108 lines
3.9 KiB
TypeScript
108 lines
3.9 KiB
TypeScript
import { resolveChatMediaInput } from "@/lib/ai/context-memory/media-input";
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import { resolveTargetAssets } from "@/lib/ai/context-memory/target-resolution";
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import { persistImageWorkflowTurn } from "@/lib/ai/conversation-state-protocol";
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import { getConversationWorkingStateByChatId, getMediaAssetsByUserAndUrls } from "@/lib/db/queries";
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import { readCanvasImages, readOwnedCanvas, type RuntimeImageNode } from "./canvas";
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import type { AgentRunSnapshot } from "./protocol";
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import { finalImageResultNodeIds } from "./results";
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import { AgentRunError } from "./store";
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export async function resolveRuntimeConversationSources(params: {
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userId: string;
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chatId: string;
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projectId: string;
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text: string;
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parts: Array<Record<string, any>>;
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nodes: RuntimeImageNode[];
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selectedNodeIds: string[];
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}) {
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const workingState = await getConversationWorkingStateByChatId({ chatId: params.chatId });
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const imageNodes = params.nodes.filter((node) => node.type === "image");
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const normalize = (url: string) => url.split("?")[0].split("#")[0];
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const files = params.parts
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.filter((part) => part.type === "file")
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.map((part) => normalize(part.url));
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if (
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files.some(
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(url) => !imageNodes.some((node) => normalize(node.data.src || node.data.url || "") === url),
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)
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) {
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throw new AgentRunError("上传图片尚未保存到画布，请稍后重试。");
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}
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const context = {
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source: "canvas" as const,
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projectId: params.projectId,
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selectedNodeIds: params.selectedNodeIds,
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canvasImageNodes: imageNodes.map((node) => ({
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nodeId: node.id,
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url: node.data.src || node.data.url || "",
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title: node.data.title,
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x: node.position.x,
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y: node.position.y,
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width: node.size?.width,
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height: node.size?.height,
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isSelected: params.selectedNodeIds.includes(node.id),
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})),
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};
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const mediaInput = resolveChatMediaInput({
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messageParts: params.parts,
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historyMessages: [],
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contextPayload: context,
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maxImages: 20,
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});
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const target = resolveTargetAssets({
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text: params.text,
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mediaInput,
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contextPayload: context,
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workingState: workingState || undefined,
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});
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const sourceNodeIds = target.targetRefs.flatMap((ref) => {
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const node = imageNodes.find(
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(item) =>
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item.id === ref.nodeId ||
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normalize(item.data.src || item.data.url || "") === normalize(ref.url),
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);
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return node ? [node.id] : [];
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});
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return {
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sourceNodeIds: [...new Set(sourceNodeIds)],
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targetReason: target.reason,
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previousContext: workingState?.lastTurnSummary
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? JSON.stringify(workingState.lastTurnSummary).slice(0, 4000)
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: undefined,
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};
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}
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export async function persistRuntimeConversationResults(params: {
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userId: string;
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chatId: string;
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projectId: string;
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assistantMessageId: string;
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snapshot: AgentRunSnapshot;
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}) {
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if (!params.snapshot.resultNodeIds.length) return;
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const current = await readOwnedCanvas(params.userId, params.projectId);
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const ids = finalImageResultNodeIds(params.snapshot, current.nodes);
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if (!ids.length) return;
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const nodes = await readCanvasImages(params.userId, params.projectId, ids);
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const urls = nodes.map((node) => node.data.src || node.data.url!);
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const assets = await getMediaAssetsByUserAndUrls({ userId: params.userId, urls });
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const assetIds = assets.map((asset) => asset.id);
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await persistImageWorkflowTurn({
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userId: params.userId,
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chatId: params.chatId,
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projectId: params.projectId,
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currentIntent: "image_edit",
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currentFocusAssetIds: assetIds,
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currentFocusImageUrls: urls,
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lastGeneratedAssetIds: assetIds,
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lastGeneratedImageUrls: urls,
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lastResolvedTargetAssetIds: assetIds,
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lastResolvedTargetImageUrls: urls,
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lastMessageId: params.assistantMessageId,
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lastUserPrompt: params.snapshot.objective,
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lastResultImageUrls: urls,
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lastRequestedImageCount: Math.min(12, urls.length),
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});
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}
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