mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-12-21 19:00:13 +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
467 lines
15 KiB
JavaScript
467 lines
15 KiB
JavaScript
const OpenAIClient = require('./OpenAIClient');
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const { CallbackManager } = require('langchain/callbacks');
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const { initializeCustomAgent, initializeFunctionsAgent } = require('./agents');
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const { addImages, buildErrorInput, buildPromptPrefix } = require('./output_parsers');
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// const { createSummaryBufferMemory } = require('./memory');
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const { formatLangChainMessages } = require('./prompts');
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const { SelfReflectionTool } = require('./tools');
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const { loadTools } = require('./tools/util');
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class PluginsClient extends OpenAIClient {
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constructor(apiKey, options = {}) {
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super(apiKey, options);
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this.sender = options.sender ?? 'Assistant';
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this.tools = [];
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this.actions = [];
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this.setOptions(options);
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this.openAIApiKey = this.apiKey;
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this.executor = null;
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}
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setOptions(options) {
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this.agentOptions = { ...options.agentOptions };
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this.functionsAgent = this.agentOptions?.agent === 'functions';
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this.agentIsGpt3 = this.agentOptions?.model?.includes('gpt-3');
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super.setOptions(options);
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if (this.functionsAgent && this.agentOptions.model && !this.useOpenRouter) {
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this.agentOptions.model = this.getFunctionModelName(this.agentOptions.model);
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}
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this.isGpt3 = this.modelOptions?.model?.includes('gpt-3');
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if (this.options.reverseProxyUrl) {
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this.langchainProxy = this.options.reverseProxyUrl.match(/.*v1/)[0];
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}
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}
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getSaveOptions() {
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return {
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chatGptLabel: this.options.chatGptLabel,
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promptPrefix: this.options.promptPrefix,
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...this.modelOptions,
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agentOptions: this.agentOptions,
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};
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}
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saveLatestAction(action) {
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this.actions.push(action);
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}
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getFunctionModelName(input) {
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if (input.includes('gpt-3.5-turbo')) {
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return 'gpt-3.5-turbo';
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} else if (input.includes('gpt-4')) {
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return 'gpt-4';
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} else {
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return 'gpt-3.5-turbo';
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}
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}
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getBuildMessagesOptions(opts) {
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return {
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isChatCompletion: true,
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promptPrefix: opts.promptPrefix,
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abortController: opts.abortController,
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};
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}
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async initialize({ user, message, onAgentAction, onChainEnd, signal }) {
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const modelOptions = {
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modelName: this.agentOptions.model,
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temperature: this.agentOptions.temperature,
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};
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const model = this.initializeLLM(modelOptions);
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if (this.options.debug) {
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console.debug(
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`<-----Agent Model: ${model.modelName} | Temp: ${model.temperature} | Functions: ${this.functionsAgent}----->`,
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);
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}
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// Map Messages to Langchain format
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const pastMessages = formatLangChainMessages(this.currentMessages.slice(0, -1), {
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userName: this.options?.name,
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});
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this.options.debug && console.debug('pastMessages: ', pastMessages);
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// TODO: implement new token efficient way of processing openAPI plugins so they can "share" memory with agent
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// const memory = createSummaryBufferMemory({ llm: this.initializeLLM(modelOptions), messages: pastMessages });
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this.tools = await loadTools({
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user,
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model,
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tools: this.options.tools,
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functions: this.functionsAgent,
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options: {
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// memory,
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openAIApiKey: this.openAIApiKey,
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conversationId: this.conversationId,
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debug: this.options?.debug,
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message,
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},
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});
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if (this.tools.length > 0 && !this.functionsAgent) {
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this.tools.push(new SelfReflectionTool({ message, isGpt3: false }));
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} else if (this.tools.length === 0) {
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return;
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}
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if (this.options.debug) {
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console.debug('Requested Tools');
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console.debug(this.options.tools);
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console.debug('Loaded Tools');
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console.debug(this.tools.map((tool) => tool.name));
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}
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const handleAction = (action, runId, callback = null) => {
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this.saveLatestAction(action);
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if (this.options.debug) {
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console.debug('Latest Agent Action ', this.actions[this.actions.length - 1]);
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}
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if (typeof callback === 'function') {
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callback(action, runId);
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}
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};
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// initialize agent
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const initializer = this.functionsAgent ? initializeFunctionsAgent : initializeCustomAgent;
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this.executor = await initializer({
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model,
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signal,
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pastMessages,
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tools: this.tools,
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currentDateString: this.currentDateString,
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verbose: this.options.debug,
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returnIntermediateSteps: true,
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callbackManager: CallbackManager.fromHandlers({
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async handleAgentAction(action, runId) {
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handleAction(action, runId, onAgentAction);
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},
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async handleChainEnd(action) {
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if (typeof onChainEnd === 'function') {
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onChainEnd(action);
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}
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},
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}),
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});
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if (this.options.debug) {
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console.debug('Loaded agent.');
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}
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}
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async executorCall(message, { signal, stream, onToolStart, onToolEnd }) {
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let errorMessage = '';
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const maxAttempts = 1;
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for (let attempts = 1; attempts <= maxAttempts; attempts++) {
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const errorInput = buildErrorInput({
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message,
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errorMessage,
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actions: this.actions,
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functionsAgent: this.functionsAgent,
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});
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const input = attempts > 1 ? errorInput : message;
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if (this.options.debug) {
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console.debug(`Attempt ${attempts} of ${maxAttempts}`);
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}
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if (this.options.debug && errorMessage.length > 0) {
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console.debug('Caught error, input:', input);
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}
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try {
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this.result = await this.executor.call({ input, signal }, [
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{
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async handleToolStart(...args) {
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await onToolStart(...args);
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},
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async handleToolEnd(...args) {
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await onToolEnd(...args);
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},
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async handleLLMEnd(output) {
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const { generations } = output;
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const { text } = generations[0][0];
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if (text && typeof stream === 'function') {
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await stream(text);
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}
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},
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},
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]);
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break; // Exit the loop if the function call is successful
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} catch (err) {
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console.error(err);
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errorMessage = err.message;
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let content = '';
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if (content) {
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errorMessage = content;
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break;
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}
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if (attempts === maxAttempts) {
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this.result.output = `Encountered an error while attempting to respond. Error: ${err.message}`;
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this.result.intermediateSteps = this.actions;
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this.result.errorMessage = errorMessage;
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break;
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}
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}
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}
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}
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async handleResponseMessage(responseMessage, saveOptions, user) {
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responseMessage.tokenCount = this.getTokenCountForResponse(responseMessage);
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responseMessage.completionTokens = responseMessage.tokenCount;
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await this.saveMessageToDatabase(responseMessage, saveOptions, user);
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delete responseMessage.tokenCount;
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return { ...responseMessage, ...this.result };
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}
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async sendMessage(message, opts = {}) {
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// If a message is edited, no tools can be used.
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const completionMode = this.options.tools.length === 0 || opts.isEdited;
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if (completionMode) {
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this.setOptions(opts);
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return super.sendMessage(message, opts);
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}
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if (this.options.debug) {
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console.log('Plugins sendMessage', message, opts);
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}
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const {
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user,
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isEdited,
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conversationId,
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responseMessageId,
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saveOptions,
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userMessage,
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onAgentAction,
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onChainEnd,
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onToolStart,
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onToolEnd,
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} = await this.handleStartMethods(message, opts);
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this.conversationId = conversationId;
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this.currentMessages.push(userMessage);
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let {
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prompt: payload,
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tokenCountMap,
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promptTokens,
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} = await this.buildMessages(
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this.currentMessages,
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userMessage.messageId,
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this.getBuildMessagesOptions({
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promptPrefix: null,
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abortController: this.abortController,
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}),
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);
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if (tokenCountMap) {
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console.dir(tokenCountMap, { depth: null });
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if (tokenCountMap[userMessage.messageId]) {
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userMessage.tokenCount = tokenCountMap[userMessage.messageId];
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console.log('userMessage.tokenCount', userMessage.tokenCount);
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}
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this.handleTokenCountMap(tokenCountMap);
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}
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this.result = {};
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if (payload) {
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this.currentMessages = payload;
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}
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await this.saveMessageToDatabase(userMessage, saveOptions, user);
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const responseMessage = {
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messageId: responseMessageId,
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conversationId,
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parentMessageId: userMessage.messageId,
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isCreatedByUser: false,
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isEdited,
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model: this.modelOptions.model,
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sender: this.sender,
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promptTokens,
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};
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await this.initialize({
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user,
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message,
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onAgentAction,
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onChainEnd,
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signal: this.abortController.signal,
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onProgress: opts.onProgress,
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});
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// const stream = async (text) => {
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// await this.generateTextStream.call(this, text, opts.onProgress, { delay: 1 });
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// };
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await this.executorCall(message, {
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signal: this.abortController.signal,
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// stream,
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onToolStart,
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onToolEnd,
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});
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// If message was aborted mid-generation
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if (this.result?.errorMessage?.length > 0 && this.result?.errorMessage?.includes('cancel')) {
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responseMessage.text = 'Cancelled.';
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return await this.handleResponseMessage(responseMessage, saveOptions, user);
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}
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if (this.agentOptions.skipCompletion && this.result.output && this.functionsAgent) {
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const partialText = opts.getPartialText();
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const trimmedPartial = opts.getPartialText().replaceAll(':::plugin:::\n', '');
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responseMessage.text =
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trimmedPartial.length === 0 ? `${partialText}${this.result.output}` : partialText;
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await this.generateTextStream(this.result.output, opts.onProgress, { delay: 5 });
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return await this.handleResponseMessage(responseMessage, saveOptions, user);
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}
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if (this.agentOptions.skipCompletion && this.result.output) {
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responseMessage.text = this.result.output;
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addImages(this.result.intermediateSteps, responseMessage);
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await this.generateTextStream(this.result.output, opts.onProgress, { delay: 5 });
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return await this.handleResponseMessage(responseMessage, saveOptions, user);
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}
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if (this.options.debug) {
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console.debug('Plugins completion phase: this.result');
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console.debug(this.result);
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}
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const promptPrefix = buildPromptPrefix({
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result: this.result,
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message,
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functionsAgent: this.functionsAgent,
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});
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if (this.options.debug) {
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console.debug('Plugins: promptPrefix');
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console.debug(promptPrefix);
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}
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payload = await this.buildCompletionPrompt({
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messages: this.currentMessages,
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promptPrefix,
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});
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if (this.options.debug) {
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console.debug('buildCompletionPrompt Payload');
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console.debug(payload);
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}
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responseMessage.text = await this.sendCompletion(payload, opts);
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return await this.handleResponseMessage(responseMessage, saveOptions, user);
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}
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async buildCompletionPrompt({ messages, promptPrefix: _promptPrefix }) {
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if (this.options.debug) {
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console.debug('buildCompletionPrompt messages', messages);
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}
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const orderedMessages = messages;
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let promptPrefix = _promptPrefix.trim();
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// If the prompt prefix doesn't end with the end token, add it.
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if (!promptPrefix.endsWith(`${this.endToken}`)) {
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promptPrefix = `${promptPrefix.trim()}${this.endToken}\n\n`;
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}
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promptPrefix = `${this.startToken}Instructions:\n${promptPrefix}`;
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const promptSuffix = `${this.startToken}${this.chatGptLabel ?? 'Assistant'}:\n`;
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const instructionsPayload = {
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role: 'system',
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name: 'instructions',
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content: promptPrefix,
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};
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const messagePayload = {
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role: 'system',
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content: promptSuffix,
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};
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if (this.isGpt3) {
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instructionsPayload.role = 'user';
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messagePayload.role = 'user';
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instructionsPayload.content += `\n${promptSuffix}`;
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}
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// testing if this works with browser endpoint
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if (!this.isGpt3 && this.options.reverseProxyUrl) {
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instructionsPayload.role = 'user';
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}
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let currentTokenCount =
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this.getTokenCountForMessage(instructionsPayload) +
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this.getTokenCountForMessage(messagePayload);
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let promptBody = '';
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const maxTokenCount = this.maxPromptTokens;
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// Iterate backwards through the messages, adding them to the prompt until we reach the max token count.
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// Do this within a recursive async function so that it doesn't block the event loop for too long.
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const buildPromptBody = async () => {
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if (currentTokenCount < maxTokenCount && orderedMessages.length > 0) {
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const message = orderedMessages.pop();
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const isCreatedByUser = message.isCreatedByUser || message.role?.toLowerCase() === 'user';
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const roleLabel = isCreatedByUser ? this.userLabel : this.chatGptLabel;
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let messageString = `${this.startToken}${roleLabel}:\n${
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message.text ?? message.content ?? ''
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}${this.endToken}\n`;
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let newPromptBody = `${messageString}${promptBody}`;
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const tokenCountForMessage = this.getTokenCount(messageString);
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const newTokenCount = currentTokenCount + tokenCountForMessage;
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if (newTokenCount > maxTokenCount) {
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if (promptBody) {
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// This message would put us over the token limit, so don't add it.
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return false;
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}
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// This is the first message, so we can't add it. Just throw an error.
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throw new Error(
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`Prompt is too long. Max token count is ${maxTokenCount}, but prompt is ${newTokenCount} tokens long.`,
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);
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}
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promptBody = newPromptBody;
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currentTokenCount = newTokenCount;
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// wait for next tick to avoid blocking the event loop
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await new Promise((resolve) => setTimeout(resolve, 0));
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return buildPromptBody();
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}
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return true;
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};
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await buildPromptBody();
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const prompt = promptBody;
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messagePayload.content = prompt;
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// Add 2 tokens for metadata after all messages have been counted.
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currentTokenCount += 2;
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if (this.isGpt3 && messagePayload.content.length > 0) {
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const context = 'Chat History:\n';
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messagePayload.content = `${context}${prompt}`;
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currentTokenCount += this.getTokenCount(context);
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}
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// Use up to `this.maxContextTokens` tokens (prompt + response), but try to leave `this.maxTokens` tokens for the response.
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this.modelOptions.max_tokens = Math.min(
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this.maxContextTokens - currentTokenCount,
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this.maxResponseTokens,
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);
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if (this.isGpt3) {
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messagePayload.content += promptSuffix;
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return [instructionsPayload, messagePayload];
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}
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const result = [messagePayload, instructionsPayload];
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if (this.functionsAgent && !this.isGpt3) {
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result[1].content = `${result[1].content}\n${this.startToken}${this.chatGptLabel}:\nSure thing! Here is the output you requested:\n`;
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}
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return result.filter((message) => message.content.length > 0);
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}
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}
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module.exports = PluginsClient;
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