47 lines
1.7 KiB
TypeScript
47 lines
1.7 KiB
TypeScript
import { tool } from "@opencode-ai/plugin";
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const internalBaseUrl =
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process.env.TJWATER_AGENT_INTERNAL_BASE_URL ?? "http://127.0.0.1:8787";
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const internalToken = process.env.TJWATER_AGENT_INTERNAL_TOKEN ?? "";
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const importDirectory =
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process.env.RESULT_REF_IMPORT_DIR ?? "./data/result-imports";
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export default tool({
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description:
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`导入 ${importDirectory} 下的受控 JSON 包装文件并返回 render_ref。文件必须是 { metadata: object, location: { file_path: string }, data: { node_area_map, area_ids?, area_colors? } }，location.file_path 必须与传入的绝对路径完全一致。只接受该目录内的真实文件，不接受目录外路径或指向目录外的符号链接。`,
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args: {
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reason: tool.schema
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.string()
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.describe(
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"为何需要将此本地渲染数据持久化为 render_ref，以便后续通过 render_junctions 渲染到前端。",
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),
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file_path: tool.schema
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.string()
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.describe(
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`位于 ${importDirectory} 内的包装 JSON 文件绝对路径。必须包含 metadata、location.file_path 和 data；data 才是 render_junctions 使用的 { node_area_map, area_ids?, area_colors? }。`,
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),
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},
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async execute(args, context) {
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const response = await fetch(
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`${internalBaseUrl}/internal/tools/store-render-ref`,
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{
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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"x-agent-internal-token": internalToken,
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},
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body: JSON.stringify({
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session_id: context.sessionID,
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file_path: args.file_path,
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}),
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},
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);
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const text = await response.text();
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if (!response.ok) {
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throw new Error(text);
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}
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return text;
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},
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});
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