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https://github.com/danny-avila/LibreChat.git
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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
This commit is contained in:
Danny Avila
GitHub
parent
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commit
29473a72db
100 changed files with 2146 additions and 627 deletions
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@ -9,75 +9,7 @@ export enum EModelEndpoint {
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gptPlugins = 'gptPlugins',
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anthropic = 'anthropic',
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assistant = 'assistant',
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}
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export const defaultEndpoints: EModelEndpoint[] = [
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EModelEndpoint.openAI,
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EModelEndpoint.assistant,
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EModelEndpoint.azureOpenAI,
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EModelEndpoint.bingAI,
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EModelEndpoint.chatGPTBrowser,
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EModelEndpoint.gptPlugins,
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EModelEndpoint.google,
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EModelEndpoint.anthropic,
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];
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export const defaultModels = {
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[EModelEndpoint.google]: [
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'gemini-pro',
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'gemini-pro-vision',
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'chat-bison',
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'chat-bison-32k',
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'codechat-bison',
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'codechat-bison-32k',
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'text-bison',
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'text-bison-32k',
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'text-unicorn',
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'code-gecko',
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'code-bison',
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'code-bison-32k',
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],
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[EModelEndpoint.anthropic]: [
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'claude-2.1',
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'claude-2',
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'claude-1.2',
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'claude-1',
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'claude-1-100k',
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'claude-instant-1',
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'claude-instant-1-100k',
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],
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[EModelEndpoint.openAI]: [
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'gpt-3.5-turbo-16k-0613',
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'gpt-3.5-turbo-16k',
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'gpt-4-1106-preview',
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'gpt-3.5-turbo',
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'gpt-3.5-turbo-1106',
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'gpt-4-vision-preview',
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'gpt-4',
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'gpt-3.5-turbo-instruct-0914',
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'gpt-3.5-turbo-0613',
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'gpt-3.5-turbo-0301',
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'gpt-3.5-turbo-instruct',
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'gpt-4-0613',
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'text-davinci-003',
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'gpt-4-0314',
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],
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};
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export const alternateName = {
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[EModelEndpoint.openAI]: 'OpenAI',
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[EModelEndpoint.assistant]: 'Assistants',
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[EModelEndpoint.azureOpenAI]: 'Azure OpenAI',
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[EModelEndpoint.bingAI]: 'Bing',
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[EModelEndpoint.chatGPTBrowser]: 'ChatGPT',
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[EModelEndpoint.gptPlugins]: 'Plugins',
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[EModelEndpoint.google]: 'Google',
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[EModelEndpoint.anthropic]: 'Anthropic',
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};
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export enum AuthKeys {
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GOOGLE_SERVICE_KEY = 'GOOGLE_SERVICE_KEY',
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GOOGLE_API_KEY = 'GOOGLE_API_KEY',
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custom = 'custom',
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}
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export const endpointSettings = {
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@ -116,41 +48,10 @@ export const endpointSettings = {
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const google = endpointSettings[EModelEndpoint.google];
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export const EndpointURLs: { [key in EModelEndpoint]: string } = {
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[EModelEndpoint.azureOpenAI]: '/api/ask/azureOpenAI',
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[EModelEndpoint.openAI]: '/api/ask/openAI',
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[EModelEndpoint.bingAI]: '/api/ask/bingAI',
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[EModelEndpoint.chatGPTBrowser]: '/api/ask/chatGPTBrowser',
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[EModelEndpoint.google]: '/api/ask/google',
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[EModelEndpoint.gptPlugins]: '/api/ask/gptPlugins',
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[EModelEndpoint.anthropic]: '/api/ask/anthropic',
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[EModelEndpoint.assistant]: '/api/assistants/chat',
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};
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export const modularEndpoints = new Set<EModelEndpoint | string>([
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EModelEndpoint.gptPlugins,
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EModelEndpoint.anthropic,
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EModelEndpoint.google,
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EModelEndpoint.openAI,
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]);
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export const supportsFiles = {
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[EModelEndpoint.openAI]: true,
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[EModelEndpoint.google]: true,
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[EModelEndpoint.assistant]: true,
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[EModelEndpoint.azureOpenAI]: true,
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};
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export const supportsBalanceCheck = {
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[EModelEndpoint.openAI]: true,
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[EModelEndpoint.azureOpenAI]: true,
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[EModelEndpoint.gptPlugins]: true,
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};
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export const visionModels = ['gpt-4-vision', 'llava-13b', 'gemini-pro-vision'];
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export const eModelEndpointSchema = z.nativeEnum(EModelEndpoint);
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export const extendedModelEndpointSchema = z.union([eModelEndpointSchema, z.string()]);
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export const tPluginAuthConfigSchema = z.object({
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authField: z.string(),
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label: z.string(),
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@ -253,6 +154,7 @@ export const tConversationSchema = z.object({
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title: z.string().nullable().or(z.literal('New Chat')).default('New Chat'),
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user: z.string().optional(),
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endpoint: eModelEndpointSchema.nullable(),
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endpointType: eModelEndpointSchema.optional(),
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suggestions: z.array(z.string()).optional(),
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messages: z.array(z.string()).optional(),
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tools: z.array(tPluginSchema).optional(),
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@ -305,8 +207,22 @@ export const tPresetSchema = tConversationSchema
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}),
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);
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export const tConvoUpdateSchema = tConversationSchema.merge(
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z.object({
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endpoint: extendedModelEndpointSchema.nullable(),
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}),
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);
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export const tPresetUpdateSchema = tConversationSchema.merge(
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z.object({
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endpoint: extendedModelEndpointSchema.nullable(),
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}),
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);
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export type TPreset = z.infer<typeof tPresetSchema>;
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// type DefaultSchemaValues = Partial<typeof google>;
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export const openAISchema = tConversationSchema
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.pick({
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model: true,
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@ -528,122 +444,6 @@ export const assistantSchema = tConversationSchema
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.transform(removeNullishValues)
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.catch(() => ({}));
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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.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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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: EModelEndpoint,
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conversation: Partial<TConversation | TPreset>,
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possibleValues?: TPossibleValues,
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) => {
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const schema = endpointSchemas[endpoint];
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if (!schema) {
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throw new Error(`Unknown endpoint: ${endpoint}`);
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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 type TEndpointOption = {
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endpoint: EModelEndpoint;
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model?: string | null;
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promptPrefix?: string;
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temperature?: number;
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chatGptLabel?: string | null;
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modelLabel?: string | null;
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jailbreak?: boolean;
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key?: string | null;
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};
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export const getResponseSender = (endpointOption: TEndpointOption): string => {
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const { model, endpoint, chatGptLabel, modelLabel, jailbreak } = 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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}
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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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return '';
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};
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export const compactOpenAISchema = tConversationSchema
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.pick({
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model: true,
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@ -809,53 +609,52 @@ export const compactPluginsSchema = tConversationSchema
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})
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.catch(() => ({}));
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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 createGoogleSchema = (customGoogle: DefaultSchemaValues) => {
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// const defaults = { ...google, ...customGoogle };
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// return tConversationSchema
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// .pick({
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// model: true,
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// modelLabel: true,
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// promptPrefix: true,
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// examples: true,
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// temperature: true,
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// maxOutputTokens: true,
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// topP: true,
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// topK: true,
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// })
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// .transform((obj) => {
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// const isGeminiPro = obj?.model?.toLowerCase()?.includes('gemini-pro');
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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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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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// const maxOutputTokensMax = isGeminiPro
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// ? defaults.maxOutputTokens.maxGeminiPro
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// : defaults.maxOutputTokens.max;
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// const maxOutputTokensDefault = isGeminiPro
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// ? defaults.maxOutputTokens.defaultGeminiPro
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// : defaults.maxOutputTokens.default;
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export const parseCompactConvo = (
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endpoint: EModelEndpoint | undefined,
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conversation: Partial<TConversation | TPreset>,
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possibleValues?: TPossibleValues,
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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 maxOutputTokens = obj.maxOutputTokens ?? maxOutputTokensDefault;
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// maxOutputTokens = Math.min(maxOutputTokens, maxOutputTokensMax);
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const schema = compactEndpointSchemas[endpoint];
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if (!schema) {
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throw new Error(`Unknown endpoint: ${endpoint}`);
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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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// return {
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// ...obj,
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// model: obj.model ?? defaults.model.default,
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// modelLabel: obj.modelLabel ?? null,
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// promptPrefix: obj.promptPrefix ?? null,
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// examples: obj.examples ?? [{ input: { content: '' }, output: { content: '' } }],
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// temperature: obj.temperature ?? defaults.temperature.default,
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// maxOutputTokens,
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// topP: obj.topP ?? defaults.topP.default,
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// topK: obj.topK ?? defaults.topK.default,
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// };
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// })
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// .catch(() => ({
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// model: defaults.model.default,
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// modelLabel: null,
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// promptPrefix: null,
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// examples: [{ input: { content: '' }, output: { content: '' } }],
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// temperature: defaults.temperature.default,
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// maxOutputTokens: defaults.maxOutputTokens.default,
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// topP: defaults.topP.default,
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// topK: defaults.topK.default,
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// }));
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// };
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