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💫 feat: Config File & Custom Endpoints (#1474)
* WIP(backend/api): custom endpoint * WIP(frontend/client): custom endpoint * chore: adjust typedefs for configs * refactor: use data-provider for cache keys and rename enums and custom endpoint for better clarity and compatibility * feat: loadYaml utility * refactor: rename back to from and proof-of-concept for creating schemas from user-defined defaults * refactor: remove custom endpoint from default endpointsConfig as it will be exclusively managed by yaml config * refactor(EndpointController): rename variables for clarity * feat: initial load custom config * feat(server/utils): add simple `isUserProvided` helper * chore(types): update TConfig type * refactor: remove custom endpoint handling from model services as will be handled by config, modularize fetching of models * feat: loadCustomConfig, loadConfigEndpoints, loadConfigModels * chore: reorganize server init imports, invoke loadCustomConfig * refactor(loadConfigEndpoints/Models): return each custom endpoint as standalone endpoint * refactor(Endpoint/ModelController): spread config values after default (temporary) * chore(client): fix type issues * WIP: first pass for multiple custom endpoints - add endpointType to Conversation schema - add update zod schemas for both convo/presets to allow non-EModelEndpoint value as endpoint (also using type assertion) - use `endpointType` value as `endpoint` where mapping to type is necessary using this field - use custom defined `endpoint` value and not type for mapping to modelsConfig - misc: add return type to `getDefaultEndpoint` - in `useNewConvo`, add the endpointType if it wasn't already added to conversation - EndpointsMenu: use user-defined endpoint name as Title in menu - TODO: custom icon via custom config, change unknown to robot icon * refactor(parseConvo): pass args as an object and change where used accordingly; chore: comment out 'create schema' code * chore: remove unused availableModels field in TConfig type * refactor(parseCompactConvo): pass args as an object and change where used accordingly * feat: chat through custom endpoint * chore(message/convoSchemas): avoid saving empty arrays * fix(BaseClient/saveMessageToDatabase): save endpointType * refactor(ChatRoute): show Spinner if endpointsQuery or modelsQuery are still loading, which is apparent with slow fetching of models/remote config on first serve * fix(useConversation): assign endpointType if it's missing * fix(SaveAsPreset): pass real endpoint and endpointType when saving Preset) * chore: recorganize types order for TConfig, add `iconURL` * feat: custom endpoint icon support: - use UnknownIcon in all icon contexts - add mistral and openrouter as known endpoints, and add their icons - iconURL support * fix(presetSchema): move endpointType to default schema definitions shared between convoSchema and defaults * refactor(Settings/OpenAI): remove legacy `isOpenAI` flag * fix(OpenAIClient): do not invoke abortCompletion on completion error * feat: add responseSender/label support for custom endpoints: - use defaultModelLabel field in endpointOption - add model defaults for custom endpoints in `getResponseSender` - add `useGetSender` hook which uses EndpointsQuery to determine `defaultModelLabel` - include defaultModelLabel from endpointConfig in custom endpoint client options - pass `endpointType` to `getResponseSender` * feat(OpenAIClient): use custom options from config file * refactor: rename `defaultModelLabel` to `modelDisplayLabel` * refactor(data-provider): separate concerns from `schemas` into `parsers`, `config`, and fix imports elsewhere * feat: `iconURL` and extract environment variables from custom endpoint config values * feat: custom config validation via zod schema, rename and move to `./projectRoot/librechat.yaml` * docs: custom config docs and examples * fix(OpenAIClient/mistral): mistral does not allow singular system message, also add `useChatCompletion` flag to use openai-node for title completions * fix(custom/initializeClient): extract env var and use `isUserProvided` function * Update librechat.example.yaml * feat(InputWithLabel): add className props, and forwardRef * fix(streamResponse): handle error edge case where either messages or convos query throws an error * fix(useSSE): handle errorHandler edge cases where error response is and is not properly formatted from API, especially when a conversationId is not yet provided, which ensures stream is properly closed on error * feat: user_provided keys for custom endpoints * fix(config/endpointSchema): do not allow default endpoint values in custom endpoint `name` * feat(loadConfigModels): extract env variables and optimize fetching models * feat: support custom endpoint iconURL for messages and Nav * feat(OpenAIClient): add/dropParams support * docs: update docs with default params, add/dropParams, and notes to use config file instead of `OPENAI_REVERSE_PROXY` * docs: update docs with additional notes * feat(maxTokensMap): add mistral models (32k context) * docs: update openrouter notes * Update ai_setup.md * docs(custom_config): add table of contents and fix note about custom name * docs(custom_config): reorder ToC * Update custom_config.md * Add note about `max_tokens` field in custom_config.md
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Danny Avila
GitHub
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commit
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100 changed files with 2146 additions and 627 deletions
225
packages/data-provider/src/parsers.ts
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225
packages/data-provider/src/parsers.ts
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import type { TConversation, TPreset } from './schemas';
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import type { TEndpointOption } from './types';
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import {
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EModelEndpoint,
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openAISchema,
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googleSchema,
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bingAISchema,
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anthropicSchema,
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chatGPTBrowserSchema,
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gptPluginsSchema,
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assistantSchema,
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compactOpenAISchema,
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compactGoogleSchema,
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compactAnthropicSchema,
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compactChatGPTSchema,
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compactPluginsSchema,
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} from './schemas';
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import { alternateName } from './config';
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type EndpointSchema =
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| typeof openAISchema
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| typeof googleSchema
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| typeof bingAISchema
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| typeof anthropicSchema
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| typeof chatGPTBrowserSchema
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| typeof gptPluginsSchema
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| typeof assistantSchema;
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const endpointSchemas: Record<EModelEndpoint, EndpointSchema> = {
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[EModelEndpoint.openAI]: openAISchema,
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[EModelEndpoint.azureOpenAI]: openAISchema,
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[EModelEndpoint.custom]: openAISchema,
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[EModelEndpoint.google]: googleSchema,
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[EModelEndpoint.bingAI]: bingAISchema,
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[EModelEndpoint.anthropic]: anthropicSchema,
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[EModelEndpoint.chatGPTBrowser]: chatGPTBrowserSchema,
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[EModelEndpoint.gptPlugins]: gptPluginsSchema,
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[EModelEndpoint.assistant]: assistantSchema,
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};
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// const schemaCreators: Record<EModelEndpoint, (customSchema: DefaultSchemaValues) => EndpointSchema> = {
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// [EModelEndpoint.google]: createGoogleSchema,
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// };
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export function getFirstDefinedValue(possibleValues: string[]) {
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let returnValue;
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for (const value of possibleValues) {
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if (value) {
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returnValue = value;
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break;
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}
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}
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return returnValue;
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}
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export type TPossibleValues = {
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models: string[];
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secondaryModels?: string[];
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};
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export const parseConvo = ({
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endpoint,
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endpointType,
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conversation,
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possibleValues,
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}: {
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endpoint: EModelEndpoint;
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endpointType?: EModelEndpoint;
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conversation: Partial<TConversation | TPreset>;
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possibleValues?: TPossibleValues;
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// TODO: POC for default schema
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// defaultSchema?: Partial<EndpointSchema>,
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}) => {
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let schema = endpointSchemas[endpoint];
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if (!schema && !endpointType) {
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throw new Error(`Unknown endpoint: ${endpoint}`);
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} else if (!schema && endpointType) {
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schema = endpointSchemas[endpointType];
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}
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// if (defaultSchema && schemaCreators[endpoint]) {
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// schema = schemaCreators[endpoint](defaultSchema);
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// }
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const convo = schema.parse(conversation) as TConversation;
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const { models, secondaryModels } = possibleValues ?? {};
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if (models && convo) {
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convo.model = getFirstDefinedValue(models) ?? convo.model;
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}
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if (secondaryModels && convo.agentOptions) {
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convo.agentOptions.model = getFirstDefinedValue(secondaryModels) ?? convo.agentOptions.model;
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}
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return convo;
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};
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export const getResponseSender = (endpointOption: TEndpointOption): string => {
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const { model, endpoint, endpointType, modelDisplayLabel, chatGptLabel, modelLabel, jailbreak } =
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endpointOption;
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if (
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[
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EModelEndpoint.openAI,
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EModelEndpoint.azureOpenAI,
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EModelEndpoint.gptPlugins,
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EModelEndpoint.chatGPTBrowser,
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].includes(endpoint)
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) {
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if (chatGptLabel) {
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return chatGptLabel;
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} else if (model && model.includes('gpt-3')) {
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return 'GPT-3.5';
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} else if (model && model.includes('gpt-4')) {
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return 'GPT-4';
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} else if (model && model.includes('mistral')) {
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return 'Mistral';
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}
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return alternateName[endpoint] ?? 'ChatGPT';
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}
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if (endpoint === EModelEndpoint.bingAI) {
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return jailbreak ? 'Sydney' : 'BingAI';
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}
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if (endpoint === EModelEndpoint.anthropic) {
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return modelLabel ?? 'Claude';
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}
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if (endpoint === EModelEndpoint.google) {
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if (modelLabel) {
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return modelLabel;
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} else if (model && model.includes('gemini')) {
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return 'Gemini';
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} else if (model && model.includes('code')) {
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return 'Codey';
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}
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return 'PaLM2';
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}
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if (endpoint === EModelEndpoint.custom || endpointType === EModelEndpoint.custom) {
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if (modelLabel) {
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return modelLabel;
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} else if (chatGptLabel) {
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return chatGptLabel;
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} else if (model && model.includes('mistral')) {
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return 'Mistral';
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} else if (model && model.includes('gpt-3')) {
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return 'GPT-3.5';
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} else if (model && model.includes('gpt-4')) {
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return 'GPT-4';
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} else if (modelDisplayLabel) {
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return modelDisplayLabel;
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}
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return 'AI';
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}
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return '';
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};
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type CompactEndpointSchema =
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| typeof compactOpenAISchema
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| typeof assistantSchema
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| typeof compactGoogleSchema
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| typeof bingAISchema
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| typeof compactAnthropicSchema
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| typeof compactChatGPTSchema
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| typeof compactPluginsSchema;
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const compactEndpointSchemas: Record<string, CompactEndpointSchema> = {
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openAI: compactOpenAISchema,
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azureOpenAI: compactOpenAISchema,
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custom: compactOpenAISchema,
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assistant: assistantSchema,
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google: compactGoogleSchema,
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/* BingAI needs all fields */
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bingAI: bingAISchema,
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anthropic: compactAnthropicSchema,
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chatGPTBrowser: compactChatGPTSchema,
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gptPlugins: compactPluginsSchema,
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};
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export const parseCompactConvo = ({
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endpoint,
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endpointType,
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conversation,
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possibleValues,
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}: {
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endpoint?: EModelEndpoint;
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endpointType?: EModelEndpoint;
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conversation: Partial<TConversation | TPreset>;
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possibleValues?: TPossibleValues;
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// TODO: POC for default schema
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// defaultSchema?: Partial<EndpointSchema>,
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}) => {
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if (!endpoint) {
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throw new Error(`undefined endpoint: ${endpoint}`);
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}
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let schema = compactEndpointSchemas[endpoint];
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if (!schema && !endpointType) {
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throw new Error(`Unknown endpoint: ${endpoint}`);
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} else if (!schema && endpointType) {
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schema = compactEndpointSchemas[endpointType];
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}
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const convo = schema.parse(conversation) as TConversation;
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// const { models, secondaryModels } = possibleValues ?? {};
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const { models } = possibleValues ?? {};
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if (models && convo) {
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convo.model = getFirstDefinedValue(models) ?? convo.model;
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}
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// if (secondaryModels && convo.agentOptions) {
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// convo.agentOptionmodel = getFirstDefinedValue(secondaryModels) ?? convo.agentOptionmodel;
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// }
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return convo;
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};
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