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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@ -6,6 +6,7 @@ const {
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isImageVisionTool,
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actionDomainSeparator,
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} = require('librechat-data-provider');
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const { tool } = require('@langchain/core/tools');
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const { encryptV2, decryptV2 } = require('~/server/utils/crypto');
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const { getActions, deleteActions } = require('~/models/Action');
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const { deleteAssistant } = require('~/models/Assistant');
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@ -101,7 +102,8 @@ async function domainParser(req, domain, inverse = false) {
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*
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* @param {Object} searchParams - The parameters for loading action sets.
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* @param {string} searchParams.user - The user identifier.
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* @param {string} searchParams.assistant_id - The assistant identifier.
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* @param {string} [searchParams.agent_id]- The agent identifier.
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* @param {string} [searchParams.assistant_id]- The assistant identifier.
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* @returns {Promise<Action[] | null>} A promise that resolves to an array of actions or `null` if no match.
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*/
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async function loadActionSets(searchParams) {
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@ -114,10 +116,14 @@ async function loadActionSets(searchParams) {
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* @param {Object} params - The parameters for loading action sets.
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* @param {Action} params.action - The action set. Necessary for decrypting authentication values.
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* @param {ActionRequest} params.requestBuilder - The ActionRequest builder class to execute the API call.
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* @returns { { _call: (toolInput: Object) => unknown} } An object with `_call` method to execute the tool input.
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* @param {string | undefined} [params.name] - The name of the tool.
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* @param {string | undefined} [params.description] - The description for the tool.
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* @param {import('zod').ZodTypeAny | undefined} [params.zodSchema] - The Zod schema for tool input validation/definition
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* @returns { Promsie<typeof tool | { _call: (toolInput: Object | string) => unknown}> } An object with `_call` method to execute the tool input.
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*/
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async function createActionTool({ action, requestBuilder }) {
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async function createActionTool({ action, requestBuilder, zodSchema, name, description }) {
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action.metadata = await decryptMetadata(action.metadata);
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/** @type {(toolInput: Object | string) => Promise<unknown>} */
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const _call = async (toolInput) => {
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try {
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requestBuilder.setParams(toolInput);
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@ -142,6 +148,14 @@ async function createActionTool({ action, requestBuilder }) {
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}
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};
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if (name) {
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return tool(_call, {
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name,
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description: description || '',
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schema: zodSchema,
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});
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}
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return {
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_call,
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};
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@ -180,7 +194,7 @@ async function encryptMetadata(metadata) {
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* Decrypts sensitive metadata values for an action.
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*
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* @param {ActionMetadata} metadata - The action metadata to decrypt.
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* @returns {ActionMetadata} The updated action metadata with decrypted values.
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* @returns {Promise<ActionMetadata>} The updated action metadata with decrypted values.
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*/
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async function decryptMetadata(metadata) {
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const decryptedMetadata = { ...metadata };
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@ -45,5 +45,7 @@ module.exports = {
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AZURE_ASSISTANTS_BASE_URL,
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EModelEndpoint.azureAssistants,
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),
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/* key will be part of separate config */
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[EModelEndpoint.agents]: generateConfig(process.env.I_AM_A_TEAPOT),
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},
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};
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@ -9,13 +9,22 @@ const { config } = require('./EndpointService');
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*/
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async function loadDefaultEndpointsConfig(req) {
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const { google, gptPlugins } = await loadAsyncEndpoints(req);
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const { openAI, assistants, azureAssistants, bingAI, anthropic, azureOpenAI, chatGPTBrowser } =
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config;
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const {
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openAI,
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agents,
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assistants,
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azureAssistants,
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bingAI,
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anthropic,
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azureOpenAI,
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chatGPTBrowser,
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} = config;
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const enabledEndpoints = getEnabledEndpoints();
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const endpointConfig = {
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[EModelEndpoint.openAI]: openAI,
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[EModelEndpoint.agents]: agents,
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[EModelEndpoint.assistants]: assistants,
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[EModelEndpoint.azureAssistants]: azureAssistants,
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[EModelEndpoint.azureOpenAI]: azureOpenAI,
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@ -29,6 +29,7 @@ async function loadDefaultModels(req) {
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return {
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[EModelEndpoint.openAI]: openAI,
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[EModelEndpoint.agents]: openAI,
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[EModelEndpoint.google]: google,
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[EModelEndpoint.anthropic]: anthropic,
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[EModelEndpoint.gptPlugins]: gptPlugins,
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30
api/server/services/Endpoints/agents/build.js
Normal file
30
api/server/services/Endpoints/agents/build.js
Normal file
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@ -0,0 +1,30 @@
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const { getAgent } = require('~/models/Agent');
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const { logger } = require('~/config');
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const buildOptions = (req, endpoint, parsedBody) => {
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const { agent_id, instructions, spec, ...rest } = parsedBody;
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const agentPromise = getAgent({
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id: agent_id,
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// TODO: better author handling
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author: req.user.id,
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}).catch((error) => {
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logger.error(`[/agents/:${agent_id}] Error retrieving agent during build options step`, error);
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return undefined;
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});
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const endpointOption = {
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agent: agentPromise,
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endpoint,
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agent_id,
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instructions,
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spec,
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modelOptions: {
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...rest,
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},
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};
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return endpointOption;
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};
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module.exports = { buildOptions };
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7
api/server/services/Endpoints/agents/index.js
Normal file
7
api/server/services/Endpoints/agents/index.js
Normal file
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@ -0,0 +1,7 @@
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const build = require('./build');
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const initialize = require('./initialize');
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module.exports = {
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...build,
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...initialize,
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};
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119
api/server/services/Endpoints/agents/initialize.js
Normal file
119
api/server/services/Endpoints/agents/initialize.js
Normal file
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@ -0,0 +1,119 @@
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// const {
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// ErrorTypes,
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// EModelEndpoint,
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// resolveHeaders,
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// mapModelToAzureConfig,
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// } = require('librechat-data-provider');
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// const { getUserKeyValues, checkUserKeyExpiry } = require('~/server/services/UserService');
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// const { isEnabled, isUserProvided } = require('~/server/utils');
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// const { getAzureCredentials } = require('~/utils');
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// const { OpenAIClient } = require('~/app');
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const { z } = require('zod');
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const { tool } = require('@langchain/core/tools');
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const { EModelEndpoint, providerEndpointMap } = require('librechat-data-provider');
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const { getDefaultHandlers } = require('~/server/controllers/agents/callbacks');
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// for testing purposes
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// const createTavilySearchTool = require('~/app/clients/tools/structured/TavilySearch');
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const initAnthropic = require('~/server/services/Endpoints/anthropic/initializeClient');
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const initOpenAI = require('~/server/services/Endpoints/openAI/initializeClient');
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const { loadAgentTools } = require('~/server/services/ToolService');
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const AgentClient = require('~/server/controllers/agents/client');
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const { getModelMaxTokens } = require('~/utils');
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/* For testing errors */
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const _getWeather = tool(
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async ({ location }) => {
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if (location === 'SAN FRANCISCO') {
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return 'It\'s 60 degrees and foggy';
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} else if (location.toLowerCase() === 'san francisco') {
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throw new Error('Input queries must be all capitals');
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} else {
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throw new Error('Invalid input.');
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}
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},
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{
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name: 'get_weather',
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description: 'Call to get the current weather',
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schema: z.object({
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location: z.string(),
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}),
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},
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);
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const providerConfigMap = {
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[EModelEndpoint.openAI]: initOpenAI,
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[EModelEndpoint.azureOpenAI]: initOpenAI,
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[EModelEndpoint.anthropic]: initAnthropic,
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};
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const initializeClient = async ({ req, res, endpointOption }) => {
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if (!endpointOption) {
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throw new Error('Endpoint option not provided');
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}
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// TODO: use endpointOption to determine options/modelOptions
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const eventHandlers = getDefaultHandlers({ res });
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// const tools = [createTavilySearchTool()];
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// const tools = [_getWeather];
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// const tool_calls = [{ name: 'getPeople_action_swapi---dev' }];
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// const tool_calls = [{ name: 'dalle' }];
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// const tool_calls = [{ name: 'getItmOptions_action_YWlhcGkzLn' }];
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// const tool_calls = [{ name: 'tavily_search_results_json' }];
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// const tool_calls = [
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// { name: 'searchListings_action_emlsbG93NT' },
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// { name: 'searchAddress_action_emlsbG93NT' },
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// { name: 'searchMLS_action_emlsbG93NT' },
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// { name: 'searchCoordinates_action_emlsbG93NT' },
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// { name: 'searchUrl_action_emlsbG93NT' },
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// { name: 'getPropertyDetails_action_emlsbG93NT' },
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// ];
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if (!endpointOption.agent) {
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throw new Error('No agent promise provided');
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}
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/** @type {Agent} */
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const agent = await endpointOption.agent;
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const { tools, toolMap } = await loadAgentTools({
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req,
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tools: agent.tools,
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agent_id: agent.id,
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// openAIApiKey: process.env.OPENAI_API_KEY,
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});
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let modelOptions = { model: agent.model };
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const getOptions = providerConfigMap[agent.provider];
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if (!getOptions) {
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throw new Error(`Provider ${agent.provider} not supported`);
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}
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// TODO: pass-in override settings that are specific to current run
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endpointOption.modelOptions.model = agent.model;
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const options = await getOptions({
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req,
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res,
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endpointOption,
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optionsOnly: true,
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overrideEndpoint: agent.provider,
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overrideModel: agent.model,
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});
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modelOptions = Object.assign(modelOptions, options.llmConfig);
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const client = new AgentClient({
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req,
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agent,
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tools,
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toolMap,
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modelOptions,
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eventHandlers,
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configOptions: options.configOptions,
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maxContextTokens:
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agent.max_context_tokens ??
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getModelMaxTokens(modelOptions.model, providerEndpointMap[agent.provider]),
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});
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return { client };
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};
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module.exports = { initializeClient };
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@ -1,8 +1,9 @@
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const { EModelEndpoint } = require('librechat-data-provider');
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const { getUserKey, checkUserKeyExpiry } = require('~/server/services/UserService');
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const { getLLMConfig } = require('~/server/services/Endpoints/anthropic/llm');
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const { AnthropicClient } = require('~/app');
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const initializeClient = async ({ req, res, endpointOption }) => {
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const initializeClient = async ({ req, res, endpointOption, optionsOnly }) => {
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const { ANTHROPIC_API_KEY, ANTHROPIC_REVERSE_PROXY, PROXY } = process.env;
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const expiresAt = req.body.key;
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const isUserProvided = ANTHROPIC_API_KEY === 'user_provided';
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@ -34,6 +35,18 @@ const initializeClient = async ({ req, res, endpointOption }) => {
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clientOptions.streamRate = allConfig.streamRate;
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}
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if (optionsOnly) {
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const requestOptions = Object.assign(
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{
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reverseProxyUrl: ANTHROPIC_REVERSE_PROXY ?? null,
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proxy: PROXY ?? null,
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modelOptions: endpointOption.modelOptions,
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},
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clientOptions,
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);
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return getLLMConfig(anthropicApiKey, requestOptions);
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}
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const client = new AnthropicClient(anthropicApiKey, {
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req,
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res,
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55
api/server/services/Endpoints/anthropic/llm.js
Normal file
55
api/server/services/Endpoints/anthropic/llm.js
Normal file
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@ -0,0 +1,55 @@
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const { HttpsProxyAgent } = require('https-proxy-agent');
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const { anthropicSettings, removeNullishValues } = require('librechat-data-provider');
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/**
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* Generates configuration options for creating an Anthropic language model (LLM) instance.
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*
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* @param {string} apiKey - The API key for authentication with Anthropic.
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* @param {Object} [options={}] - Additional options for configuring the LLM.
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* @param {Object} [options.modelOptions] - Model-specific options.
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* @param {string} [options.modelOptions.model] - The name of the model to use.
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* @param {number} [options.modelOptions.maxOutputTokens] - The maximum number of tokens to generate.
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* @param {number} [options.modelOptions.temperature] - Controls randomness in output generation.
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* @param {number} [options.modelOptions.topP] - Controls diversity of output generation.
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* @param {number} [options.modelOptions.topK] - Controls the number of top tokens to consider.
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* @param {string[]} [options.modelOptions.stop] - Sequences where the API will stop generating further tokens.
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* @param {boolean} [options.modelOptions.stream] - Whether to stream the response.
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* @param {string} [options.proxy] - Proxy server URL.
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* @param {string} [options.reverseProxyUrl] - URL for a reverse proxy, if used.
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*
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* @returns {Object} Configuration options for creating an Anthropic LLM instance, with null and undefined values removed.
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*/
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function getLLMConfig(apiKey, options = {}) {
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const defaultOptions = {
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model: anthropicSettings.model.default,
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maxOutputTokens: anthropicSettings.maxOutputTokens.default,
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stream: true,
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};
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const mergedOptions = Object.assign(defaultOptions, options.modelOptions);
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const requestOptions = {
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apiKey,
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model: mergedOptions.model,
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stream: mergedOptions.stream,
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temperature: mergedOptions.temperature,
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top_p: mergedOptions.topP,
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top_k: mergedOptions.topK,
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stop_sequences: mergedOptions.stop,
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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