mirror of
https://github.com/danny-avila/LibreChat.git
synced 2025-09-22 08:12:00 +02:00

* fix: agent initialization, add `collectedUsage` handling * style: improve side panel styling * refactor(loadAgent): Optimize order agent project ID retrieval * feat: code execution * fix: typing issues * feat: ExecuteCode content part * refactor: use local state for default collapsed state of analysis content parts * fix: code parsing in ExecuteCode component * chore: bump agents package, export loadAuthValues * refactor: Update handleTools.js to use EnvVar for code execution tool authentication * WIP * feat: download code outputs * fix(useEventHandlers): type issues * feat: backend handling for code outputs * Refactor: Remove console.log statement in Part.tsx * refactor: add attachments to TMessage/messageSchema * WIP: prelim handling for code outputs * feat: attachments rendering * refactor: improve attachments rendering * fix: attachments, nullish edge case, handle attachments from event stream, bump agents package * fix filename download * fix: tool assignment for 'run code' on agent creation * fix: image handling by adding attachments * refactor: prevent agent creation without provider/model * refactor: remove unnecessary space in agent creation success message * refactor: select first model if selecting provider from empty on form * fix: Agent avatar bug * fix: `defaultAgentFormValues` causing boolean typing issue and typeerror * fix: capabilities counting as tools, causing duplication of them * fix: formatted messages edge case where consecutive content text type parts with the latter having tool_call_ids would cause consecutive AI messages to be created. furthermore, content could not be an array for tool_use messages (anthropic limitation) * chore: bump @librechat/agents dependency to version 1.6.9 * feat: bedrock agents * feat: new Agents icon * feat: agent titling * feat: agent landing * refactor: allow sharing agent globally only if user is admin or author * feat: initial AgentPanelSkeleton * feat: AgentPanelSkeleton * feat: collaborative agents * chore: add potential authorName as part of schema * chore: Remove unnecessary console.log statement * WIP: agent model parameters * chore: ToolsDialog typing and tool related localization chnages * refactor: update tool instance type (latest langchain class), and rename google tool to 'google' proper * chore: add back tools * feat: Agent knowledge files upload * refactor: better verbiage for disabled knowledge * chore: debug logs for file deletions * chore: debug logs for file deletions * feat: upload/delete agent knowledge/file-search files * feat: file search UI for agents * feat: first pass, file search tool * chore: update default agent capabilities and info
611 lines
18 KiB
JavaScript
611 lines
18 KiB
JavaScript
// const { HttpsProxyAgent } = require('https-proxy-agent');
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// const {
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// Constants,
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// ImageDetail,
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// EModelEndpoint,
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// resolveHeaders,
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// validateVisionModel,
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// mapModelToAzureConfig,
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// } = require('librechat-data-provider');
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const { Callback, createMetadataAggregator } = require('@librechat/agents');
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const {
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Constants,
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openAISchema,
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EModelEndpoint,
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anthropicSchema,
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bedrockOutputParser,
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providerEndpointMap,
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removeNullishValues,
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} = require('librechat-data-provider');
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const {
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extractBaseURL,
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// constructAzureURL,
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// genAzureChatCompletion,
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} = require('~/utils');
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const {
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formatMessage,
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formatAgentMessages,
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createContextHandlers,
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} = require('~/app/clients/prompts');
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const { encodeAndFormat } = require('~/server/services/Files/images/encode');
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const Tokenizer = require('~/server/services/Tokenizer');
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const { spendTokens } = require('~/models/spendTokens');
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const BaseClient = require('~/app/clients/BaseClient');
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// const { sleep } = require('~/server/utils');
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const { createRun } = require('./run');
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const { logger } = require('~/config');
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/** @typedef {import('@librechat/agents').MessageContentComplex} MessageContentComplex */
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const providerParsers = {
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[EModelEndpoint.openAI]: openAISchema,
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[EModelEndpoint.azureOpenAI]: openAISchema,
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[EModelEndpoint.anthropic]: anthropicSchema,
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[EModelEndpoint.bedrock]: bedrockOutputParser,
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};
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class AgentClient extends BaseClient {
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constructor(options = {}) {
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super(null, options);
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/** @type {'discard' | 'summarize'} */
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this.contextStrategy = 'discard';
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/** @deprecated @type {true} - Is a Chat Completion Request */
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this.isChatCompletion = true;
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/** @type {AgentRun} */
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this.run;
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const {
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contentParts,
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collectedUsage,
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artifactPromises,
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maxContextTokens,
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modelOptions = {},
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...clientOptions
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} = options;
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this.modelOptions = modelOptions;
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this.maxContextTokens = maxContextTokens;
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/** @type {MessageContentComplex[]} */
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this.contentParts = contentParts;
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/** @type {Array<UsageMetadata>} */
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this.collectedUsage = collectedUsage;
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/** @type {ArtifactPromises} */
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this.artifactPromises = artifactPromises;
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this.options = Object.assign({ endpoint: options.endpoint }, clientOptions);
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}
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/**
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* Returns the aggregated content parts for the current run.
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* @returns {MessageContentComplex[]} */
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getContentParts() {
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return this.contentParts;
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}
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setOptions(options) {
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logger.info('[api/server/controllers/agents/client.js] setOptions', options);
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}
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/**
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*
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* Checks if the model is a vision model based on request attachments and sets the appropriate options:
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* - Sets `this.modelOptions.model` to `gpt-4-vision-preview` if the request is a vision request.
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* - Sets `this.isVisionModel` to `true` if vision request.
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* - Deletes `this.modelOptions.stop` if vision request.
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* @param {MongoFile[]} attachments
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*/
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checkVisionRequest(attachments) {
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logger.info(
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'[api/server/controllers/agents/client.js #checkVisionRequest] not implemented',
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attachments,
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);
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// if (!attachments) {
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// return;
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// }
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// const availableModels = this.options.modelsConfig?.[this.options.endpoint];
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// if (!availableModels) {
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// return;
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// }
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// let visionRequestDetected = false;
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// for (const file of attachments) {
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// if (file?.type?.includes('image')) {
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// visionRequestDetected = true;
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// break;
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// }
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// }
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// if (!visionRequestDetected) {
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// return;
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// }
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// this.isVisionModel = validateVisionModel({ model: this.modelOptions.model, availableModels });
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// if (this.isVisionModel) {
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// delete this.modelOptions.stop;
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// return;
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// }
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// for (const model of availableModels) {
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// if (!validateVisionModel({ model, availableModels })) {
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// continue;
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// }
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// this.modelOptions.model = model;
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// this.isVisionModel = true;
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// delete this.modelOptions.stop;
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// return;
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// }
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// if (!availableModels.includes(this.defaultVisionModel)) {
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// return;
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// }
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// if (!validateVisionModel({ model: this.defaultVisionModel, availableModels })) {
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// return;
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// }
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// this.modelOptions.model = this.defaultVisionModel;
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// this.isVisionModel = true;
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// delete this.modelOptions.stop;
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}
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getSaveOptions() {
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const parseOptions = providerParsers[this.options.endpoint];
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let runOptions =
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this.options.endpoint === EModelEndpoint.agents
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? {
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model: undefined,
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// TODO:
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// would need to be override settings; otherwise, model needs to be undefined
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// model: this.override.model,
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// instructions: this.override.instructions,
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// additional_instructions: this.override.additional_instructions,
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}
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: {};
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if (parseOptions) {
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runOptions = parseOptions(this.modelOptions);
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}
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return removeNullishValues(
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Object.assign(
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{
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endpoint: this.options.endpoint,
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agent_id: this.options.agent.id,
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modelLabel: this.options.modelLabel,
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maxContextTokens: this.options.maxContextTokens,
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resendFiles: this.options.resendFiles,
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imageDetail: this.options.imageDetail,
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spec: this.options.spec,
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},
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// TODO: PARSE OPTIONS BY PROVIDER, MAY CONTAIN SENSITIVE DATA
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runOptions,
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),
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);
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}
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getBuildMessagesOptions(opts) {
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return {
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instructions: opts.instructions,
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additional_instructions: opts.additional_instructions,
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};
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}
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async addImageURLs(message, attachments) {
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const { files, image_urls } = await encodeAndFormat(
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this.options.req,
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attachments,
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this.options.agent.provider,
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);
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message.image_urls = image_urls.length ? image_urls : undefined;
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return files;
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}
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async buildMessages(
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messages,
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parentMessageId,
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{ instructions = null, additional_instructions = null },
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opts,
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) {
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let orderedMessages = this.constructor.getMessagesForConversation({
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messages,
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parentMessageId,
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summary: this.shouldSummarize,
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});
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let payload;
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/** @type {{ role: string; name: string; content: string } | undefined} */
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let systemMessage;
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/** @type {number | undefined} */
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let promptTokens;
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/** @type {string} */
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let systemContent = `${instructions ?? ''}${additional_instructions ?? ''}`;
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if (this.options.attachments) {
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const attachments = await this.options.attachments;
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if (this.message_file_map) {
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this.message_file_map[orderedMessages[orderedMessages.length - 1].messageId] = attachments;
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} else {
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this.message_file_map = {
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[orderedMessages[orderedMessages.length - 1].messageId]: attachments,
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};
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}
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const files = await this.addImageURLs(
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orderedMessages[orderedMessages.length - 1],
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attachments,
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);
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this.options.attachments = files;
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}
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if (this.message_file_map) {
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this.contextHandlers = createContextHandlers(
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this.options.req,
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orderedMessages[orderedMessages.length - 1].text,
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);
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}
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const formattedMessages = orderedMessages.map((message, i) => {
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const formattedMessage = formatMessage({
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message,
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userName: this.options?.name,
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assistantName: this.options?.modelLabel,
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});
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const needsTokenCount = this.contextStrategy && !orderedMessages[i].tokenCount;
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/* If tokens were never counted, or, is a Vision request and the message has files, count again */
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if (needsTokenCount || (this.isVisionModel && (message.image_urls || message.files))) {
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orderedMessages[i].tokenCount = this.getTokenCountForMessage(formattedMessage);
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}
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/* If message has files, calculate image token cost */
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// if (this.message_file_map && this.message_file_map[message.messageId]) {
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// const attachments = this.message_file_map[message.messageId];
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// for (const file of attachments) {
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// if (file.embedded) {
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// this.contextHandlers?.processFile(file);
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// continue;
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// }
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// orderedMessages[i].tokenCount += this.calculateImageTokenCost({
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// width: file.width,
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// height: file.height,
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// detail: this.options.imageDetail ?? ImageDetail.auto,
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// });
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// }
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// }
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return formattedMessage;
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});
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if (this.contextHandlers) {
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this.augmentedPrompt = await this.contextHandlers.createContext();
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systemContent = this.augmentedPrompt + systemContent;
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}
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if (systemContent) {
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systemContent = `${systemContent.trim()}`;
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systemMessage = {
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role: 'system',
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name: 'instructions',
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content: systemContent,
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};
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if (this.contextStrategy) {
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const instructionTokens = this.getTokenCountForMessage(systemMessage);
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if (instructionTokens >= 0) {
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const firstMessageTokens = orderedMessages[0].tokenCount ?? 0;
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orderedMessages[0].tokenCount = firstMessageTokens + instructionTokens;
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}
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}
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}
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if (this.contextStrategy) {
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({ payload, promptTokens, messages } = await this.handleContextStrategy({
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orderedMessages,
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formattedMessages,
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/* prefer usage_metadata from final message */
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buildTokenMap: false,
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}));
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}
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const result = {
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prompt: payload,
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promptTokens,
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messages,
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};
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if (promptTokens >= 0 && typeof opts?.getReqData === 'function') {
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opts.getReqData({ promptTokens });
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}
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return result;
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}
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/** @type {sendCompletion} */
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async sendCompletion(payload, opts = {}) {
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this.modelOptions.user = this.user;
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await this.chatCompletion({
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payload,
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onProgress: opts.onProgress,
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abortController: opts.abortController,
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});
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return this.contentParts;
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}
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/**
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* @param {Object} params
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* @param {string} [params.model]
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* @param {string} [params.context='message']
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* @param {UsageMetadata[]} [params.collectedUsage=this.collectedUsage]
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*/
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async recordCollectedUsage({ model, context = 'message', collectedUsage = this.collectedUsage }) {
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for (const usage of collectedUsage) {
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await spendTokens(
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{
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context,
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model: model ?? this.modelOptions.model,
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conversationId: this.conversationId,
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user: this.user ?? this.options.req.user?.id,
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endpointTokenConfig: this.options.endpointTokenConfig,
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},
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{ promptTokens: usage.input_tokens, completionTokens: usage.output_tokens },
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);
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}
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}
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async chatCompletion({ payload, abortController = null }) {
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try {
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if (!abortController) {
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abortController = new AbortController();
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}
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const baseURL = extractBaseURL(this.completionsUrl);
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logger.debug('[api/server/controllers/agents/client.js] chatCompletion', {
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baseURL,
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payload,
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});
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// if (this.useOpenRouter) {
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// opts.defaultHeaders = {
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// 'HTTP-Referer': 'https://librechat.ai',
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// 'X-Title': 'LibreChat',
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// };
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// }
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// if (this.options.headers) {
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// opts.defaultHeaders = { ...opts.defaultHeaders, ...this.options.headers };
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// }
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// if (this.options.proxy) {
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// opts.httpAgent = new HttpsProxyAgent(this.options.proxy);
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// }
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// if (this.isVisionModel) {
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// modelOptions.max_tokens = 4000;
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// }
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// /** @type {TAzureConfig | undefined} */
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// const azureConfig = this.options?.req?.app?.locals?.[EModelEndpoint.azureOpenAI];
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// if (
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// (this.azure && this.isVisionModel && azureConfig) ||
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// (azureConfig && this.isVisionModel && this.options.endpoint === EModelEndpoint.azureOpenAI)
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// ) {
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// const { modelGroupMap, groupMap } = azureConfig;
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// const {
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// azureOptions,
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// baseURL,
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// headers = {},
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// serverless,
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// } = mapModelToAzureConfig({
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// modelName: modelOptions.model,
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// modelGroupMap,
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// groupMap,
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// });
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// opts.defaultHeaders = resolveHeaders(headers);
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// this.langchainProxy = extractBaseURL(baseURL);
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// this.apiKey = azureOptions.azureOpenAIApiKey;
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// const groupName = modelGroupMap[modelOptions.model].group;
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// this.options.addParams = azureConfig.groupMap[groupName].addParams;
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// this.options.dropParams = azureConfig.groupMap[groupName].dropParams;
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// // Note: `forcePrompt` not re-assigned as only chat models are vision models
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// this.azure = !serverless && azureOptions;
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// this.azureEndpoint =
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// !serverless && genAzureChatCompletion(this.azure, modelOptions.model, this);
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// }
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// if (this.azure || this.options.azure) {
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// /* Azure Bug, extremely short default `max_tokens` response */
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// if (!modelOptions.max_tokens && modelOptions.model === 'gpt-4-vision-preview') {
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// modelOptions.max_tokens = 4000;
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// }
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// /* Azure does not accept `model` in the body, so we need to remove it. */
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// delete modelOptions.model;
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// opts.baseURL = this.langchainProxy
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// ? constructAzureURL({
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// baseURL: this.langchainProxy,
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// azureOptions: this.azure,
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// })
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// : this.azureEndpoint.split(/(?<!\/)\/(chat|completion)\//)[0];
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// opts.defaultQuery = { 'api-version': this.azure.azureOpenAIApiVersion };
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// opts.defaultHeaders = { ...opts.defaultHeaders, 'api-key': this.apiKey };
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// }
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// if (process.env.OPENAI_ORGANIZATION) {
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// opts.organization = process.env.OPENAI_ORGANIZATION;
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// }
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// if (this.options.addParams && typeof this.options.addParams === 'object') {
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// modelOptions = {
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// ...modelOptions,
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// ...this.options.addParams,
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// };
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// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] added params', {
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// addParams: this.options.addParams,
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// modelOptions,
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// });
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// }
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// if (this.options.dropParams && Array.isArray(this.options.dropParams)) {
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// this.options.dropParams.forEach((param) => {
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// delete modelOptions[param];
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// });
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// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] dropped params', {
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// dropParams: this.options.dropParams,
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// modelOptions,
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// });
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// }
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const run = await createRun({
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req: this.options.req,
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agent: this.options.agent,
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tools: this.options.tools,
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toolMap: this.options.toolMap,
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runId: this.responseMessageId,
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modelOptions: this.modelOptions,
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customHandlers: this.options.eventHandlers,
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});
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const config = {
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configurable: {
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provider: providerEndpointMap[this.options.agent.provider],
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thread_id: this.conversationId,
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},
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signal: abortController.signal,
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streamMode: 'values',
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version: 'v2',
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};
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if (!run) {
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throw new Error('Failed to create run');
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}
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this.run = run;
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const messages = formatAgentMessages(payload);
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await run.processStream({ messages }, config, {
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[Callback.TOOL_ERROR]: (graph, error, toolId) => {
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logger.error(
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'[api/server/controllers/agents/client.js #chatCompletion] Tool Error',
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error,
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toolId,
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);
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},
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});
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this.recordCollectedUsage({ context: 'message' }).catch((err) => {
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logger.error(
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'[api/server/controllers/agents/client.js #chatCompletion] Error recording collected usage',
|
|
err,
|
|
);
|
|
});
|
|
} catch (err) {
|
|
if (!abortController.signal.aborted) {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #sendCompletion] Unhandled error type',
|
|
err,
|
|
);
|
|
throw err;
|
|
}
|
|
|
|
logger.warn(
|
|
'[api/server/controllers/agents/client.js #sendCompletion] Operation aborted',
|
|
err,
|
|
);
|
|
}
|
|
}
|
|
|
|
/**
|
|
*
|
|
* @param {Object} params
|
|
* @param {string} params.text
|
|
* @param {string} params.conversationId
|
|
*/
|
|
async titleConvo({ text }) {
|
|
if (!this.run) {
|
|
throw new Error('Run not initialized');
|
|
}
|
|
const { handleLLMEnd, collected: collectedMetadata } = createMetadataAggregator();
|
|
const clientOptions = {};
|
|
const providerConfig = this.options.req.app.locals[this.options.agent.provider];
|
|
if (
|
|
providerConfig &&
|
|
providerConfig.titleModel &&
|
|
providerConfig.titleModel !== Constants.CURRENT_MODEL
|
|
) {
|
|
clientOptions.model = providerConfig.titleModel;
|
|
}
|
|
try {
|
|
const titleResult = await this.run.generateTitle({
|
|
inputText: text,
|
|
contentParts: this.contentParts,
|
|
clientOptions,
|
|
chainOptions: {
|
|
callbacks: [
|
|
{
|
|
handleLLMEnd,
|
|
},
|
|
],
|
|
},
|
|
});
|
|
|
|
const collectedUsage = collectedMetadata.map((item) => {
|
|
let input_tokens, output_tokens;
|
|
|
|
if (item.usage) {
|
|
input_tokens = item.usage.input_tokens || item.usage.inputTokens;
|
|
output_tokens = item.usage.output_tokens || item.usage.outputTokens;
|
|
} else if (item.tokenUsage) {
|
|
input_tokens = item.tokenUsage.promptTokens;
|
|
output_tokens = item.tokenUsage.completionTokens;
|
|
}
|
|
|
|
return {
|
|
input_tokens: input_tokens,
|
|
output_tokens: output_tokens,
|
|
};
|
|
});
|
|
|
|
this.recordCollectedUsage({
|
|
model: clientOptions.model,
|
|
context: 'title',
|
|
collectedUsage,
|
|
}).catch((err) => {
|
|
logger.error(
|
|
'[api/server/controllers/agents/client.js #titleConvo] Error recording collected usage',
|
|
err,
|
|
);
|
|
});
|
|
|
|
return titleResult.title;
|
|
} catch (err) {
|
|
logger.error('[api/server/controllers/agents/client.js #titleConvo] Error', err);
|
|
return;
|
|
}
|
|
}
|
|
|
|
getEncoding() {
|
|
return this.modelOptions.model?.includes('gpt-4o') ? 'o200k_base' : 'cl100k_base';
|
|
}
|
|
|
|
/**
|
|
* Returns the token count of a given text. It also checks and resets the tokenizers if necessary.
|
|
* @param {string} text - The text to get the token count for.
|
|
* @returns {number} The token count of the given text.
|
|
*/
|
|
getTokenCount(text) {
|
|
const encoding = this.getEncoding();
|
|
return Tokenizer.getTokenCount(text, encoding);
|
|
}
|
|
}
|
|
|
|
module.exports = AgentClient;
|