refactor: collapse 8 provider functions into one adapter table
src/lib/providers.ts holds a per-provider spec (url, headers, body, extractor, default model) and a single callProvider() covering both the action-prompt and custom-prompt families (~250 lines -> one path). getSystemPrompt, fetchWithTimeout, and listModels move along with it; background.ts drops from 553 to 130 lines. Behavior pinned by 20 parity tests written from the old functions (request shapes, headers, defaults, error strings). One deliberate change: max_tokens now scales with input length (1024-8192) instead of a hard-coded 1024, so Expand no longer truncates long selections. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
john kevin asprec
@@ -2,52 +2,7 @@ import { defineBackground } from 'wxt/utils/define-background';
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import type { AnalyzePayload, LexAIConfig, LexAIResponse } from '@lib/types';
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import type { AnalyzePayload, LexAIConfig, LexAIResponse } from '@lib/types';
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import { ACTIONS, ACTION_LABELS, CONTEXT_MENU_STYLES } from '@lib/actions';
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import { ACTIONS, ACTION_LABELS, CONTEXT_MENU_STYLES } from '@lib/actions';
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import { decryptApiKey } from '@lib/crypto';
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import { decryptApiKey } from '@lib/crypto';
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import { callProvider, getSystemPrompt, listModels } from '@lib/providers';
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// ─── Fetch with timeout ───────────────────────────────────────────────────────
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async function fetchWithTimeout(url: string, options: RequestInit, timeoutMs = 30000): Promise<Response> {
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const controller = new AbortController();
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const id = setTimeout(() => controller.abort(), timeoutMs);
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try {
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return await fetch(url, { ...options, signal: controller.signal });
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} finally {
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clearTimeout(id);
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}
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}
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// ─── System prompts ───────────────────────────────────────────────────────────
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function getSystemPrompt(action: string, style?: string): string {
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// Normalize 'fix' (used by context menu) to 'grammar'
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const normalizedAction = action === 'fix' ? 'grammar' : action;
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const prompts: Record<string, string> = {
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grammar:
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'You are a professional grammar editor. Fix all grammar, spelling, and punctuation errors in the provided text. ' +
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'Preserve the original meaning and tone as closely as possible. ' +
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'Return ONLY the corrected text — no explanations, no preamble.',
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rephrase:
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'You are a skilled writing assistant. Rephrase the provided text to make it clearer, more engaging, and more professional. ' +
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'Keep the same meaning and approximate length. ' +
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'Return ONLY the rephrased text — no explanations.',
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shorten:
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'You are a concise editor. Shorten the provided text by at least 30% while preserving the core message. ' +
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'Remove filler words, redundant phrases, and unnecessary detail. ' +
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'Return ONLY the shortened text.',
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expand:
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'You are an experienced writer. Expand the provided text with more detail, context, and supporting points. ' +
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'Make it richer and more informative while staying on topic. ' +
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'Return ONLY the expanded text.',
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explain:
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'You are a helpful teacher. Explain the following text in simple, easy-to-understand language. ' +
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'Break down complex terms, jargon, or concepts so anyone can understand. ' +
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'Be concise but clear. Return only the explanation, no extra commentary.',
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};
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const base = prompts[normalizedAction] ?? prompts.grammar;
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const styleModifier = style && style !== 'Default'
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? ` Write in a ${style.toLowerCase()} style.`
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: '';
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return base + styleModifier;
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}
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// Resolve the usable API key from stored config: prefer the encrypted path,
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// Resolve the usable API key from stored config: prefer the encrypted path,
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// fall back to plaintext for backward compat. Returns null if none is set.
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// fall back to plaintext for backward compat. Returns null if none is set.
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@@ -61,331 +16,6 @@ async function resolveApiKey(config: LexAIConfig): Promise<string | null> {
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return apiKey.trim();
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return apiKey.trim();
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}
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}
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// ─── Provider implementations ─────────────────────────────────────────────────
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async function callOpenAI(payload: AnalyzePayload, config: LexAIConfig): Promise<LexAIResponse> {
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const model = config.model || 'gpt-4o-mini';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.openai.com/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify({
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model,
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messages: [
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{ role: 'system', content: getSystemPrompt(payload.action, payload.style) },
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{ role: 'user', content: payload.text },
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],
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max_tokens: 1024,
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temperature: 0.7,
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}),
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});
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} catch (err) {
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return { error: `Network error reaching OpenAI: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) {
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const msg = data?.error?.message ?? `HTTP ${res.status}`;
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return { error: `OpenAI error: ${msg}` };
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}
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const result = data?.choices?.[0]?.message?.content as string | undefined;
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if (!result) return { error: 'OpenAI returned an empty response.' };
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return { result: result.trim() };
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}
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async function callAnthropic(payload: AnalyzePayload, config: LexAIConfig): Promise<LexAIResponse> {
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const model = config.model || 'claude-3-5-haiku-20241022';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.anthropic.com/v1/messages', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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'x-api-key': config.apiKey!,
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'anthropic-version': '2023-06-01',
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},
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body: JSON.stringify({
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model,
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max_tokens: 1024,
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system: getSystemPrompt(payload.action, payload.style),
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messages: [{ role: 'user', content: payload.text }],
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}),
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});
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} catch (err) {
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return { error: `Network error reaching Anthropic: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) {
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const msg = data?.error?.message ?? `HTTP ${res.status}`;
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return { error: `Anthropic error: ${msg}` };
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}
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const result = data?.content?.[0]?.text as string | undefined;
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if (!result) return { error: 'Anthropic returned an empty response.' };
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return { result: result.trim() };
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}
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async function callGroq(payload: AnalyzePayload, config: LexAIConfig): Promise<LexAIResponse> {
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const model = config.model || 'llama-3.3-70b-versatile';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.groq.com/openai/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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},
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body: JSON.stringify({
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model,
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messages: [
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{ role: 'system', content: getSystemPrompt(payload.action, payload.style) },
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{ role: 'user', content: payload.text },
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],
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max_tokens: 1024,
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temperature: 0.7,
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}),
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});
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} catch (err) {
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return { error: `Network error reaching Groq: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) {
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const msg = data?.error?.message ?? `HTTP ${res.status}`;
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return { error: `Groq error: ${msg}` };
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}
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const result = data?.choices?.[0]?.message?.content as string | undefined;
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if (!result) return { error: 'Groq returned an empty response.' };
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return { result: result.trim() };
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}
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async function callOpenRouter(payload: AnalyzePayload, config: LexAIConfig): Promise<LexAIResponse> {
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const model = config.model || 'openai/gpt-4o-mini';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://openrouter.ai/api/v1/chat/completions', {
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method: 'POST',
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headers: {
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'Content-Type': 'application/json',
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Authorization: `Bearer ${config.apiKey}`,
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'HTTP-Referer': 'https://lexai.dev',
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'X-Title': 'LexAI',
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},
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body: JSON.stringify({
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model,
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messages: [
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{ role: 'system', content: getSystemPrompt(payload.action, payload.style) },
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{ role: 'user', content: payload.text },
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],
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max_tokens: 1024,
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}),
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});
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} catch (err) {
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return { error: `Network error reaching OpenRouter: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) {
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const msg = data?.error?.message ?? `HTTP ${res.status}`;
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return { error: `OpenRouter error: ${msg}` };
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}
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const result = data?.choices?.[0]?.message?.content as string | undefined;
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if (!result) return { error: 'OpenRouter returned an empty response.' };
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return { result: result.trim() };
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}
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// ─── Generic provider call (used by copy-as and future features) ──────────────
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async function callProvider(config: LexAIConfig, text: string, systemPrompt: string): Promise<LexAIResponse> {
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const payload: AnalyzePayload = { text, action: '__custom__' };
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const provider = config.provider || 'openai';
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switch (provider) {
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case 'openai': return callOpenAIWithPrompt(payload, config, systemPrompt);
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case 'anthropic': return callAnthropicWithPrompt(payload, config, systemPrompt);
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case 'groq': return callGroqWithPrompt(payload, config, systemPrompt);
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case 'openrouter': return callOpenRouterWithPrompt(payload, config, systemPrompt);
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default: return { error: `Unknown provider: "${provider}". Please check LexAI settings.` };
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}
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}
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async function callOpenAIWithPrompt(payload: AnalyzePayload, config: LexAIConfig, systemPrompt: string): Promise<LexAIResponse> {
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const model = config.model || 'gpt-4o-mini';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.openai.com/v1/chat/completions', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${config.apiKey}` },
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body: JSON.stringify({
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model,
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messages: [
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: payload.text },
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],
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max_tokens: 1024,
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temperature: 0.7,
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}),
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});
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} catch (err) {
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return { error: `Network error reaching OpenAI: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) return { error: `OpenAI error: ${data?.error?.message ?? `HTTP ${res.status}`}` };
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const result = data?.choices?.[0]?.message?.content as string | undefined;
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if (!result) return { error: 'OpenAI returned an empty response.' };
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return { result: result.trim() };
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}
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async function callAnthropicWithPrompt(payload: AnalyzePayload, config: LexAIConfig, systemPrompt: string): Promise<LexAIResponse> {
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const model = config.model || 'claude-3-5-haiku-20241022';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.anthropic.com/v1/messages', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json', 'x-api-key': config.apiKey!, 'anthropic-version': '2023-06-01' },
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body: JSON.stringify({
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model,
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max_tokens: 1024,
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system: systemPrompt,
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messages: [{ role: 'user', content: payload.text }],
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}),
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});
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} catch (err) {
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return { error: `Network error reaching Anthropic: ${String(err)}` };
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}
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const data = await res.json();
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if (!res.ok) return { error: `Anthropic error: ${data?.error?.message ?? `HTTP ${res.status}`}` };
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const result = data?.content?.[0]?.text as string | undefined;
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if (!result) return { error: 'Anthropic returned an empty response.' };
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return { result: result.trim() };
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}
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async function callGroqWithPrompt(payload: AnalyzePayload, config: LexAIConfig, systemPrompt: string): Promise<LexAIResponse> {
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const model = config.model || 'llama-3.3-70b-versatile';
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let res: Response;
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try {
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res = await fetchWithTimeout('https://api.groq.com/openai/v1/chat/completions', {
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method: 'POST',
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headers: { 'Content-Type': 'application/json', Authorization: `Bearer ${config.apiKey}` },
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body: JSON.stringify({
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model,
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messages: [
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{ role: 'system', content: systemPrompt },
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{ role: 'user', content: payload.text },
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||||||
],
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max_tokens: 1024,
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temperature: 0.7,
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||||||
}),
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||||||
});
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} catch (err) {
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return { error: `Network error reaching Groq: ${String(err)}` };
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|
||||||
}
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const data = await res.json();
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||||||
if (!res.ok) return { error: `Groq error: ${data?.error?.message ?? `HTTP ${res.status}`}` };
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const result = data?.choices?.[0]?.message?.content as string | undefined;
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||||||
if (!result) return { error: 'Groq returned an empty response.' };
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return { result: result.trim() };
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}
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async function callOpenRouterWithPrompt(payload: AnalyzePayload, config: LexAIConfig, systemPrompt: string): Promise<LexAIResponse> {
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const model = config.model || 'openai/gpt-4o-mini';
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||||||
let res: Response;
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|
||||||
try {
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||||||
res = await fetchWithTimeout('https://openrouter.ai/api/v1/chat/completions', {
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||||||
method: 'POST',
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||||||
headers: {
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||||||
'Content-Type': 'application/json',
|
|
||||||
Authorization: `Bearer ${config.apiKey}`,
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||||||
'HTTP-Referer': 'https://lexai.dev',
|
|
||||||
'X-Title': 'LexAI',
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|
||||||
},
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||||||
body: JSON.stringify({
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||||||
model,
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||||||
messages: [
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||||||
{ role: 'system', content: systemPrompt },
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||||||
{ role: 'user', content: payload.text },
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|
||||||
],
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||||||
max_tokens: 1024,
|
|
||||||
}),
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|
||||||
});
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|
||||||
} catch (err) {
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||||||
return { error: `Network error reaching OpenRouter: ${String(err)}` };
|
|
||||||
}
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||||||
const data = await res.json();
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|
||||||
if (!res.ok) return { error: `OpenRouter error: ${data?.error?.message ?? `HTTP ${res.status}`}` };
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||||||
const result = data?.choices?.[0]?.message?.content as string | undefined;
|
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||||||
if (!result) return { error: 'OpenRouter returned an empty response.' };
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||||||
return { result: result.trim() };
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||||||
}
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||||||
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// ─── Live model listing ───────────────────────────────────────────────────────
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||||||
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||||||
const MODEL_LIST_ENDPOINTS: Record<string, string> = {
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||||||
openai: 'https://api.openai.com/v1/models',
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||||||
groq: 'https://api.groq.com/openai/v1/models',
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openrouter: 'https://openrouter.ai/api/v1/models',
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||||||
anthropic: 'https://api.anthropic.com/v1/models',
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||||||
};
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||||||
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||||||
// Drop non-chat models (embeddings, audio, image, etc.) so the picker stays useful.
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|
||||||
const NON_CHAT_MODEL_RE = /embedding|whisper|tts|dall-e|audio|realtime|moderation|image|guard|transcribe|speech|rerank/i;
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|
||||||
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|
||||||
async function listModels(provider: string, apiKey?: string): Promise<{ models?: string[]; error?: string }> {
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|
||||||
const url = MODEL_LIST_ENDPOINTS[provider];
|
|
||||||
if (!url) return { error: `Unknown provider: "${provider}". Please check LexAI settings.` };
|
|
||||||
|
|
||||||
const headers: Record<string, string> = { 'Content-Type': 'application/json' };
|
|
||||||
if (provider === 'anthropic') {
|
|
||||||
if (!apiKey) return { error: 'Anthropic requires an API key to list models.' };
|
|
||||||
headers['x-api-key'] = apiKey;
|
|
||||||
headers['anthropic-version'] = '2023-06-01';
|
|
||||||
// Allow the extension origin to call Anthropic directly (avoids a CORS 403).
|
|
||||||
headers['anthropic-dangerous-direct-browser-access'] = 'true';
|
|
||||||
} else if (apiKey) {
|
|
||||||
// OpenRouter's list is public, so the key is optional there; OpenAI/Groq require it.
|
|
||||||
headers['Authorization'] = `Bearer ${apiKey}`;
|
|
||||||
}
|
|
||||||
|
|
||||||
let res: Response;
|
|
||||||
try {
|
|
||||||
res = await fetchWithTimeout(url, { method: 'GET', headers }, 15000);
|
|
||||||
} catch (err) {
|
|
||||||
return { error: `Network error reaching ${provider}: ${String(err)}` };
|
|
||||||
}
|
|
||||||
|
|
||||||
const data = await res.json().catch(() => null);
|
|
||||||
if (!res.ok) {
|
|
||||||
const msg = data?.error?.message ?? data?.error ?? `HTTP ${res.status}`;
|
|
||||||
return { error: `${provider} error: ${msg}` };
|
|
||||||
}
|
|
||||||
|
|
||||||
const raw = Array.isArray(data?.data) ? data.data : [];
|
|
||||||
const ids = raw
|
|
||||||
.map((m: any) => (typeof m === 'string' ? m : m?.id))
|
|
||||||
.filter((id: unknown): id is string => typeof id === 'string' && id.length > 0)
|
|
||||||
.filter((id: string) => !NON_CHAT_MODEL_RE.test(id))
|
|
||||||
.sort((a: string, b: string) => a.localeCompare(b));
|
|
||||||
|
|
||||||
if (ids.length === 0) return { error: `No models returned by ${provider}.` };
|
|
||||||
return { models: ids };
|
|
||||||
}
|
|
||||||
|
|
||||||
// ─── Main handler ─────────────────────────────────────────────────────────────
|
// ─── Main handler ─────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
async function handleAnalyzeText(payload: AnalyzePayload): Promise<LexAIResponse> {
|
async function handleAnalyzeText(payload: AnalyzePayload): Promise<LexAIResponse> {
|
||||||
@@ -398,20 +28,8 @@ async function handleAnalyzeText(payload: AnalyzePayload): Promise<LexAIResponse
|
|||||||
}
|
}
|
||||||
|
|
||||||
const resolvedConfig: LexAIConfig = { ...config, apiKey };
|
const resolvedConfig: LexAIConfig = { ...config, apiKey };
|
||||||
const provider = config.provider || 'openai';
|
const systemPrompt = getSystemPrompt(payload.action, payload.style);
|
||||||
|
return callProvider(resolvedConfig, payload.text, systemPrompt);
|
||||||
switch (provider) {
|
|
||||||
case 'openai':
|
|
||||||
return callOpenAI(payload, resolvedConfig);
|
|
||||||
case 'anthropic':
|
|
||||||
return callAnthropic(payload, resolvedConfig);
|
|
||||||
case 'groq':
|
|
||||||
return callGroq(payload, resolvedConfig);
|
|
||||||
case 'openrouter':
|
|
||||||
return callOpenRouter(payload, resolvedConfig);
|
|
||||||
default:
|
|
||||||
return { error: `Unknown provider: "${provider}". Please check LexAI settings.` };
|
|
||||||
}
|
|
||||||
}
|
}
|
||||||
|
|
||||||
// ─── Background entry ─────────────────────────────────────────────────────────
|
// ─── Background entry ─────────────────────────────────────────────────────────
|
||||||
|
|||||||
237
src/lib/providers.ts
Normal file
237
src/lib/providers.ts
Normal file
@@ -0,0 +1,237 @@
|
|||||||
|
// Provider adapter table + the single LLM call path. Replaces the eight
|
||||||
|
// hand-rolled per-provider functions (callX / callXWithPrompt families) that
|
||||||
|
// previously lived in the background worker. Behavior-preserving: URLs,
|
||||||
|
// headers, request bodies, default models, and error strings match the old
|
||||||
|
// implementations — pinned by tests/unit/providers.test.ts.
|
||||||
|
|
||||||
|
import type { LexAIConfig, LexAIResponse } from './types';
|
||||||
|
|
||||||
|
export async function fetchWithTimeout(
|
||||||
|
url: string,
|
||||||
|
options: RequestInit,
|
||||||
|
timeoutMs = 30000,
|
||||||
|
): Promise<Response> {
|
||||||
|
const controller = new AbortController();
|
||||||
|
const id = setTimeout(() => controller.abort(), timeoutMs);
|
||||||
|
try {
|
||||||
|
return await fetch(url, { ...options, signal: controller.signal });
|
||||||
|
} finally {
|
||||||
|
clearTimeout(id);
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
// ─── System prompts ───────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
export function getSystemPrompt(action: string, style?: string): string {
|
||||||
|
// Normalize 'fix' (used by context menu and popup) to 'grammar'
|
||||||
|
const normalizedAction = action === 'fix' ? 'grammar' : action;
|
||||||
|
const prompts: Record<string, string> = {
|
||||||
|
grammar:
|
||||||
|
'You are a professional grammar editor. Fix all grammar, spelling, and punctuation errors in the provided text. ' +
|
||||||
|
'Preserve the original meaning and tone as closely as possible. ' +
|
||||||
|
'Return ONLY the corrected text — no explanations, no preamble.',
|
||||||
|
rephrase:
|
||||||
|
'You are a skilled writing assistant. Rephrase the provided text to make it clearer, more engaging, and more professional. ' +
|
||||||
|
'Keep the same meaning and approximate length. ' +
|
||||||
|
'Return ONLY the rephrased text — no explanations.',
|
||||||
|
shorten:
|
||||||
|
'You are a concise editor. Shorten the provided text by at least 30% while preserving the core message. ' +
|
||||||
|
'Remove filler words, redundant phrases, and unnecessary detail. ' +
|
||||||
|
'Return ONLY the shortened text.',
|
||||||
|
expand:
|
||||||
|
'You are an experienced writer. Expand the provided text with more detail, context, and supporting points. ' +
|
||||||
|
'Make it richer and more informative while staying on topic. ' +
|
||||||
|
'Return ONLY the expanded text.',
|
||||||
|
explain:
|
||||||
|
'You are a helpful teacher. Explain the following text in simple, easy-to-understand language. ' +
|
||||||
|
'Break down complex terms, jargon, or concepts so anyone can understand. ' +
|
||||||
|
'Be concise but clear. Return only the explanation, no extra commentary.',
|
||||||
|
};
|
||||||
|
const base = prompts[normalizedAction] ?? prompts.grammar;
|
||||||
|
const styleModifier = style && style !== 'Default'
|
||||||
|
? ` Write in a ${style.toLowerCase()} style.`
|
||||||
|
: '';
|
||||||
|
return base + styleModifier;
|
||||||
|
}
|
||||||
|
|
||||||
|
// ─── Adapter table ────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
export interface ProviderSpec {
|
||||||
|
label: string; // human name used in error messages ('OpenAI error: …')
|
||||||
|
chatUrl: string;
|
||||||
|
modelsUrl: string;
|
||||||
|
defaultModel: string;
|
||||||
|
headers: (apiKey: string) => Record<string, string>;
|
||||||
|
body: (model: string, systemPrompt: string, text: string, maxTokens: number) => Record<string, unknown>;
|
||||||
|
extract: (data: any) => string | undefined;
|
||||||
|
}
|
||||||
|
|
||||||
|
function bearerHeaders(apiKey: string): Record<string, string> {
|
||||||
|
return { 'Content-Type': 'application/json', Authorization: `Bearer ${apiKey}` };
|
||||||
|
}
|
||||||
|
|
||||||
|
// OpenAI-compatible chat body (OpenAI, Groq, OpenRouter). OpenRouter's old
|
||||||
|
// implementation sent no temperature — preserve that.
|
||||||
|
function openAiStyleBody(temperature?: number) {
|
||||||
|
return (model: string, systemPrompt: string, text: string, maxTokens: number) => ({
|
||||||
|
model,
|
||||||
|
messages: [
|
||||||
|
{ role: 'system', content: systemPrompt },
|
||||||
|
{ role: 'user', content: text },
|
||||||
|
],
|
||||||
|
max_tokens: maxTokens,
|
||||||
|
...(temperature !== undefined ? { temperature } : {}),
|
||||||
|
});
|
||||||
|
}
|
||||||
|
|
||||||
|
const extractOpenAiStyle = (data: any): string | undefined => data?.choices?.[0]?.message?.content;
|
||||||
|
|
||||||
|
export const PROVIDER_SPECS: Record<string, ProviderSpec> = {
|
||||||
|
openai: {
|
||||||
|
label: 'OpenAI',
|
||||||
|
chatUrl: 'https://api.openai.com/v1/chat/completions',
|
||||||
|
modelsUrl: 'https://api.openai.com/v1/models',
|
||||||
|
defaultModel: 'gpt-4o-mini',
|
||||||
|
headers: bearerHeaders,
|
||||||
|
body: openAiStyleBody(0.7),
|
||||||
|
extract: extractOpenAiStyle,
|
||||||
|
},
|
||||||
|
anthropic: {
|
||||||
|
label: 'Anthropic',
|
||||||
|
chatUrl: 'https://api.anthropic.com/v1/messages',
|
||||||
|
modelsUrl: 'https://api.anthropic.com/v1/models',
|
||||||
|
defaultModel: 'claude-3-5-haiku-20241022',
|
||||||
|
headers: (apiKey) => ({
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
'x-api-key': apiKey,
|
||||||
|
'anthropic-version': '2023-06-01',
|
||||||
|
}),
|
||||||
|
body: (model, systemPrompt, text, maxTokens) => ({
|
||||||
|
model,
|
||||||
|
max_tokens: maxTokens,
|
||||||
|
system: systemPrompt,
|
||||||
|
messages: [{ role: 'user', content: text }],
|
||||||
|
}),
|
||||||
|
extract: (data) => data?.content?.[0]?.text,
|
||||||
|
},
|
||||||
|
groq: {
|
||||||
|
label: 'Groq',
|
||||||
|
chatUrl: 'https://api.groq.com/openai/v1/chat/completions',
|
||||||
|
modelsUrl: 'https://api.groq.com/openai/v1/models',
|
||||||
|
defaultModel: 'llama-3.3-70b-versatile',
|
||||||
|
headers: bearerHeaders,
|
||||||
|
body: openAiStyleBody(0.7),
|
||||||
|
extract: extractOpenAiStyle,
|
||||||
|
},
|
||||||
|
openrouter: {
|
||||||
|
label: 'OpenRouter',
|
||||||
|
chatUrl: 'https://openrouter.ai/api/v1/chat/completions',
|
||||||
|
modelsUrl: 'https://openrouter.ai/api/v1/models',
|
||||||
|
defaultModel: 'openai/gpt-4o-mini',
|
||||||
|
headers: (apiKey) => ({
|
||||||
|
...bearerHeaders(apiKey),
|
||||||
|
'HTTP-Referer': 'https://lexai.dev',
|
||||||
|
'X-Title': 'LexAI',
|
||||||
|
}),
|
||||||
|
body: openAiStyleBody(undefined),
|
||||||
|
extract: extractOpenAiStyle,
|
||||||
|
},
|
||||||
|
};
|
||||||
|
|
||||||
|
// ─── Chat call ────────────────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
// Scale the output budget with the input instead of the old hard-coded 1024
|
||||||
|
// (which truncated "Expand" on long selections). chars ≈ tokens × 4, so this
|
||||||
|
// allows roughly 4× the input length in output, clamped to a sane range.
|
||||||
|
export function defaultMaxTokens(text: string): number {
|
||||||
|
return Math.max(1024, Math.min(8192, Math.ceil(text.length)));
|
||||||
|
}
|
||||||
|
|
||||||
|
export interface CallOptions {
|
||||||
|
maxTokens?: number;
|
||||||
|
}
|
||||||
|
|
||||||
|
export async function callProvider(
|
||||||
|
config: LexAIConfig,
|
||||||
|
text: string,
|
||||||
|
systemPrompt: string,
|
||||||
|
opts?: CallOptions,
|
||||||
|
): Promise<LexAIResponse> {
|
||||||
|
const provider = config.provider || 'openai';
|
||||||
|
const spec = PROVIDER_SPECS[provider];
|
||||||
|
if (!spec) {
|
||||||
|
return { error: `Unknown provider: "${provider}". Please check LexAI settings.` };
|
||||||
|
}
|
||||||
|
|
||||||
|
const model = config.model || spec.defaultModel;
|
||||||
|
const maxTokens = opts?.maxTokens ?? defaultMaxTokens(text);
|
||||||
|
|
||||||
|
let res: Response;
|
||||||
|
try {
|
||||||
|
res = await fetchWithTimeout(spec.chatUrl, {
|
||||||
|
method: 'POST',
|
||||||
|
headers: spec.headers(config.apiKey ?? ''),
|
||||||
|
body: JSON.stringify(spec.body(model, systemPrompt, text, maxTokens)),
|
||||||
|
});
|
||||||
|
} catch (err) {
|
||||||
|
return { error: `Network error reaching ${spec.label}: ${String(err)}` };
|
||||||
|
}
|
||||||
|
|
||||||
|
const data = await res.json();
|
||||||
|
if (!res.ok) {
|
||||||
|
const msg = data?.error?.message ?? `HTTP ${res.status}`;
|
||||||
|
return { error: `${spec.label} error: ${msg}` };
|
||||||
|
}
|
||||||
|
|
||||||
|
const result = spec.extract(data);
|
||||||
|
if (!result) return { error: `${spec.label} returned an empty response.` };
|
||||||
|
return { result: result.trim() };
|
||||||
|
}
|
||||||
|
|
||||||
|
// ─── Live model listing ───────────────────────────────────────────────────────
|
||||||
|
|
||||||
|
// Drop non-chat models (embeddings, audio, image, etc.) so the picker stays useful.
|
||||||
|
const NON_CHAT_MODEL_RE = /embedding|whisper|tts|dall-e|audio|realtime|moderation|image|guard|transcribe|speech|rerank/i;
|
||||||
|
|
||||||
|
export async function listModels(
|
||||||
|
provider: string,
|
||||||
|
apiKey?: string,
|
||||||
|
): Promise<{ models?: string[]; error?: string }> {
|
||||||
|
const spec = PROVIDER_SPECS[provider];
|
||||||
|
if (!spec) return { error: `Unknown provider: "${provider}". Please check LexAI settings.` };
|
||||||
|
|
||||||
|
const headers: Record<string, string> = { 'Content-Type': 'application/json' };
|
||||||
|
if (provider === 'anthropic') {
|
||||||
|
if (!apiKey) return { error: 'Anthropic requires an API key to list models.' };
|
||||||
|
headers['x-api-key'] = apiKey;
|
||||||
|
headers['anthropic-version'] = '2023-06-01';
|
||||||
|
// Allow the extension origin to call Anthropic directly (avoids a CORS 403).
|
||||||
|
headers['anthropic-dangerous-direct-browser-access'] = 'true';
|
||||||
|
} else if (apiKey) {
|
||||||
|
// OpenRouter's list is public, so the key is optional there; OpenAI/Groq require it.
|
||||||
|
headers['Authorization'] = `Bearer ${apiKey}`;
|
||||||
|
}
|
||||||
|
|
||||||
|
let res: Response;
|
||||||
|
try {
|
||||||
|
res = await fetchWithTimeout(spec.modelsUrl, { method: 'GET', headers }, 15000);
|
||||||
|
} catch (err) {
|
||||||
|
return { error: `Network error reaching ${provider}: ${String(err)}` };
|
||||||
|
}
|
||||||
|
|
||||||
|
const data = await res.json().catch(() => null);
|
||||||
|
if (!res.ok) {
|
||||||
|
const msg = data?.error?.message ?? data?.error ?? `HTTP ${res.status}`;
|
||||||
|
return { error: `${provider} error: ${msg}` };
|
||||||
|
}
|
||||||
|
|
||||||
|
const raw = Array.isArray(data?.data) ? data.data : [];
|
||||||
|
const ids = raw
|
||||||
|
.map((m: any) => (typeof m === 'string' ? m : m?.id))
|
||||||
|
.filter((id: unknown): id is string => typeof id === 'string' && id.length > 0)
|
||||||
|
.filter((id: string) => !NON_CHAT_MODEL_RE.test(id))
|
||||||
|
.sort((a: string, b: string) => a.localeCompare(b));
|
||||||
|
|
||||||
|
if (ids.length === 0) return { error: `No models returned by ${provider}.` };
|
||||||
|
return { models: ids };
|
||||||
|
}
|
||||||
213
tests/unit/providers.test.ts
Normal file
213
tests/unit/providers.test.ts
Normal file
@@ -0,0 +1,213 @@
|
|||||||
|
import { describe, it, expect, vi, beforeEach, afterEach } from 'vitest';
|
||||||
|
import {
|
||||||
|
callProvider,
|
||||||
|
listModels,
|
||||||
|
getSystemPrompt,
|
||||||
|
defaultMaxTokens,
|
||||||
|
PROVIDER_SPECS,
|
||||||
|
} from '@lib/providers';
|
||||||
|
|
||||||
|
function mockFetchOnce(data: unknown, { ok = true, status = 200 } = {}) {
|
||||||
|
const fn = vi.fn().mockResolvedValue({ ok, status, json: async () => data });
|
||||||
|
vi.stubGlobal('fetch', fn);
|
||||||
|
return fn;
|
||||||
|
}
|
||||||
|
|
||||||
|
beforeEach(() => vi.restoreAllMocks());
|
||||||
|
afterEach(() => vi.unstubAllGlobals());
|
||||||
|
|
||||||
|
// ─── Request shapes pinned to the ORIGINAL callX/callXWithPrompt functions ────
|
||||||
|
// These bodies/headers/URLs are copied from the pre-refactor background.ts.
|
||||||
|
// maxTokens is forced to 1024 to match the old hard-coded value exactly.
|
||||||
|
|
||||||
|
const openAiResponse = { choices: [{ message: { content: ' fixed text ' } }] };
|
||||||
|
const anthropicResponse = { content: [{ text: ' fixed text ' }] };
|
||||||
|
|
||||||
|
describe('callProvider request shapes (parity with old implementations)', () => {
|
||||||
|
it('OpenAI: url, bearer auth, system message, temperature 0.7, default model', async () => {
|
||||||
|
const fetch = mockFetchOnce(openAiResponse);
|
||||||
|
const res = await callProvider({ provider: 'openai', apiKey: 'sk-x' }, 'hello', 'SYS', { maxTokens: 1024 });
|
||||||
|
|
||||||
|
expect(res).toEqual({ result: 'fixed text' });
|
||||||
|
const [url, init] = fetch.mock.calls[0];
|
||||||
|
expect(url).toBe('https://api.openai.com/v1/chat/completions');
|
||||||
|
expect(init.method).toBe('POST');
|
||||||
|
expect(init.headers).toEqual({ 'Content-Type': 'application/json', Authorization: 'Bearer sk-x' });
|
||||||
|
expect(JSON.parse(init.body)).toEqual({
|
||||||
|
model: 'gpt-4o-mini',
|
||||||
|
messages: [
|
||||||
|
{ role: 'system', content: 'SYS' },
|
||||||
|
{ role: 'user', content: 'hello' },
|
||||||
|
],
|
||||||
|
max_tokens: 1024,
|
||||||
|
temperature: 0.7,
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
it('Anthropic: x-api-key + version headers, top-level system, no temperature', async () => {
|
||||||
|
const fetch = mockFetchOnce(anthropicResponse);
|
||||||
|
const res = await callProvider({ provider: 'anthropic', apiKey: 'sk-ant' }, 'hello', 'SYS', { maxTokens: 1024 });
|
||||||
|
|
||||||
|
expect(res).toEqual({ result: 'fixed text' });
|
||||||
|
const [url, init] = fetch.mock.calls[0];
|
||||||
|
expect(url).toBe('https://api.anthropic.com/v1/messages');
|
||||||
|
expect(init.headers).toEqual({
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
'x-api-key': 'sk-ant',
|
||||||
|
'anthropic-version': '2023-06-01',
|
||||||
|
});
|
||||||
|
expect(JSON.parse(init.body)).toEqual({
|
||||||
|
model: 'claude-3-5-haiku-20241022',
|
||||||
|
max_tokens: 1024,
|
||||||
|
system: 'SYS',
|
||||||
|
messages: [{ role: 'user', content: 'hello' }],
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
it('Groq: OpenAI-compatible endpoint and body with temperature', async () => {
|
||||||
|
const fetch = mockFetchOnce(openAiResponse);
|
||||||
|
await callProvider({ provider: 'groq', apiKey: 'gsk-x' }, 'hello', 'SYS', { maxTokens: 1024 });
|
||||||
|
|
||||||
|
const [url, init] = fetch.mock.calls[0];
|
||||||
|
expect(url).toBe('https://api.groq.com/openai/v1/chat/completions');
|
||||||
|
expect(init.headers).toEqual({ 'Content-Type': 'application/json', Authorization: 'Bearer gsk-x' });
|
||||||
|
const body = JSON.parse(init.body);
|
||||||
|
expect(body.model).toBe('llama-3.3-70b-versatile');
|
||||||
|
expect(body.temperature).toBe(0.7);
|
||||||
|
expect(body.max_tokens).toBe(1024);
|
||||||
|
});
|
||||||
|
|
||||||
|
it('OpenRouter: referer/title headers, NO temperature', async () => {
|
||||||
|
const fetch = mockFetchOnce(openAiResponse);
|
||||||
|
await callProvider({ provider: 'openrouter', apiKey: 'sk-or' }, 'hello', 'SYS', { maxTokens: 1024 });
|
||||||
|
|
||||||
|
const [url, init] = fetch.mock.calls[0];
|
||||||
|
expect(url).toBe('https://openrouter.ai/api/v1/chat/completions');
|
||||||
|
expect(init.headers).toEqual({
|
||||||
|
'Content-Type': 'application/json',
|
||||||
|
Authorization: 'Bearer sk-or',
|
||||||
|
'HTTP-Referer': 'https://lexai.dev',
|
||||||
|
'X-Title': 'LexAI',
|
||||||
|
});
|
||||||
|
const body = JSON.parse(init.body);
|
||||||
|
expect(body.model).toBe('openai/gpt-4o-mini');
|
||||||
|
expect(body).not.toHaveProperty('temperature');
|
||||||
|
});
|
||||||
|
|
||||||
|
it('uses the configured model over the default', async () => {
|
||||||
|
const fetch = mockFetchOnce(openAiResponse);
|
||||||
|
await callProvider({ provider: 'openai', apiKey: 'k', model: 'gpt-4o' }, 'x', 'SYS');
|
||||||
|
expect(JSON.parse(fetch.mock.calls[0][1].body).model).toBe('gpt-4o');
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
describe('callProvider error handling (parity with old implementations)', () => {
|
||||||
|
it('surfaces provider error messages with the provider label', async () => {
|
||||||
|
mockFetchOnce({ error: { message: 'invalid api key' } }, { ok: false, status: 401 });
|
||||||
|
const res = await callProvider({ provider: 'openai', apiKey: 'bad' }, 'x', 'SYS');
|
||||||
|
expect(res).toEqual({ error: 'OpenAI error: invalid api key' });
|
||||||
|
});
|
||||||
|
|
||||||
|
it('falls back to HTTP status when the error body has no message', async () => {
|
||||||
|
mockFetchOnce({}, { ok: false, status: 500 });
|
||||||
|
const res = await callProvider({ provider: 'groq', apiKey: 'k' }, 'x', 'SYS');
|
||||||
|
expect(res).toEqual({ error: 'Groq error: HTTP 500' });
|
||||||
|
});
|
||||||
|
|
||||||
|
it('reports network failures with the provider label', async () => {
|
||||||
|
vi.stubGlobal('fetch', vi.fn().mockRejectedValue(new Error('offline')));
|
||||||
|
const res = await callProvider({ provider: 'anthropic', apiKey: 'k' }, 'x', 'SYS');
|
||||||
|
expect(res.error).toMatch(/^Network error reaching Anthropic:/);
|
||||||
|
});
|
||||||
|
|
||||||
|
it('reports empty responses', async () => {
|
||||||
|
mockFetchOnce({ choices: [] });
|
||||||
|
const res = await callProvider({ provider: 'openrouter', apiKey: 'k' }, 'x', 'SYS');
|
||||||
|
expect(res).toEqual({ error: 'OpenRouter returned an empty response.' });
|
||||||
|
});
|
||||||
|
|
||||||
|
it('rejects unknown providers without fetching', async () => {
|
||||||
|
const fetch = mockFetchOnce({});
|
||||||
|
const res = await callProvider({ provider: 'bogus', apiKey: 'k' }, 'x', 'SYS');
|
||||||
|
expect(res.error).toContain('Unknown provider: "bogus"');
|
||||||
|
expect(fetch).not.toHaveBeenCalled();
|
||||||
|
});
|
||||||
|
|
||||||
|
it('defaults to openai when no provider is configured', async () => {
|
||||||
|
const fetch = mockFetchOnce(openAiResponse);
|
||||||
|
await callProvider({ apiKey: 'k' }, 'x', 'SYS');
|
||||||
|
expect(fetch.mock.calls[0][0]).toBe('https://api.openai.com/v1/chat/completions');
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
describe('getSystemPrompt', () => {
|
||||||
|
it("normalizes 'fix' to the grammar prompt", () => {
|
||||||
|
expect(getSystemPrompt('fix')).toBe(getSystemPrompt('grammar'));
|
||||||
|
expect(getSystemPrompt('fix')).toContain('grammar editor');
|
||||||
|
});
|
||||||
|
|
||||||
|
it('falls back to grammar for unknown actions', () => {
|
||||||
|
expect(getSystemPrompt('nonsense')).toBe(getSystemPrompt('grammar'));
|
||||||
|
});
|
||||||
|
|
||||||
|
it('appends a style modifier except for Default', () => {
|
||||||
|
expect(getSystemPrompt('rephrase', 'Formal')).toMatch(/Write in a formal style\.$/);
|
||||||
|
expect(getSystemPrompt('rephrase', 'Default')).not.toContain('style.');
|
||||||
|
expect(getSystemPrompt('rephrase')).not.toContain('Write in a');
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
describe('defaultMaxTokens', () => {
|
||||||
|
it('never goes below the old 1024 budget', () => {
|
||||||
|
expect(defaultMaxTokens('short')).toBe(1024);
|
||||||
|
});
|
||||||
|
|
||||||
|
it('scales with input length and clamps at 8192', () => {
|
||||||
|
expect(defaultMaxTokens('a'.repeat(4000))).toBe(4000);
|
||||||
|
expect(defaultMaxTokens('a'.repeat(50000))).toBe(8192);
|
||||||
|
});
|
||||||
|
});
|
||||||
|
|
||||||
|
describe('listModels', () => {
|
||||||
|
it('requires a key for Anthropic and sends the direct-browser-access header', async () => {
|
||||||
|
expect(await listModels('anthropic')).toEqual({
|
||||||
|
error: 'Anthropic requires an API key to list models.',
|
||||||
|
});
|
||||||
|
|
||||||
|
const fetch = mockFetchOnce({ data: [{ id: 'claude-3-5-haiku-20241022' }] });
|
||||||
|
await listModels('anthropic', 'sk-ant');
|
||||||
|
const [url, init] = fetch.mock.calls[0];
|
||||||
|
expect(url).toBe('https://api.anthropic.com/v1/models');
|
||||||
|
expect(init.headers['anthropic-dangerous-direct-browser-access']).toBe('true');
|
||||||
|
expect(init.headers['x-api-key']).toBe('sk-ant');
|
||||||
|
});
|
||||||
|
|
||||||
|
it('allows keyless listing (OpenRouter) and sends bearer auth when a key exists', async () => {
|
||||||
|
const noKey = mockFetchOnce({ data: [{ id: 'openai/gpt-4o' }] });
|
||||||
|
await listModels('openrouter');
|
||||||
|
expect(noKey.mock.calls[0][1].headers).not.toHaveProperty('Authorization');
|
||||||
|
|
||||||
|
const withKey = mockFetchOnce({ data: [{ id: 'gpt-4o' }] });
|
||||||
|
await listModels('openai', 'sk-x');
|
||||||
|
expect(withKey.mock.calls[0][1].headers['Authorization']).toBe('Bearer sk-x');
|
||||||
|
});
|
||||||
|
|
||||||
|
it('filters non-chat models and sorts ids', async () => {
|
||||||
|
mockFetchOnce({
|
||||||
|
data: [
|
||||||
|
{ id: 'gpt-4o' },
|
||||||
|
{ id: 'text-embedding-3-small' },
|
||||||
|
{ id: 'whisper-1' },
|
||||||
|
{ id: 'dall-e-3' },
|
||||||
|
{ id: 'gpt-4o-mini' },
|
||||||
|
],
|
||||||
|
});
|
||||||
|
expect(await listModels('openai', 'k')).toEqual({ models: ['gpt-4o', 'gpt-4o-mini'] });
|
||||||
|
});
|
||||||
|
|
||||||
|
it('errors on empty lists and unknown providers', async () => {
|
||||||
|
mockFetchOnce({ data: [] });
|
||||||
|
expect((await listModels('openai', 'k')).error).toBe('No models returned by openai.');
|
||||||
|
expect((await listModels('bogus', 'k')).error).toContain('Unknown provider');
|
||||||
|
});
|
||||||
|
});
|
||||||
Reference in New Issue
Block a user