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https://github.com/danny-avila/LibreChat.git
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* 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
90 lines
3.5 KiB
JavaScript
90 lines
3.5 KiB
JavaScript
const { HumanMessage, AIMessage, SystemMessage } = require('langchain/schema');
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/**
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* Formats a message to OpenAI payload format based on the provided options.
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*
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* @param {Object} params - The parameters for formatting.
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* @param {Object} params.message - The message object to format.
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* @param {string} [params.message.role] - The role of the message sender (e.g., 'user', 'assistant').
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* @param {string} [params.message._name] - The name associated with the message.
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* @param {string} [params.message.sender] - The sender of the message.
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* @param {string} [params.message.text] - The text content of the message.
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* @param {string} [params.message.content] - The content of the message.
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* @param {string} [params.userName] - The name of the user.
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* @param {string} [params.assistantName] - The name of the assistant.
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* @param {boolean} [params.langChain=false] - Whether to return a LangChain message object.
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* @returns {(Object|HumanMessage|AIMessage|SystemMessage)} - The formatted message.
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*/
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const formatMessage = ({ message, userName, assistantName, langChain = false }) => {
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let { role: _role, _name, sender, text, content: _content, lc_id } = message;
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if (lc_id && lc_id[2] && !langChain) {
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const roleMapping = {
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SystemMessage: 'system',
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HumanMessage: 'user',
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AIMessage: 'assistant',
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};
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_role = roleMapping[lc_id[2]];
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}
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const role = _role ?? (sender && sender?.toLowerCase() === 'user' ? 'user' : 'assistant');
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const content = text ?? _content ?? '';
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const formattedMessage = {
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role,
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content,
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};
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if (_name) {
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formattedMessage.name = _name;
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}
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if (userName && formattedMessage.role === 'user') {
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formattedMessage.name = userName;
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}
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if (assistantName && formattedMessage.role === 'assistant') {
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formattedMessage.name = assistantName;
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}
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if (!langChain) {
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return formattedMessage;
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}
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if (role === 'user') {
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return new HumanMessage(formattedMessage);
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} else if (role === 'assistant') {
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return new AIMessage(formattedMessage);
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} else {
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return new SystemMessage(formattedMessage);
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}
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};
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/**
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* Formats an array of messages for LangChain.
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*
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* @param {Array<Object>} messages - The array of messages to format.
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* @param {Object} formatOptions - The options for formatting each message.
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* @param {string} [formatOptions.userName] - The name of the user.
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* @param {string} [formatOptions.assistantName] - The name of the assistant.
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* @returns {Array<(HumanMessage|AIMessage|SystemMessage)>} - The array of formatted LangChain messages.
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*/
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const formatLangChainMessages = (messages, formatOptions) =>
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messages.map((msg) => formatMessage({ ...formatOptions, message: msg, langChain: true }));
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/**
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* Formats a LangChain message object by merging properties from `lc_kwargs` or `kwargs` and `additional_kwargs`.
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*
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* @param {Object} message - The message object to format.
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* @param {Object} [message.lc_kwargs] - Contains properties to be merged. Either this or `message.kwargs` should be provided.
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* @param {Object} [message.kwargs] - Contains properties to be merged. Either this or `message.lc_kwargs` should be provided.
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* @param {Object} [message.kwargs.additional_kwargs] - Additional properties to be merged.
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*
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* @returns {Object} The formatted LangChain message.
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*/
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const formatFromLangChain = (message) => {
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const { additional_kwargs, ...message_kwargs } = message.lc_kwargs ?? message.kwargs;
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return {
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...message_kwargs,
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...additional_kwargs,
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};
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};
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module.exports = { formatMessage, formatLangChainMessages, formatFromLangChain };
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