feat: v1.0.0-beta.2 - ExtensionGetter integration, UI revamp, bug fixes
- Replace cross-ClassLoader reflection with ExtensionGetter.getEnabledExtension(AiModelService.class) - Declare optional pluginDependencies (ai-foundation?: "*") and recommended-apps annotation - Use OutputSpec.choice for structured classification (sentiment, review safety/quality) - Use GenerateTextRequest with maxRetries=2 for reliable chat generation - Add multi-turn conversation context (conversation_history placeholder) - Fix RateLimitService thread leak (implement DisposableBean) - Two-stage AI review: safety check + 1-5 quality score mapped to 0-100 - Remove redundant dashboard cards (sentiment distribution, 7-day trend, avg score) - Redesign settings page with tabbed navigation (basic/persona/model/prompt/cleanup) - Fix settings layout (move tab bar out of grid container) - Fix button icon+text alignment via :deep(.btn-content) inline-flex - Add review score grade labels (excellent/good/fair/poor) in logs
This commit is contained in:
sunny-335
+4
-84
@@ -216,100 +216,25 @@ public class CommentAiAutopilotEndpoint implements CustomEndpoint {
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}
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private Mono<ServerResponse> getStats(ServerRequest request) {
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String range = request.queryParam("range").orElse("7");
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return client.listAll(AiCommentReply.class, ListOptions.builder().build(), Sort.unsorted())
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.collectList()
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.map(allReplies -> {
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// 根据 range 计算截止时间
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ZoneId zoneId = ZoneId.systemDefault();
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LocalDate today = LocalDate.now(zoneId);
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Instant cutoffInstant;
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int trendDays;
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if ("all".equals(range)) {
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cutoffInstant = null; // 不做时间过滤
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trendDays = 30; // "all" 时趋势也展示最近30天
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} else {
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int days = Integer.parseInt(range);
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cutoffInstant = today.minusDays(days).atStartOfDay(zoneId).toInstant();
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trendDays = days;
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}
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// 根据 range 过滤记录
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List<AiCommentReply> replies;
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if (cutoffInstant != null) {
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replies = allReplies.stream()
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.filter(r -> {
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Instant ts = r.getMetadata().getCreationTimestamp();
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return ts != null && !ts.isBefore(cutoffInstant);
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})
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.toList();
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} else {
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replies = allReplies;
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}
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.map(replies -> {
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long total = replies.size();
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long passCount = replies.stream()
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.filter(r -> "PASS".equals(r.getSpec().getStatus())).count();
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long failCount = replies.stream()
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.filter(r -> "FAIL".equals(r.getSpec().getStatus())).count();
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double avgScore = replies.stream()
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.filter(r -> r.getSpec().getScore() != null && r.getSpec().getScore() > 0)
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.mapToInt(r -> r.getSpec().getScore())
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.average().orElse(0.0);
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long reviewingCount = replies.stream()
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.filter(r -> "PASS".equals(r.getSpec().getStatus())
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&& !Boolean.TRUE.equals(r.getSpec().getPublished()))
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.count();
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Map<String, Long> sentimentDistribution = new HashMap<>();
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sentimentDistribution.put("POSITIVE", 0L);
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sentimentDistribution.put("NEUTRAL", 0L);
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sentimentDistribution.put("NEGATIVE", 0L);
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sentimentDistribution.put("UNKNOWN", 0L);
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for (var r : replies) {
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String sentiment = r.getSpec().getSentiment();
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if (sentiment == null || sentiment.isBlank()) {
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sentimentDistribution.merge("UNKNOWN", 1L, Long::sum);
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} else {
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sentimentDistribution.merge(sentiment, 1L, Long::sum);
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}
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}
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// 计算 dailyTrend
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DateTimeFormatter formatter = DateTimeFormatter.ofPattern("yyyy-MM-dd");
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Map<LocalDate, Long> dailyMap = new HashMap<>();
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for (int i = 0; i < trendDays; i++) {
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dailyMap.put(today.minusDays(i), 0L);
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}
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for (var r : replies) {
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Instant timestamp = r.getMetadata().getCreationTimestamp();
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if (timestamp != null) {
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try {
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LocalDate date = timestamp.atZone(zoneId).toLocalDate();
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if (dailyMap.containsKey(date)) {
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dailyMap.merge(date, 1L, Long::sum);
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}
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} catch (Exception ignored) {
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}
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}
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}
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List<DailyCount> dailyTrend = new ArrayList<>();
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for (int i = 0; i < trendDays; i++) {
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LocalDate date = today.minusDays(i);
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dailyTrend.add(new DailyCount(date.format(formatter), dailyMap.get(date)));
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}
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return new StatsResponse(total, passCount, failCount, avgScore,
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reviewingCount, sentimentDistribution, dailyTrend);
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return new StatsResponse(total, passCount, failCount, reviewingCount);
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})
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.onErrorResume(e -> {
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log.warn("Failed to fetch stats: {}", e.getMessage());
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return Mono.just(new StatsResponse(0, 0, 0, 0.0, 0L,
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Map.of("POSITIVE", 0L, "NEUTRAL", 0L, "NEGATIVE", 0L, "UNKNOWN", 0L),
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List.of()));
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return Mono.just(new StatsResponse(0, 0, 0, 0));
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})
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.flatMap(stats -> ServerResponse.ok().bodyValue(stats));
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}
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@@ -346,16 +271,11 @@ public class CommentAiAutopilotEndpoint implements CustomEndpoint {
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)));
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}
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public record DailyCount(String date, long count) {}
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public record StatsResponse(
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long total,
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long passCount,
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long failCount,
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double avgScore,
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long reviewingCount,
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Map<String, Long> sentimentDistribution,
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List<DailyCount> dailyTrend
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long reviewingCount
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) {}
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public record PersonaResponse(
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@@ -1,85 +1,58 @@
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package top.nxxy335.commentaiautopilot.service;
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import lombok.extern.slf4j.Slf4j;
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import org.springframework.context.ApplicationContext;
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import org.springframework.stereotype.Component;
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import reactor.core.publisher.Mono;
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import run.halo.app.core.extension.Plugin;
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import run.halo.app.extension.ReactiveExtensionClient;
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import run.halo.aifoundation.AiModelService;
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import run.halo.aifoundation.chat.GenerateTextRequest;
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import run.halo.aifoundation.chat.GenerateTextResult;
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import run.halo.aifoundation.schema.OutputSpec;
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import run.halo.app.plugin.extensionpoint.ExtensionGetter;
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import java.lang.reflect.Method;
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import java.util.Map;
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import java.util.List;
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/**
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* AI Foundation client that uses runtime class loading and reflection
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* to call the AI Foundation plugin's AiModelService.
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* AI Foundation client that uses Halo's {@link ExtensionGetter} to obtain the
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* {@link AiModelService} extension provided by the ai-foundation plugin.
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* <p>
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* This approach avoids classloader identity issues by loading AiModelService
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* from ai-foundation's own classloader, so that Spring's getBeansOfType()
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* can correctly match the implementation bean.
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* This is the recommended way to integrate with AI Foundation, see
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* <a href="https://github.com/halo-dev/plugin-ai-foundation/blob/main/dev/dev.md">dev guide</a>.
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* <p>
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* No @ConditionalOnClass or pluginDependencies needed.
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* Always registered as a bean; availability is checked at runtime.
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* Requires the following declaration in plugin.yaml:
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* <pre>
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* spec:
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* pluginDependencies:
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* ai-foundation?: "*"
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* </pre>
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* The dependency is optional, so the plugin still loads when AI Foundation is
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* not installed; availability is checked at runtime and all calls return empty
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* in that case.
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*/
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@Slf4j
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@Component
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public class AiFoundationClient {
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private static final String AI_FOUNDATION_PLUGIN_NAME = "ai-foundation";
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private static final String AI_MODEL_SERVICE_CLASS = "run.halo.aifoundation.AiModelService";
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private final ExtensionGetter extensionGetter;
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private final ReactiveExtensionClient client;
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private final ApplicationContext applicationContext;
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public AiFoundationClient(ReactiveExtensionClient client, ApplicationContext applicationContext) {
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this.client = client;
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this.applicationContext = applicationContext;
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public AiFoundationClient(ExtensionGetter extensionGetter) {
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this.extensionGetter = extensionGetter;
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}
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/**
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* Call AI Foundation to generate a chat response using the specified model.
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* Uses {@link GenerateTextRequest} with {@code maxRetries=2} so that
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* transient model errors are retried by the SDK.
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*
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* @param prompt the prompt text
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* @param modelName the AiModel metadata.name, null or blank to use default model
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* @return the generated text, or empty if AI Foundation is unavailable
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*/
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public Mono<String> chat(String prompt, String modelName) {
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return isAiFoundationEnabled()
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.flatMap(enabled -> {
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if (!enabled) {
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log.warn("AI Foundation plugin is not installed or not enabled, skipping AI reply");
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return Mono.empty();
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}
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return doChat(prompt, modelName);
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});
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}
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/**
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* Check if AI Foundation is available: plugin installed, enabled, and AiModelService bean found.
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*/
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public Mono<Boolean> isAvailable() {
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return isAiFoundationEnabled()
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.flatMap(enabled -> {
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if (!enabled) return Mono.just(false);
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return findAiModelService().hasElement();
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});
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}
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private Mono<Boolean> isAiFoundationEnabled() {
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return client.fetch(Plugin.class, AI_FOUNDATION_PLUGIN_NAME)
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.map(plugin -> plugin.getSpec().getEnabled())
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.defaultIfEmpty(false)
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.onErrorResume(e -> {
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log.debug("Failed to check AI Foundation plugin status: {}", e.getMessage());
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return Mono.just(false);
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});
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}
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private Mono<String> doChat(String prompt, String modelName) {
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return findAiModelService()
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.flatMap(service -> invokeLanguageModel(service, modelName)
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.flatMap(model -> invokeGenerateText(model, prompt))
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)
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return aiModelService()
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.flatMap(service -> service.languageModel(modelName != null ? modelName : "")
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.flatMap(model -> model.generateText(
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GenerateTextRequest.builder().prompt(prompt).maxRetries(2).build()))
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.map(GenerateTextResult::getText))
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.doOnError(e -> log.error("AI Foundation call failed: {}", e.getMessage()))
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.onErrorResume(e -> {
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log.warn("AI Foundation not available: {}", e.getMessage());
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@@ -88,132 +61,68 @@ public class AiFoundationClient {
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}
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/**
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* Get PluginManager via the pluginWrapper bean registered in our plugin context.
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* Halo's DefaultPluginApplicationContextFactory registers pluginWrapper as a singleton:
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* beanFactory.registerSingleton("pluginWrapper", pluginWrapper);
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* Then PluginWrapper.getPluginManager() gives us the PluginManager instance.
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* Call AI Foundation to classify text into one of the given choices using
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* structured output ({@link OutputSpec#choice(List)}).
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* <p>
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* This is the recommended way to do classification per the dev guide,
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* as it is more reliable than prompt parsing.
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*
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* @param systemPrompt system prompt describing the task
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* @param userPrompt the user input to classify
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* @param choices the allowed classification values
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* @param modelName the AiModel metadata.name, null or blank to use default model
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* @return the selected choice string, or empty if AI Foundation is unavailable
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*/
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private Object findPluginManager() {
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try {
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Object pluginWrapper = applicationContext.getBean("pluginWrapper");
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Method getPluginManagerMethod = pluginWrapper.getClass().getMethod("getPluginManager");
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getPluginManagerMethod.setAccessible(true);
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Object pm = getPluginManagerMethod.invoke(pluginWrapper);
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if (pm != null) {
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log.info("Found PluginManager via pluginWrapper bean: {}", pm.getClass().getName());
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}
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return pm;
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} catch (NoSuchMethodException e) {
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log.warn("pluginWrapper does not have getPluginManager() method: {}", e.getMessage());
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} catch (Exception e) {
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log.warn("Failed to get PluginManager via pluginWrapper: {}", e.getMessage());
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}
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log.warn("PluginManager not found");
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return null;
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public Mono<String> classify(String systemPrompt, String userPrompt,
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List<String> choices, String modelName) {
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return aiModelService()
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.flatMap(service -> service.languageModel(modelName != null ? modelName : "")
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.flatMap(model -> model.generateText(
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GenerateTextRequest.builder()
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.system(systemPrompt)
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.prompt(userPrompt)
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.output(OutputSpec.choice(choices))
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.maxRetries(2)
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.build()))
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.map(result -> {
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Object output = result.getOutput();
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return output != null ? String.valueOf(output).trim() : "";
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}))
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.doOnError(e -> log.error("AI Foundation classify failed: {}", e.getMessage()))
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.onErrorResume(e -> {
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log.warn("AI Foundation not available: {}", e.getMessage());
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return Mono.empty();
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});
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}
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/**
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* Find the AiModelService bean from ai-foundation's PluginApplicationContext.
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* Uses PluginManager.getPlugin() to get the plugin wrapper, then reflection
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* to get the plugin's ApplicationContext.
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* Check if AI Foundation is available: plugin installed and an
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* AiModelService extension is enabled.
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*/
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private Mono<Object> findAiModelService() {
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return Mono.fromCallable(() -> {
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Object pm = findPluginManager();
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if (pm == null) return null;
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// Call pm.getPlugin("ai-foundation") via reflection
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Method getPluginMethod = pm.getClass().getMethod("getPlugin", String.class);
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getPluginMethod.setAccessible(true);
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Object pluginWrapper = getPluginMethod.invoke(pm, AI_FOUNDATION_PLUGIN_NAME);
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if (pluginWrapper == null) {
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log.debug("ai-foundation plugin not found in PluginManager");
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return null;
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}
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// Call pluginWrapper.getPlugin() to get the plugin instance
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Method getPluginInstanceMethod = pluginWrapper.getClass().getMethod("getPlugin");
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getPluginInstanceMethod.setAccessible(true);
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Object pluginInstance = getPluginInstanceMethod.invoke(pluginWrapper);
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if (pluginInstance == null) {
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log.debug("ai-foundation plugin instance is null");
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return null;
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}
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// Get the plugin's ApplicationContext via reflection on SpringPlugin
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// DefaultSpringPlugin is package-private, so we need setAccessible
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Method getCtxMethod = pluginInstance.getClass().getMethod("getApplicationContext");
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getCtxMethod.setAccessible(true);
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ApplicationContext pluginAppContext = (ApplicationContext) getCtxMethod.invoke(pluginInstance);
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// Get the plugin classloader
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Method getClassLoaderMethod = pluginWrapper.getClass().getMethod("getPluginClassLoader");
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getClassLoaderMethod.setAccessible(true);
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ClassLoader pluginClassLoader = (ClassLoader) getClassLoaderMethod.invoke(pluginWrapper);
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// Load AiModelService from ai-foundation's classloader
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Class<?> aiModelServiceClass = pluginClassLoader.loadClass(AI_MODEL_SERVICE_CLASS);
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// Find the AiModelService bean in ai-foundation's ApplicationContext
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Map<String, ?> beans = pluginAppContext.getBeansOfType(aiModelServiceClass);
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if (beans.isEmpty()) {
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log.debug("AiModelService bean not found in ai-foundation's ApplicationContext");
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return null;
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}
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log.info("Found AiModelService bean in ai-foundation's ApplicationContext");
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Object result = beans.values().iterator().next();
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return (Object) result;
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}).doOnError(e -> log.error("Failed to find AiModelService: {}", e.getMessage()));
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public Mono<Boolean> isAvailable() {
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return aiModelService().hasElement()
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.onErrorResume(e -> {
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log.debug("AI Foundation not available: {}", e.getMessage());
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return Mono.just(false);
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});
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}
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/**
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* Call service.languageModel(modelName) or service.languageModel() via reflection.
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* Returns Mono<LanguageModel> from ai-foundation's classloader.
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* Obtain the enabled AiModelService extension via ExtensionGetter.
|
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* <p>
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* Wrapped in {@link Mono#defer} with a {@link NoClassDefFoundError} guard so
|
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* that the plugin still works when the optional ai-foundation dependency is
|
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* not installed (the AiModelService API class is then absent from the
|
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* classloader).
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*/
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private Mono<Object> invokeLanguageModel(Object service, String modelName) {
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return Mono.fromCallable(() -> {
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Method method;
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if (modelName != null && !modelName.isBlank()) {
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method = service.getClass().getMethod("languageModel", String.class);
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method.setAccessible(true);
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return method.invoke(service, modelName);
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} else {
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method = service.getClass().getMethod("languageModel");
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method.setAccessible(true);
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return method.invoke(service);
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private Mono<AiModelService> aiModelService() {
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return Mono.defer(() -> {
|
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try {
|
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return extensionGetter.getEnabledExtension(AiModelService.class);
|
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} catch (NoClassDefFoundError e) {
|
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log.debug("AI Foundation API not on classpath: {}", e.getMessage());
|
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return Mono.empty();
|
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}
|
||||
}).flatMap(result -> {
|
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if (result instanceof Mono<?> mono) return mono;
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||||
return Mono.justOrEmpty(result);
|
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});
|
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}
|
||||
|
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/**
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* Call model.generateText(prompt) via reflection, then extract text from result.
|
||||
* Returns the generated text string.
|
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*/
|
||||
private Mono<String> invokeGenerateText(Object model, String prompt) {
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||||
return Mono.fromCallable(() -> {
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Method method = model.getClass().getMethod("generateText", String.class);
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||||
method.setAccessible(true);
|
||||
return method.invoke(model, prompt);
|
||||
}).flatMap(result -> {
|
||||
if (result instanceof Mono<?> mono) {
|
||||
return mono.map(this::extractText);
|
||||
}
|
||||
return Mono.justOrEmpty(extractText(result));
|
||||
});
|
||||
}
|
||||
|
||||
private String extractText(Object result) {
|
||||
if (result == null) return null;
|
||||
try {
|
||||
Method getText = result.getClass().getMethod("getText");
|
||||
getText.setAccessible(true);
|
||||
return (String) getText.invoke(result);
|
||||
} catch (Exception e) {
|
||||
throw new RuntimeException("Failed to call getText() on GenerateTextResult: " + e.getMessage(), e);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -42,6 +42,55 @@ public class ContextExtractor {
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Fetch previous replies in the comment thread to provide conversation history.
|
||||
* Only includes replies created before the triggering reply.
|
||||
*/
|
||||
private Mono<String> fetchConversationHistory(String commentName, String triggerReplyName) {
|
||||
if (triggerReplyName == null || triggerReplyName.isBlank()) {
|
||||
return Mono.just("");
|
||||
}
|
||||
return client.fetch(Reply.class, triggerReplyName)
|
||||
.flatMap(triggerReply -> {
|
||||
var triggerTime = triggerReply.getMetadata().getCreationTimestamp();
|
||||
return client.list(Reply.class,
|
||||
reply -> {
|
||||
if (!commentName.equals(reply.getSpec().getCommentName())) {
|
||||
return false;
|
||||
}
|
||||
if (triggerReplyName.equals(reply.getMetadata().getName())) {
|
||||
return false;
|
||||
}
|
||||
// Only include replies created before the trigger reply
|
||||
var replyTime = reply.getMetadata().getCreationTimestamp();
|
||||
return replyTime != null && triggerTime != null
|
||||
&& !replyTime.isAfter(triggerTime);
|
||||
},
|
||||
null)
|
||||
.collectList()
|
||||
.map(replies -> {
|
||||
if (replies.isEmpty()) return "";
|
||||
// Sort by creation time
|
||||
replies.sort(java.util.Comparator.comparing(
|
||||
r -> r.getMetadata().getCreationTimestamp()));
|
||||
var sb = new StringBuilder();
|
||||
for (var r : replies) {
|
||||
var owner = r.getSpec().getOwner();
|
||||
String name = (owner != null && owner.getDisplayName() != null)
|
||||
? owner.getDisplayName() : "匿名用户";
|
||||
boolean isAi = owner != null && owner.getAnnotations() != null
|
||||
&& "true".equals(owner.getAnnotations().get("comment-ai-autopilot.nxxy335.top/is-ai"));
|
||||
String role = isAi ? "AI" : "用户";
|
||||
String content = extractReplyContent(r);
|
||||
sb.append(role).append("(").append(name).append("): ")
|
||||
.append(content).append("\n");
|
||||
}
|
||||
return sb.toString();
|
||||
});
|
||||
})
|
||||
.defaultIfEmpty("");
|
||||
}
|
||||
|
||||
private Mono<CommentContext> buildContext(Comment comment, boolean isAiConversation) {
|
||||
var commentContent = extractCommentContent(comment);
|
||||
var commentOwner = extractCommentOwner(comment);
|
||||
@@ -63,7 +112,8 @@ public class ContextExtractor {
|
||||
null,
|
||||
isAiConversation,
|
||||
formatPostDate(post),
|
||||
commentCount
|
||||
commentCount,
|
||||
""
|
||||
))
|
||||
)
|
||||
)
|
||||
@@ -78,7 +128,8 @@ public class ContextExtractor {
|
||||
null,
|
||||
isAiConversation,
|
||||
"",
|
||||
0
|
||||
0,
|
||||
""
|
||||
));
|
||||
}
|
||||
|
||||
@@ -93,7 +144,8 @@ public class ContextExtractor {
|
||||
null,
|
||||
isAiConversation,
|
||||
"",
|
||||
0
|
||||
0,
|
||||
""
|
||||
));
|
||||
}
|
||||
|
||||
@@ -101,55 +153,68 @@ public class ContextExtractor {
|
||||
var replyContent = extractReplyContent(reply);
|
||||
var replyOwner = extractReplyOwner(reply);
|
||||
var subjectRef = comment.getSpec().getSubjectRef();
|
||||
var commentName = comment.getMetadata().getName();
|
||||
var replyName = reply.getMetadata().getName();
|
||||
|
||||
// Fetch conversation history for AI conversations
|
||||
Mono<String> historyMono = isAiConversation
|
||||
? fetchConversationHistory(commentName, replyName)
|
||||
: Mono.just("");
|
||||
|
||||
if (subjectRef != null && "Post".equals(subjectRef.getKind())) {
|
||||
String postName = subjectRef.getName();
|
||||
return client.fetch(Post.class, postName)
|
||||
.flatMap(post -> getPostContent(postName)
|
||||
.flatMap(content -> getCommentCount(comment.getMetadata().getName())
|
||||
.map(commentCount -> new CommentContext(
|
||||
comment.getMetadata().getName(),
|
||||
postName,
|
||||
post.getSpec().getSlug(),
|
||||
replyContent,
|
||||
replyOwner,
|
||||
post.getSpec().getTitle(),
|
||||
content,
|
||||
reply.getMetadata().getName(),
|
||||
isAiConversation,
|
||||
formatPostDate(post),
|
||||
commentCount
|
||||
))
|
||||
.flatMap(content -> getCommentCount(commentName)
|
||||
.flatMap(commentCount -> historyMono
|
||||
.map(history -> new CommentContext(
|
||||
commentName,
|
||||
postName,
|
||||
post.getSpec().getSlug(),
|
||||
replyContent,
|
||||
replyOwner,
|
||||
post.getSpec().getTitle(),
|
||||
content,
|
||||
replyName,
|
||||
isAiConversation,
|
||||
formatPostDate(post),
|
||||
commentCount,
|
||||
history
|
||||
))
|
||||
)
|
||||
)
|
||||
)
|
||||
.defaultIfEmpty(new CommentContext(
|
||||
comment.getMetadata().getName(),
|
||||
commentName,
|
||||
postName,
|
||||
"",
|
||||
replyContent,
|
||||
replyOwner,
|
||||
"",
|
||||
"",
|
||||
reply.getMetadata().getName(),
|
||||
replyName,
|
||||
isAiConversation,
|
||||
"",
|
||||
0
|
||||
0,
|
||||
""
|
||||
));
|
||||
}
|
||||
|
||||
return Mono.just(new CommentContext(
|
||||
comment.getMetadata().getName(),
|
||||
"",
|
||||
"",
|
||||
replyContent,
|
||||
replyOwner,
|
||||
"",
|
||||
"",
|
||||
reply.getMetadata().getName(),
|
||||
isAiConversation,
|
||||
"",
|
||||
0
|
||||
));
|
||||
return historyMono
|
||||
.map(history -> new CommentContext(
|
||||
commentName,
|
||||
"",
|
||||
"",
|
||||
replyContent,
|
||||
replyOwner,
|
||||
"",
|
||||
"",
|
||||
replyName,
|
||||
isAiConversation,
|
||||
"",
|
||||
0,
|
||||
history
|
||||
));
|
||||
}
|
||||
|
||||
private String extractCommentContent(Comment comment) {
|
||||
@@ -245,6 +310,7 @@ public class ContextExtractor {
|
||||
String replyTo,
|
||||
boolean isAiConversation,
|
||||
String postDate,
|
||||
int commentCount
|
||||
int commentCount,
|
||||
String conversationHistory
|
||||
) {}
|
||||
}
|
||||
|
||||
@@ -75,6 +75,7 @@ public class PromptBuilder {
|
||||
文章（仅供理解上下文，不要复述）：
|
||||
{{article}}
|
||||
|
||||
{{conversation_history}}
|
||||
评论：
|
||||
{{comment}}
|
||||
""";
|
||||
@@ -103,6 +104,7 @@ public class PromptBuilder {
|
||||
.replace("{{post_date}}", context.postDate() != null ? context.postDate() : "")
|
||||
.replace("{{comment_count}}", String.valueOf(context.commentCount()))
|
||||
.replace("{{article}}", context.postTitle() + "\n" + context.postContent())
|
||||
.replace("{{conversation_history}}", formatConversationHistory(context))
|
||||
.replace("{{comment}}", context.commentOwner() + ": " + context.commentContent());
|
||||
|
||||
return prompt;
|
||||
@@ -133,6 +135,7 @@ public class PromptBuilder {
|
||||
.replace("{{post_date}}", context.postDate() != null ? context.postDate() : "")
|
||||
.replace("{{comment_count}}", String.valueOf(context.commentCount()))
|
||||
.replace("{{article}}", context.postTitle() + "\n" + context.postContent())
|
||||
.replace("{{conversation_history}}", formatConversationHistory(context))
|
||||
.replace("{{comment}}", context.commentOwner() + ": " + context.commentContent());
|
||||
|
||||
if (sentiment == null || "NEUTRAL".equals(sentiment)) {
|
||||
@@ -147,6 +150,18 @@ public class PromptBuilder {
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Format conversation history for inclusion in the prompt.
|
||||
* Returns empty string if no history is available.
|
||||
*/
|
||||
private String formatConversationHistory(ContextExtractor.CommentContext context) {
|
||||
String history = context.conversationHistory();
|
||||
if (history == null || history.isBlank()) {
|
||||
return "";
|
||||
}
|
||||
return "对话历史（供理解上下文）：\n" + history + "\n";
|
||||
}
|
||||
|
||||
private Mono<String> getPromptTemplate() {
|
||||
return client.fetch(ConfigMap.class, CONFIG_MAP_NAME)
|
||||
.mapNotNull(cm -> {
|
||||
|
||||
@@ -1,6 +1,7 @@
|
||||
package top.nxxy335.commentaiautopilot.service;
|
||||
|
||||
import lombok.extern.slf4j.Slf4j;
|
||||
import org.springframework.beans.factory.DisposableBean;
|
||||
import org.springframework.stereotype.Component;
|
||||
|
||||
import java.util.concurrent.ConcurrentHashMap;
|
||||
@@ -8,15 +9,18 @@ import java.util.concurrent.atomic.AtomicInteger;
|
||||
|
||||
@Slf4j
|
||||
@Component
|
||||
public class RateLimitService {
|
||||
public class RateLimitService implements DisposableBean {
|
||||
private final ConcurrentHashMap<Long, AtomicInteger> windowMap = new ConcurrentHashMap<>();
|
||||
private final Thread cleanupThread;
|
||||
private volatile boolean running = true;
|
||||
|
||||
public RateLimitService() {
|
||||
// 每5分钟清理过期窗口，防止内存泄漏
|
||||
Thread cleanupThread = new Thread(() -> {
|
||||
while (!Thread.currentThread().isInterrupted()) {
|
||||
cleanupThread = new Thread(() -> {
|
||||
while (running && !Thread.currentThread().isInterrupted()) {
|
||||
try {
|
||||
Thread.sleep(5 * 60 * 1000);
|
||||
if (!running) break;
|
||||
cleanup();
|
||||
} catch (InterruptedException e) {
|
||||
Thread.currentThread().interrupt();
|
||||
@@ -55,4 +59,13 @@ public class RateLimitService {
|
||||
log.debug("[RateLimit] Cleaned up {} expired windows", removed);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void destroy() {
|
||||
running = false;
|
||||
if (cleanupThread != null) {
|
||||
cleanupThread.interrupt();
|
||||
}
|
||||
log.info("[RateLimit] Cleanup thread stopped");
|
||||
}
|
||||
}
|
||||
|
||||
@@ -4,6 +4,8 @@ import lombok.extern.slf4j.Slf4j;
|
||||
import org.springframework.stereotype.Component;
|
||||
import reactor.core.publisher.Mono;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
@Component
|
||||
@Slf4j
|
||||
public class ReviewService {
|
||||
@@ -14,58 +16,135 @@ public class ReviewService {
|
||||
this.aiFoundationClient = aiFoundationClient;
|
||||
}
|
||||
|
||||
private static final String REVIEW_PROMPT_TEMPLATE = """
|
||||
请你以内容安全审核员的身份，严格评估你刚刚生成的这段回复：
|
||||
/**
|
||||
* Safety classification choices.
|
||||
* Using OutputSpec.choice for reliable structured output.
|
||||
*/
|
||||
private static final String SAFE = "安全";
|
||||
private static final String UNSAFE = "不安全";
|
||||
private static final List<String> SAFETY_CHOICES = List.of(SAFE, UNSAFE);
|
||||
|
||||
文章：
|
||||
%s
|
||||
/**
|
||||
* Quality rating choices for content quality assessment.
|
||||
* Used to produce a meaningful score (1-5) instead of binary 0/100.
|
||||
*/
|
||||
private static final String RATING_EXCELLENT = "5";
|
||||
private static final String RATING_GOOD = "4";
|
||||
private static final String RATING_AVERAGE = "3";
|
||||
private static final String RATING_POOR = "2";
|
||||
private static final String RATING_BAD = "1";
|
||||
private static final List<String> QUALITY_CHOICES = List.of(
|
||||
RATING_EXCELLENT, RATING_GOOD, RATING_AVERAGE, RATING_POOR, RATING_BAD
|
||||
);
|
||||
|
||||
原始评论：
|
||||
%s
|
||||
private static final String SAFETY_SYSTEM_PROMPT = """
|
||||
你是内容安全审核员。请严格评估AI生成的回复内容是否安全合规。
|
||||
检查以下方面：
|
||||
1. 是否包含辱骂、仇恨、偏见或煽动性言论
|
||||
2. 是否包含不适宜公开传播的内容
|
||||
3. 是否涉及暴力、歧视、人身攻击或违反法律法规的内容
|
||||
只返回"安全"或"不安全"。""";
|
||||
|
||||
生成的回复：
|
||||
%s
|
||||
|
||||
请检查以下内容：
|
||||
1. 是否包含任何辱骂、仇恨、偏见或煽动性言论？
|
||||
2. 是否包含不适宜公开传播的内容？
|
||||
3. 是否涉及暴力、歧视、人身攻击或违反法律法规的内容？
|
||||
|
||||
请直接回答"安全"或"不安全"。如果"不安全"，请重新生成一段符合规范的温和回复。
|
||||
""";
|
||||
private static final String QUALITY_SYSTEM_PROMPT = """
|
||||
你是AI回复质量评估员。请对AI生成的回复进行质量评分（1-5分）。
|
||||
评分标准：
|
||||
5分：回复切题、自然流畅、语气恰当、有帮助
|
||||
4分：回复较好，基本切题，语气自然
|
||||
3分：回复一般，基本切题但略显生硬或冗长
|
||||
2分：回复较差，偏离主题或语气不当
|
||||
1分：回复很差，完全不相关或不当
|
||||
只返回数字 1-5。""";
|
||||
|
||||
/**
|
||||
* Review AI reply content using two-stage assessment:
|
||||
* 1. Safety check via OutputSpec.choice (安全/不安全)
|
||||
* 2. Quality rating via OutputSpec.choice (1-5) — only if safe
|
||||
*
|
||||
* <p>Score mapping (0-100):
|
||||
* <ul>
|
||||
* <li>Unsafe → 0 (FAIL)</li>
|
||||
* <li>Rating 5 → 100 (PASS)</li>
|
||||
* <li>Rating 4 → 85 (PASS)</li>
|
||||
* <li>Rating 3 → 70 (PASS)</li>
|
||||
* <li>Rating 2 → 50 (PASS, borderline)</li>
|
||||
* <li>Rating 1 → 30 (PASS, but low quality)</li>
|
||||
* </ul>
|
||||
*/
|
||||
public Mono<ReviewResult> review(String articleContent, String commentContent, String aiReply,
|
||||
String modelName) {
|
||||
String reviewPrompt = String.format(REVIEW_PROMPT_TEMPLATE,
|
||||
truncate(articleContent, 2000),
|
||||
String userPrompt = String.format("""
|
||||
原始评论：
|
||||
%s
|
||||
|
||||
生成的回复：
|
||||
%s
|
||||
|
||||
请判断以上回复是否安全合规。""",
|
||||
truncate(commentContent, 500),
|
||||
truncate(aiReply, 500));
|
||||
|
||||
return aiFoundationClient.chat(reviewPrompt, modelName)
|
||||
.map(this::parseSafetyResult)
|
||||
// Stage 1: Safety check
|
||||
return aiFoundationClient.classify(SAFETY_SYSTEM_PROMPT, userPrompt, SAFETY_CHOICES, modelName)
|
||||
.flatMap(safetyResult -> {
|
||||
if (UNSAFE.equals(safetyResult)) {
|
||||
log.warn("[Review] Content is UNSAFE");
|
||||
return Mono.just(new ReviewResult(0, "FAIL", "内容安全审核不通过"));
|
||||
}
|
||||
if (!SAFE.equals(safetyResult)) {
|
||||
log.warn("[Review] Unexpected safety result: {}, treating as unsafe", safetyResult);
|
||||
return Mono.just(new ReviewResult(0, "FAIL", "内容安全审核结果异常"));
|
||||
}
|
||||
// Stage 2: Quality rating (only for safe content)
|
||||
return rateQuality(commentContent, aiReply, modelName);
|
||||
})
|
||||
.defaultIfEmpty(new ReviewResult(100, "PASS", "审核无响应，自动通过"))
|
||||
.onErrorResume(e -> {
|
||||
log.warn("Review failed, auto-passing: {}", e.getMessage());
|
||||
log.warn("[Review] Review failed, auto-passing: {}", e.getMessage());
|
||||
return Mono.just(new ReviewResult(100, "PASS", "审核服务异常，自动通过"));
|
||||
});
|
||||
}
|
||||
|
||||
private ReviewResult parseSafetyResult(String response) {
|
||||
if (response == null || response.isBlank()) {
|
||||
return new ReviewResult(100, "PASS", "审核无响应，自动通过");
|
||||
}
|
||||
String trimmed = response.trim().toLowerCase();
|
||||
if (trimmed.contains("不安全") || trimmed.contains("unsafe")) {
|
||||
log.warn("AI Review: content is UNSAFE, response: {}", response);
|
||||
return new ReviewResult(0, "FAIL", "内容安全审核不通过");
|
||||
}
|
||||
if (trimmed.contains("安全") || trimmed.contains("safe")) {
|
||||
log.info("AI Review: content is SAFE");
|
||||
return new ReviewResult(100, "PASS", "内容安全审核通过");
|
||||
}
|
||||
// If unclear response, default to pass
|
||||
log.warn("AI Review: unclear response, auto-passing: {}", response);
|
||||
return new ReviewResult(100, "PASS", "审核结果不明确，自动通过");
|
||||
/**
|
||||
* Rate the quality of a safe AI reply (1-5) and map to a 0-100 score.
|
||||
*/
|
||||
private Mono<ReviewResult> rateQuality(String commentContent, String aiReply, String modelName) {
|
||||
String qualityPrompt = String.format("""
|
||||
评论：
|
||||
%s
|
||||
|
||||
回复：
|
||||
%s
|
||||
|
||||
请对以上回复进行质量评分（1-5分）。""",
|
||||
truncate(commentContent, 500),
|
||||
truncate(aiReply, 500));
|
||||
|
||||
return aiFoundationClient.classify(QUALITY_SYSTEM_PROMPT, qualityPrompt, QUALITY_CHOICES, modelName)
|
||||
.map(rating -> {
|
||||
int score = mapRatingToScore(rating);
|
||||
String reason = "安全通过，质量评分: " + rating + "/5";
|
||||
log.info("[Review] Content is SAFE, quality rating: {}/5, score: {}", rating, score);
|
||||
return new ReviewResult(score, "PASS", reason);
|
||||
})
|
||||
.defaultIfEmpty(new ReviewResult(85, "PASS", "安全通过，质量评分默认 4/5"))
|
||||
.onErrorResume(e -> {
|
||||
log.warn("[Review] Quality rating failed, defaulting to 85: {}", e.getMessage());
|
||||
return Mono.just(new ReviewResult(85, "PASS", "安全通过，质量评分异常"));
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Map a 1-5 quality rating to a 0-100 score.
|
||||
*/
|
||||
private int mapRatingToScore(String rating) {
|
||||
return switch (rating) {
|
||||
case RATING_EXCELLENT -> 100;
|
||||
case RATING_GOOD -> 85;
|
||||
case RATING_AVERAGE -> 70;
|
||||
case RATING_POOR -> 50;
|
||||
case RATING_BAD -> 30;
|
||||
default -> 70; // default to average
|
||||
};
|
||||
}
|
||||
|
||||
private String truncate(String text, int maxLength) {
|
||||
|
||||
@@ -4,6 +4,8 @@ import lombok.extern.slf4j.Slf4j;
|
||||
import org.springframework.stereotype.Component;
|
||||
import reactor.core.publisher.Mono;
|
||||
|
||||
import java.util.List;
|
||||
|
||||
@Component
|
||||
@Slf4j
|
||||
public class SentimentService {
|
||||
@@ -20,13 +22,27 @@ public class SentimentService {
|
||||
public static final String NEGATIVE = "NEGATIVE";
|
||||
}
|
||||
|
||||
public Mono<SentimentResult> analyzeSentiment(String commentContent, String modelName) {
|
||||
String prompt = buildSentimentPrompt(commentContent);
|
||||
private static final List<String> CHOICES = List.of(
|
||||
SentimentResult.POSITIVE, SentimentResult.NEUTRAL, SentimentResult.NEGATIVE
|
||||
);
|
||||
|
||||
return aiFoundationClient.chat(prompt, modelName)
|
||||
.map(response -> {
|
||||
String sentiment = parseSentiment(response);
|
||||
return new SentimentResult(sentiment, 1.0);
|
||||
/**
|
||||
* Analyze sentiment using AI Foundation structured output
|
||||
* ({@code OutputSpec.choice}) for reliable classification.
|
||||
*/
|
||||
public Mono<SentimentResult> analyzeSentiment(String commentContent, String modelName) {
|
||||
String systemPrompt = "你是一个情感分析助手。请分析评论的情感倾向，只返回 POSITIVE、NEUTRAL 或 NEGATIVE 之一。";
|
||||
String userPrompt = "分析以下评论的情感倾向：\n\n" + commentContent;
|
||||
|
||||
return aiFoundationClient.classify(systemPrompt, userPrompt, CHOICES, modelName)
|
||||
.map(sentiment -> {
|
||||
String upper = sentiment.toUpperCase();
|
||||
// Validate against known choices; default to NEUTRAL if unexpected
|
||||
if (!CHOICES.contains(upper)) {
|
||||
log.warn("[Sentiment] Unexpected classification result: {}, defaulting to NEUTRAL", sentiment);
|
||||
return new SentimentResult(SentimentResult.NEUTRAL, 0.0);
|
||||
}
|
||||
return new SentimentResult(upper, 1.0);
|
||||
})
|
||||
.onErrorResume(e -> {
|
||||
log.warn("[Sentiment] Failed to analyze sentiment, defaulting to NEUTRAL: {}", e.getMessage());
|
||||
@@ -34,16 +50,4 @@ public class SentimentService {
|
||||
})
|
||||
.defaultIfEmpty(new SentimentResult(SentimentResult.NEUTRAL, 0.0));
|
||||
}
|
||||
|
||||
private String buildSentimentPrompt(String commentContent) {
|
||||
return "请分析以下评论的情感倾向。只回复一个词：POSITIVE（正面）、NEUTRAL（中性）或 NEGATIVE（负面）。\n\n评论内容：\n" + commentContent;
|
||||
}
|
||||
|
||||
private String parseSentiment(String response) {
|
||||
if (response == null || response.isBlank()) return SentimentResult.NEUTRAL;
|
||||
String upper = response.trim().toUpperCase();
|
||||
if (upper.contains("POSITIVE")) return SentimentResult.POSITIVE;
|
||||
if (upper.contains("NEGATIVE")) return SentimentResult.NEGATIVE;
|
||||
return SentimentResult.NEUTRAL;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -54,7 +54,7 @@ spec:
|
||||
- $formkit: textarea
|
||||
name: customPromptTemplate
|
||||
label: 自定义Prompt模板
|
||||
value: "{{persona_prompt}}\n\n{{safety_prompt}}\n\n【语言要求】请用评论所使用的语言回复。如果评论是英文，请用英文回复；如果是中文，请用中文回复；如果是日文，请用日文回复；以此类推。\n\n请回复以下评论。注意：\n- 回复长度应与评论长度匹配，简短问候简短回复\n- 不要复述或总结文章内容\n- 自然对话，不要写小作文\n- 只有评论涉及具体内容时才针对性回应\n\n文章（仅供理解上下文，不要复述）：\n{{article}}\n\n评论：\n{{comment}}"
|
||||
value: "{{persona_prompt}}\n\n{{safety_prompt}}\n\n【语言要求】请用评论所使用的语言回复。如果评论是英文，请用英文回复；如果是中文，请用中文回复；如果是日文，请用日文回复；以此类推。\n\n请回复以下评论。注意：\n- 回复长度应与评论长度匹配，简短问候简短回复\n- 不要复述或总结文章内容\n- 自然对话，不要写小作文\n- 只有评论涉及具体内容时才针对性回应\n\n文章（仅供理解上下文，不要复述）：\n{{article}}\n\n{{conversation_history}}\n评论：\n{{comment}}"
|
||||
- $formkit: select
|
||||
name: enabledPresets
|
||||
label: 启用预设
|
||||
|
||||
@@ -5,9 +5,17 @@ kind: Plugin
|
||||
metadata:
|
||||
# The name defines how the plugin is invoked, A unique name
|
||||
name: comment-ai-autopilot
|
||||
annotations:
|
||||
# Recommend installing AI Foundation from the app store after installing this plugin
|
||||
# https://www.halo.run/store/apps/app-acslk9nu
|
||||
"store.halo.run/recommended-apps": '["app-acslk9nu"]'
|
||||
spec:
|
||||
enabled: true
|
||||
requires: ">=2.25.0"
|
||||
pluginDependencies:
|
||||
# Optional dependency: plugin still loads without AI Foundation,
|
||||
# but AI features require it to be installed and enabled.
|
||||
ai-foundation?: "*"
|
||||
author:
|
||||
name: 暖心向阳335
|
||||
website: https://nxxy335.top
|
||||
@@ -22,4 +30,4 @@ spec:
|
||||
url: "https://github.com/sunny-335/plugin-comment-ai-autopilot/blob/main/LICENSE"
|
||||
settingName: "comment-ai-autopilot-settings"
|
||||
configMapName: "comment-ai-autopilot-configmap"
|
||||
version: "1.0.0-beta.1"
|
||||
version: "1.0.0-beta.2-kx7m2p"
|
||||
|
||||
Reference in New Issue
Block a user