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https://github.com/danny-avila/LibreChat.git
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* 📨 feat: Custom Headers on Built-in Provider Endpoints Add a `headers` config option to the built-in `openAI`, `anthropic`, and `google` endpoints (incl. Anthropic/Google Vertex), mirroring the custom endpoint header mechanism. Values support the same placeholder resolution (env vars, `{{LIBRECHAT_USER_*}}`, `{{LIBRECHAT_BODY_CONVERSATIONID}}`) and are resolved at request time so dynamic values like conversationId resolve against the live request — without losing provider-native request shaping. Closes #13082. Covers #13713: forwarding conversationId to a reverse proxy is now `X-Conversation-Id: '{{LIBRECHAT_BODY_CONVERSATIONID}}'` — an unknown header is ignored by the native Anthropic API, so no 400 and no metadata gating needed. - Schema: `headers` on `baseEndpointSchema` (openAI/google/anthropic/all). - New `mergeHeaders`/`resolveConfigHeaders` utils centralize the per-provider header locations (`configuration.defaultHeaders`, Anthropic `clientOptions.defaultHeaders`, Google `customHeaders`); provider-managed headers (auth, `anthropic-beta`) always win on collision. - Each initializer threads configured headers (endpoint over `all`) into the right place; request-time resolution runs across all locations in the main and title flows. * 🩹 fix: Cast endpoints.all to TEndpoint for headers DeepPartial widening Adding `headers` (a Record) to `baseEndpointSchema` makes `DeepPartial<TCustomConfig>` widen its value type to `string | undefined`, which is not assignable to the concrete `TEndpoint['headers']: Record<string, string>` at the `loadedEndpoints.all` assignment. Cast at the assignment site, mirroring the existing `anthropicConfig as TAnthropicEndpoint` cast in the same function. * 🛡️ fix: Harden built-in endpoint custom headers (Codex review) Address Codex P2 findings on the custom-headers feature: - Anthropic title requests: `omitTitleOptions` strips the `clientOptions` carrier, which dropped its `defaultHeaders`. Preserve just the header carrier so gateway/reverse-proxy metadata still reaches title generation. - mergeHeaders: match header names case-insensitively so an override (e.g. a provider-managed `Authorization`/`anthropic-beta`) replaces/uniones a case-variant from the base instead of emitting two names a client may collapse. - OpenAI: withhold admin-configured headers when the user supplies the base URL (`user_provided`), since values may carry `${SECRET}`/token placeholders that must not reach a user-controlled endpoint — mirrors the custom-endpoint guard. - Azure: honor global `endpoints.all` headers (same OpenAI carrier) while keeping Azure-managed `api-key`/version headers authoritative. Adds tests for each. * 🔐 fix: Resolve-once + provider-managed header safety (Codex review round 2) Address Codex P2 findings: - Azure: keep global `endpoints.all` headers unresolved at init and let request-time `resolveConfigHeaders` resolve them once, avoiding a second-order env expansion of already-substituted user values. - Google: `resolveConfigHeaders` no longer template-resolves the provider-managed `Authorization` header (built from a possibly user-provided key), so a user key like `${ENV}` can't leak server environment values. - Model fetches: thread configured headers (endpoint over `all`) + user object through `getOpenAIModels`/`getAnthropicModels` → `fetchModels`, so a gateway-fronted built-in provider receives the header on `/models` too. Fixed `fetchModels` to merge custom headers for Anthropic instead of overwriting them (managed `x-api-key`/version still win). Adds/updates tests for each. * 🧯 fix: Header provenance, memory/title coverage, idempotency (Codex round 3) Address Codex P2 findings, including two regressions from the prior round: - Google auth (findings 6 & 8): move native Google header resolution to init (`initializeGoogle`), resolving admin templates BEFORE the key-derived auth header is built. resolveConfigHeaders no longer touches Google `customHeaders`, so admin `Authorization` templates resolve again (fixes the round-2 regression) while the SDK auth header (possibly a user-provided key) is never env-expanded. - Memory runs: memory extraction now calls `resolveConfigHeaders`, so native Anthropic (and OpenAI) headers resolve for memory requests too. - Vertex titles: restore the ORIGINAL `clientOptions` object reference (not a copy) when preserving headers across `omitTitleOptions`, so the Vertex `createClient` closure and the resolved headers stay on the same object. - Reuse: `resolveConfigHeaders` is now idempotent (resolve-once per header map), preventing a second pass from env-expanding values already substituted with user/body data when an agent object flows through buildAgentInput twice. Adds/updates tests for each.
96 lines
3.2 KiB
JavaScript
96 lines
3.2 KiB
JavaScript
const { logger } = require('@librechat/data-schemas');
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const { EModelEndpoint } = require('librechat-data-provider');
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const {
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mergeHeaders,
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getAnthropicModels,
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getBedrockModels,
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getOpenAIModels,
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getGoogleModels,
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} = require('@librechat/api');
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const { getAppConfig } = require('./app');
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/**
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* Loads the default models for the application.
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* @async
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* @function
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* @param {ServerRequest} req - The Express request object.
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*/
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async function loadDefaultModels(req) {
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try {
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const appConfig =
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req.config ??
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(await getAppConfig({
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role: req.user?.role,
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userId: req.user?.id,
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tenantId: req.user?.tenantId,
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}));
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const vertexConfig = appConfig?.endpoints?.[EModelEndpoint.anthropic]?.vertexConfig;
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/** Forward configured custom headers (endpoint over global `all`) so model
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* fetches reach a gateway-fronted provider the same as chat requests. */
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const allHeaders = appConfig?.endpoints?.all?.headers;
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const openAIHeaders = mergeHeaders(
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allHeaders,
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appConfig?.endpoints?.[EModelEndpoint.openAI]?.headers,
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);
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const anthropicHeaders = mergeHeaders(
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allHeaders,
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appConfig?.endpoints?.[EModelEndpoint.anthropic]?.headers,
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);
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const [openAI, anthropic, azureOpenAI, assistants, azureAssistants, google, bedrock] =
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await Promise.all([
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getOpenAIModels({ user: req.user.id, headers: openAIHeaders, userObject: req.user }).catch(
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(error) => {
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logger.error('Error fetching OpenAI models:', error);
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return [];
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},
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),
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getAnthropicModels({
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user: req.user.id,
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vertexModels: vertexConfig?.modelNames,
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headers: anthropicHeaders,
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userObject: req.user,
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}).catch((error) => {
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logger.error('Error fetching Anthropic models:', error);
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return [];
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}),
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getOpenAIModels({ user: req.user.id, azure: true }).catch((error) => {
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logger.error('Error fetching Azure OpenAI models:', error);
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return [];
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}),
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getOpenAIModels({ assistants: true }).catch((error) => {
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logger.error('Error fetching OpenAI Assistants API models:', error);
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return [];
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}),
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getOpenAIModels({ azureAssistants: true }).catch((error) => {
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logger.error('Error fetching Azure OpenAI Assistants API models:', error);
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return [];
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}),
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Promise.resolve(getGoogleModels()).catch((error) => {
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logger.error('Error getting Google models:', error);
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return [];
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}),
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Promise.resolve(getBedrockModels()).catch((error) => {
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logger.error('Error getting Bedrock models:', error);
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return [];
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}),
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]);
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return {
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[EModelEndpoint.openAI]: openAI,
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[EModelEndpoint.google]: google,
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[EModelEndpoint.anthropic]: anthropic,
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[EModelEndpoint.azureOpenAI]: azureOpenAI,
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[EModelEndpoint.assistants]: assistants,
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[EModelEndpoint.azureAssistants]: azureAssistants,
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[EModelEndpoint.bedrock]: bedrock,
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};
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} catch (error) {
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logger.error('Error fetching default models:', error);
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throw new Error(`Failed to load default models: ${error.message}`);
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}
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}
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module.exports = loadDefaultModels;
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