mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-23 20:00:15 +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
263 lines
6.8 KiB
JavaScript
263 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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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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