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https://github.com/danny-avila/LibreChat.git
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* feat: update PaLM icons * feat: add additional google models * POC: formatting inputs for Vertex AI streaming * refactor: move endpoints services outside of /routes dir to /services/Endpoints * refactor: shorten schemas import * refactor: rename PALM to GOOGLE * feat: make Google editable endpoint * feat: reusable Ask and Edit controllers based off Anthropic * chore: organize imports/logic * fix(parseConvo): include examples in googleSchema * fix: google only allows odd number of messages to be sent * fix: pass proxy to AnthropicClient * refactor: change `google` altName to `Google` * refactor: update getModelMaxTokens and related functions to handle maxTokensMap with nested endpoint model key/values * refactor: google Icon and response sender changes (Codey and Google logo instead of PaLM in all cases) * feat: google support for maxTokensMap * feat: google updated endpoints with Ask/Edit controllers, buildOptions, and initializeClient * feat(GoogleClient): now builds prompt for text models and supports real streaming from Vertex AI through langchain * chore(GoogleClient): remove comments, left before for reference in git history * docs: update google instructions (WIP) * docs(apis_and_tokens.md): add images to google instructions * docs: remove typo apis_and_tokens.md * Update apis_and_tokens.md * feat(Google): use default settings map, fully support context for both text and chat models, fully support examples for chat models * chore: update more PaLM references to Google * chore: move playwright out of workflows to avoid failing tests
86 lines
2.4 KiB
JavaScript
86 lines
2.4 KiB
JavaScript
const { promptTokensEstimate } = require('openai-chat-tokens');
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const { EModelEndpoint } = require('~/server/services/Endpoints');
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const { formatFromLangChain } = require('~/app/clients/prompts');
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const checkBalance = require('~/models/checkBalance');
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const { isEnabled } = require('~/server/utils');
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const createStartHandler = ({
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context,
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conversationId,
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tokenBuffer = 0,
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initialMessageCount,
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manager,
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}) => {
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return async (_llm, _messages, runId, parentRunId, extraParams) => {
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const { invocation_params } = extraParams;
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const { model, functions, function_call } = invocation_params;
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const messages = _messages[0].map(formatFromLangChain);
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if (manager.debug) {
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console.log(`handleChatModelStart: ${context}`);
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console.dir({ model, functions, function_call }, { depth: null });
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}
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const payload = { messages };
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let prelimPromptTokens = 1;
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if (functions) {
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payload.functions = functions;
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prelimPromptTokens += 2;
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}
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if (function_call) {
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payload.function_call = function_call;
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prelimPromptTokens -= 5;
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}
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prelimPromptTokens += promptTokensEstimate(payload);
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if (manager.debug) {
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console.log('Prelim Prompt Tokens & Token Buffer', prelimPromptTokens, tokenBuffer);
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}
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prelimPromptTokens += tokenBuffer;
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try {
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if (isEnabled(process.env.CHECK_BALANCE)) {
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const generations =
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initialMessageCount && messages.length > initialMessageCount
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? messages.slice(initialMessageCount)
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: null;
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await checkBalance({
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req: manager.req,
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res: manager.res,
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txData: {
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user: manager.user,
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tokenType: 'prompt',
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amount: prelimPromptTokens,
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debug: manager.debug,
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generations,
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model,
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endpoint: EModelEndpoint.openAI,
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},
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});
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}
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} catch (err) {
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console.error(`[${context}] checkBalance error`, err);
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manager.abortController.abort();
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if (context === 'summary' || context === 'plugins') {
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manager.addRun(runId, { conversationId, error: err.message });
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throw new Error(err);
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}
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return;
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}
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manager.addRun(runId, {
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model,
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messages,
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functions,
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function_call,
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runId,
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parentRunId,
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conversationId,
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prelimPromptTokens,
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});
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};
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};
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module.exports = createStartHandler;
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