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399 lines
13 KiB
JavaScript
399 lines
13 KiB
JavaScript
import { PROVIDER_MODELS, PROVIDER_ID_TO_ALIAS } from "@/shared/constants/models";
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import {
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AI_PROVIDERS,
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getProviderAlias,
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isAnthropicCompatibleProvider,
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isOpenAICompatibleProvider,
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} from "@/shared/constants/providers";
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import { getProviderConnections, getCombos, getCustomModels, getModelAliases } from "@/lib/localDb";
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const parseOpenAIStyleModels = (data) => {
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if (Array.isArray(data)) return data;
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return data?.data || data?.models || data?.results || [];
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};
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// Matches provider IDs that are upstream/cross-instance connections (contain a UUID suffix)
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const UPSTREAM_CONNECTION_RE = /[-_][0-9a-f]{8,}$/i;
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// LLM kind sentinel — combos/models with no explicit kind default to LLM
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const LLM_KIND = "llm";
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// Map per-model `type` field (in PROVIDER_MODELS) to service kind.
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// Models without `type` are treated as LLM.
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const MODEL_TYPE_TO_KIND = {
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image: "image",
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tts: "tts",
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embedding: "embedding",
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stt: "stt",
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imageToText: "imageToText",
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};
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function modelKind(model) {
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if (!model?.type) return LLM_KIND;
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return MODEL_TYPE_TO_KIND[model.type] || LLM_KIND;
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}
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// For dynamic/unknown model IDs (compatible providers, alias map, custom models)
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// fall back to provider-level kind matching when per-model type is unavailable.
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function inferKindFromUnknownModelId(modelId) {
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const lower = String(modelId).toLowerCase();
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if (/embed/.test(lower)) return "embedding";
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if (/tts|speech|audio|voice/.test(lower)) return "tts";
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if (/image|imagen|dall-?e|flux|sdxl|sd-|stable-diffusion/.test(lower)) return "image";
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return LLM_KIND;
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}
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async function fetchCompatibleModelIds(connection) {
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if (!connection?.apiKey) return [];
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const baseUrl = typeof connection?.providerSpecificData?.baseUrl === "string"
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? connection.providerSpecificData.baseUrl.trim().replace(/\/$/, "")
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: "";
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if (!baseUrl) return [];
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let url = `${baseUrl}/models`;
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const headers = {
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"Content-Type": "application/json",
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};
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if (isOpenAICompatibleProvider(connection.provider)) {
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headers.Authorization = `Bearer ${connection.apiKey}`;
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} else if (isAnthropicCompatibleProvider(connection.provider)) {
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if (url.endsWith("/messages/models")) {
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url = url.slice(0, -9);
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} else if (url.endsWith("/messages")) {
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url = `${url.slice(0, -9)}/models`;
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}
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headers["x-api-key"] = connection.apiKey;
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headers["anthropic-version"] = "2023-06-01";
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headers.Authorization = `Bearer ${connection.apiKey}`;
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} else {
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return [];
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}
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try {
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const controller = new AbortController();
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const timeoutId = setTimeout(() => controller.abort(), 5000);
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const response = await fetch(url, {
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method: "GET",
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headers,
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cache: "no-store",
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signal: controller.signal,
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});
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clearTimeout(timeoutId);
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if (!response.ok) return [];
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const data = await response.json();
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const rawModels = parseOpenAIStyleModels(data);
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return Array.from(
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new Set(
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rawModels
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.map((model) => model?.id || model?.name || model?.model)
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.filter((modelId) => typeof modelId === "string" && modelId.trim() !== "")
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)
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);
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} catch {
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return [];
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}
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}
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// Provider matches kindFilter when its serviceKinds intersect the requested kinds.
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// LLM is the default kind for providers missing serviceKinds.
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function providerMatchesKinds(providerId, kindFilter) {
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const provider = AI_PROVIDERS[providerId];
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const kinds = Array.isArray(provider?.serviceKinds) && provider.serviceKinds.length > 0
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? provider.serviceKinds
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: [LLM_KIND];
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return kindFilter.some((k) => kinds.includes(k));
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}
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// Combo matches kindFilter when its `kind` field is in the list.
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// Combos with no kind are treated as LLM.
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function comboMatchesKinds(combo, kindFilter) {
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const kind = combo?.kind || LLM_KIND;
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return kindFilter.includes(kind);
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}
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/**
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* Build OpenAI-format models list filtered by service kinds.
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* @param {string[]} kindFilter - List of service kinds to include (e.g. ["llm"], ["webSearch","webFetch"]).
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*/
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export async function buildModelsList(kindFilter) {
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let connections = [];
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try {
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connections = await getProviderConnections();
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connections = connections.filter(c => c.isActive !== false);
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} catch (e) {
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console.log("Could not fetch providers, returning all models");
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}
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let combos = [];
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try {
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combos = await getCombos();
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} catch (e) {
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console.log("Could not fetch combos");
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}
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let customModels = [];
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try {
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customModels = await getCustomModels();
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} catch (e) {
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console.log("Could not fetch custom models");
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}
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let modelAliases = {};
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try {
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modelAliases = await getModelAliases();
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} catch (e) {
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console.log("Could not fetch model aliases");
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}
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const activeConnectionByProvider = new Map();
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for (const conn of connections) {
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if (!activeConnectionByProvider.has(conn.provider)) {
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activeConnectionByProvider.set(conn.provider, conn);
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}
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}
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const models = [];
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const timestamp = Math.floor(Date.now() / 1000);
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// Combos first (filtered by kind). Web combos expose `kind` so AI knows search vs fetch.
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for (const combo of combos) {
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if (!comboMatchesKinds(combo, kindFilter)) continue;
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const entry = {
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id: combo.name,
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object: "model",
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created: timestamp,
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owned_by: "combo",
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};
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if (combo.kind === "webSearch" || combo.kind === "webFetch") {
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entry.kind = combo.kind;
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}
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models.push(entry);
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}
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if (connections.length === 0) {
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// DB unavailable -> return static models, filtered by per-model kind
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const aliasToProviderId = Object.fromEntries(
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Object.entries(PROVIDER_ID_TO_ALIAS).map(([id, alias]) => [alias, id])
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);
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for (const [alias, providerModels] of Object.entries(PROVIDER_MODELS)) {
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const providerId = aliasToProviderId[alias] || alias;
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if (!providerMatchesKinds(providerId, kindFilter)) continue;
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for (const model of providerModels) {
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if (!kindFilter.includes(modelKind(model))) continue;
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models.push({
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id: `${alias}/${model.id}`,
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object: "model",
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created: timestamp,
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owned_by: alias,
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});
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}
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}
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for (const customModel of customModels) {
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if (!customModel?.id || (customModel.type && customModel.type !== "llm")) continue;
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// Custom models without active connection are LLM-only by current schema
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if (!kindFilter.includes(LLM_KIND)) continue;
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const providerAlias = customModel.providerAlias;
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if (!providerAlias) continue;
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const modelId = String(customModel.id).trim();
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if (!modelId) continue;
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models.push({
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id: `${providerAlias}/${modelId}`,
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object: "model",
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created: timestamp,
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owned_by: providerAlias,
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});
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}
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} else {
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for (const [providerId, conn] of activeConnectionByProvider.entries()) {
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if (!providerMatchesKinds(providerId, kindFilter)) continue;
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const staticAlias = PROVIDER_ID_TO_ALIAS[providerId] || providerId;
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const outputAlias = (
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conn?.providerSpecificData?.prefix
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|| getProviderAlias(providerId)
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|| staticAlias
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).trim();
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const providerModels = PROVIDER_MODELS[staticAlias] || [];
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const enabledModels = conn?.providerSpecificData?.enabledModels;
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const hasExplicitEnabledModels =
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Array.isArray(enabledModels) && enabledModels.length > 0;
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const isCompatibleProvider =
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isOpenAICompatibleProvider(providerId) || isAnthropicCompatibleProvider(providerId);
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// Build kind lookup for static models so we can filter even when only IDs are exposed
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const staticModelKindById = new Map(
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providerModels.map((m) => [m.id, modelKind(m)])
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);
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let rawModelIds = hasExplicitEnabledModels
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? Array.from(
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new Set(
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enabledModels.filter(
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(modelId) => typeof modelId === "string" && modelId.trim() !== "",
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),
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),
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)
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: providerModels.map((model) => model.id);
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if (isCompatibleProvider && rawModelIds.length === 0 && !UPSTREAM_CONNECTION_RE.test(providerId)) {
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rawModelIds = await fetchCompatibleModelIds(conn);
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}
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const modelIds = rawModelIds
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.map((modelId) => {
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if (modelId.startsWith(`${outputAlias}/`)) {
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return modelId.slice(outputAlias.length + 1);
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}
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if (modelId.startsWith(`${staticAlias}/`)) {
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return modelId.slice(staticAlias.length + 1);
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}
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if (modelId.startsWith(`${providerId}/`)) {
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return modelId.slice(providerId.length + 1);
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}
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return modelId;
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})
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.filter((modelId) => typeof modelId === "string" && modelId.trim() !== "");
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const customModelIds = customModels
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.filter((m) => {
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if (!m?.id || (m.type && m.type !== "llm")) return false;
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const alias = m.providerAlias;
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return alias === staticAlias || alias === outputAlias || alias === providerId;
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})
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.map((m) => String(m.id).trim())
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.filter((modelId) => modelId !== "");
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const aliasModelIds = Object.values(modelAliases || {})
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.filter((fullModel) => {
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if (typeof fullModel !== "string" || !fullModel.includes("/")) return false;
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return (
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fullModel.startsWith(`${outputAlias}/`) ||
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fullModel.startsWith(`${staticAlias}/`) ||
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fullModel.startsWith(`${providerId}/`)
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);
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})
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.map((fullModel) => {
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if (fullModel.startsWith(`${outputAlias}/`)) {
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return fullModel.slice(outputAlias.length + 1);
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}
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if (fullModel.startsWith(`${staticAlias}/`)) {
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return fullModel.slice(staticAlias.length + 1);
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}
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if (fullModel.startsWith(`${providerId}/`)) {
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return fullModel.slice(providerId.length + 1);
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}
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return fullModel;
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})
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.filter((modelId) => typeof modelId === "string" && modelId.trim() !== "");
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const mergedModelIds = Array.from(new Set([...modelIds, ...customModelIds, ...aliasModelIds]));
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for (const modelId of mergedModelIds) {
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// Resolve kind: prefer static metadata, otherwise infer from ID heuristics
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const kind = staticModelKindById.get(modelId) || inferKindFromUnknownModelId(modelId);
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if (!kindFilter.includes(kind)) continue;
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models.push({
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id: `${outputAlias}/${modelId}`,
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object: "model",
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created: timestamp,
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owned_by: outputAlias,
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});
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}
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// Merge sub-config models (TTS / embedding) that live on AI_PROVIDERS, not PROVIDER_MODELS
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const providerInfo = AI_PROVIDERS[providerId];
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const subConfigModels = [];
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if (kindFilter.includes("tts") && Array.isArray(providerInfo?.ttsConfig?.models)) {
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for (const m of providerInfo.ttsConfig.models) {
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if (m?.id) subConfigModels.push(m.id);
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}
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}
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if (kindFilter.includes("embedding") && Array.isArray(providerInfo?.embeddingConfig?.models)) {
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for (const m of providerInfo.embeddingConfig.models) {
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if (m?.id) subConfigModels.push(m.id);
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}
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}
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for (const subId of subConfigModels) {
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models.push({
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id: `${outputAlias}/${subId}`,
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object: "model",
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created: timestamp,
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owned_by: outputAlias,
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});
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}
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// Web search/fetch — provider IS the model, expose as {alias}/search and/or {alias}/fetch with explicit kind
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if (kindFilter.includes("webSearch") && providerInfo?.searchConfig) {
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models.push({
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id: `${outputAlias}/search`,
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object: "model",
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kind: "webSearch",
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created: timestamp,
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owned_by: outputAlias,
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});
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}
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if (kindFilter.includes("webFetch") && providerInfo?.fetchConfig) {
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models.push({
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id: `${outputAlias}/fetch`,
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object: "model",
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kind: "webFetch",
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created: timestamp,
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owned_by: outputAlias,
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});
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}
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}
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}
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const dedupedModels = [];
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const seenModelIds = new Set();
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for (const model of models) {
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if (!model?.id || seenModelIds.has(model.id)) continue;
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seenModelIds.add(model.id);
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dedupedModels.push(model);
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}
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return dedupedModels;
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}
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/**
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* Handle CORS preflight
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*/
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export async function OPTIONS() {
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return new Response(null, {
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headers: {
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "GET, OPTIONS",
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"Access-Control-Allow-Headers": "*",
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},
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});
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}
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/**
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* GET /v1/models - OpenAI compatible models list (LLM/chat models only by default).
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* For other capabilities use /v1/models/{kind} (image, tts, stt, embedding, image-to-text, web).
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*/
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export async function GET() {
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try {
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const data = await buildModelsList([LLM_KIND]);
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return Response.json({ object: "list", data }, {
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headers: { "Access-Control-Allow-Origin": "*" },
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});
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} catch (error) {
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console.log("Error fetching models:", error);
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return Response.json(
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{ error: { message: error.message, type: "server_error" } },
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{ status: 500 }
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);
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}
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}
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