ai-chatbot/lib/ai/conversation-memory-composer.ts

import "server-only";

import type { AgentThreadState } from "@/lib/ai/runtime/agent-thread-state";
import type { ImageWorkflowMemory } from "@/lib/ai/image-workflow-memory";
import type { PromptMemoryEntry } from "@/lib/ai/prompt-memory";
import {
  applyContextLineLimit,
  applyMemoryHintLimit,
  type MemoryRelevancePolicy,
} from "@/lib/ai/runtime/memory-relevance";
import type { StoryboardPreferenceMemory } from "@/lib/video-agent/storyboard-preference-memory";
import type {
  ResolvedChatMediaInput,
  ResolvedTargetAssets,
} from "@/lib/ai/context-memory/types";

export interface ComposedConversationMemory {
  contextSummary: string;
  memoryHints: string[];
  metadata: Record<string, unknown>;
}

function normalizeText(value: unknown, max = 400): string | undefined {
  const normalized = String(value || "").trim();
  if (!normalized) return undefined;
  return normalized.slice(0, max);
}

function uniqueStrings(values: Array<string | undefined>) {
  return Array.from(
    new Set(
      values
        .map((item) => normalizeText(item, 240))
        .filter((item): item is string => Boolean(item))
    )
  );
}

function summarizeTargetResolution(target?: ResolvedTargetAssets | null) {
  if (!target) return { lines: [] as string[], metadata: {} as Record<string, unknown> };
  const lines: string[] = [];
  lines.push(
    `当前目标解析：mode=${target.resolutionMode}; confidence=${target.confidence}; fromHistory=${
      target.fromHistory ? "yes" : "no"
    }`
  );
  if (target.targetImageUrls.length > 0) {
    lines.push(`当前命中的目标图片数：${target.targetImageUrls.length}`);
  }
  if (target.reason) {
    lines.push(`目标解析原因：${target.reason}`);
  }
  return {
    lines,
    metadata: {
      targetResolutionMode: target.resolutionMode,
      targetResolutionConfidence: target.confidence,
      targetImageCount: target.targetImageUrls.length,
      targetReason: target.reason,
    },
  };
}

function summarizeResolvedMedia(media?: ResolvedChatMediaInput | null) {
  if (!media) return { lines: [] as string[], metadata: {} as Record<string, unknown> };
  return {
    lines: [
      `当前媒体输入：source=${media.source}; hasImage=${media.hasImage ? "yes" : "no"}`,
      media.currentMessageImageUrls.length > 0
        ? `本轮上传图片数：${media.currentMessageImageUrls.length}`
        : "",
      media.selectedImageUrls.length > 0
        ? `当前选中图片数：${media.selectedImageUrls.length}`
        : "",
      media.historyAssistantImageUrls.length + media.historyUserImageUrls.length > 0
        ? `历史可参考图片数：${
            media.historyAssistantImageUrls.length + media.historyUserImageUrls.length
          }`
        : "",
    ].filter(Boolean),
    metadata: {
      mediaSource: media.source,
      hasImage: media.hasImage,
      currentImageCount: media.currentMessageImageUrls.length,
      selectedImageCount: media.selectedImageUrls.length,
      historyImageCount:
        media.historyAssistantImageUrls.length + media.historyUserImageUrls.length,
    },
  };
}

function summarizePromptMemory(promptMemory?: PromptMemoryEntry | null) {
  if (!promptMemory?.optimizedPrompt) {
    return { lines: [] as string[], hints: [] as string[], metadata: {} as Record<string, unknown> };
  }
  const preview = promptMemory.optimizedPrompt.replace(/\s+/g, " ").trim().slice(0, 220);
  return {
    lines: [`上一轮可复用提示词：${preview}`],
    hints: [
      "如果用户明确要求沿用上一轮提示词继续做图，优先把这段提示词作为本轮生成基础。",
    ],
    metadata: {
      promptMemory,
    },
  };
}

function summarizeImageWorkflow(memory?: ImageWorkflowMemory | null) {
  if (!memory) {
    return { lines: [] as string[], hints: [] as string[], metadata: {} as Record<string, unknown> };
  }
  const lines = [
    memory.lastOptimizedPrompt
      ? `上一轮优化提示词摘要：${memory.lastOptimizedPrompt.replace(/\s+/g, " ").slice(0, 180)}`
      : "",
    memory.lastRequestedImageCount
      ? `上一轮目标出图数：${memory.lastRequestedImageCount} 张`
      : "",
    memory.lastAspectRatio ? `上一轮画幅：${memory.lastAspectRatio}` : "",
    memory.lastImageSize ? `上一轮清晰度：${memory.lastImageSize}` : "",
    memory.lastSourceImageUrls?.length
      ? `上一轮参考/目标图数量：${memory.lastSourceImageUrls.length}`
      : "",
    memory.lastResultImageUrls?.length
      ? `上一轮生成结果数量：${memory.lastResultImageUrls.length}`
      : "",
  ].filter(Boolean);
  const hints = memory.lastOptimizedPrompt
    ? ["如果用户要求沿用上一轮提示词继续做图，应优先复用这段提示词。"]
    : [];
  return {
    lines,
    hints,
    metadata: {
      imageWorkflowMemory: memory,
    },
  };
}

function summarizeStoryboardPreference(memory?: StoryboardPreferenceMemory | null) {
  if (!memory) {
    return { lines: [] as string[], hints: [] as string[], metadata: {} as Record<string, unknown> };
  }
  const lines = [
    memory.preferredGridType ? `用户偏好宫格：${memory.preferredGridType}` : "",
    memory.preferredShotCount ? `用户偏好镜头数：${memory.preferredShotCount}` : "",
    memory.prefersConfirmation === true ? "用户偏好先确认再执行" : "",
    memory.lastUsedGridType && memory.lastUsedShotCount
      ? `最近一次分镜任务：${memory.lastUsedGridType} / ${memory.lastUsedShotCount} 段`
      : "",
  ].filter(Boolean);
  const hints = memory.prefersConfirmation
    ? ["如果当前任务涉及分镜或高成本执行，优先先确认再推进。"]
    : [];
  return {
    lines,
    hints,
    metadata: {
      storyboardPreferenceMemory: memory,
    },
  };
}

export function composeConversationMemory(params: {
  autoContextText?: string;
  resolvedMediaInput?: ResolvedChatMediaInput | null;
  targetResolution?: ResolvedTargetAssets | null;
  promptMemory?: PromptMemoryEntry | null;
  imageWorkflowMemory?: ImageWorkflowMemory | null;
  storyboardPreferenceMemory?: StoryboardPreferenceMemory | null;
  threadState?: AgentThreadState | null;
  memoryRelevancePolicy?: MemoryRelevancePolicy;
}) {
  const targetSummary = summarizeTargetResolution(params.targetResolution);
  const mediaSummary = summarizeResolvedMedia(params.resolvedMediaInput);
  const promptSummary = summarizePromptMemory(params.promptMemory);
  const imageWorkflowSummary = summarizeImageWorkflow(params.imageWorkflowMemory);
  const storyboardSummary = summarizeStoryboardPreference(params.storyboardPreferenceMemory);

  const contextSummary = uniqueStrings([
    params.autoContextText,
    ...mediaSummary.lines,
    ...targetSummary.lines,
    ...promptSummary.lines,
    ...imageWorkflowSummary.lines,
    ...storyboardSummary.lines,
  ]).join("\n");

  const memoryHints = uniqueStrings([
    ...(params.threadState?.memoryHints || []),
    ...promptSummary.hints,
    ...imageWorkflowSummary.hints,
    ...storyboardSummary.hints,
  ]);

  const metadata: Record<string, unknown> = {
    ...(params.threadState?.metadata || {}),
    ...mediaSummary.metadata,
    ...targetSummary.metadata,
    ...promptSummary.metadata,
    ...imageWorkflowSummary.metadata,
    ...storyboardSummary.metadata,
  };

  return {
    contextSummary: params.memoryRelevancePolicy
      ? applyContextLineLimit(contextSummary, params.memoryRelevancePolicy.maxContextLines)
      : contextSummary,
    memoryHints: params.memoryRelevancePolicy
      ? applyMemoryHintLimit(memoryHints, params.memoryRelevancePolicy.maxMemoryHints)
      : memoryHints,
    metadata,
  } satisfies ComposedConversationMemory;
}