
sender: 'assistant',
|
|
text: 'Hi there',
|
|
},
|
|
];
|
|
const formatOptions = {
|
|
userName: 'John',
|
|
assistantName: 'Assistant',
|
|
};
|
|
const result = formatLangChainMessages(messages, formatOptions);
|
|
expect(result).toHaveLength(3);
|
|
expect(result[0]).toBeInstanceOf(SystemMessage);
|
|
expect(result[1]).toBeInstanceOf(HumanMessage);
|
|
expect(result[2]).toBeInstanceOf(AIMessage);
|
|
|
|
expect(result[0].lc_kwargs.content).toEqual(messages[0].content);
|
|
expect(result[1].lc_kwargs.content).toEqual(messages[1].text);
|
|
expect(result[2].lc_kwargs.content).toEqual(messages[2].text);
|
|
|
|
expect(result[1].lc_kwargs.name).toEqual(formatOptions.userName);
|
|
expect(result[2].lc_kwargs.name).toEqual(formatOptions.assistantName);
|
|
});
|
|
|
|
describe('formatFromLangChain', () => {
|
|
it('should merge kwargs and additional_kwargs', () => {
|
|
const message = {
|
|
kwargs: {
|
|
content: 'some content',
|
|
name: 'dan',
|
|
additional_kwargs: {
|
|
function_call: {
|
|
name: 'dall-e',
|
|
arguments: '{\n "input": "Subject: hedgehog, Style: cute"\n}',
|
|
},
|
|
},
|
|
},
|
|
};
|
|
|
|
const expected = {
|
|
content: 'some content',
|
|
name: 'dan',
|
|
function_call: {
|
|
name: 'dall-e',
|
|
arguments: '{\n "input": "Subject: hedgehog, Style: cute"\n}',
|
|
},
|
|
};
|
|
|
|
expect(formatFromLangChain(message)).toEqual(expected);
|
|
});
|
|
|
|
it('should handle messages without additional_kwargs', () => {
|
|
const message = {
|
|
kwargs: {
|
|
content: 'some content',
|
|
name: 'dan',
|
|
},
|
|
};
|
|
|
|
const expected = {
|
|
content: 'some content',
|
|
name: 'dan',
|
|
};
|
|
|
|
expect(formatFromLangChain(message)).toEqual(expected);
|
|
});
|
|
|
|
it('should handle empty messages', () => {
|
|
const message = {
|
|
kwargs: {},
|
|
};
|
|
|
|
const expected = {};
|
|
|
|
expect(formatFromLangChain(message)).toEqual(expected);
|
|
});
|
|
});
|
|
});
|