feat: add role management and usage tracking for AI code review
- Implemented role parsing and loading from markdown files, including attributes like name, side, focus, badge, color, and personality. - Created functions to build prompts for analysis, line location, and verdicts based on roles. - Added tests for role management functionalities to ensure correct parsing and loading of roles. - Developed usage tracking for AI assistant interactions, including token usage and rate limits. - Implemented functions to extract and record usage data from various LLM providers. - Added tests for usage tracking functionalities to validate correct accumulation and reporting of usage statistics.
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import axios from 'axios';
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import { getLLMConfig, getOpenCodeHttpsAgent } from './config.js';
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import { recordUsage, recordRateLimit } from './usage.js';
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import { line, error } from './log.js';
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function isOpenAIGpt55(provider, model) {
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return provider === 'openai' && /^gpt-5\.5(?:-|$)/i.test(model || '');
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}
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function chatEndpoint(baseURL, provider, model) {
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const base = baseURL.replace(/\/$/, '');
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return isOpenAIGpt55(provider, model) ? `${base}/responses` : `${base}/chat/completions`;
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}
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function chatPayload(provider, model, systemPrompt, userContent) {
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if (isOpenAIGpt55(provider, model)) {
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return { model, instructions: systemPrompt, input: userContent, temperature: 0.2 };
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}
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return { model, messages: [{ role: 'system', content: systemPrompt }, { role: 'user', content: userContent }], temperature: 0.2 };
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}
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function extractContent(provider, model, data) {
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if (!isOpenAIGpt55(provider, model)) return data.choices[0].message.content;
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if (typeof data.output_text === 'string') return data.output_text;
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const parts = data.output?.flatMap(item => item.content || []) || [];
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const text = parts
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.map(part => {
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if (typeof part.text === 'string') return part.text;
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if (typeof part.content === 'string') return part.content;
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return '';
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})
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.filter(Boolean)
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.join('');
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if (text) return text;
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return data.choices?.[0]?.message?.content || '';
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}
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function opencodeModelConfig(model) {
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const [providerID, modelID] = model.includes('/') ? model.split('/', 2) : [process.env.OPENCODE_PROVIDER || 'google', model];
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return { providerID, modelID };
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}
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function applyOpenCodeAuth(headers) {
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const password = process.env.OPENCODE_SERVER_PASSWORD;
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if (!password) return;
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const username = process.env.OPENCODE_SERVER_USERNAME || 'opencode';
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headers['Authorization'] = `Basic ${Buffer.from(`${username}:${password}`).toString('base64')}`;
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}
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function opencodeAxiosOptions(headers) {
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return {
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headers,
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httpsAgent: getOpenCodeHttpsAgent(),
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};
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}
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function extractOpenCodeContent(data) {
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const parts = data.parts || data.data?.parts || data.info?.content || data.data?.info?.content || [];
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return parts
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.map(part => part.text || part.content || '')
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.filter(Boolean)
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.join('');
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}
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async function chatOpenCode(baseURL, model, systemPrompt, userContent, headers) {
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const base = baseURL.replace(/\/$/, '');
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const { providerID, modelID } = opencodeModelConfig(model);
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const session = await axios.post(
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`${base}/session`,
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{ title: 'AI Code Review', model: { providerID, id: modelID } },
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opencodeAxiosOptions(headers)
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);
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const sessionID = session.data.id || session.data.data?.id;
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if (!sessionID) throw new Error('OpenCode session 建立失敗：回應中沒有 session id');
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const resp = await axios.post(
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`${base}/session/${sessionID}/message`,
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{
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model: { providerID, modelID },
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system: systemPrompt,
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parts: [{ type: 'text', text: userContent }],
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},
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opencodeAxiosOptions(headers)
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);
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return { content: extractOpenCodeContent(resp.data), data: resp.data };
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}
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export async function chat(systemPrompt, userContent) {
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const { provider, apiKeys, baseURL, model } = getLLMConfig();
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if (!provider) throw new Error('未設定任何 LLM API Key');
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line(`[LLM] provider=${provider} model=${model}`);
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const headers = { 'Content-Type': 'application/json' };
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if (provider === 'claude') headers['anthropic-version'] = '2023-06-01';
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const shuffled = [...apiKeys].sort(() => Math.random() - 0.5);
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for (let i = 0; i < shuffled.length; i++) {
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if (provider !== 'ollama' && provider !== 'opencode') headers['Authorization'] = `Bearer ${shuffled[i]}`;
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try {
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if (provider === 'opencode') {
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applyOpenCodeAuth(headers);
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const { content, data } = await chatOpenCode(baseURL, model, systemPrompt, userContent, headers);
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recordUsage(data);
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return content;
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}
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const resp = await axios.post(
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chatEndpoint(baseURL, provider, model),
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chatPayload(provider, model, systemPrompt, userContent),
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{ headers }
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);
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recordUsage(resp.data);
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recordRateLimit(resp.headers);
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return extractContent(provider, model, resp.data);
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} catch (e) {
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line(`[LLM] key[${i + 1}/${shuffled.length}] 失敗: ${e.message}`);
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}
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}
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error('[LLM] 所有 API Key 均失敗，終止流程');
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process.exit(1);
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}
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export async function chatJSON(systemPrompt, userContent) {
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const text = await chat(systemPrompt, userContent);
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try {
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return JSON.parse(extractJSONText(text));
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} catch (e) {
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line(`[LLM] JSON 解析失敗: ${e.message}`);
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return [];
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}
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}
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function stripOuterFence(text) {
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return String(text)
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.trim()
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.replace(/^```[a-zA-Z0-9_-]*\n?/, '')
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.replace(/```$/, '')
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.trim();
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}
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function extractBalancedJSON(text, startIndex) {
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const source = String(text);
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const open = source[startIndex];
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const close = open === '{' ? '}' : ']';
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let depth = 0;
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let inString = false;
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let escaped = false;
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for (let i = startIndex; i < source.length; i++) {
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const ch = source[i];
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if (inString) {
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if (escaped) {
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escaped = false;
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} else if (ch === '\\') {
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escaped = true;
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} else if (ch === '"') {
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inString = false;
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}
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continue;
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}
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if (ch === '"') {
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inString = true;
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continue;
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}
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if (ch === open) depth += 1;
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else if (ch === close) {
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depth -= 1;
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if (depth === 0) return source.slice(startIndex, i + 1);
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}
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}
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return null;
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}
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function extractJSONText(text) {
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const stripped = stripOuterFence(text);
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try {
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JSON.parse(stripped);
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return stripped;
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} catch {}
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for (let i = 0; i < stripped.length; i++) {
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if (stripped[i] !== '[' && stripped[i] !== '{') continue;
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const candidate = extractBalancedJSON(stripped, i);
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if (!candidate) continue;
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try {
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JSON.parse(candidate);
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return candidate;
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} catch {}
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}
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return stripped;
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}
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