mirror of
https://github.com/danny-avila/LibreChat.git
synced 2026-01-06 02:28:51 +01:00
🚧 chore: merge latest dev build to main repo (#3844)
* agents - phase 1 (#30) * chore: copy assistant files * feat: frontend and data-provider * feat: backend get endpoint test * fix(MessageEndpointIcon): switched to AgentName and AgentAvatar * fix: small fixes * fix: agent endpoint config * fix: show Agent Builder * chore: install agentus * chore: initial scaffolding for agents * fix: updated Assistant logic to Agent Logic for some Agent components * WIP first pass, demo of agent package * WIP: initial backend infra for agents * fix: agent list error * wip: agents routing * chore: Refactor useSSE hook to handle different data events * wip: correctly emit events * chore: Update @librechat/agentus npm dependency to version 1.0.9 * remove comment * first pass: streaming agent text * chore: Remove @librechat/agentus root-level workspace npm dependency * feat: Agent Schema and Model * fix: content handling fixes * fix: content message save * WIP: new content data * fix: run step issue with tool calls * chore: Update @librechat/agentus npm dependency to version 1.1.5 * feat: update controller and agent routes * wip: initial backend tool and tool error handling support * wip: tool chunks * chore: Update @librechat/agentus npm dependency to version 1.1.7 * chore: update tool_call typing, add test conditions and logs * fix: create agent * fix: create agent * first pass: render completed content parts * fix: remove logging, fix step handler typing * chore: Update @librechat/agentus npm dependency to version 1.1.9 * refactor: cleanup maps on unmount * chore: Update BaseClient.js to safely count tokens for string, number, and boolean values * fix: support subsequent messages with tool_calls * chore: export order * fix: select agent * fix: tool call types and handling * chore: switch to anthropic for testing * fix: AgentSelect * refactor: experimental: OpenAIClient to use array for intermediateReply * fix(useSSE): revert old condition for streaming legacy client tokens * fix: lint * revert `agent_id` to `id` * chore: update localization keys for agent-related components * feat: zod schema handling for actions * refactor(actions): if no params, no zodSchema * chore: Update @librechat/agentus npm dependency to version 1.2.1 * feat: first pass, actions * refactor: empty schema for actions without params * feat: Update createRun function to accept additional options * fix: message payload formatting; feat: add more client options * fix: ToolCall component rendering when action has no args but has output * refactor(ToolCall): allow non-stringy args * WIP: first pass, correctly formatted tool_calls between providers * refactor: Remove duplicate import of 'roles' module * refactor: Exclude 'vite.config.ts' from TypeScript compilation * refactor: fix agent related types > - no need to use endpoint/model fields for identifying agent metadata > - add `provider` distinction for agent-configured 'endpoint' - no need for agent-endpoint map - reduce complexity of tools as functions into tools as string[] - fix types related to above changes - reduce unnecessary variables for queries/mutations and corresponding react-query keys * refactor: Add tools and tool_kwargs fields to agent schema * refactor: Remove unused code and update dependencies * refactor: Update updateAgentHandler to use req.body directly * refactor: Update AgentSelect component to use localized hooks * refactor: Update agent schema to include tools and provider fields * refactor(AgentPanel): add scrollbar gutter, add provider field to form, fix agent schema required values * refactor: Update AgentSwitcher component to use selectedAgentId instead of selectedAgent * refactor: Update AgentPanel component to include alternateName import and defaultAgentFormValues * refactor(SelectDropDown): allow setting value as option while still supporting legacy usage (string values only) * refactor: SelectDropdown changes - Only necessary when the available values are objects with label/value fields and the selected value is expected to be a string. * refactor: TypeError issues and handle provider as option * feat: Add placeholder for provider selection in AgentPanel component * refactor: Update agent schema to include author and provider fields * fix: show expected 'create agent' placeholder when creating agent * chore: fix localization strings, hide capabilities form for now * chore: typing * refactor: import order and use compact agents schema for now * chore: typing * refactor: Update AgentForm type to use AgentCapabilities * fix agent form agent selection issues * feat: responsive agent selection * fix: Handle cancelled fetch in useSelectAgent hook * fix: reset agent form on accordion close/open * feat: Add agent_id to default conversation for agents endpoint * feat: agents endpoint request handling * refactor: reset conversation model on agent select * refactor: add `additional_instructions` to conversation schema, organize other fields * chore: casing * chore: types * refactor(loadAgentTools): explicitly pass agent_id, do not pass `model` to loadAgentTools for now, load action sets by agent_id * WIP: initial draft of real agent client initialization * WIP: first pass, anthropic agent requests * feat: remember last selected agent * feat: openai and azure connected * fix: prioritize agent model for runs unless an explicit override model is passed from client * feat: Agent Actions * fix: save agent id to convo * feat: model panel (#29) * feat: model panel * bring back comments * fix: method still null * fix: AgentPanel FormContext * feat: add more parameters * fix: style issues; refactor: Agent Controller * fix: cherry-pick * fix: Update AgentAvatar component to use AssistantIcon instead of BrainCircuit * feat: OGDialog for delete agent; feat(assistant): update Agent types, introduced `model_parameters` * feat: icon and general `model_parameters` update * feat: use react-hook-form better * fix: agent builder form reset issue when switching panels * refactor: modularize agent builder form --------- Co-authored-by: Danny Avila <danny@librechat.ai> * fix: AgentPanel and ModelPanel type issues and use `useFormContext` and `watch` instead of `methods` directly and `useWatch`. * fix: tool call issues due to invalid input (anthropic) of empty string * fix: handle empty text in Part component --------- Co-authored-by: Marco Beretta <81851188+berry-13@users.noreply.github.com> * refactor: remove form ModelPanel and fixed nested ternary expressions in AgentConfig * fix: Model Parameters not saved correctly * refactor: remove console log * feat: avatar upload and get for Agents (#36) Co-authored-by: Marco Beretta <81851188+berry-13@users.noreply.github.com> * chore: update to public package * fix: typing, optional chaining * fix: cursor not showing for content parts * chore: conditionally enable agents * ci: fix azure test * ci: fix frontend tests, fix eslint api * refactor: Remove unused errorContentPart variable * continue of the agent message PR (#40) * last fixes * fix: agentMap * pr merge test (#41) * fix: model icon not fetching correctly * remove console logs * feat: agent name * refactor: pass documentsMap as a prop to allow re-render of assistant form * refactor: pass documentsMap as a prop to allow re-render of assistant form * chore: Bump version to 0.7.419 * fix: TypeError: Cannot read properties of undefined (reading 'id') * refactor: update AgentSwitcher component to use ControlCombobox instead of Combobox --------- Co-authored-by: Marco Beretta <81851188+berry-13@users.noreply.github.com>
This commit is contained in:
Danny Avila
GitHub
parent
618be4bf2b
commit
a0291ed155
141 changed files with 14473 additions and 5714 deletions
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@ -2,6 +2,9 @@ const { CacheKeys } = require('librechat-data-provider');
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const { loadDefaultModels, loadConfigModels } = require('~/server/services/Config');
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const { getLogStores } = require('~/cache');
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/**
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* @param {ServerRequest} req
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*/
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const getModelsConfig = async (req) => {
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const cache = getLogStores(CacheKeys.CONFIG_STORE);
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let modelsConfig = await cache.get(CacheKeys.MODELS_CONFIG);
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@ -14,7 +17,7 @@ const getModelsConfig = async (req) => {
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/**
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* Loads the models from the config.
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* @param {Express.Request} req - The Express request object.
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* @param {ServerRequest} req - The Express request object.
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* @returns {Promise<TModelsConfig>} The models config.
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*/
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async function loadModels(req) {
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83
api/server/controllers/agents/callbacks.js
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83
api/server/controllers/agents/callbacks.js
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@ -0,0 +1,83 @@
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const { GraphEvents, ToolEndHandler, ChatModelStreamHandler } = require('@librechat/agents');
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/** @typedef {import('@librechat/agents').EventHandler} EventHandler */
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/** @typedef {import('@librechat/agents').ChatModelStreamHandler} ChatModelStreamHandler */
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/** @typedef {import('@librechat/agents').GraphEvents} GraphEvents */
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/**
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* Sends message data in Server Sent Events format.
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* @param {ServerResponse} res - The server response.
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* @param {{ data: string | Record<string, unknown>, event?: string }} event - The message event.
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* @param {string} event.event - The type of event.
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* @param {string} event.data - The message to be sent.
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*/
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const sendEvent = (res, event) => {
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if (typeof event.data === 'string' && event.data.length === 0) {
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return;
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}
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res.write(`event: message\ndata: ${JSON.stringify(event)}\n\n`);
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};
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/**
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* Get default handlers for stream events.
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* @param {{ res?: ServerResponse }} options - The options object.
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* @returns {Record<string, t.EventHandler>} The default handlers.
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* @throws {Error} If the request is not found.
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*/
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function getDefaultHandlers({ res }) {
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if (!res) {
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throw new Error('Request not found');
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}
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const handlers = {
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// [GraphEvents.CHAT_MODEL_END]: new ModelEndHandler(),
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[GraphEvents.TOOL_END]: new ToolEndHandler(),
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[GraphEvents.CHAT_MODEL_STREAM]: new ChatModelStreamHandler(),
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[GraphEvents.ON_RUN_STEP]: {
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/**
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* Handle ON_RUN_STEP event.
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* @param {string} event - The event name.
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* @param {StreamEventData} data - The event data.
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*/
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handle: (event, data) => {
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sendEvent(res, { event, data });
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},
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},
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[GraphEvents.ON_RUN_STEP_DELTA]: {
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/**
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* Handle ON_RUN_STEP_DELTA event.
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* @param {string} event - The event name.
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* @param {StreamEventData} data - The event data.
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*/
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handle: (event, data) => {
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sendEvent(res, { event, data });
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},
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},
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[GraphEvents.ON_RUN_STEP_COMPLETED]: {
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/**
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* Handle ON_RUN_STEP_COMPLETED event.
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* @param {string} event - The event name.
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* @param {StreamEventData & { result: ToolEndData }} data - The event data.
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*/
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handle: (event, data) => {
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sendEvent(res, { event, data });
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},
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},
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[GraphEvents.ON_MESSAGE_DELTA]: {
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/**
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* Handle ON_MESSAGE_DELTA event.
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* @param {string} event - The event name.
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* @param {StreamEventData} data - The event data.
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*/
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handle: (event, data) => {
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sendEvent(res, { event, data });
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},
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},
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};
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return handlers;
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}
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module.exports = {
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sendEvent,
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getDefaultHandlers,
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};
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462
api/server/controllers/agents/client.js
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462
api/server/controllers/agents/client.js
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@ -0,0 +1,462 @@
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// 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 } = require('@librechat/agents');
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const {
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EModelEndpoint,
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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 Tokenizer = require('~/server/services/Tokenizer');
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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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class AgentClient extends BaseClient {
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constructor(options = {}) {
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super(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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const { maxContextTokens, modelOptions = {}, ...clientOptions } = options;
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this.modelOptions = modelOptions;
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this.maxContextTokens = maxContextTokens;
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this.options = Object.assign({ endpoint: EModelEndpoint.agents }, clientOptions);
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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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return removeNullishValues(
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Object.assign(
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{
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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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this.modelOptions,
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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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);
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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 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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return 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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}
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// async recordTokenUsage({ promptTokens, completionTokens, context = 'message' }) {
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// await spendTokens(
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// {
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// context,
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// model: this.modelOptions.model,
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// conversationId: this.conversationId,
|
||||
// user: this.user ?? this.options.req.user?.id,
|
||||
// endpointTokenConfig: this.options.endpointTokenConfig,
|
||||
// },
|
||||
// { promptTokens, completionTokens },
|
||||
// );
|
||||
// }
|
||||
|
||||
async chatCompletion({ payload, abortController = null }) {
|
||||
try {
|
||||
if (!abortController) {
|
||||
abortController = new AbortController();
|
||||
}
|
||||
|
||||
const baseURL = extractBaseURL(this.completionsUrl);
|
||||
logger.debug('[api/server/controllers/agents/client.js] chatCompletion', {
|
||||
baseURL,
|
||||
payload,
|
||||
});
|
||||
|
||||
// if (this.useOpenRouter) {
|
||||
// opts.defaultHeaders = {
|
||||
// 'HTTP-Referer': 'https://librechat.ai',
|
||||
// 'X-Title': 'LibreChat',
|
||||
// };
|
||||
// }
|
||||
|
||||
// if (this.options.headers) {
|
||||
// opts.defaultHeaders = { ...opts.defaultHeaders, ...this.options.headers };
|
||||
// }
|
||||
|
||||
// if (this.options.proxy) {
|
||||
// opts.httpAgent = new HttpsProxyAgent(this.options.proxy);
|
||||
// }
|
||||
|
||||
// if (this.isVisionModel) {
|
||||
// modelOptions.max_tokens = 4000;
|
||||
// }
|
||||
|
||||
// /** @type {TAzureConfig | undefined} */
|
||||
// const azureConfig = this.options?.req?.app?.locals?.[EModelEndpoint.azureOpenAI];
|
||||
|
||||
// if (
|
||||
// (this.azure && this.isVisionModel && azureConfig) ||
|
||||
// (azureConfig && this.isVisionModel && this.options.endpoint === EModelEndpoint.azureOpenAI)
|
||||
// ) {
|
||||
// const { modelGroupMap, groupMap } = azureConfig;
|
||||
// const {
|
||||
// azureOptions,
|
||||
// baseURL,
|
||||
// headers = {},
|
||||
// serverless,
|
||||
// } = mapModelToAzureConfig({
|
||||
// modelName: modelOptions.model,
|
||||
// modelGroupMap,
|
||||
// groupMap,
|
||||
// });
|
||||
// opts.defaultHeaders = resolveHeaders(headers);
|
||||
// this.langchainProxy = extractBaseURL(baseURL);
|
||||
// this.apiKey = azureOptions.azureOpenAIApiKey;
|
||||
|
||||
// const groupName = modelGroupMap[modelOptions.model].group;
|
||||
// this.options.addParams = azureConfig.groupMap[groupName].addParams;
|
||||
// this.options.dropParams = azureConfig.groupMap[groupName].dropParams;
|
||||
// // Note: `forcePrompt` not re-assigned as only chat models are vision models
|
||||
|
||||
// this.azure = !serverless && azureOptions;
|
||||
// this.azureEndpoint =
|
||||
// !serverless && genAzureChatCompletion(this.azure, modelOptions.model, this);
|
||||
// }
|
||||
|
||||
// if (this.azure || this.options.azure) {
|
||||
// /* Azure Bug, extremely short default `max_tokens` response */
|
||||
// if (!modelOptions.max_tokens && modelOptions.model === 'gpt-4-vision-preview') {
|
||||
// modelOptions.max_tokens = 4000;
|
||||
// }
|
||||
|
||||
// /* Azure does not accept `model` in the body, so we need to remove it. */
|
||||
// delete modelOptions.model;
|
||||
|
||||
// opts.baseURL = this.langchainProxy
|
||||
// ? constructAzureURL({
|
||||
// baseURL: this.langchainProxy,
|
||||
// azureOptions: this.azure,
|
||||
// })
|
||||
// : this.azureEndpoint.split(/(?<!\/)\/(chat|completion)\//)[0];
|
||||
|
||||
// opts.defaultQuery = { 'api-version': this.azure.azureOpenAIApiVersion };
|
||||
// opts.defaultHeaders = { ...opts.defaultHeaders, 'api-key': this.apiKey };
|
||||
// }
|
||||
|
||||
// if (process.env.OPENAI_ORGANIZATION) {
|
||||
// opts.organization = process.env.OPENAI_ORGANIZATION;
|
||||
// }
|
||||
|
||||
// if (this.options.addParams && typeof this.options.addParams === 'object') {
|
||||
// modelOptions = {
|
||||
// ...modelOptions,
|
||||
// ...this.options.addParams,
|
||||
// };
|
||||
// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] added params', {
|
||||
// addParams: this.options.addParams,
|
||||
// modelOptions,
|
||||
// });
|
||||
// }
|
||||
|
||||
// if (this.options.dropParams && Array.isArray(this.options.dropParams)) {
|
||||
// this.options.dropParams.forEach((param) => {
|
||||
// delete modelOptions[param];
|
||||
// });
|
||||
// logger.debug('[api/server/controllers/agents/client.js #chatCompletion] dropped params', {
|
||||
// dropParams: this.options.dropParams,
|
||||
// modelOptions,
|
||||
// });
|
||||
// }
|
||||
|
||||
// const streamRate = this.options.streamRate ?? Constants.DEFAULT_STREAM_RATE;
|
||||
|
||||
const run = await createRun({
|
||||
agent: this.options.agent,
|
||||
tools: this.options.tools,
|
||||
toolMap: this.options.toolMap,
|
||||
runId: this.responseMessageId,
|
||||
modelOptions: this.modelOptions,
|
||||
customHandlers: this.options.eventHandlers,
|
||||
});
|
||||
|
||||
const config = {
|
||||
configurable: {
|
||||
provider: providerEndpointMap[this.options.agent.provider],
|
||||
thread_id: this.conversationId,
|
||||
},
|
||||
run_id: this.responseMessageId,
|
||||
streamMode: 'values',
|
||||
version: 'v2',
|
||||
};
|
||||
|
||||
if (!run) {
|
||||
throw new Error('Failed to create run');
|
||||
}
|
||||
|
||||
const messages = formatAgentMessages(payload);
|
||||
const runMessages = await run.processStream({ messages }, config, {
|
||||
[Callback.TOOL_ERROR]: (graph, error, toolId) => {
|
||||
logger.error(
|
||||
'[api/server/controllers/agents/client.js #chatCompletion] Tool Error',
|
||||
error,
|
||||
toolId,
|
||||
);
|
||||
},
|
||||
});
|
||||
// console.dir(runMessages, { depth: null });
|
||||
return runMessages;
|
||||
} catch (err) {
|
||||
logger.error(
|
||||
'[api/server/controllers/agents/client.js #chatCompletion] Unhandled error type',
|
||||
err,
|
||||
);
|
||||
throw err;
|
||||
}
|
||||
}
|
||||
|
||||
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;
|
||||
44
api/server/controllers/agents/demo.js
Normal file
44
api/server/controllers/agents/demo.js
Normal file
|
|
@ -0,0 +1,44 @@
|
|||
// Import the necessary modules
|
||||
const path = require('path');
|
||||
const base = path.resolve(__dirname, '..', '..', '..', '..', 'api');
|
||||
console.log(base);
|
||||
//api/server/controllers/agents/demo.js
|
||||
require('module-alias')({ base });
|
||||
const connectDb = require('~/lib/db/connectDb');
|
||||
const AgentClient = require('./client');
|
||||
|
||||
// Define the user and message options
|
||||
const user = 'user123';
|
||||
const parentMessageId = 'pmid123';
|
||||
const conversationId = 'cid456';
|
||||
const maxContextTokens = 200000;
|
||||
const req = {
|
||||
user: { id: user },
|
||||
};
|
||||
const progressOptions = {
|
||||
res: {},
|
||||
};
|
||||
|
||||
// Define the message options
|
||||
const messageOptions = {
|
||||
user,
|
||||
parentMessageId,
|
||||
conversationId,
|
||||
progressOptions,
|
||||
};
|
||||
|
||||
async function main() {
|
||||
await connectDb();
|
||||
const client = new AgentClient({ req, maxContextTokens });
|
||||
|
||||
const text = 'Hello, this is a test message.';
|
||||
|
||||
try {
|
||||
let response = await client.sendMessage(text, messageOptions);
|
||||
console.log('Response:', response);
|
||||
} catch (error) {
|
||||
console.error('Error sending message:', error);
|
||||
}
|
||||
}
|
||||
|
||||
main();
|
||||
153
api/server/controllers/agents/errors.js
Normal file
153
api/server/controllers/agents/errors.js
Normal file
|
|
@ -0,0 +1,153 @@
|
|||
// errorHandler.js
|
||||
const { logger } = require('~/config');
|
||||
const getLogStores = require('~/cache/getLogStores');
|
||||
const { CacheKeys, ViolationTypes } = require('librechat-data-provider');
|
||||
const { recordUsage } = require('~/server/services/Threads');
|
||||
const { getConvo } = require('~/models/Conversation');
|
||||
const { sendResponse } = require('~/server/utils');
|
||||
|
||||
/**
|
||||
* @typedef {Object} ErrorHandlerContext
|
||||
* @property {OpenAIClient} openai - The OpenAI client
|
||||
* @property {string} run_id - The run ID
|
||||
* @property {boolean} completedRun - Whether the run has completed
|
||||
* @property {string} assistant_id - The assistant ID
|
||||
* @property {string} conversationId - The conversation ID
|
||||
* @property {string} parentMessageId - The parent message ID
|
||||
* @property {string} responseMessageId - The response message ID
|
||||
* @property {string} endpoint - The endpoint being used
|
||||
* @property {string} cacheKey - The cache key for the current request
|
||||
*/
|
||||
|
||||
/**
|
||||
* @typedef {Object} ErrorHandlerDependencies
|
||||
* @property {Express.Request} req - The Express request object
|
||||
* @property {Express.Response} res - The Express response object
|
||||
* @property {() => ErrorHandlerContext} getContext - Function to get the current context
|
||||
* @property {string} [originPath] - The origin path for the error handler
|
||||
*/
|
||||
|
||||
/**
|
||||
* Creates an error handler function with the given dependencies
|
||||
* @param {ErrorHandlerDependencies} dependencies - The dependencies for the error handler
|
||||
* @returns {(error: Error) => Promise<void>} The error handler function
|
||||
*/
|
||||
const createErrorHandler = ({ req, res, getContext, originPath = '/assistants/chat/' }) => {
|
||||
const cache = getLogStores(CacheKeys.ABORT_KEYS);
|
||||
|
||||
/**
|
||||
* Handles errors that occur during the chat process
|
||||
* @param {Error} error - The error that occurred
|
||||
* @returns {Promise<void>}
|
||||
*/
|
||||
return async (error) => {
|
||||
const {
|
||||
openai,
|
||||
run_id,
|
||||
endpoint,
|
||||
cacheKey,
|
||||
completedRun,
|
||||
assistant_id,
|
||||
conversationId,
|
||||
parentMessageId,
|
||||
responseMessageId,
|
||||
} = getContext();
|
||||
|
||||
const defaultErrorMessage =
|
||||
'The Assistant run failed to initialize. Try sending a message in a new conversation.';
|
||||
const messageData = {
|
||||
assistant_id,
|
||||
conversationId,
|
||||
parentMessageId,
|
||||
sender: 'System',
|
||||
user: req.user.id,
|
||||
shouldSaveMessage: false,
|
||||
messageId: responseMessageId,
|
||||
endpoint,
|
||||
};
|
||||
|
||||
if (error.message === 'Run cancelled') {
|
||||
return res.end();
|
||||
} else if (error.message === 'Request closed' && completedRun) {
|
||||
return;
|
||||
} else if (error.message === 'Request closed') {
|
||||
logger.debug(`[${originPath}] Request aborted on close`);
|
||||
} else if (/Files.*are invalid/.test(error.message)) {
|
||||
const errorMessage = `Files are invalid, or may not have uploaded yet.${
|
||||
endpoint === 'azureAssistants'
|
||||
? ' If using Azure OpenAI, files are only available in the region of the assistant\'s model at the time of upload.'
|
||||
: ''
|
||||
}`;
|
||||
return sendResponse(req, res, messageData, errorMessage);
|
||||
} else if (error?.message?.includes('string too long')) {
|
||||
return sendResponse(
|
||||
req,
|
||||
res,
|
||||
messageData,
|
||||
'Message too long. The Assistants API has a limit of 32,768 characters per message. Please shorten it and try again.',
|
||||
);
|
||||
} else if (error?.message?.includes(ViolationTypes.TOKEN_BALANCE)) {
|
||||
return sendResponse(req, res, messageData, error.message);
|
||||
} else {
|
||||
logger.error(`[${originPath}]`, error);
|
||||
}
|
||||
|
||||
if (!openai || !run_id) {
|
||||
return sendResponse(req, res, messageData, defaultErrorMessage);
|
||||
}
|
||||
|
||||
await new Promise((resolve) => setTimeout(resolve, 2000));
|
||||
|
||||
try {
|
||||
const status = await cache.get(cacheKey);
|
||||
if (status === 'cancelled') {
|
||||
logger.debug(`[${originPath}] Run already cancelled`);
|
||||
return res.end();
|
||||
}
|
||||
await cache.delete(cacheKey);
|
||||
// const cancelledRun = await openai.beta.threads.runs.cancel(thread_id, run_id);
|
||||
// logger.debug(`[${originPath}] Cancelled run:`, cancelledRun);
|
||||
} catch (error) {
|
||||
logger.error(`[${originPath}] Error cancelling run`, error);
|
||||
}
|
||||
|
||||
await new Promise((resolve) => setTimeout(resolve, 2000));
|
||||
|
||||
let run;
|
||||
try {
|
||||
// run = await openai.beta.threads.runs.retrieve(thread_id, run_id);
|
||||
await recordUsage({
|
||||
...run.usage,
|
||||
model: run.model,
|
||||
user: req.user.id,
|
||||
conversationId,
|
||||
});
|
||||
} catch (error) {
|
||||
logger.error(`[${originPath}] Error fetching or processing run`, error);
|
||||
}
|
||||
|
||||
let finalEvent;
|
||||
try {
|
||||
// const errorContentPart = {
|
||||
// text: {
|
||||
// value:
|
||||
// error?.message ?? 'There was an error processing your request. Please try again later.',
|
||||
// },
|
||||
// type: ContentTypes.ERROR,
|
||||
// };
|
||||
|
||||
finalEvent = {
|
||||
final: true,
|
||||
conversation: await getConvo(req.user.id, conversationId),
|
||||
// runMessages,
|
||||
};
|
||||
} catch (error) {
|
||||
logger.error(`[${originPath}] Error finalizing error process`, error);
|
||||
return sendResponse(req, res, messageData, 'The Assistant run failed');
|
||||
}
|
||||
|
||||
return sendResponse(req, res, finalEvent);
|
||||
};
|
||||
};
|
||||
|
||||
module.exports = { createErrorHandler };
|
||||
106
api/server/controllers/agents/llm.js
Normal file
106
api/server/controllers/agents/llm.js
Normal file
|
|
@ -0,0 +1,106 @@
|
|||
const { HttpsProxyAgent } = require('https-proxy-agent');
|
||||
const { resolveHeaders } = require('librechat-data-provider');
|
||||
const { createLLM } = require('~/app/clients/llm');
|
||||
|
||||
/**
|
||||
* Initializes and returns a Language Learning Model (LLM) instance.
|
||||
*
|
||||
* @param {Object} options - Configuration options for the LLM.
|
||||
* @param {string} options.model - The model identifier.
|
||||
* @param {string} options.modelName - The specific name of the model.
|
||||
* @param {number} options.temperature - The temperature setting for the model.
|
||||
* @param {number} options.presence_penalty - The presence penalty for the model.
|
||||
* @param {number} options.frequency_penalty - The frequency penalty for the model.
|
||||
* @param {number} options.max_tokens - The maximum number of tokens for the model output.
|
||||
* @param {boolean} options.streaming - Whether to use streaming for the model output.
|
||||
* @param {Object} options.context - The context for the conversation.
|
||||
* @param {number} options.tokenBuffer - The token buffer size.
|
||||
* @param {number} options.initialMessageCount - The initial message count.
|
||||
* @param {string} options.conversationId - The ID of the conversation.
|
||||
* @param {string} options.user - The user identifier.
|
||||
* @param {string} options.langchainProxy - The langchain proxy URL.
|
||||
* @param {boolean} options.useOpenRouter - Whether to use OpenRouter.
|
||||
* @param {Object} options.options - Additional options.
|
||||
* @param {Object} options.options.headers - Custom headers for the request.
|
||||
* @param {string} options.options.proxy - Proxy URL.
|
||||
* @param {Object} options.options.req - The request object.
|
||||
* @param {Object} options.options.res - The response object.
|
||||
* @param {boolean} options.options.debug - Whether to enable debug mode.
|
||||
* @param {string} options.apiKey - The API key for authentication.
|
||||
* @param {Object} options.azure - Azure-specific configuration.
|
||||
* @param {Object} options.abortController - The AbortController instance.
|
||||
* @returns {Object} The initialized LLM instance.
|
||||
*/
|
||||
function initializeLLM(options) {
|
||||
const {
|
||||
model,
|
||||
modelName,
|
||||
temperature,
|
||||
presence_penalty,
|
||||
frequency_penalty,
|
||||
max_tokens,
|
||||
streaming,
|
||||
user,
|
||||
langchainProxy,
|
||||
useOpenRouter,
|
||||
options: { headers, proxy },
|
||||
apiKey,
|
||||
azure,
|
||||
} = options;
|
||||
|
||||
const modelOptions = {
|
||||
modelName: modelName || model,
|
||||
temperature,
|
||||
presence_penalty,
|
||||
frequency_penalty,
|
||||
user,
|
||||
};
|
||||
|
||||
if (max_tokens) {
|
||||
modelOptions.max_tokens = max_tokens;
|
||||
}
|
||||
|
||||
const configOptions = {};
|
||||
|
||||
if (langchainProxy) {
|
||||
configOptions.basePath = langchainProxy;
|
||||
}
|
||||
|
||||
if (useOpenRouter) {
|
||||
configOptions.basePath = 'https://openrouter.ai/api/v1';
|
||||
configOptions.baseOptions = {
|
||||
headers: {
|
||||
'HTTP-Referer': 'https://librechat.ai',
|
||||
'X-Title': 'LibreChat',
|
||||
},
|
||||
};
|
||||
}
|
||||
|
||||
if (headers && typeof headers === 'object' && !Array.isArray(headers)) {
|
||||
configOptions.baseOptions = {
|
||||
headers: resolveHeaders({
|
||||
...headers,
|
||||
...configOptions?.baseOptions?.headers,
|
||||
}),
|
||||
};
|
||||
}
|
||||
|
||||
if (proxy) {
|
||||
configOptions.httpAgent = new HttpsProxyAgent(proxy);
|
||||
configOptions.httpsAgent = new HttpsProxyAgent(proxy);
|
||||
}
|
||||
|
||||
const llm = createLLM({
|
||||
modelOptions,
|
||||
configOptions,
|
||||
openAIApiKey: apiKey,
|
||||
azure,
|
||||
streaming,
|
||||
});
|
||||
|
||||
return llm;
|
||||
}
|
||||
|
||||
module.exports = {
|
||||
initializeLLM,
|
||||
};
|
||||
150
api/server/controllers/agents/request.js
Normal file
150
api/server/controllers/agents/request.js
Normal file
|
|
@ -0,0 +1,150 @@
|
|||
const { Constants, getResponseSender } = require('librechat-data-provider');
|
||||
const { createAbortController, handleAbortError } = require('~/server/middleware');
|
||||
const { sendMessage } = require('~/server/utils');
|
||||
const { saveMessage } = require('~/models');
|
||||
const { logger } = require('~/config');
|
||||
|
||||
const AgentController = async (req, res, next, initializeClient, addTitle) => {
|
||||
let {
|
||||
text,
|
||||
endpointOption,
|
||||
conversationId,
|
||||
modelDisplayLabel,
|
||||
parentMessageId = null,
|
||||
overrideParentMessageId = null,
|
||||
} = req.body;
|
||||
|
||||
let userMessage;
|
||||
let userMessagePromise;
|
||||
let promptTokens;
|
||||
let userMessageId;
|
||||
let responseMessageId;
|
||||
|
||||
const sender = getResponseSender({
|
||||
...endpointOption,
|
||||
model: endpointOption.modelOptions.model,
|
||||
modelDisplayLabel,
|
||||
});
|
||||
const newConvo = !conversationId;
|
||||
const user = req.user.id;
|
||||
|
||||
const getReqData = (data = {}) => {
|
||||
for (let key in data) {
|
||||
if (key === 'userMessage') {
|
||||
userMessage = data[key];
|
||||
userMessageId = data[key].messageId;
|
||||
} else if (key === 'userMessagePromise') {
|
||||
userMessagePromise = data[key];
|
||||
} else if (key === 'responseMessageId') {
|
||||
responseMessageId = data[key];
|
||||
} else if (key === 'promptTokens') {
|
||||
promptTokens = data[key];
|
||||
} else if (!conversationId && key === 'conversationId') {
|
||||
conversationId = data[key];
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
try {
|
||||
const { client } = await initializeClient({ req, res, endpointOption });
|
||||
|
||||
const getAbortData = () => ({
|
||||
sender,
|
||||
userMessage,
|
||||
promptTokens,
|
||||
conversationId,
|
||||
userMessagePromise,
|
||||
// text: getPartialText(),
|
||||
messageId: responseMessageId,
|
||||
parentMessageId: overrideParentMessageId ?? userMessageId,
|
||||
});
|
||||
|
||||
const { abortController, onStart } = createAbortController(req, res, getAbortData, getReqData);
|
||||
|
||||
res.on('close', () => {
|
||||
logger.debug('[AgentController] Request closed');
|
||||
if (!abortController) {
|
||||
return;
|
||||
} else if (abortController.signal.aborted) {
|
||||
return;
|
||||
} else if (abortController.requestCompleted) {
|
||||
return;
|
||||
}
|
||||
|
||||
abortController.abort();
|
||||
logger.debug('[AgentController] Request aborted on close');
|
||||
});
|
||||
|
||||
const messageOptions = {
|
||||
user,
|
||||
onStart,
|
||||
getReqData,
|
||||
conversationId,
|
||||
parentMessageId,
|
||||
abortController,
|
||||
overrideParentMessageId,
|
||||
progressOptions: {
|
||||
res,
|
||||
// parentMessageId: overrideParentMessageId || userMessageId,
|
||||
},
|
||||
};
|
||||
|
||||
let response = await client.sendMessage(text, messageOptions);
|
||||
|
||||
if (overrideParentMessageId) {
|
||||
response.parentMessageId = overrideParentMessageId;
|
||||
}
|
||||
|
||||
response.endpoint = endpointOption.endpoint;
|
||||
|
||||
const { conversation = {} } = await client.responsePromise;
|
||||
conversation.title =
|
||||
conversation && !conversation.title ? null : conversation?.title || 'New Chat';
|
||||
|
||||
if (client.options.attachments) {
|
||||
userMessage.files = client.options.attachments;
|
||||
conversation.model = endpointOption.modelOptions.model;
|
||||
delete userMessage.image_urls;
|
||||
}
|
||||
|
||||
if (!abortController.signal.aborted) {
|
||||
sendMessage(res, {
|
||||
final: true,
|
||||
conversation,
|
||||
title: conversation.title,
|
||||
requestMessage: userMessage,
|
||||
responseMessage: response,
|
||||
});
|
||||
res.end();
|
||||
|
||||
await saveMessage(
|
||||
req,
|
||||
{ ...response, user },
|
||||
{ context: 'api/server/controllers/agents/request.js - response end' },
|
||||
);
|
||||
}
|
||||
|
||||
if (!client.skipSaveUserMessage) {
|
||||
await saveMessage(req, userMessage, {
|
||||
context: 'api/server/controllers/agents/request.js - don\'t skip saving user message',
|
||||
});
|
||||
}
|
||||
|
||||
if (addTitle && parentMessageId === Constants.NO_PARENT && newConvo) {
|
||||
addTitle(req, {
|
||||
text,
|
||||
response,
|
||||
client,
|
||||
});
|
||||
}
|
||||
} catch (error) {
|
||||
handleAbortError(res, req, error, {
|
||||
conversationId,
|
||||
sender,
|
||||
messageId: responseMessageId,
|
||||
parentMessageId: userMessageId ?? parentMessageId,
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
module.exports = AgentController;
|
||||
59
api/server/controllers/agents/run.js
Normal file
59
api/server/controllers/agents/run.js
Normal file
|
|
@ -0,0 +1,59 @@
|
|||
const { Run } = require('@librechat/agents');
|
||||
const { providerEndpointMap } = require('librechat-data-provider');
|
||||
|
||||
/**
|
||||
* @typedef {import('@librechat/agents').t} t
|
||||
* @typedef {import('@librechat/agents').StreamEventData} StreamEventData
|
||||
* @typedef {import('@librechat/agents').ClientOptions} ClientOptions
|
||||
* @typedef {import('@librechat/agents').EventHandler} EventHandler
|
||||
* @typedef {import('@librechat/agents').GraphEvents} GraphEvents
|
||||
* @typedef {import('@librechat/agents').IState} IState
|
||||
*/
|
||||
|
||||
/**
|
||||
* Creates a new Run instance with custom handlers and configuration.
|
||||
*
|
||||
* @param {Object} options - The options for creating the Run instance.
|
||||
* @param {Agent} options.agent - The agent for this run.
|
||||
* @param {StructuredTool[] | undefined} [options.tools] - The tools to use in the run.
|
||||
* @param {Record<string, StructuredTool[]> | undefined} [options.toolMap] - The tool map for the run.
|
||||
* @param {Record<GraphEvents, EventHandler> | undefined} [options.customHandlers] - Custom event handlers.
|
||||
* @param {string | undefined} [options.runId] - Optional run ID; otherwise, a new run ID will be generated.
|
||||
* @param {ClientOptions} [options.modelOptions] - Optional model to use; if not provided, it will use the default from modelMap.
|
||||
* @param {boolean} [options.streaming=true] - Whether to use streaming.
|
||||
* @param {boolean} [options.streamUsage=true] - Whether to stream usage information.
|
||||
* @returns {Promise<Run<IState>>} A promise that resolves to a new Run instance.
|
||||
*/
|
||||
async function createRun({
|
||||
runId,
|
||||
tools,
|
||||
agent,
|
||||
toolMap,
|
||||
modelOptions,
|
||||
customHandlers,
|
||||
streaming = true,
|
||||
streamUsage = true,
|
||||
}) {
|
||||
const llmConfig = Object.assign(
|
||||
{
|
||||
provider: providerEndpointMap[agent.provider],
|
||||
streaming,
|
||||
streamUsage,
|
||||
},
|
||||
modelOptions,
|
||||
);
|
||||
|
||||
return Run.create({
|
||||
graphConfig: {
|
||||
runId,
|
||||
llmConfig,
|
||||
tools,
|
||||
toolMap,
|
||||
instructions: agent.instructions,
|
||||
additional_instructions: agent.additional_instructions,
|
||||
},
|
||||
customHandlers,
|
||||
});
|
||||
}
|
||||
|
||||
module.exports = { createRun };
|
||||
208
api/server/controllers/agents/v1.js
Normal file
208
api/server/controllers/agents/v1.js
Normal file
|
|
@ -0,0 +1,208 @@
|
|||
const { nanoid } = require('nanoid');
|
||||
const { FileContext } = require('librechat-data-provider');
|
||||
const {
|
||||
getAgent,
|
||||
createAgent,
|
||||
updateAgent,
|
||||
deleteAgent,
|
||||
getListAgents,
|
||||
} = require('~/models/Agent');
|
||||
const { getStrategyFunctions } = require('~/server/services/Files/strategies');
|
||||
const { uploadImageBuffer } = require('~/server/services/Files/process');
|
||||
const { deleteFileByFilter } = require('~/models/File');
|
||||
const { logger } = require('~/config');
|
||||
|
||||
/**
|
||||
* Creates an Agent.
|
||||
* @route POST /Agents
|
||||
* @param {ServerRequest} req - The request object.
|
||||
* @param {AgentCreateParams} req.body - The request body.
|
||||
* @param {ServerResponse} res - The response object.
|
||||
* @returns {Agent} 201 - success response - application/json
|
||||
*/
|
||||
const createAgentHandler = async (req, res) => {
|
||||
try {
|
||||
const { tools = [], provider, name, description, instructions, model, ...agentData } = req.body;
|
||||
const { id: userId } = req.user;
|
||||
|
||||
agentData.tools = tools
|
||||
.map((tool) => (typeof tool === 'string' ? req.app.locals.availableTools[tool] : tool))
|
||||
.filter(Boolean);
|
||||
|
||||
Object.assign(agentData, {
|
||||
author: userId,
|
||||
name,
|
||||
description,
|
||||
instructions,
|
||||
provider,
|
||||
model,
|
||||
});
|
||||
|
||||
agentData.id = `agent_${nanoid()}`;
|
||||
const agent = await createAgent(agentData);
|
||||
res.status(201).json(agent);
|
||||
} catch (error) {
|
||||
logger.error('[/Agents] Error creating agent', error);
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Retrieves an Agent by ID.
|
||||
* @route GET /Agents/:id
|
||||
* @param {object} req - Express Request
|
||||
* @param {object} req.params - Request params
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @returns {Agent} 200 - success response - application/json
|
||||
* @returns {Error} 404 - Agent not found
|
||||
*/
|
||||
const getAgentHandler = async (req, res) => {
|
||||
try {
|
||||
const id = req.params.id;
|
||||
const agent = await getAgent({ id });
|
||||
if (!agent) {
|
||||
return res.status(404).json({ error: 'Agent not found' });
|
||||
}
|
||||
return res.status(200).json(agent);
|
||||
} catch (error) {
|
||||
logger.error('[/Agents/:id] Error retrieving agent', error);
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Updates an Agent.
|
||||
* @route PATCH /Agents/:id
|
||||
* @param {object} req - Express Request
|
||||
* @param {object} req.params - Request params
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @param {AgentUpdateParams} req.body - The Agent update parameters.
|
||||
* @returns {Agent} 200 - success response - application/json
|
||||
*/
|
||||
const updateAgentHandler = async (req, res) => {
|
||||
try {
|
||||
const id = req.params.id;
|
||||
const updatedAgent = await updateAgent({ id, author: req.user.id }, req.body);
|
||||
return res.json(updatedAgent);
|
||||
} catch (error) {
|
||||
logger.error('[/Agents/:id] Error updating Agent', error);
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Deletes an Agent based on the provided ID.
|
||||
* @route DELETE /Agents/:id
|
||||
* @param {object} req - Express Request
|
||||
* @param {object} req.params - Request params
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @returns {Agent} 200 - success response - application/json
|
||||
*/
|
||||
const deleteAgentHandler = async (req, res) => {
|
||||
try {
|
||||
const id = req.params.id;
|
||||
const agent = await getAgent({ id });
|
||||
if (!agent) {
|
||||
return res.status(404).json({ error: 'Agent not found' });
|
||||
}
|
||||
await deleteAgent({ id, author: req.user.id });
|
||||
return res.json({ message: 'Agent deleted' });
|
||||
} catch (error) {
|
||||
logger.error('[/Agents/:id] Error deleting Agent', error);
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
*
|
||||
* @route GET /Agents
|
||||
* @param {object} req - Express Request
|
||||
* @param {object} req.query - Request query
|
||||
* @param {string} [req.query.user] - The user ID of the agent's author.
|
||||
* @returns {AgentListResponse} 200 - success response - application/json
|
||||
*/
|
||||
const getListAgentsHandler = async (req, res) => {
|
||||
try {
|
||||
const { user } = req.query;
|
||||
const filter = user ? { author: user } : {};
|
||||
const data = await getListAgents(filter);
|
||||
return res.json(data);
|
||||
} catch (error) {
|
||||
logger.error('[/Agents] Error listing Agents', error);
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
};
|
||||
|
||||
/**
|
||||
* Uploads and updates an avatar for a specific agent.
|
||||
* @route POST /avatar/:agent_id
|
||||
* @param {object} req - Express Request
|
||||
* @param {object} req.params - Request params
|
||||
* @param {string} req.params.agent_id - The ID of the agent.
|
||||
* @param {Express.Multer.File} req.file - The avatar image file.
|
||||
* @param {object} req.body - Request body
|
||||
* @param {string} [req.body.avatar] - Optional avatar for the agent's avatar.
|
||||
* @returns {Object} 200 - success response - application/json
|
||||
*/
|
||||
const uploadAgentAvatarHandler = async (req, res) => {
|
||||
try {
|
||||
const { agent_id } = req.params;
|
||||
if (!agent_id) {
|
||||
return res.status(400).json({ message: 'Agent ID is required' });
|
||||
}
|
||||
|
||||
let { avatar: _avatar = '{}' } = req.body;
|
||||
|
||||
const image = await uploadImageBuffer({
|
||||
req,
|
||||
context: FileContext.avatar,
|
||||
metadata: {
|
||||
buffer: req.file.buffer,
|
||||
},
|
||||
});
|
||||
|
||||
try {
|
||||
_avatar = JSON.parse(_avatar);
|
||||
} catch (error) {
|
||||
logger.error('[/avatar/:agent_id] Error parsing avatar', error);
|
||||
_avatar = {};
|
||||
}
|
||||
|
||||
if (_avatar && _avatar.source) {
|
||||
const { deleteFile } = getStrategyFunctions(_avatar.source);
|
||||
try {
|
||||
await deleteFile(req, { filepath: _avatar.filepath });
|
||||
await deleteFileByFilter({ filepath: _avatar.filepath });
|
||||
} catch (error) {
|
||||
logger.error('[/avatar/:agent_id] Error deleting old avatar', error);
|
||||
}
|
||||
}
|
||||
|
||||
const promises = [];
|
||||
|
||||
const data = {
|
||||
avatar: {
|
||||
filepath: image.filepath,
|
||||
source: req.app.locals.fileStrategy,
|
||||
},
|
||||
};
|
||||
|
||||
promises.push(await updateAgent({ id: agent_id, author: req.user.id }, data));
|
||||
|
||||
const resolved = await Promise.all(promises);
|
||||
res.status(201).json(resolved[0]);
|
||||
} catch (error) {
|
||||
const message = 'An error occurred while updating the Agent Avatar';
|
||||
logger.error(message, error);
|
||||
res.status(500).json({ message });
|
||||
}
|
||||
};
|
||||
|
||||
module.exports = {
|
||||
createAgent: createAgentHandler,
|
||||
getAgent: getAgentHandler,
|
||||
updateAgent: updateAgentHandler,
|
||||
deleteAgent: deleteAgentHandler,
|
||||
getListAgents: getListAgentsHandler,
|
||||
uploadAgentAvatar: uploadAgentAvatarHandler,
|
||||
};
|
||||
|
|
@ -105,6 +105,7 @@ const startServer = async () => {
|
|||
app.use('/images/', validateImageRequest, routes.staticRoute);
|
||||
app.use('/api/share', routes.share);
|
||||
app.use('/api/roles', routes.roles);
|
||||
app.use('/api/agents', routes.agents);
|
||||
|
||||
app.use('/api/tags', routes.tags);
|
||||
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
const { parseCompactConvo, EModelEndpoint } = require('librechat-data-provider');
|
||||
const { parseCompactConvo, EModelEndpoint, isAgentsEndpoint } = require('librechat-data-provider');
|
||||
const { getModelsConfig } = require('~/server/controllers/ModelController');
|
||||
const azureAssistants = require('~/server/services/Endpoints/azureAssistants');
|
||||
const assistants = require('~/server/services/Endpoints/assistants');
|
||||
|
|
@ -6,6 +6,7 @@ const gptPlugins = require('~/server/services/Endpoints/gptPlugins');
|
|||
const { processFiles } = require('~/server/services/Files/process');
|
||||
const anthropic = require('~/server/services/Endpoints/anthropic');
|
||||
const openAI = require('~/server/services/Endpoints/openAI');
|
||||
const agents = require('~/server/services/Endpoints/agents');
|
||||
const custom = require('~/server/services/Endpoints/custom');
|
||||
const google = require('~/server/services/Endpoints/google');
|
||||
const enforceModelSpec = require('./enforceModelSpec');
|
||||
|
|
@ -15,6 +16,7 @@ const buildFunction = {
|
|||
[EModelEndpoint.openAI]: openAI.buildOptions,
|
||||
[EModelEndpoint.google]: google.buildOptions,
|
||||
[EModelEndpoint.custom]: custom.buildOptions,
|
||||
[EModelEndpoint.agents]: agents.buildOptions,
|
||||
[EModelEndpoint.azureOpenAI]: openAI.buildOptions,
|
||||
[EModelEndpoint.anthropic]: anthropic.buildOptions,
|
||||
[EModelEndpoint.gptPlugins]: gptPlugins.buildOptions,
|
||||
|
|
@ -59,12 +61,13 @@ async function buildEndpointOption(req, res, next) {
|
|||
}
|
||||
}
|
||||
|
||||
req.body.endpointOption = buildFunction[endpointType ?? endpoint](
|
||||
endpoint,
|
||||
parsedBody,
|
||||
endpointType,
|
||||
);
|
||||
const endpointFn = buildFunction[endpointType ?? endpoint];
|
||||
const builder = isAgentsEndpoint(endpoint) ? (...args) => endpointFn(req, ...args) : endpointFn;
|
||||
|
||||
// TODO: use object params
|
||||
req.body.endpointOption = builder(endpoint, parsedBody, endpointType);
|
||||
|
||||
// TODO: use `getModelsConfig` only when necessary
|
||||
const modelsConfig = await getModelsConfig(req);
|
||||
req.body.endpointOption.modelsConfig = modelsConfig;
|
||||
|
||||
|
|
|
|||
166
api/server/routes/agents/actions.js
Normal file
166
api/server/routes/agents/actions.js
Normal file
|
|
@ -0,0 +1,166 @@
|
|||
const express = require('express');
|
||||
const { nanoid } = require('nanoid');
|
||||
const { actionDelimiter } = require('librechat-data-provider');
|
||||
const { encryptMetadata, domainParser } = require('~/server/services/ActionService');
|
||||
const { updateAction, getActions, deleteAction } = require('~/models/Action');
|
||||
const { getAgent, updateAgent } = require('~/models/Agent');
|
||||
const { logger } = require('~/config');
|
||||
|
||||
const router = express.Router();
|
||||
|
||||
/**
|
||||
* Retrieves all user's actions
|
||||
* @route GET /actions/
|
||||
* @param {string} req.params.id - Assistant identifier.
|
||||
* @returns {Action[]} 200 - success response - application/json
|
||||
*/
|
||||
router.get('/', async (req, res) => {
|
||||
try {
|
||||
res.json(await getActions({ user: req.user.id }));
|
||||
} catch (error) {
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
});
|
||||
|
||||
/**
|
||||
* Adds or updates actions for a specific agent.
|
||||
* @route POST /actions/:agent_id
|
||||
* @param {string} req.params.agent_id - The ID of the agent.
|
||||
* @param {FunctionTool[]} req.body.functions - The functions to be added or updated.
|
||||
* @param {string} [req.body.action_id] - Optional ID for the action.
|
||||
* @param {ActionMetadata} req.body.metadata - Metadata for the action.
|
||||
* @returns {Object} 200 - success response - application/json
|
||||
*/
|
||||
router.post('/:agent_id', async (req, res) => {
|
||||
try {
|
||||
const { agent_id } = req.params;
|
||||
|
||||
/** @type {{ functions: FunctionTool[], action_id: string, metadata: ActionMetadata }} */
|
||||
const { functions, action_id: _action_id, metadata: _metadata } = req.body;
|
||||
if (!functions.length) {
|
||||
return res.status(400).json({ message: 'No functions provided' });
|
||||
}
|
||||
|
||||
let metadata = encryptMetadata(_metadata);
|
||||
|
||||
let { domain } = metadata;
|
||||
domain = await domainParser(req, domain, true);
|
||||
|
||||
if (!domain) {
|
||||
return res.status(400).json({ message: 'No domain provided' });
|
||||
}
|
||||
|
||||
const action_id = _action_id ?? nanoid();
|
||||
const initialPromises = [];
|
||||
|
||||
// TODO: share agents
|
||||
initialPromises.push(getAgent({ id: agent_id, author: req.user.id }));
|
||||
if (_action_id) {
|
||||
initialPromises.push(getActions({ action_id }, true));
|
||||
}
|
||||
|
||||
/** @type {[Agent, [Action|undefined]]} */
|
||||
const [agent, actions_result] = await Promise.all(initialPromises);
|
||||
if (!agent) {
|
||||
return res.status(404).json({ message: 'Agent not found for adding action' });
|
||||
}
|
||||
|
||||
if (actions_result && actions_result.length) {
|
||||
const action = actions_result[0];
|
||||
metadata = { ...action.metadata, ...metadata };
|
||||
}
|
||||
|
||||
const { actions: _actions = [] } = agent ?? {};
|
||||
const actions = [];
|
||||
for (const action of _actions) {
|
||||
const [_action_domain, current_action_id] = action.split(actionDelimiter);
|
||||
if (current_action_id === action_id) {
|
||||
continue;
|
||||
}
|
||||
|
||||
actions.push(action);
|
||||
}
|
||||
|
||||
actions.push(`${domain}${actionDelimiter}${action_id}`);
|
||||
|
||||
/** @type {string[]}} */
|
||||
const { tools: _tools = [] } = agent;
|
||||
|
||||
const tools = _tools
|
||||
.filter((tool) => !(tool && (tool.includes(domain) || tool.includes(action_id))))
|
||||
.concat(functions.map((tool) => `${tool.function.name}${actionDelimiter}${domain}`));
|
||||
|
||||
const updatedAgent = await updateAgent(
|
||||
{ id: agent_id, author: req.user.id },
|
||||
{ tools, actions },
|
||||
);
|
||||
/** @type {[Action]} */
|
||||
const updatedAction = await updateAction(
|
||||
{ action_id },
|
||||
{ metadata, agent_id, user: req.user.id },
|
||||
);
|
||||
|
||||
const sensitiveFields = ['api_key', 'oauth_client_id', 'oauth_client_secret'];
|
||||
for (let field of sensitiveFields) {
|
||||
if (updatedAction.metadata[field]) {
|
||||
delete updatedAction.metadata[field];
|
||||
}
|
||||
}
|
||||
|
||||
res.json([updatedAgent, updatedAction]);
|
||||
} catch (error) {
|
||||
const message = 'Trouble updating the Agent Action';
|
||||
logger.error(message, error);
|
||||
res.status(500).json({ message });
|
||||
}
|
||||
});
|
||||
|
||||
/**
|
||||
* Deletes an action for a specific agent.
|
||||
* @route DELETE /actions/:agent_id/:action_id
|
||||
* @param {string} req.params.agent_id - The ID of the agent.
|
||||
* @param {string} req.params.action_id - The ID of the action to delete.
|
||||
* @returns {Object} 200 - success response - application/json
|
||||
*/
|
||||
router.delete('/:agent_id/:action_id', async (req, res) => {
|
||||
try {
|
||||
const { agent_id, action_id } = req.params;
|
||||
|
||||
const agent = await getAgent({ id: agent_id, author: req.user.id });
|
||||
if (!agent) {
|
||||
return res.status(404).json({ message: 'Agent not found for deleting action' });
|
||||
}
|
||||
|
||||
const { tools = [], actions = [] } = agent;
|
||||
|
||||
let domain = '';
|
||||
const updatedActions = actions.filter((action) => {
|
||||
if (action.includes(action_id)) {
|
||||
[domain] = action.split(actionDelimiter);
|
||||
return false;
|
||||
}
|
||||
return true;
|
||||
});
|
||||
|
||||
domain = await domainParser(req, domain, true);
|
||||
|
||||
if (!domain) {
|
||||
return res.status(400).json({ message: 'No domain provided' });
|
||||
}
|
||||
|
||||
const updatedTools = tools.filter((tool) => !(tool && tool.includes(domain)));
|
||||
|
||||
await updateAgent(
|
||||
{ id: agent_id, author: req.user.id },
|
||||
{ tools: updatedTools, actions: updatedActions },
|
||||
);
|
||||
await deleteAction({ action_id });
|
||||
res.status(200).json({ message: 'Action deleted successfully' });
|
||||
} catch (error) {
|
||||
const message = 'Trouble deleting the Agent Action';
|
||||
logger.error(message, error);
|
||||
res.status(500).json({ message });
|
||||
}
|
||||
});
|
||||
|
||||
module.exports = router;
|
||||
35
api/server/routes/agents/chat.js
Normal file
35
api/server/routes/agents/chat.js
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
const express = require('express');
|
||||
|
||||
const router = express.Router();
|
||||
const {
|
||||
setHeaders,
|
||||
handleAbort,
|
||||
// validateModel,
|
||||
// validateEndpoint,
|
||||
buildEndpointOption,
|
||||
} = require('~/server/middleware');
|
||||
const { initializeClient } = require('~/server/services/Endpoints/agents');
|
||||
const AgentController = require('~/server/controllers/agents/request');
|
||||
|
||||
router.post('/abort', handleAbort());
|
||||
|
||||
/**
|
||||
* @route POST /
|
||||
* @desc Chat with an assistant
|
||||
* @access Public
|
||||
* @param {express.Request} req - The request object, containing the request data.
|
||||
* @param {express.Response} res - The response object, used to send back a response.
|
||||
* @returns {void}
|
||||
*/
|
||||
router.post(
|
||||
'/',
|
||||
// validateModel,
|
||||
// validateEndpoint,
|
||||
buildEndpointOption,
|
||||
setHeaders,
|
||||
async (req, res, next) => {
|
||||
await AgentController(req, res, next, initializeClient);
|
||||
},
|
||||
);
|
||||
|
||||
module.exports = router;
|
||||
21
api/server/routes/agents/index.js
Normal file
21
api/server/routes/agents/index.js
Normal file
|
|
@ -0,0 +1,21 @@
|
|||
const express = require('express');
|
||||
const router = express.Router();
|
||||
const {
|
||||
uaParser,
|
||||
checkBan,
|
||||
requireJwtAuth,
|
||||
// concurrentLimiter,
|
||||
// messageIpLimiter,
|
||||
// messageUserLimiter,
|
||||
} = require('~/server/middleware');
|
||||
|
||||
const v1 = require('./v1');
|
||||
const chat = require('./chat');
|
||||
|
||||
router.use(requireJwtAuth);
|
||||
router.use(checkBan);
|
||||
router.use(uaParser);
|
||||
router.use('/', v1);
|
||||
router.use('/chat', chat);
|
||||
|
||||
module.exports = router;
|
||||
77
api/server/routes/agents/v1.js
Normal file
77
api/server/routes/agents/v1.js
Normal file
|
|
@ -0,0 +1,77 @@
|
|||
const multer = require('multer');
|
||||
const express = require('express');
|
||||
const v1 = require('~/server/controllers/agents/v1');
|
||||
const actions = require('./actions');
|
||||
|
||||
const upload = multer();
|
||||
const router = express.Router();
|
||||
|
||||
/**
|
||||
* Agent actions route.
|
||||
* @route GET|POST /agents/actions
|
||||
*/
|
||||
router.use('/actions', actions);
|
||||
|
||||
/**
|
||||
* Get a list of available tools for agents.
|
||||
* @route GET /agents/tools
|
||||
* @returns {TPlugin[]} 200 - application/json
|
||||
*/
|
||||
router.use('/tools', (req, res) => {
|
||||
res.json([]);
|
||||
});
|
||||
|
||||
/**
|
||||
* Creates an agent.
|
||||
* @route POST /agents
|
||||
* @param {AgentCreateParams} req.body - The agent creation parameters.
|
||||
* @returns {Agent} 201 - Success response - application/json
|
||||
*/
|
||||
router.post('/', v1.createAgent);
|
||||
|
||||
/**
|
||||
* Retrieves an agent.
|
||||
* @route GET /agents/:id
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @returns {Agent} 200 - Success response - application/json
|
||||
*/
|
||||
router.get('/:id', v1.getAgent);
|
||||
|
||||
/**
|
||||
* Updates an agent.
|
||||
* @route PATCH /agents/:id
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @param {AgentUpdateParams} req.body - The agent update parameters.
|
||||
* @returns {Agent} 200 - Success response - application/json
|
||||
*/
|
||||
router.patch('/:id', v1.updateAgent);
|
||||
|
||||
/**
|
||||
* Deletes an agent.
|
||||
* @route DELETE /agents/:id
|
||||
* @param {string} req.params.id - Agent identifier.
|
||||
* @returns {Agent} 200 - success response - application/json
|
||||
*/
|
||||
router.delete('/:id', v1.deleteAgent);
|
||||
|
||||
/**
|
||||
* Returns a list of agents.
|
||||
* @route GET /agents
|
||||
* @param {AgentListParams} req.query - The agent list parameters for pagination and sorting.
|
||||
* @returns {AgentListResponse} 200 - success response - application/json
|
||||
*/
|
||||
router.get('/', v1.getListAgents);
|
||||
|
||||
// TODO: handle private agents
|
||||
|
||||
/**
|
||||
* Uploads and updates an avatar for a specific agent.
|
||||
* @route POST /avatar/:agent_id
|
||||
* @param {string} req.params.agent_id - The ID of the agent.
|
||||
* @param {Express.Multer.File} req.file - The avatar image file.
|
||||
* @param {string} [req.body.metadata] - Optional metadata for the agent's avatar.
|
||||
* @returns {Object} 200 - success response - application/json
|
||||
*/
|
||||
router.post('/avatar/:agent_id', upload.single('file'), v1.uploadAgentAvatar);
|
||||
|
||||
module.exports = router;
|
||||
|
|
@ -1,5 +1,5 @@
|
|||
const { v4 } = require('uuid');
|
||||
const express = require('express');
|
||||
const { nanoid } = require('nanoid');
|
||||
const { encryptMetadata, domainParser } = require('~/server/services/ActionService');
|
||||
const { actionDelimiter, EModelEndpoint } = require('librechat-data-provider');
|
||||
const { getOpenAIClient } = require('~/server/controllers/assistants/helpers');
|
||||
|
|
@ -9,20 +9,6 @@ const { logger } = require('~/config');
|
|||
|
||||
const router = express.Router();
|
||||
|
||||
/**
|
||||
* Retrieves all user's actions
|
||||
* @route GET /actions/
|
||||
* @param {string} req.params.id - Assistant identifier.
|
||||
* @returns {Action[]} 200 - success response - application/json
|
||||
*/
|
||||
router.get('/', async (req, res) => {
|
||||
try {
|
||||
res.json(await getActions());
|
||||
} catch (error) {
|
||||
res.status(500).json({ error: error.message });
|
||||
}
|
||||
});
|
||||
|
||||
/**
|
||||
* Adds or updates actions for a specific assistant.
|
||||
* @route POST /actions/:assistant_id
|
||||
|
|
@ -51,7 +37,7 @@ router.post('/:assistant_id', async (req, res) => {
|
|||
return res.status(400).json({ message: 'No domain provided' });
|
||||
}
|
||||
|
||||
const action_id = _action_id ?? v4();
|
||||
const action_id = _action_id ?? nanoid();
|
||||
const initialPromises = [];
|
||||
|
||||
const { openai } = await getOpenAIClient({ req, res });
|
||||
|
|
@ -178,6 +164,10 @@ router.delete('/:assistant_id/:action_id/:model', async (req, res) => {
|
|||
|
||||
domain = await domainParser(req, domain, true);
|
||||
|
||||
if (!domain) {
|
||||
return res.status(400).json({ message: 'No domain provided' });
|
||||
}
|
||||
|
||||
const updatedTools = tools.filter(
|
||||
(tool) => !(tool.function && tool.function.name.includes(domain)),
|
||||
);
|
||||
|
|
|
|||
|
|
@ -1,51 +1,53 @@
|
|||
const ask = require('./ask');
|
||||
const edit = require('./edit');
|
||||
const assistants = require('./assistants');
|
||||
const categories = require('./categories');
|
||||
const tokenizer = require('./tokenizer');
|
||||
const endpoints = require('./endpoints');
|
||||
const staticRoute = require('./static');
|
||||
const messages = require('./messages');
|
||||
const convos = require('./convos');
|
||||
const presets = require('./presets');
|
||||
const prompts = require('./prompts');
|
||||
const search = require('./search');
|
||||
const tokenizer = require('./tokenizer');
|
||||
const auth = require('./auth');
|
||||
const keys = require('./keys');
|
||||
const oauth = require('./oauth');
|
||||
const endpoints = require('./endpoints');
|
||||
const balance = require('./balance');
|
||||
const models = require('./models');
|
||||
const plugins = require('./plugins');
|
||||
const user = require('./user');
|
||||
const search = require('./search');
|
||||
const models = require('./models');
|
||||
const convos = require('./convos');
|
||||
const config = require('./config');
|
||||
const assistants = require('./assistants');
|
||||
const files = require('./files');
|
||||
const staticRoute = require('./static');
|
||||
const share = require('./share');
|
||||
const categories = require('./categories');
|
||||
const agents = require('./agents');
|
||||
const roles = require('./roles');
|
||||
const oauth = require('./oauth');
|
||||
const files = require('./files');
|
||||
const share = require('./share');
|
||||
const tags = require('./tags');
|
||||
const auth = require('./auth');
|
||||
const edit = require('./edit');
|
||||
const keys = require('./keys');
|
||||
const user = require('./user');
|
||||
const ask = require('./ask');
|
||||
|
||||
module.exports = {
|
||||
search,
|
||||
ask,
|
||||
edit,
|
||||
messages,
|
||||
convos,
|
||||
presets,
|
||||
prompts,
|
||||
auth,
|
||||
keys,
|
||||
oauth,
|
||||
user,
|
||||
tokenizer,
|
||||
endpoints,
|
||||
balance,
|
||||
tags,
|
||||
roles,
|
||||
oauth,
|
||||
files,
|
||||
share,
|
||||
agents,
|
||||
convos,
|
||||
search,
|
||||
prompts,
|
||||
config,
|
||||
models,
|
||||
plugins,
|
||||
config,
|
||||
presets,
|
||||
balance,
|
||||
messages,
|
||||
endpoints,
|
||||
tokenizer,
|
||||
assistants,
|
||||
files,
|
||||
staticRoute,
|
||||
share,
|
||||
categories,
|
||||
roles,
|
||||
tags,
|
||||
staticRoute,
|
||||
};
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ const {
|
|||
isImageVisionTool,
|
||||
actionDomainSeparator,
|
||||
} = require('librechat-data-provider');
|
||||
const { tool } = require('@langchain/core/tools');
|
||||
const { encryptV2, decryptV2 } = require('~/server/utils/crypto');
|
||||
const { getActions, deleteActions } = require('~/models/Action');
|
||||
const { deleteAssistant } = require('~/models/Assistant');
|
||||
|
|
@ -101,7 +102,8 @@ async function domainParser(req, domain, inverse = false) {
|
|||
*
|
||||
* @param {Object} searchParams - The parameters for loading action sets.
|
||||
* @param {string} searchParams.user - The user identifier.
|
||||
* @param {string} searchParams.assistant_id - The assistant identifier.
|
||||
* @param {string} [searchParams.agent_id]- The agent identifier.
|
||||
* @param {string} [searchParams.assistant_id]- The assistant identifier.
|
||||
* @returns {Promise<Action[] | null>} A promise that resolves to an array of actions or `null` if no match.
|
||||
*/
|
||||
async function loadActionSets(searchParams) {
|
||||
|
|
@ -114,10 +116,14 @@ async function loadActionSets(searchParams) {
|
|||
* @param {Object} params - The parameters for loading action sets.
|
||||
* @param {Action} params.action - The action set. Necessary for decrypting authentication values.
|
||||
* @param {ActionRequest} params.requestBuilder - The ActionRequest builder class to execute the API call.
|
||||
* @returns { { _call: (toolInput: Object) => unknown} } An object with `_call` method to execute the tool input.
|
||||
* @param {string | undefined} [params.name] - The name of the tool.
|
||||
* @param {string | undefined} [params.description] - The description for the tool.
|
||||
* @param {import('zod').ZodTypeAny | undefined} [params.zodSchema] - The Zod schema for tool input validation/definition
|
||||
* @returns { Promsie<typeof tool | { _call: (toolInput: Object | string) => unknown}> } An object with `_call` method to execute the tool input.
|
||||
*/
|
||||
async function createActionTool({ action, requestBuilder }) {
|
||||
async function createActionTool({ action, requestBuilder, zodSchema, name, description }) {
|
||||
action.metadata = await decryptMetadata(action.metadata);
|
||||
/** @type {(toolInput: Object | string) => Promise<unknown>} */
|
||||
const _call = async (toolInput) => {
|
||||
try {
|
||||
requestBuilder.setParams(toolInput);
|
||||
|
|
@ -142,6 +148,14 @@ async function createActionTool({ action, requestBuilder }) {
|
|||
}
|
||||
};
|
||||
|
||||
if (name) {
|
||||
return tool(_call, {
|
||||
name,
|
||||
description: description || '',
|
||||
schema: zodSchema,
|
||||
});
|
||||
}
|
||||
|
||||
return {
|
||||
_call,
|
||||
};
|
||||
|
|
@ -180,7 +194,7 @@ async function encryptMetadata(metadata) {
|
|||
* Decrypts sensitive metadata values for an action.
|
||||
*
|
||||
* @param {ActionMetadata} metadata - The action metadata to decrypt.
|
||||
* @returns {ActionMetadata} The updated action metadata with decrypted values.
|
||||
* @returns {Promise<ActionMetadata>} The updated action metadata with decrypted values.
|
||||
*/
|
||||
async function decryptMetadata(metadata) {
|
||||
const decryptedMetadata = { ...metadata };
|
||||
|
|
|
|||
|
|
@ -45,5 +45,7 @@ module.exports = {
|
|||
AZURE_ASSISTANTS_BASE_URL,
|
||||
EModelEndpoint.azureAssistants,
|
||||
),
|
||||
/* key will be part of separate config */
|
||||
[EModelEndpoint.agents]: generateConfig(process.env.I_AM_A_TEAPOT),
|
||||
},
|
||||
};
|
||||
|
|
|
|||
|
|
@ -9,13 +9,22 @@ const { config } = require('./EndpointService');
|
|||
*/
|
||||
async function loadDefaultEndpointsConfig(req) {
|
||||
const { google, gptPlugins } = await loadAsyncEndpoints(req);
|
||||
const { openAI, assistants, azureAssistants, bingAI, anthropic, azureOpenAI, chatGPTBrowser } =
|
||||
config;
|
||||
const {
|
||||
openAI,
|
||||
agents,
|
||||
assistants,
|
||||
azureAssistants,
|
||||
bingAI,
|
||||
anthropic,
|
||||
azureOpenAI,
|
||||
chatGPTBrowser,
|
||||
} = config;
|
||||
|
||||
const enabledEndpoints = getEnabledEndpoints();
|
||||
|
||||
const endpointConfig = {
|
||||
[EModelEndpoint.openAI]: openAI,
|
||||
[EModelEndpoint.agents]: agents,
|
||||
[EModelEndpoint.assistants]: assistants,
|
||||
[EModelEndpoint.azureAssistants]: azureAssistants,
|
||||
[EModelEndpoint.azureOpenAI]: azureOpenAI,
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ async function loadDefaultModels(req) {
|
|||
|
||||
return {
|
||||
[EModelEndpoint.openAI]: openAI,
|
||||
[EModelEndpoint.agents]: openAI,
|
||||
[EModelEndpoint.google]: google,
|
||||
[EModelEndpoint.anthropic]: anthropic,
|
||||
[EModelEndpoint.gptPlugins]: gptPlugins,
|
||||
|
|
|
|||
30
api/server/services/Endpoints/agents/build.js
Normal file
30
api/server/services/Endpoints/agents/build.js
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
const { getAgent } = require('~/models/Agent');
|
||||
const { logger } = require('~/config');
|
||||
|
||||
const buildOptions = (req, endpoint, parsedBody) => {
|
||||
const { agent_id, instructions, spec, ...rest } = parsedBody;
|
||||
|
||||
const agentPromise = getAgent({
|
||||
id: agent_id,
|
||||
// TODO: better author handling
|
||||
author: req.user.id,
|
||||
}).catch((error) => {
|
||||
logger.error(`[/agents/:${agent_id}] Error retrieving agent during build options step`, error);
|
||||
return undefined;
|
||||
});
|
||||
|
||||
const endpointOption = {
|
||||
agent: agentPromise,
|
||||
endpoint,
|
||||
agent_id,
|
||||
instructions,
|
||||
spec,
|
||||
modelOptions: {
|
||||
...rest,
|
||||
},
|
||||
};
|
||||
|
||||
return endpointOption;
|
||||
};
|
||||
|
||||
module.exports = { buildOptions };
|
||||
7
api/server/services/Endpoints/agents/index.js
Normal file
7
api/server/services/Endpoints/agents/index.js
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
const build = require('./build');
|
||||
const initialize = require('./initialize');
|
||||
|
||||
module.exports = {
|
||||
...build,
|
||||
...initialize,
|
||||
};
|
||||
119
api/server/services/Endpoints/agents/initialize.js
Normal file
119
api/server/services/Endpoints/agents/initialize.js
Normal file
|
|
@ -0,0 +1,119 @@
|
|||
// const {
|
||||
// ErrorTypes,
|
||||
// EModelEndpoint,
|
||||
// resolveHeaders,
|
||||
// mapModelToAzureConfig,
|
||||
// } = require('librechat-data-provider');
|
||||
// const { getUserKeyValues, checkUserKeyExpiry } = require('~/server/services/UserService');
|
||||
// const { isEnabled, isUserProvided } = require('~/server/utils');
|
||||
// const { getAzureCredentials } = require('~/utils');
|
||||
// const { OpenAIClient } = require('~/app');
|
||||
|
||||
const { z } = require('zod');
|
||||
const { tool } = require('@langchain/core/tools');
|
||||
const { EModelEndpoint, providerEndpointMap } = require('librechat-data-provider');
|
||||
const { getDefaultHandlers } = require('~/server/controllers/agents/callbacks');
|
||||
// for testing purposes
|
||||
// const createTavilySearchTool = require('~/app/clients/tools/structured/TavilySearch');
|
||||
const initAnthropic = require('~/server/services/Endpoints/anthropic/initializeClient');
|
||||
const initOpenAI = require('~/server/services/Endpoints/openAI/initializeClient');
|
||||
const { loadAgentTools } = require('~/server/services/ToolService');
|
||||
const AgentClient = require('~/server/controllers/agents/client');
|
||||
const { getModelMaxTokens } = require('~/utils');
|
||||
|
||||
/* For testing errors */
|
||||
const _getWeather = tool(
|
||||
async ({ location }) => {
|
||||
if (location === 'SAN FRANCISCO') {
|
||||
return 'It\'s 60 degrees and foggy';
|
||||
} else if (location.toLowerCase() === 'san francisco') {
|
||||
throw new Error('Input queries must be all capitals');
|
||||
} else {
|
||||
throw new Error('Invalid input.');
|
||||
}
|
||||
},
|
||||
{
|
||||
name: 'get_weather',
|
||||
description: 'Call to get the current weather',
|
||||
schema: z.object({
|
||||
location: z.string(),
|
||||
}),
|
||||
},
|
||||
);
|
||||
|
||||
const providerConfigMap = {
|
||||
[EModelEndpoint.openAI]: initOpenAI,
|
||||
[EModelEndpoint.azureOpenAI]: initOpenAI,
|
||||
[EModelEndpoint.anthropic]: initAnthropic,
|
||||
};
|
||||
|
||||
const initializeClient = async ({ req, res, endpointOption }) => {
|
||||
if (!endpointOption) {
|
||||
throw new Error('Endpoint option not provided');
|
||||
}
|
||||
|
||||
// TODO: use endpointOption to determine options/modelOptions
|
||||
const eventHandlers = getDefaultHandlers({ res });
|
||||
|
||||
// const tools = [createTavilySearchTool()];
|
||||
// const tools = [_getWeather];
|
||||
// const tool_calls = [{ name: 'getPeople_action_swapi---dev' }];
|
||||
// const tool_calls = [{ name: 'dalle' }];
|
||||
// const tool_calls = [{ name: 'getItmOptions_action_YWlhcGkzLn' }];
|
||||
// const tool_calls = [{ name: 'tavily_search_results_json' }];
|
||||
// const tool_calls = [
|
||||
// { name: 'searchListings_action_emlsbG93NT' },
|
||||
// { name: 'searchAddress_action_emlsbG93NT' },
|
||||
// { name: 'searchMLS_action_emlsbG93NT' },
|
||||
// { name: 'searchCoordinates_action_emlsbG93NT' },
|
||||
// { name: 'searchUrl_action_emlsbG93NT' },
|
||||
// { name: 'getPropertyDetails_action_emlsbG93NT' },
|
||||
// ];
|
||||
|
||||
if (!endpointOption.agent) {
|
||||
throw new Error('No agent promise provided');
|
||||
}
|
||||
|
||||
/** @type {Agent} */
|
||||
const agent = await endpointOption.agent;
|
||||
const { tools, toolMap } = await loadAgentTools({
|
||||
req,
|
||||
tools: agent.tools,
|
||||
agent_id: agent.id,
|
||||
// openAIApiKey: process.env.OPENAI_API_KEY,
|
||||
});
|
||||
|
||||
let modelOptions = { model: agent.model };
|
||||
const getOptions = providerConfigMap[agent.provider];
|
||||
if (!getOptions) {
|
||||
throw new Error(`Provider ${agent.provider} not supported`);
|
||||
}
|
||||
|
||||
// TODO: pass-in override settings that are specific to current run
|
||||
endpointOption.modelOptions.model = agent.model;
|
||||
const options = await getOptions({
|
||||
req,
|
||||
res,
|
||||
endpointOption,
|
||||
optionsOnly: true,
|
||||
overrideEndpoint: agent.provider,
|
||||
overrideModel: agent.model,
|
||||
});
|
||||
modelOptions = Object.assign(modelOptions, options.llmConfig);
|
||||
|
||||
const client = new AgentClient({
|
||||
req,
|
||||
agent,
|
||||
tools,
|
||||
toolMap,
|
||||
modelOptions,
|
||||
eventHandlers,
|
||||
configOptions: options.configOptions,
|
||||
maxContextTokens:
|
||||
agent.max_context_tokens ??
|
||||
getModelMaxTokens(modelOptions.model, providerEndpointMap[agent.provider]),
|
||||
});
|
||||
return { client };
|
||||
};
|
||||
|
||||
module.exports = { initializeClient };
|
||||
|
|
@ -1,8 +1,9 @@
|
|||
const { EModelEndpoint } = require('librechat-data-provider');
|
||||
const { getUserKey, checkUserKeyExpiry } = require('~/server/services/UserService');
|
||||
const { getLLMConfig } = require('~/server/services/Endpoints/anthropic/llm');
|
||||
const { AnthropicClient } = require('~/app');
|
||||
|
||||
const initializeClient = async ({ req, res, endpointOption }) => {
|
||||
const initializeClient = async ({ req, res, endpointOption, optionsOnly }) => {
|
||||
const { ANTHROPIC_API_KEY, ANTHROPIC_REVERSE_PROXY, PROXY } = process.env;
|
||||
const expiresAt = req.body.key;
|
||||
const isUserProvided = ANTHROPIC_API_KEY === 'user_provided';
|
||||
|
|
@ -34,6 +35,18 @@ const initializeClient = async ({ req, res, endpointOption }) => {
|
|||
clientOptions.streamRate = allConfig.streamRate;
|
||||
}
|
||||
|
||||
if (optionsOnly) {
|
||||
const requestOptions = Object.assign(
|
||||
{
|
||||
reverseProxyUrl: ANTHROPIC_REVERSE_PROXY ?? null,
|
||||
proxy: PROXY ?? null,
|
||||
modelOptions: endpointOption.modelOptions,
|
||||
},
|
||||
clientOptions,
|
||||
);
|
||||
return getLLMConfig(anthropicApiKey, requestOptions);
|
||||
}
|
||||
|
||||
const client = new AnthropicClient(anthropicApiKey, {
|
||||
req,
|
||||
res,
|
||||
|
|
|
|||
55
api/server/services/Endpoints/anthropic/llm.js
Normal file
55
api/server/services/Endpoints/anthropic/llm.js
Normal file
|
|
@ -0,0 +1,55 @@
|
|||
const { HttpsProxyAgent } = require('https-proxy-agent');
|
||||
const { anthropicSettings, removeNullishValues } = require('librechat-data-provider');
|
||||
|
||||
/**
|
||||
* Generates configuration options for creating an Anthropic language model (LLM) instance.
|
||||
*
|
||||
* @param {string} apiKey - The API key for authentication with Anthropic.
|
||||
* @param {Object} [options={}] - Additional options for configuring the LLM.
|
||||
* @param {Object} [options.modelOptions] - Model-specific options.
|
||||
* @param {string} [options.modelOptions.model] - The name of the model to use.
|
||||
* @param {number} [options.modelOptions.maxOutputTokens] - The maximum number of tokens to generate.
|
||||
* @param {number} [options.modelOptions.temperature] - Controls randomness in output generation.
|
||||
* @param {number} [options.modelOptions.topP] - Controls diversity of output generation.
|
||||
* @param {number} [options.modelOptions.topK] - Controls the number of top tokens to consider.
|
||||
* @param {string[]} [options.modelOptions.stop] - Sequences where the API will stop generating further tokens.
|
||||
* @param {boolean} [options.modelOptions.stream] - Whether to stream the response.
|
||||
* @param {string} [options.proxy] - Proxy server URL.
|
||||
* @param {string} [options.reverseProxyUrl] - URL for a reverse proxy, if used.
|
||||
*
|
||||
* @returns {Object} Configuration options for creating an Anthropic LLM instance, with null and undefined values removed.
|
||||
*/
|
||||
function getLLMConfig(apiKey, options = {}) {
|
||||
const defaultOptions = {
|
||||
model: anthropicSettings.model.default,
|
||||
maxOutputTokens: anthropicSettings.maxOutputTokens.default,
|
||||
stream: true,
|
||||
};
|
||||
|
||||
const mergedOptions = Object.assign(defaultOptions, options.modelOptions);
|
||||
|
||||
const requestOptions = {
|
||||
apiKey,
|
||||
model: mergedOptions.model,
|
||||
stream: mergedOptions.stream,
|
||||
temperature: mergedOptions.temperature,
|
||||
top_p: mergedOptions.topP,
|
||||
top_k: mergedOptions.topK,
|
||||
stop_sequences: mergedOptions.stop,
|
||||
max_tokens:
|
||||
mergedOptions.maxOutputTokens || anthropicSettings.maxOutputTokens.reset(mergedOptions.model),
|
||||
};
|
||||
|
||||
const configOptions = {};
|
||||
if (options.proxy) {
|
||||
configOptions.httpAgent = new HttpsProxyAgent(options.proxy);
|
||||
}
|
||||
|
||||
if (options.reverseProxyUrl) {
|
||||
configOptions.baseURL = options.reverseProxyUrl;
|
||||
}
|
||||
|
||||
return { llmConfig: removeNullishValues(requestOptions), configOptions };
|
||||
}
|
||||
|
||||
module.exports = { getLLMConfig };
|
||||
|
|
@ -5,11 +5,19 @@ const {
|
|||
mapModelToAzureConfig,
|
||||
} = require('librechat-data-provider');
|
||||
const { getUserKeyValues, checkUserKeyExpiry } = require('~/server/services/UserService');
|
||||
const { getLLMConfig } = require('~/server/services/Endpoints/openAI/llm');
|
||||
const { isEnabled, isUserProvided } = require('~/server/utils');
|
||||
const { getAzureCredentials } = require('~/utils');
|
||||
const { OpenAIClient } = require('~/app');
|
||||
|
||||
const initializeClient = async ({ req, res, endpointOption }) => {
|
||||
const initializeClient = async ({
|
||||
req,
|
||||
res,
|
||||
endpointOption,
|
||||
optionsOnly,
|
||||
overrideEndpoint,
|
||||
overrideModel,
|
||||
}) => {
|
||||
const {
|
||||
PROXY,
|
||||
OPENAI_API_KEY,
|
||||
|
|
@ -19,7 +27,9 @@ const initializeClient = async ({ req, res, endpointOption }) => {
|
|||
OPENAI_SUMMARIZE,
|
||||
DEBUG_OPENAI,
|
||||
} = process.env;
|
||||
const { key: expiresAt, endpoint, model: modelName } = req.body;
|
||||
const { key: expiresAt } = req.body;
|
||||
const modelName = overrideModel ?? req.body.model;
|
||||
const endpoint = overrideEndpoint ?? req.body.endpoint;
|
||||
const contextStrategy = isEnabled(OPENAI_SUMMARIZE) ? 'summarize' : null;
|
||||
|
||||
const credentials = {
|
||||
|
|
@ -45,12 +55,10 @@ const initializeClient = async ({ req, res, endpointOption }) => {
|
|||
let baseURL = userProvidesURL ? userValues?.baseURL : baseURLOptions[endpoint];
|
||||
|
||||
const clientOptions = {
|
||||
debug: isEnabled(DEBUG_OPENAI),
|
||||
contextStrategy,
|
||||
reverseProxyUrl: baseURL ? baseURL : null,
|
||||
proxy: PROXY ?? null,
|
||||
req,
|
||||
res,
|
||||
debug: isEnabled(DEBUG_OPENAI),
|
||||
reverseProxyUrl: baseURL ? baseURL : null,
|
||||
...endpointOption,
|
||||
};
|
||||
|
||||
|
|
@ -119,7 +127,17 @@ const initializeClient = async ({ req, res, endpointOption }) => {
|
|||
throw new Error(`${endpoint} API Key not provided.`);
|
||||
}
|
||||
|
||||
const client = new OpenAIClient(apiKey, clientOptions);
|
||||
if (optionsOnly) {
|
||||
const requestOptions = Object.assign(
|
||||
{
|
||||
modelOptions: endpointOption.modelOptions,
|
||||
},
|
||||
clientOptions,
|
||||
);
|
||||
return getLLMConfig(apiKey, requestOptions);
|
||||
}
|
||||
|
||||
const client = new OpenAIClient(apiKey, Object.assign({ req, res }, clientOptions));
|
||||
return {
|
||||
client,
|
||||
openAIApiKey: apiKey,
|
||||
|
|
|
|||
120
api/server/services/Endpoints/openAI/llm.js
Normal file
120
api/server/services/Endpoints/openAI/llm.js
Normal file
|
|
@ -0,0 +1,120 @@
|
|||
const { HttpsProxyAgent } = require('https-proxy-agent');
|
||||
const { sanitizeModelName, constructAzureURL } = require('~/utils');
|
||||
const { isEnabled } = require('~/server/utils');
|
||||
|
||||
/**
|
||||
* Generates configuration options for creating a language model (LLM) instance.
|
||||
* @param {string} apiKey - The API key for authentication.
|
||||
* @param {Object} options - Additional options for configuring the LLM.
|
||||
* @param {Object} [options.modelOptions] - Model-specific options.
|
||||
* @param {string} [options.modelOptions.model] - The name of the model to use.
|
||||
* @param {number} [options.modelOptions.temperature] - Controls randomness in output generation (0-2).
|
||||
* @param {number} [options.modelOptions.top_p] - Controls diversity via nucleus sampling (0-1).
|
||||
* @param {number} [options.modelOptions.frequency_penalty] - Reduces repetition of token sequences (-2 to 2).
|
||||
* @param {number} [options.modelOptions.presence_penalty] - Encourages discussing new topics (-2 to 2).
|
||||
* @param {number} [options.modelOptions.max_tokens] - The maximum number of tokens to generate.
|
||||
* @param {string[]} [options.modelOptions.stop] - Sequences where the API will stop generating further tokens.
|
||||
* @param {string} [options.reverseProxyUrl] - URL for a reverse proxy, if used.
|
||||
* @param {boolean} [options.useOpenRouter] - Flag to use OpenRouter API.
|
||||
* @param {Object} [options.headers] - Additional headers for API requests.
|
||||
* @param {string} [options.proxy] - Proxy server URL.
|
||||
* @param {Object} [options.azure] - Azure-specific configurations.
|
||||
* @param {boolean} [options.streaming] - Whether to use streaming mode.
|
||||
* @param {Object} [options.addParams] - Additional parameters to add to the model options.
|
||||
* @param {string[]} [options.dropParams] - Parameters to remove from the model options.
|
||||
* @returns {Object} Configuration options for creating an LLM instance.
|
||||
*/
|
||||
function getLLMConfig(apiKey, options = {}) {
|
||||
const {
|
||||
modelOptions = {},
|
||||
reverseProxyUrl,
|
||||
useOpenRouter,
|
||||
headers,
|
||||
proxy,
|
||||
azure,
|
||||
streaming = true,
|
||||
addParams,
|
||||
dropParams,
|
||||
} = options;
|
||||
|
||||
let llmConfig = {
|
||||
model: 'gpt-4o-mini',
|
||||
streaming,
|
||||
};
|
||||
|
||||
Object.assign(llmConfig, modelOptions);
|
||||
|
||||
if (addParams && typeof addParams === 'object') {
|
||||
Object.assign(llmConfig, addParams);
|
||||
}
|
||||
|
||||
if (dropParams && Array.isArray(dropParams)) {
|
||||
dropParams.forEach((param) => {
|
||||
delete llmConfig[param];
|
||||
});
|
||||
}
|
||||
|
||||
const configOptions = {};
|
||||
|
||||
// Handle OpenRouter or custom reverse proxy
|
||||
if (useOpenRouter || reverseProxyUrl === 'https://openrouter.ai/api/v1') {
|
||||
configOptions.basePath = 'https://openrouter.ai/api/v1';
|
||||
configOptions.baseOptions = {
|
||||
headers: Object.assign(
|
||||
{
|
||||
'HTTP-Referer': 'https://librechat.ai',
|
||||
'X-Title': 'LibreChat',
|
||||
},
|
||||
headers,
|
||||
),
|
||||
};
|
||||
} else if (reverseProxyUrl) {
|
||||
configOptions.basePath = reverseProxyUrl;
|
||||
if (headers) {
|
||||
configOptions.baseOptions = { headers };
|
||||
}
|
||||
}
|
||||
|
||||
if (proxy) {
|
||||
const proxyAgent = new HttpsProxyAgent(proxy);
|
||||
Object.assign(configOptions, {
|
||||
httpAgent: proxyAgent,
|
||||
httpsAgent: proxyAgent,
|
||||
});
|
||||
}
|
||||
|
||||
if (azure) {
|
||||
const useModelName = isEnabled(process.env.AZURE_USE_MODEL_AS_DEPLOYMENT_NAME);
|
||||
azure.azureOpenAIApiDeploymentName = useModelName
|
||||
? sanitizeModelName(llmConfig.model)
|
||||
: azure.azureOpenAIApiDeploymentName;
|
||||
|
||||
if (process.env.AZURE_OPENAI_DEFAULT_MODEL) {
|
||||
llmConfig.model = process.env.AZURE_OPENAI_DEFAULT_MODEL;
|
||||
}
|
||||
|
||||
if (configOptions.basePath) {
|
||||
const azureURL = constructAzureURL({
|
||||
baseURL: configOptions.basePath,
|
||||
azureOptions: azure,
|
||||
});
|
||||
azure.azureOpenAIBasePath = azureURL.split(`/${azure.azureOpenAIApiDeploymentName}`)[0];
|
||||
}
|
||||
|
||||
Object.assign(llmConfig, azure);
|
||||
llmConfig.model = llmConfig.azureOpenAIApiDeploymentName;
|
||||
} else {
|
||||
llmConfig.openAIApiKey = apiKey;
|
||||
// Object.assign(llmConfig, {
|
||||
// configuration: { apiKey },
|
||||
// });
|
||||
}
|
||||
|
||||
if (process.env.OPENAI_ORGANIZATION && this.azure) {
|
||||
llmConfig.organization = process.env.OPENAI_ORGANIZATION;
|
||||
}
|
||||
|
||||
return { llmConfig, configOptions };
|
||||
}
|
||||
|
||||
module.exports = { getLLMConfig };
|
||||
64
api/server/services/Tokenizer.js
Normal file
64
api/server/services/Tokenizer.js
Normal file
|
|
@ -0,0 +1,64 @@
|
|||
const { encoding_for_model: encodingForModel, get_encoding: getEncoding } = require('tiktoken');
|
||||
const { logger } = require('~/config');
|
||||
|
||||
class Tokenizer {
|
||||
constructor() {
|
||||
this.tokenizersCache = {};
|
||||
this.tokenizerCallsCount = 0;
|
||||
}
|
||||
|
||||
getTokenizer(encoding, isModelName = false, extendSpecialTokens = {}) {
|
||||
let tokenizer;
|
||||
if (this.tokenizersCache[encoding]) {
|
||||
tokenizer = this.tokenizersCache[encoding];
|
||||
} else {
|
||||
if (isModelName) {
|
||||
tokenizer = encodingForModel(encoding, extendSpecialTokens);
|
||||
} else {
|
||||
tokenizer = getEncoding(encoding, extendSpecialTokens);
|
||||
}
|
||||
this.tokenizersCache[encoding] = tokenizer;
|
||||
}
|
||||
return tokenizer;
|
||||
}
|
||||
|
||||
freeAndResetAllEncoders() {
|
||||
try {
|
||||
Object.keys(this.tokenizersCache).forEach((key) => {
|
||||
if (this.tokenizersCache[key]) {
|
||||
this.tokenizersCache[key].free();
|
||||
delete this.tokenizersCache[key];
|
||||
}
|
||||
});
|
||||
this.tokenizerCallsCount = 1;
|
||||
} catch (error) {
|
||||
logger.error('[Tokenizer] Free and reset encoders error', error);
|
||||
}
|
||||
}
|
||||
|
||||
resetTokenizersIfNecessary() {
|
||||
if (this.tokenizerCallsCount >= 25) {
|
||||
if (this.options?.debug) {
|
||||
logger.debug('[Tokenizer] freeAndResetAllEncoders: reached 25 encodings, resetting...');
|
||||
}
|
||||
this.freeAndResetAllEncoders();
|
||||
}
|
||||
this.tokenizerCallsCount++;
|
||||
}
|
||||
|
||||
getTokenCount(text, encoding = 'cl100k_base') {
|
||||
this.resetTokenizersIfNecessary();
|
||||
try {
|
||||
const tokenizer = this.getTokenizer(encoding);
|
||||
return tokenizer.encode(text, 'all').length;
|
||||
} catch (error) {
|
||||
this.freeAndResetAllEncoders();
|
||||
const tokenizer = this.getTokenizer(encoding);
|
||||
return tokenizer.encode(text, 'all').length;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const tokenizerService = new Tokenizer();
|
||||
|
||||
module.exports = tokenizerService;
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
const fs = require('fs');
|
||||
const path = require('path');
|
||||
const { StructuredTool } = require('langchain/tools');
|
||||
const { tool: toolFn } = require('@langchain/core/tools');
|
||||
const { zodToJsonSchema } = require('zod-to-json-schema');
|
||||
const { Calculator } = require('langchain/tools/calculator');
|
||||
const {
|
||||
|
|
@ -180,7 +181,7 @@ async function processRequiredActions(client, requiredActions) {
|
|||
const tools = requiredActions.map((action) => action.tool);
|
||||
const loadedTools = await loadTools({
|
||||
user: client.req.user.id,
|
||||
model: client.req.body.model ?? 'gpt-3.5-turbo-1106',
|
||||
model: client.req.body.model ?? 'gpt-4o-mini',
|
||||
tools,
|
||||
functions: true,
|
||||
options: {
|
||||
|
|
@ -372,8 +373,120 @@ async function processRequiredActions(client, requiredActions) {
|
|||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Processes the runtime tool calls and returns a combined toolMap.
|
||||
* @param {Object} params - Run params containing user and request information.
|
||||
* @param {ServerRequest} params.req - The request object.
|
||||
* @param {string} params.agent_id - The agent ID.
|
||||
* @param {string[]} params.tools - The agent's available tools.
|
||||
* @param {string | undefined} [params.openAIApiKey] - The OpenAI API key.
|
||||
* @returns {Promise<{ tools?: StructuredTool[]; toolMap?: Record<string, StructuredTool>}>} The combined toolMap.
|
||||
*/
|
||||
async function loadAgentTools({ req, agent_id, tools, openAIApiKey }) {
|
||||
if (!tools || tools.length === 0) {
|
||||
return {};
|
||||
}
|
||||
const loadedTools = await loadTools({
|
||||
user: req.user.id,
|
||||
// model: req.body.model ?? 'gpt-4o-mini',
|
||||
tools,
|
||||
functions: true,
|
||||
options: {
|
||||
req,
|
||||
openAIApiKey,
|
||||
returnMetadata: true,
|
||||
processFileURL,
|
||||
uploadImageBuffer,
|
||||
fileStrategy: req.app.locals.fileStrategy,
|
||||
},
|
||||
skipSpecs: true,
|
||||
});
|
||||
|
||||
const agentTools = [];
|
||||
for (let i = 0; i < loadedTools.length; i++) {
|
||||
const tool = loadedTools[i];
|
||||
|
||||
const toolInstance = toolFn(
|
||||
async (...args) => {
|
||||
return tool['_call'](...args);
|
||||
},
|
||||
{
|
||||
name: tool.name,
|
||||
description: tool.description,
|
||||
schema: tool.schema,
|
||||
},
|
||||
);
|
||||
|
||||
agentTools.push(toolInstance);
|
||||
}
|
||||
|
||||
const ToolMap = loadedTools.reduce((map, tool) => {
|
||||
map[tool.name] = tool;
|
||||
return map;
|
||||
}, {});
|
||||
|
||||
let actionSets = [];
|
||||
const ActionToolMap = {};
|
||||
|
||||
for (const toolName of tools) {
|
||||
if (!ToolMap[toolName]) {
|
||||
if (!actionSets.length) {
|
||||
actionSets = (await loadActionSets({ agent_id })) ?? [];
|
||||
}
|
||||
|
||||
let actionSet = null;
|
||||
let currentDomain = '';
|
||||
for (let action of actionSets) {
|
||||
const domain = await domainParser(req, action.metadata.domain, true);
|
||||
if (toolName.includes(domain)) {
|
||||
currentDomain = domain;
|
||||
actionSet = action;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (actionSet) {
|
||||
const validationResult = validateAndParseOpenAPISpec(actionSet.metadata.raw_spec);
|
||||
if (validationResult.spec) {
|
||||
const { requestBuilders, functionSignatures, zodSchemas } = openapiToFunction(
|
||||
validationResult.spec,
|
||||
true,
|
||||
);
|
||||
const functionName = toolName.replace(`${actionDelimiter}${currentDomain}`, '');
|
||||
const functionSig = functionSignatures.find((sig) => sig.name === functionName);
|
||||
const requestBuilder = requestBuilders[functionName];
|
||||
const zodSchema = zodSchemas[functionName];
|
||||
|
||||
if (requestBuilder) {
|
||||
const tool = await createActionTool({
|
||||
action: actionSet,
|
||||
requestBuilder,
|
||||
zodSchema,
|
||||
name: toolName,
|
||||
description: functionSig.description,
|
||||
});
|
||||
agentTools.push(tool);
|
||||
ActionToolMap[toolName] = tool;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (tools.length > 0 && agentTools.length === 0) {
|
||||
throw new Error('No tools found for the specified tool calls.');
|
||||
}
|
||||
|
||||
const toolMap = { ...ToolMap, ...ActionToolMap };
|
||||
return {
|
||||
tools: agentTools,
|
||||
toolMap,
|
||||
};
|
||||
}
|
||||
|
||||
module.exports = {
|
||||
formatToOpenAIAssistantTool,
|
||||
loadAgentTools,
|
||||
loadAndFormatTools,
|
||||
processRequiredActions,
|
||||
formatToOpenAIAssistantTool,
|
||||
};
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue