mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-09-22 06:00:56 +02:00

* 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
264 lines
6.8 KiB
JavaScript
264 lines
6.8 KiB
JavaScript
const { getUserPluginAuthValue } = require('../../../../server/services/PluginService');
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const { OpenAIEmbeddings } = require('langchain/embeddings/openai');
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const { ZapierToolKit } = require('langchain/agents');
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const { SerpAPI, ZapierNLAWrapper } = require('langchain/tools');
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const { ChatOpenAI } = require('langchain/chat_models/openai');
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const { Calculator } = require('langchain/tools/calculator');
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const { WebBrowser } = require('langchain/tools/webbrowser');
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const {
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availableTools,
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CodeInterpreter,
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AIPluginTool,
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GoogleSearchAPI,
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WolframAlphaAPI,
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StructuredWolfram,
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HttpRequestTool,
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OpenAICreateImage,
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StableDiffusionAPI,
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StructuredSD,
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AzureCognitiveSearch,
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StructuredACS,
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E2BTools,
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CodeSherpa,
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CodeSherpaTools,
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CodeBrew,
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} = require('../');
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const { loadSpecs } = require('./loadSpecs');
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const { loadToolSuite } = require('./loadToolSuite');
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const getOpenAIKey = async (options, user) => {
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let openAIApiKey = options.openAIApiKey ?? process.env.OPENAI_API_KEY;
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openAIApiKey = openAIApiKey === 'user_provided' ? null : openAIApiKey;
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return openAIApiKey || (await getUserPluginAuthValue(user, 'OPENAI_API_KEY'));
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};
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const validateTools = async (user, tools = []) => {
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try {
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const validToolsSet = new Set(tools);
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const availableToolsToValidate = availableTools.filter((tool) =>
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validToolsSet.has(tool.pluginKey),
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);
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const validateCredentials = async (authField, toolName) => {
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const adminAuth = process.env[authField];
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if (adminAuth && adminAuth.length > 0) {
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return;
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}
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const userAuth = await getUserPluginAuthValue(user, authField);
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if (userAuth && userAuth.length > 0) {
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return;
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}
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validToolsSet.delete(toolName);
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};
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for (const tool of availableToolsToValidate) {
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if (!tool.authConfig || tool.authConfig.length === 0) {
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continue;
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}
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for (const auth of tool.authConfig) {
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await validateCredentials(auth.authField, tool.pluginKey);
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}
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}
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return Array.from(validToolsSet.values());
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} catch (err) {
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console.log('There was a problem validating tools', err);
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throw new Error(err);
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}
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};
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const loadToolWithAuth = async (user, authFields, ToolConstructor, options = {}) => {
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return async function () {
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let authValues = {};
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for (const authField of authFields) {
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let authValue = process.env[authField];
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if (!authValue) {
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authValue = await getUserPluginAuthValue(user, authField);
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}
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authValues[authField] = authValue;
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}
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return new ToolConstructor({ ...options, ...authValues });
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};
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};
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const loadTools = async ({
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user,
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model,
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functions = null,
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returnMap = false,
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tools = [],
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options = {},
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}) => {
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const toolConstructors = {
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calculator: Calculator,
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codeinterpreter: CodeInterpreter,
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google: GoogleSearchAPI,
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wolfram: functions ? StructuredWolfram : WolframAlphaAPI,
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'dall-e': OpenAICreateImage,
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'stable-diffusion': functions ? StructuredSD : StableDiffusionAPI,
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'azure-cognitive-search': functions ? StructuredACS : AzureCognitiveSearch,
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CodeBrew: CodeBrew,
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};
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const openAIApiKey = await getOpenAIKey(options, user);
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const customConstructors = {
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e2b_code_interpreter: async () => {
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if (!functions) {
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return null;
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}
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return await loadToolSuite({
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pluginKey: 'e2b_code_interpreter',
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tools: E2BTools,
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user,
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options: {
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model,
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openAIApiKey,
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...options,
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},
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});
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},
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codesherpa_tools: async () => {
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if (!functions) {
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return null;
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}
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return await loadToolSuite({
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pluginKey: 'codesherpa_tools',
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tools: CodeSherpaTools,
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user,
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options,
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});
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},
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'web-browser': async () => {
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// let openAIApiKey = options.openAIApiKey ?? process.env.OPENAI_API_KEY;
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// openAIApiKey = openAIApiKey === 'user_provided' ? null : openAIApiKey;
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// openAIApiKey = openAIApiKey || (await getUserPluginAuthValue(user, 'OPENAI_API_KEY'));
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const browser = new WebBrowser({ model, embeddings: new OpenAIEmbeddings({ openAIApiKey }) });
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browser.description_for_model = browser.description;
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return browser;
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},
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serpapi: async () => {
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let apiKey = process.env.SERPAPI_API_KEY;
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if (!apiKey) {
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apiKey = await getUserPluginAuthValue(user, 'SERPAPI_API_KEY');
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}
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return new SerpAPI(apiKey, {
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location: 'Austin,Texas,United States',
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hl: 'en',
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gl: 'us',
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});
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},
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zapier: async () => {
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let apiKey = process.env.ZAPIER_NLA_API_KEY;
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if (!apiKey) {
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apiKey = await getUserPluginAuthValue(user, 'ZAPIER_NLA_API_KEY');
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}
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const zapier = new ZapierNLAWrapper({ apiKey });
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return ZapierToolKit.fromZapierNLAWrapper(zapier);
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},
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plugins: async () => {
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return [
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new HttpRequestTool(),
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await AIPluginTool.fromPluginUrl(
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'https://www.klarna.com/.well-known/ai-plugin.json',
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new ChatOpenAI({ openAIApiKey: options.openAIApiKey, temperature: 0 }),
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),
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];
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},
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};
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const requestedTools = {};
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if (functions) {
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toolConstructors.codesherpa = CodeSherpa;
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}
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const toolOptions = {
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serpapi: { location: 'Austin,Texas,United States', hl: 'en', gl: 'us' },
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};
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const toolAuthFields = {};
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availableTools.forEach((tool) => {
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if (customConstructors[tool.pluginKey]) {
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return;
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}
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toolAuthFields[tool.pluginKey] = tool.authConfig.map((auth) => auth.authField);
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});
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const remainingTools = [];
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for (const tool of tools) {
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if (customConstructors[tool]) {
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requestedTools[tool] = customConstructors[tool];
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continue;
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}
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if (toolConstructors[tool]) {
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const options = toolOptions[tool] || {};
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const toolInstance = await loadToolWithAuth(
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user,
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toolAuthFields[tool],
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toolConstructors[tool],
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options,
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);
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requestedTools[tool] = toolInstance;
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continue;
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}
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if (functions) {
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remainingTools.push(tool);
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}
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}
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let specs = null;
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if (functions && remainingTools.length > 0) {
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specs = await loadSpecs({
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llm: model,
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user,
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message: options.message,
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memory: options.memory,
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signal: options.signal,
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tools: remainingTools,
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map: true,
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verbose: false,
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});
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}
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for (const tool of remainingTools) {
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if (specs && specs[tool]) {
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requestedTools[tool] = specs[tool];
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}
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}
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if (returnMap) {
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return requestedTools;
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}
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// load tools
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let result = [];
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for (const tool of tools) {
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const validTool = requestedTools[tool];
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const plugin = await validTool();
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if (Array.isArray(plugin)) {
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result = [...result, ...plugin];
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} else if (plugin) {
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result.push(plugin);
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}
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}
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return result;
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};
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module.exports = {
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validateTools,
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loadTools,
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};
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