mirror of
https://github.com/modelstudioai/cli.git
synced 2026-09-14 19:49:23 +08:00
feat: model recommend beta version
This commit is contained in:
故璃
@@ -43,7 +43,9 @@
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"check": "vp check"
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},
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"dependencies": {
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"bailian-cli-core": "workspace:*"
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"bailian-cli-core": "workspace:*",
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"boxen": "catalog:",
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"chalk": "catalog:"
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},
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"devDependencies": {
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"@clack/prompts": "^0.7.0",
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@@ -0,0 +1,302 @@
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import {
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analyzeIntent,
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buildDocLink,
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type Config,
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defineCommand,
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detectOutputFormat,
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type GetModelsOptions,
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type GlobalFlags,
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getModels,
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type IntentProfile,
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isInteractive,
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type PipelineStep,
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type RecommendedModel,
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type RecommendResult,
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rankModels,
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recallSemantic,
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} from "bailian-cli-core";
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import boxen from "boxen";
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import chalk, { Chalk, type ChalkInstance } from "chalk";
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import { emitBare, emitResult } from "../../output/output.ts";
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import { createSpinner } from "../../output/progress.ts";
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import { failIfMissing, promptText } from "../../output/prompt.ts";
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function formatContextWindow(tokens: number): string {
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if (tokens >= 1_000_000)
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return `${(tokens / 1_000_000).toFixed(tokens % 1_000_000 === 0 ? 0 : 1)}M`;
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if (tokens >= 1_000) return `${(tokens / 1_000).toFixed(tokens % 1_000 === 0 ? 0 : 1)}K`;
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return String(tokens);
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}
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const MODALITY_LABELS: Record<string, string> = {
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Text: "文本",
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Image: "图片",
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Video: "视频",
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Audio: "音频",
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};
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const CAPABILITY_LABELS: Record<string, string> = {
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TG: "文本生成",
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VU: "视觉理解",
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IG: "图像生成",
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VG: "视频生成",
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TTS: "语音合成",
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ASR: "语音识别",
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Reasoning: "推理",
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};
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const BUDGET_LABELS: Record<string, string> = {
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low: "低成本优先",
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medium: "适中",
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high: "高投入",
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};
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const QUALITY_LABELS: Record<string, string> = {
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flagship: "旗舰优先",
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balanced: "均衡",
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"cost-optimized": "性价比优先",
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};
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function formatIntentSummary(intent: IntentProfile, noColor: boolean): string {
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const colorize = noColor ? new Chalk({ level: 0 }) : chalk;
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const lines: string[] = [];
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lines.push(colorize.cyan.bold("需求理解"));
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if (intent.taskSummary) {
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lines.push("");
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lines.push(intent.taskSummary);
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}
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if (intent.scenarioHints.length) {
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lines.push("");
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lines.push(`${colorize.dim("场景特征")} ${intent.scenarioHints.join(" · ")}`);
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}
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const inputLabels = intent.inputModality.map((mod) => MODALITY_LABELS[mod] ?? mod);
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const outputLabels = intent.outputModality.map((mod) => MODALITY_LABELS[mod] ?? mod);
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if (inputLabels.length || outputLabels.length) {
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lines.push("");
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const parts: string[] = [];
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if (inputLabels.length) parts.push(`${colorize.dim("输入")} ${inputLabels.join(", ")}`);
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if (outputLabels.length) parts.push(`${colorize.dim("输出")} ${outputLabels.join(", ")}`);
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lines.push(parts.join(" "));
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}
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const capLabels = intent.requiredCapabilities.map((cap) => CAPABILITY_LABELS[cap] ?? cap);
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if (capLabels.length) {
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lines.push(`${colorize.dim("所需能力")} ${capLabels.join(", ")}`);
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}
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const budgetLabel = BUDGET_LABELS[intent.budget] ?? intent.budget;
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const qualityLabel = QUALITY_LABELS[intent.qualityPreference] ?? intent.qualityPreference;
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lines.push("");
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lines.push(
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`${colorize.dim("预算倾向")} ${budgetLabel} ${colorize.dim("质量偏好")} ${qualityLabel}`,
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);
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if (intent.segments?.length) {
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lines.push("");
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lines.push(colorize.dim("任务拆解"));
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for (const [idx, segment] of intent.segments.entries()) {
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const outMods = segment.outputModality.map((mod) => MODALITY_LABELS[mod] ?? mod).join(", ");
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lines.push(
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` ${colorize.dim(`${idx + 1}.`)} ${segment.step}${outMods ? colorize.dim(` → ${outMods}`) : ""}`,
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);
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}
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}
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return boxen(lines.join("\n"), {
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padding: { top: 0, bottom: 0, left: 1, right: 1 },
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margin: { top: 0, bottom: 0, left: 1, right: 0 },
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borderColor: "cyan",
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borderStyle: "round",
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dimBorder: true,
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});
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}
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const RECOMMEND_LABELS = ["最佳推荐", "次优选择", "备选参考"];
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function renderCard(rec: RecommendedModel, index: number, colorize: ChalkInstance): string {
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const labelColors = [colorize.green.bold, colorize.blue.bold, colorize.magenta.bold];
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const colorFn = labelColors[index] ?? colorize.white.bold;
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const label = RECOMMEND_LABELS[index] ?? `推荐 #${index + 1}`;
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const lines: string[] = [];
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lines.push(colorFn(`⬢ 推荐 #${index + 1} — ${label}`));
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lines.push("");
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lines.push(`${colorize.bold(rec.name)} ${colorize.dim(`(${rec.model})`)}`);
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lines.push("");
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lines.push(`${colorize.cyan("推荐理由")} ${rec.reason}`);
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if (rec.highlights.length) {
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lines.push("");
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lines.push(
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rec.highlights.map((highlight) => colorize.bgGray.white(` ${highlight} `)).join(" "),
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);
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}
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const meta: string[] = [];
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if (rec.contextWindow) meta.push(`上下文 ${formatContextWindow(rec.contextWindow)}`);
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if (rec.maxOutputTokens) meta.push(`最大输出 ${formatContextWindow(rec.maxOutputTokens)}`);
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if (meta.length) {
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lines.push("");
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lines.push(colorize.dim(meta.join(" · ")));
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}
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const docLink = buildDocLink(rec.docUrl);
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if (docLink) {
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lines.push("");
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lines.push(colorize.dim(`文档 ${docLink}`));
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}
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return boxen(lines.join("\n"), {
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padding: { top: 0, bottom: 0, left: 1, right: 1 },
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margin: { top: 0, bottom: 0, left: 1, right: 0 },
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borderColor: "gray",
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borderStyle: "round",
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dimBorder: true,
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});
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}
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function formatSingleResult(results: RecommendedModel[], noColor: boolean): string {
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const colorize = noColor ? new Chalk({ level: 0 }) : chalk;
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return results.map((rec, idx) => renderCard(rec, idx, colorize)).join("\n");
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}
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function formatPipelineResult(summary: string, steps: PipelineStep[], noColor: boolean): string {
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const colorize = noColor ? new Chalk({ level: 0 }) : chalk;
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const lines: string[] = [];
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lines.push(` ${colorize.yellow.bold("⚡ 组合方案")} ${summary}`);
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for (const [stepIdx, { step, recommendations, warnings }] of steps.entries()) {
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lines.push("");
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lines.push(colorize.bold(` ━━━ Step ${stepIdx + 1}: ${step} ━━━`));
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if (warnings?.length) {
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for (const warning of warnings) {
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lines.push(` ${colorize.yellow("⚠")} ${colorize.yellow(warning)}`);
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}
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}
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lines.push("");
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lines.push(recommendations.map((rec, idx) => renderCard(rec, idx, colorize)).join("\n"));
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}
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return lines.join("\n");
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}
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function formatResult(result: RecommendResult, noColor: boolean): string {
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if (result.type === "pipeline") {
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return formatPipelineResult(result.summary, result.steps, noColor);
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}
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return formatSingleResult(result.recommendations, noColor);
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}
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function isEmptyResult(result: RecommendResult): boolean {
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if (result.type === "pipeline") return result.steps.length === 0;
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return result.recommendations.length === 0;
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}
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export default defineCommand({
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name: "advisor recommend",
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description:
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"Recommend the best models for your use case (intent analysis → candidate recall → LLM ranking)",
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usage: "bl advisor recommend <prompt> [flags]",
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options: [
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{
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flag: "--message <text>",
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description: "Describe your requirements (alternative to positional prompt)",
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},
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{
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flag: "--dry-run",
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description: "Show intent analysis and candidate list without LLM ranking",
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},
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{
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flag: "--output <format>",
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description: "Output format: text (default in TTY), json, yaml",
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},
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],
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examples: [
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'bl advisor recommend --message "我要做一个能理解图片的客服机器人"',
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'bl advisor recommend --message "做一个Agent自动根据用户意图生成动画片"',
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'bl advisor recommend --message "法律合同审查，要求高精准度"',
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'bl advisor recommend --message "做一个低成本高并发的在线客服" --output json',
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'bl advisor recommend --message "长文本摘要" --dry-run',
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"bl advisor recommend # 交互式输入需求",
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],
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async run(config: Config, flags: GlobalFlags) {
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const positional = ((flags as Record<string, unknown>)._positional as string[]) ?? [];
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let userInput = (flags.message as string) || positional.join(" ");
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if (!userInput.trim()) {
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if (isInteractive({ nonInteractive: config.nonInteractive })) {
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const hint = await promptText({ message: "描述你的需求:" });
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if (!hint) {
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process.stderr.write("已取消。\n");
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process.exit(1);
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}
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userInput = hint;
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} else {
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failIfMissing("message", 'bl advisor recommend "你的需求"');
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}
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}
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const top = 3;
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const format = detectOutputFormat(config.output);
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const modelsOptions: GetModelsOptions = {
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onPrepareStart: () => process.stderr.write("初始化中...\n"),
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};
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process.stderr.write("正在分析需求...\n");
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const [allModels, intent] = await Promise.all([
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getModels(config, modelsOptions),
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analyzeIntent(config, userInput),
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]);
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if (intent.confidence === 0) {
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process.stderr.write("需求分析超时，使用默认参数继续...\n");
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} else {
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process.stderr.write("\n");
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}
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// Stage 2: Candidate Recall (semantic recall, auto-builds embeddings on first run)
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const candidates = await recallSemantic(config, allModels, userInput, 50, intent);
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if (config.dryRun) {
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emitResult(
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{
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userInput,
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intent,
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candidateCount: candidates.length,
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candidates: candidates.map(({ model, score }) => ({
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model: model.model,
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score,
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})),
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top,
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},
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format,
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);
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return;
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}
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// Stage 3: LLM Ranking
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const spinner = createSpinner("正在推荐最佳模型...");
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spinner.start();
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const result = await rankModels(config, candidates, intent, userInput, top);
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spinner.stop();
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if (isEmptyResult(result)) {
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emitBare("暂无满足该需求的模型。");
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return;
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}
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if (format !== "text") {
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emitResult(result, format);
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return;
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}
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emitBare(formatIntentSummary(intent, config.noColor));
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emitBare("");
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emitBare(formatResult(result, config.noColor));
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},
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});
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@@ -35,6 +35,7 @@ import consoleCall from "./console/call.ts";
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import usageFree from "./usage/free.ts";
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import pipelineRun from "./pipeline/run.ts";
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import pipelineValidate from "./pipeline/validate.ts";
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import advisorRecommend from "./advisor/recommend.ts";
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/** Command registry map (no dependency on registry.ts — safe for build-time import). */
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export const commands: Record<string, Command> = {
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@@ -72,5 +73,6 @@ export const commands: Record<string, Command> = {
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"config show": configShow,
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"config set": configSet,
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"config export-schema": configExportSchema,
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"advisor recommend": advisorRecommend,
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update: update,
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};
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@@ -63,7 +63,8 @@ const NO_AUTH_SETUP = [
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];
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async function main() {
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const argv = process.argv.slice(2);
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let argv = process.argv.slice(2);
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if (argv[0] === "--") argv = argv.slice(1);
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if (argv.includes("--version") || argv.includes("-v")) {
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process.stdout.write(`bl ${CLI_VERSION}\n`);
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@@ -0,0 +1,79 @@
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import { describe, expect, test } from "vite-plus/test";
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import { isDashScopeE2EReady, parseStdoutJson, runCli } from "./helpers.ts";
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describe("e2e: advisor recommend", () => {
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test("advisor 分组展示子命令帮助且成功退出", async () => {
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const { stdout, stderr, exitCode } = await runCli(["advisor"]);
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expect(exitCode, stderr).toBe(0);
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expect(`${stdout}\n${stderr}`).toMatch(/advisor|recommend/i);
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});
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test("advisor recommend --help 正常退出", async () => {
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const { stderr, exitCode } = await runCli(["advisor", "recommend", "--help"]);
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expect(exitCode, stderr).toBe(0);
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expect(stderr).toMatch(/recommend|--message|dry-run/i);
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});
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});
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describe.skipIf(!isDashScopeE2EReady())("e2e: advisor recommend（DashScope）", () => {
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test("advisor recommend 缺少 --message 时打印帮助并退出 (0)", async () => {
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const { stdout, stderr, exitCode } = await runCli([
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"advisor",
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"recommend",
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"--non-interactive",
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]);
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expect(exitCode).toBe(0);
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expect(`${stdout}\n${stderr}`).toMatch(/--message|Usage:/i);
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});
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test("advisor recommend --dry-run 输出意图分析和候选列表", async () => {
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const { stdout, stderr, exitCode } = await runCli([
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"advisor",
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"recommend",
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"--dry-run",
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"--message",
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"我想做一个能理解图片的客服机器人",
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"--non-interactive",
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"--output",
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"json",
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]);
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expect(exitCode, stderr).toBe(0);
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const data = parseStdoutJson<{
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userInput?: string;
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intent?: { requiredCapabilities?: string[]; inputModality?: string[] };
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candidateCount?: number;
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candidates?: Array<{ model?: string; score?: number }>;
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}>(stdout);
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expect(data.userInput).toBe("我想做一个能理解图片的客服机器人");
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expect(data.intent?.requiredCapabilities).toContain("VU");
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expect(data.intent?.inputModality).toContain("Image");
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expect(data.candidateCount).toBeGreaterThan(0);
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expect(data.candidates?.[0]?.model).toBeDefined();
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expect(data.candidates?.[0]?.score).toBeGreaterThan(0);
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}, 60_000);
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test("advisor recommend 完整推荐流程返回结果", async () => {
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const { stdout, stderr, exitCode } = await runCli([
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"advisor",
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"recommend",
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"--message",
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"低成本高并发的在线客服",
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"--non-interactive",
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"--output",
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"json",
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]);
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expect(exitCode, stderr).toBe(0);
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const data = parseStdoutJson<{
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type?: string;
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recommendations?: Array<{
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model?: string;
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name?: string;
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reason?: string;
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}>;
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}>(stdout);
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expect(data.type).toBe("single");
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expect(data.recommendations?.length).toBeGreaterThan(0);
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expect(data.recommendations?.[0]?.model).toBeDefined();
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expect(data.recommendations?.[0]?.reason).toBeDefined();
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}, 120_000);
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});
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