feat(anthropic, gemini, openai): Replace static model lists with dynamic fetching from respective APIs
This commit is contained in:
Willie Zutz
parent
15cefdfbcd
commit
15b75328d2
3 changed files with 178 additions and 148 deletions
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@ -7,41 +7,29 @@ export const PROVIDER_INFO = {
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displayName: 'Anthropic',
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};
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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const ANTHROPIC_MODELS_ENDPOINT = 'https://api.anthropic.com/v1/models';
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const anthropicChatModels: Record<string, string>[] = [
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{
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displayName: 'Claude 4 Opus',
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key: 'claude-opus-4-20250514',
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async function fetchAnthropicModels(apiKey: string): Promise<any[]> {
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const resp = await fetch(ANTHROPIC_MODELS_ENDPOINT, {
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method: 'GET',
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headers: {
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'x-api-key': apiKey,
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'anthropic-version': '2023-06-01',
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'Content-Type': 'application/json',
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},
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{
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displayName: 'Claude 4 Sonnet',
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key: 'claude-sonnet-4-20250514',
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},
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{
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displayName: 'Claude 3.7 Sonnet',
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key: 'claude-3-7-sonnet-20250219',
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},
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{
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displayName: 'Claude 3.5 Haiku',
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key: 'claude-3-5-haiku-20241022',
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},
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{
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displayName: 'Claude 3.5 Sonnet v2',
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key: 'claude-3-5-sonnet-20241022',
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},
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{
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displayName: 'Claude 3.5 Sonnet',
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key: 'claude-3-5-sonnet-20240620',
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},
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{
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displayName: 'Claude 3 Opus',
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key: 'claude-3-opus-20240229',
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},
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{
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displayName: 'Claude 3 Haiku',
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key: 'claude-3-haiku-20240307',
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},
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];
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});
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if (!resp.ok) {
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throw new Error(`Anthropic models endpoint returned ${resp.status}`);
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}
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const data = await resp.json();
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if (!data || !Array.isArray(data.data)) {
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throw new Error('Unexpected Anthropic models response format');
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}
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return data.data;
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}
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export const loadAnthropicChatModels = async () => {
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const anthropicApiKey = getAnthropicApiKey();
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@ -49,14 +37,25 @@ export const loadAnthropicChatModels = async () => {
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if (!anthropicApiKey) return {};
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try {
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const models = await fetchAnthropicModels(anthropicApiKey);
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const anthropicChatModels = models
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.map((model: any) => {
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const id = model && model.id ? String(model.id) : '';
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const display =
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model && model.display_name ? String(model.display_name) : id;
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return { id, display };
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})
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.filter((model: any) => model.id)
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.sort((a: any, b: any) => a.display.localeCompare(b.display));
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const chatModels: Record<string, ChatModel> = {};
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anthropicChatModels.forEach((model) => {
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chatModels[model.key] = {
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displayName: model.displayName,
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anthropicChatModels.forEach((model: any) => {
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chatModels[model.id] = {
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displayName: model.display,
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model: new ChatAnthropic({
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apiKey: anthropicApiKey,
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modelName: model.key,
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modelName: model.id,
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//temperature: 0.7,
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}) as unknown as BaseChatModel,
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};
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@ -12,55 +12,34 @@ export const PROVIDER_INFO = {
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import { Embeddings } from '@langchain/core/embeddings';
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const geminiChatModels: Record<string, string>[] = [
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{
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displayName: 'Gemini 2.5 Flash Preview 05-20',
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key: 'gemini-2.5-flash-preview-05-20',
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},
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{
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displayName: 'Gemini 2.5 Pro Preview',
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key: 'gemini-2.5-pro-preview-05-06',
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},
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{
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displayName: 'Gemini 2.5 Pro Experimental',
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key: 'gemini-2.5-pro-preview-05-06',
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},
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{
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displayName: 'Gemini 2.0 Flash',
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key: 'gemini-2.0-flash',
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},
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{
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displayName: 'Gemini 2.0 Flash-Lite',
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key: 'gemini-2.0-flash-lite',
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},
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{
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displayName: 'Gemini 2.0 Flash Thinking Experimental',
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key: 'gemini-2.0-flash-thinking-exp-01-21',
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},
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{
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displayName: 'Gemini 1.5 Flash',
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key: 'gemini-1.5-flash',
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},
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{
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displayName: 'Gemini 1.5 Flash-8B',
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key: 'gemini-1.5-flash-8b',
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},
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{
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displayName: 'Gemini 1.5 Pro',
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key: 'gemini-1.5-pro',
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},
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];
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// Replace static model lists with dynamic fetch from Gemini API
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const GEMINI_MODELS_ENDPOINT =
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'https://generativelanguage.googleapis.com/v1beta/models';
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const geminiEmbeddingModels: Record<string, string>[] = [
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{
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displayName: 'Text Embedding 004',
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key: 'models/text-embedding-004',
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async function fetchGeminiModels(apiKey: string): Promise<any[]> {
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const url = `${GEMINI_MODELS_ENDPOINT}?key=${encodeURIComponent(
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apiKey,
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)}&pageSize=1000`;
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const resp = await fetch(url, {
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method: 'GET',
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headers: {
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'Content-Type': 'application/json',
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},
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{
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displayName: 'Embedding 001',
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key: 'models/embedding-001',
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},
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];
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});
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if (!resp.ok) {
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throw new Error(`Gemini models endpoint returned ${resp.status}`);
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}
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const data = await resp.json();
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if (!data || !Array.isArray(data.models)) {
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throw new Error('Unexpected Gemini models response format');
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}
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return data.models;
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}
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export const loadGeminiChatModels = async () => {
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const geminiApiKey = getGeminiApiKey();
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@ -68,9 +47,32 @@ export const loadGeminiChatModels = async () => {
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if (!geminiApiKey) return {};
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try {
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const models = await fetchGeminiModels(geminiApiKey);
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const geminiChatModels = models
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.map((model: any) => {
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const rawName = model && model.name ? String(model.name) : '';
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const stripped = rawName.replace(/^models\//i, '');
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return {
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rawName,
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key: stripped,
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displayName:
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model && model.displayName ? String(model.displayName) : stripped,
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};
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})
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.filter((model: any) => {
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const key = model.key.toLowerCase();
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const display = (model.displayName || '').toLowerCase();
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const excluded = ['audio', 'embedding', 'image', 'tts'];
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return (
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key.startsWith('gemini') &&
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!excluded.some((s) => key.includes(s) || display.includes(s))
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);
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})
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.sort((a: any, b: any) => a.key.localeCompare(b.key));
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const chatModels: Record<string, ChatModel> = {};
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geminiChatModels.forEach((model) => {
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geminiChatModels.forEach((model: any) => {
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chatModels[model.key] = {
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displayName: model.displayName,
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model: new ChatGoogleGenerativeAI({
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@ -83,7 +85,7 @@ export const loadGeminiChatModels = async () => {
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return chatModels;
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} catch (err) {
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console.error(`Error loading Gemini models: ${err}`);
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console.error(`Error loading Gemini chat models: ${err}`);
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return {};
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}
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};
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@ -94,9 +96,28 @@ export const loadGeminiEmbeddingModels = async () => {
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if (!geminiApiKey) return {};
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try {
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const models = await fetchGeminiModels(geminiApiKey);
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const geminiEmbeddingModels = models
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.map((model: any) => {
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const rawName = model && model.name ? String(model.name) : '';
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const display =
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model && model.displayName ? String(model.displayName) : rawName;
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return {
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rawName,
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key: rawName,
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displayName: display,
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};
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})
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.filter(
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(model: any) =>
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model.key.toLowerCase().includes('embedding') ||
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model.displayName.toLowerCase().includes('embedding'),
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)
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.sort((a: any, b: any) => a.key.localeCompare(b.key));
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const embeddingModels: Record<string, EmbeddingModel> = {};
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geminiEmbeddingModels.forEach((model) => {
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geminiEmbeddingModels.forEach((model: any) => {
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embeddingModels[model.key] = {
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displayName: model.displayName,
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model: new GoogleGenerativeAIEmbeddings({
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@ -108,7 +129,7 @@ export const loadGeminiEmbeddingModels = async () => {
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return embeddingModels;
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} catch (err) {
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console.error(`Error loading OpenAI embeddings models: ${err}`);
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console.error(`Error loading Gemini embedding models: ${err}`);
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return {};
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}
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};
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@ -9,51 +9,32 @@ export const PROVIDER_INFO = {
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import { Embeddings } from '@langchain/core/embeddings';
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const openaiChatModels: Record<string, string>[] = [
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{
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displayName: 'GPT-3.5 Turbo',
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key: 'gpt-3.5-turbo',
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},
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{
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displayName: 'GPT-4',
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key: 'gpt-4',
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},
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{
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displayName: 'GPT-4 turbo',
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key: 'gpt-4-turbo',
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},
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{
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displayName: 'GPT-4 omni',
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key: 'gpt-4o',
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},
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{
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displayName: 'GPT-4 omni mini',
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key: 'gpt-4o-mini',
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},
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{
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displayName: 'GPT 4.1 nano',
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key: 'gpt-4.1-nano',
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},
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{
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displayName: 'GPT 4.1 mini',
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key: 'gpt-4.1-mini',
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},
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{
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displayName: 'GPT 4.1',
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key: 'gpt-4.1',
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},
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];
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// Dynamically discover models from OpenAI instead of hardcoding
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const OPENAI_MODELS_ENDPOINT = 'https://api.openai.com/v1/models';
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const openaiEmbeddingModels: Record<string, string>[] = [
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{
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displayName: 'Text Embedding 3 Small',
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key: 'text-embedding-3-small',
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async function fetchOpenAIModels(apiKey: string): Promise<string[]> {
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const resp = await fetch(OPENAI_MODELS_ENDPOINT, {
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method: 'GET',
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headers: {
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Authorization: `Bearer ${apiKey}`,
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'Content-Type': 'application/json',
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},
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{
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displayName: 'Text Embedding 3 Large',
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key: 'text-embedding-3-large',
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},
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];
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});
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if (!resp.ok) {
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throw new Error(`OpenAI models endpoint returned ${resp.status}`);
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}
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const data = await resp.json();
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if (!data || !Array.isArray(data.data)) {
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throw new Error('Unexpected OpenAI models response format');
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}
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return data.data
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.map((model: any) => (model && model.id ? String(model.id) : undefined))
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.filter(Boolean) as string[];
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}
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export const loadOpenAIChatModels = async () => {
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const openaiApiKey = getOpenaiApiKey();
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@ -61,22 +42,41 @@ export const loadOpenAIChatModels = async () => {
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if (!openaiApiKey) return {};
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try {
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const modelIds = (await fetchOpenAIModels(openaiApiKey)).sort((a, b) =>
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a.localeCompare(b),
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);
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const chatModels: Record<string, ChatModel> = {};
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openaiChatModels.forEach((model) => {
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chatModels[model.key] = {
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displayName: model.displayName,
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modelIds.forEach((model) => {
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const lid = model.toLowerCase();
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const excludedSubstrings = [
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'audio',
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'embedding',
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'image',
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'omni-moderation',
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'transcribe',
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'tts',
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];
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const isChat =
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(lid.startsWith('gpt') || lid.startsWith('o')) &&
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!excludedSubstrings.some((s) => lid.includes(s));
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if (!isChat) return;
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chatModels[model] = {
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displayName: model,
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model: new ChatOpenAI({
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openAIApiKey: openaiApiKey,
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modelName: model.key,
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// temperature: 0.7,
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apiKey: openaiApiKey,
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modelName: model,
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//temperature: model.includes('gpt-5') ? 1 : 0.7,
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}) as unknown as BaseChatModel,
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};
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});
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return chatModels;
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} catch (err) {
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console.error(`Error loading OpenAI models: ${err}`);
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console.error(`Error loading OpenAI chat models: ${err}`);
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return {};
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}
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};
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@ -87,21 +87,31 @@ export const loadOpenAIEmbeddingModels = async () => {
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if (!openaiApiKey) return {};
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try {
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const modelIds = (await fetchOpenAIModels(openaiApiKey)).sort((a, b) =>
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a.localeCompare(b),
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);
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const embeddingModels: Record<string, EmbeddingModel> = {};
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openaiEmbeddingModels.forEach((model) => {
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embeddingModels[model.key] = {
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displayName: model.displayName,
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modelIds.forEach((model) => {
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const lid = model.toLowerCase();
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const isEmbedding = lid.includes('embedding');
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if (!isEmbedding) return;
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embeddingModels[model] = {
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displayName: model,
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model: new OpenAIEmbeddings({
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openAIApiKey: openaiApiKey,
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modelName: model.key,
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apiKey: openaiApiKey,
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modelName: model,
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}) as unknown as Embeddings,
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};
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});
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return embeddingModels;
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} catch (err) {
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console.error(`Error loading OpenAI embeddings models: ${err}`);
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console.error(`Error loading OpenAI embedding models: ${err}`);
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return {};
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}
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};
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