mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-17 17:00:15 +01:00
* 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
112 lines
3.2 KiB
JavaScript
112 lines
3.2 KiB
JavaScript
const BaseClient = require('../BaseClient');
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const { getModelMaxTokens } = require('../../../utils');
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class FakeClient extends BaseClient {
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constructor(apiKey, options = {}) {
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super(apiKey, options);
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this.sender = 'AI Assistant';
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this.setOptions(options);
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}
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setOptions(options) {
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if (this.options && !this.options.replaceOptions) {
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this.options.modelOptions = {
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...this.options.modelOptions,
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...options.modelOptions,
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};
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delete options.modelOptions;
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this.options = {
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...this.options,
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...options,
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};
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} else {
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this.options = options;
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}
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if (this.options.openaiApiKey) {
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this.apiKey = this.options.openaiApiKey;
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}
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const modelOptions = this.options.modelOptions || {};
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if (!this.modelOptions) {
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this.modelOptions = {
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...modelOptions,
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model: modelOptions.model || 'gpt-3.5-turbo',
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temperature:
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typeof modelOptions.temperature === 'undefined' ? 0.8 : modelOptions.temperature,
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top_p: typeof modelOptions.top_p === 'undefined' ? 1 : modelOptions.top_p,
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presence_penalty:
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typeof modelOptions.presence_penalty === 'undefined' ? 1 : modelOptions.presence_penalty,
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stop: modelOptions.stop,
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};
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}
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this.maxContextTokens = getModelMaxTokens(this.modelOptions.model) ?? 4097;
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}
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getCompletion() {}
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buildMessages() {}
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getTokenCount(str) {
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return str.length;
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}
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getTokenCountForMessage(message) {
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return message?.content?.length || message.length;
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}
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}
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const initializeFakeClient = (apiKey, options, fakeMessages) => {
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let TestClient = new FakeClient(apiKey);
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TestClient.options = options;
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TestClient.abortController = { abort: jest.fn() };
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TestClient.saveMessageToDatabase = jest.fn();
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TestClient.loadHistory = jest
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.fn()
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.mockImplementation((conversationId, parentMessageId = null) => {
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if (!conversationId) {
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TestClient.currentMessages = [];
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return Promise.resolve([]);
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}
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const orderedMessages = TestClient.constructor.getMessagesForConversation({
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messages: fakeMessages,
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parentMessageId,
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});
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TestClient.currentMessages = orderedMessages;
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return Promise.resolve(orderedMessages);
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});
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TestClient.getSaveOptions = jest.fn().mockImplementation(() => {
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return {};
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});
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TestClient.getBuildMessagesOptions = jest.fn().mockImplementation(() => {
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return {};
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});
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TestClient.sendCompletion = jest.fn(async () => {
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return 'Mock response text';
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});
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TestClient.buildMessages = jest.fn(async (messages, parentMessageId) => {
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const orderedMessages = TestClient.constructor.getMessagesForConversation({
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messages,
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parentMessageId,
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});
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const formattedMessages = orderedMessages.map((message) => {
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let { role: _role, sender, text } = message;
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const role = _role ?? sender;
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const content = text ?? '';
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return {
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role: role?.toLowerCase() === 'user' ? 'user' : 'assistant',
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content,
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};
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});
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return {
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prompt: formattedMessages,
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tokenCountMap: null, // Simplified for the mock
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};
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});
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return TestClient;
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};
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module.exports = { FakeClient, initializeFakeClient };
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