feat(ai): 新增 AI 模块并集成至备课/错题集/试卷/改题四大业务场景
- 新增 src/modules/ai 独立模块，遵循三层架构（actions → services → shared/lib/ai） - 通过 AiClientProvider + useAiClient 实现 React Context 依赖注入，业务组件零直接 import - 6 个 Server Actions 均调用 requirePermission() 权限校验，返回 ActionState<T> - withAiTracking 统一埋点，覆盖 chat/similar_question/grading_assist/lesson_content/question_variant/weakness_analysis - 集成场景：作业批改 AiGradingAssist、错题集 AiErrorBookAnalysis、备课 AiLessonContentGenerator、试卷 AiQuestionVariantGenerator - 全量 i18n（en/zh-CN ai.json），Error Boundary + Skeleton 边界处理 - 同步架构图 004/005，新增审计报告 ai-module-audit-report.md
This commit is contained in:
SpecialX
346
src/modules/ai/services/ai-service.ts
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346
src/modules/ai/services/ai-service.ts
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import "server-only"
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import { env } from "@/env.mjs"
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import { createAiChatCompletion, getAiErrorMessage } from "@/shared/lib/ai"
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import {
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GRADING_ASSIST_SYSTEM_PROMPT,
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LESSON_CONTENT_SYSTEM_PROMPT,
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QUESTION_VARIANT_SYSTEM_PROMPT,
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SIMILAR_QUESTION_SYSTEM_PROMPT,
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WEAKNESS_ANALYSIS_SYSTEM_PROMPT,
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} from "./prompt-templates"
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import { withAiTracking } from "./usage-tracker"
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import {
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GradingSuggestionSchema,
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LessonContentResultSchema,
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QuestionVariantResultSchema,
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SimilarQuestionListSchema,
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WeaknessAnalysisResultSchema,
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} from "../schema"
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import type {
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AiChatMessage,
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AiChatOptions,
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AiChatResult,
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AiService,
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GradingInput,
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GradingSuggestion,
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LessonContentInput,
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LessonContentResult,
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QuestionVariantInput,
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QuestionVariantResult,
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SimilarQuestionInput,
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SimilarQuestionResult,
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WeaknessAnalysisInput,
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WeaknessAnalysisResult,
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} from "../types"
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// ---------------------------------------------------------------------------
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// JSON 提取工具（从 AI 返回文本中提取 JSON）
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// ---------------------------------------------------------------------------
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const extractBalancedJsonSegment = (value: string): string | null => {
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const startBrace = value.indexOf("{")
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const startBracket = value.indexOf("[")
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const start =
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startBrace === -1
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? startBracket
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: startBracket === -1
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? startBrace
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: Math.min(startBrace, startBracket)
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if (start === -1) return null
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const opening = value[start]
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const closing = opening === "{" ? "}" : "]"
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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 = start; i < value.length; i += 1) {
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const char = value[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 (char === "\\") {
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escaped = true
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} else if (char === '"') {
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inString = false
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}
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continue
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}
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if (char === '"') {
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inString = true
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continue
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}
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if (char === opening) {
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depth += 1
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continue
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}
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if (char === closing) {
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depth -= 1
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if (depth === 0) {
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return value.slice(start, i + 1)
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}
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}
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}
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return null
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}
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const tryParseJson = (value: string): unknown | null => {
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try {
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return JSON.parse(value)
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} catch {
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return null
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}
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}
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const extractJson = (raw: string): unknown => {
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const trimmed = raw.trim()
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const candidates: string[] = []
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const fencedMatches = [...trimmed.matchAll(/```(?:json)?\s*([\s\S]*?)```/gi)]
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if (fencedMatches.length > 0) {
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candidates.push(...fencedMatches.map((match) => (match[1] ?? "").trim()))
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}
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candidates.push(trimmed)
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for (const candidate of candidates) {
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const direct = tryParseJson(candidate)
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if (direct !== null) return direct
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const segment = extractBalancedJsonSegment(candidate)
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if (!segment) continue
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const parsed = tryParseJson(segment)
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if (parsed !== null) return parsed
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}
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throw new Error("Invalid AI response: cannot parse JSON")
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}
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// ---------------------------------------------------------------------------
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// AiService 实现
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// ---------------------------------------------------------------------------
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const DEFAULT_MODEL = () => String(env.AI_MODEL ?? "gpt-4o-mini")
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const buildChatMessages = (
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systemPrompt: string,
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userContent: string
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): AiChatMessage[] => [
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{ role: "system", content: systemPrompt },
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{ role: "user", content: userContent },
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]
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const callAi = async (
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messages: AiChatMessage[],
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options?: AiChatOptions
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): Promise<{ content: string; model?: string; tokenUsage?: number }> => {
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const result = await createAiChatCompletion({
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messages,
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model: options?.model ?? DEFAULT_MODEL(),
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temperature: options?.temperature ?? 0.3,
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...(typeof options?.maxTokens === "number" ? { maxTokens: options.maxTokens } : {}),
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...(options?.providerId ? { providerId: options.providerId } : {}),
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})
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const tokenUsage =
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result.usage && typeof result.usage === "object" && "total_tokens" in result.usage
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? Number((result.usage as unknown as Record<string, unknown>).total_tokens ?? 0)
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: undefined
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return { content: result.content, tokenUsage }
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}
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/**
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* 默认 AI 服务实现
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*
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* 封装 shared/lib/ai 的底层 SDK 调用，提供业务语义化接口。
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* 所有业务模块通过此服务调用 AI，不直接 import shared/lib/ai。
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*/
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export class DefaultAiService implements AiService {
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constructor(private readonly userId: string) {}
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async chat(
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messages: AiChatMessage[],
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options?: AiChatOptions
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): Promise<AiChatResult> {
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return withAiTracking(this.userId, "chat", options?.providerId, async () => {
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const { content, tokenUsage } = await callAi(messages, {
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...options,
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temperature: options?.temperature ?? 0.7,
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})
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return { result: { content, usage: null }, tokenUsage }
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})
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}
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async suggestSimilarQuestions(
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input: SimilarQuestionInput
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): Promise<SimilarQuestionResult[]> {
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return withAiTracking(this.userId, "similar_question", undefined, async () => {
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const count = input.count ?? 3
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const userLines = [
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`Question Type: ${input.questionType}`,
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input.subject ? `Subject: ${input.subject}` : "",
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input.knowledgePointIds?.length
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? `Knowledge Points: ${input.knowledgePointIds.join(", ")}`
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: "",
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`Generate ${count} similar questions.`,
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`Original Question:\n${input.questionText}`,
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].filter((line) => line.length > 0)
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const { content } = await callAi(
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buildChatMessages(SIMILAR_QUESTION_SYSTEM_PROMPT, userLines.join("\n\n")),
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{ temperature: 0.5, maxTokens: 3000 }
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)
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const parsed = extractJson(content)
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const list =
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parsed && typeof parsed === "object" && "questions" in parsed
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? (parsed as Record<string, unknown>).questions
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: parsed
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const validated = SimilarQuestionListSchema.safeParse(list)
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if (!validated.success) return { result: [] }
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return { result: validated.data }
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})
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}
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async suggestGrading(input: GradingInput): Promise<GradingSuggestion> {
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return withAiTracking(this.userId, "grading_assist", undefined, async () => {
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const userLines = [
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`Question Type: ${input.questionType}`,
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`Max Score: ${input.maxScore}`,
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input.subject ? `Subject: ${input.subject}` : "",
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`Question:\n${input.questionText}`,
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`Student Answer:\n${input.studentAnswer}`,
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input.correctAnswer ? `Correct Answer:\n${input.correctAnswer}` : "",
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].filter((line) => line.length > 0)
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const { content } = await callAi(
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buildChatMessages(GRADING_ASSIST_SYSTEM_PROMPT, userLines.join("\n\n")),
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{ temperature: 0.2, maxTokens: 1000 }
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)
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const parsed = extractJson(content)
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const validated = GradingSuggestionSchema.safeParse(parsed)
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if (!validated.success) {
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return {
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result: {
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suggestedScore: 0,
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confidence: 0,
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feedback: "AI grading unavailable",
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reasoning: "AI response format invalid",
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},
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}
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}
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const data = validated.data
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return {
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result: {
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suggestedScore: Math.min(Math.max(data.suggestedScore, 0), input.maxScore),
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confidence: data.confidence,
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feedback: data.feedback,
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reasoning: data.reasoning,
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},
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}
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})
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}
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async generateLessonContent(
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input: LessonContentInput
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): Promise<LessonContentResult> {
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return withAiTracking(this.userId, "lesson_content", undefined, async () => {
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const userLines = [
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`Topic: ${input.topic}`,
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`Content Type: ${input.contentType}`,
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input.subject ? `Subject: ${input.subject}` : "",
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input.grade ? `Grade: ${input.grade}` : "",
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input.additionalContext ? `Additional Context:\n${input.additionalContext}` : "",
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].filter((line) => line.length > 0)
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const { content } = await callAi(
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buildChatMessages(LESSON_CONTENT_SYSTEM_PROMPT, userLines.join("\n\n")),
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{ temperature: 0.7, maxTokens: 4000 }
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)
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const parsed = extractJson(content)
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const validated = LessonContentResultSchema.safeParse(parsed)
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if (!validated.success) {
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return {
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result: {
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title: input.topic,
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content: content,
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},
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}
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}
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return { result: validated.data }
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})
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}
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async generateQuestionVariant(
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input: QuestionVariantInput
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): Promise<QuestionVariantResult> {
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return withAiTracking(this.userId, "question_variant", undefined, async () => {
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const userLines = [
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`Variant Type: ${input.variantType}`,
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input.subject ? `Subject: ${input.subject}` : "",
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`Original Question:\n${JSON.stringify(input.originalQuestion, null, 2)}`,
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].filter((line) => line.length > 0)
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const { content } = await callAi(
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buildChatMessages(QUESTION_VARIANT_SYSTEM_PROMPT, userLines.join("\n\n")),
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{ temperature: 0.6, maxTokens: 2000 }
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)
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const parsed = extractJson(content)
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const validated = QuestionVariantResultSchema.safeParse(parsed)
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if (!validated.success) {
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throw new Error("AI question variant format invalid")
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}
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return { result: validated.data }
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})
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}
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async analyzeWeakness(
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input: WeaknessAnalysisInput
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): Promise<WeaknessAnalysisResult> {
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return withAiTracking(this.userId, "weakness_analysis", undefined, async () => {
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const userLines = [
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`Student ID: ${input.studentId}`,
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input.subjectId ? `Subject ID: ${input.subjectId}` : "",
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`Error Items (${input.errorItems.length}):`,
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JSON.stringify(
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input.errorItems.map((item) => ({
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questionText: item.questionText,
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questionType: item.questionType,
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errorCount: item.errorCount,
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masteryLevel: item.masteryLevel,
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})),
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null,
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2
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),
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].filter((line) => line.length > 0)
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const { content } = await callAi(
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buildChatMessages(WEAKNESS_ANALYSIS_SYSTEM_PROMPT, userLines.join("\n\n")),
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{ temperature: 0.3, maxTokens: 2000 }
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)
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const parsed = extractJson(content)
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const validated = WeaknessAnalysisResultSchema.safeParse(parsed)
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if (!validated.success) {
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return {
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result: {
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weakAreas: [],
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studyPlan: "Analysis unavailable",
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recommendedResources: [],
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},
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}
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}
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return { result: validated.data }
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})
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}
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}
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/**
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* 创建 AI 服务实例
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*
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* 在 Server Action 中调用，传入当前用户 ID。
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* 测试时可替换为 mock 实现。
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*/
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export const createAiService = (userId: string): AiService =>
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new DefaultAiService(userId)
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/**
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* 安全执行 AI 调用，捕获异常并返回错误消息
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*/
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export const safeAiCall = async <T>(
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fn: () => Promise<T>
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): Promise<{ ok: true; data: T } | { ok: false; message: string }> => {
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try {
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const data = await fn()
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return { ok: true, data }
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} catch (error) {
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return { ok: false, message: getAiErrorMessage(error) }
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}
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}
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