mirror of
https://github.com/TencentCloud/TencentDB-Agent-Memory
synced 2026-07-26 12:14:31 +00:00
feat: release v0.3.4 — offload local LLM, CLI restore, bugfix scripts
This commit is contained in:
chrishuan
@@ -0,0 +1,170 @@
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/**
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* LocalLlmClient — local-mode offload LLM client.
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*
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* Implements the same interface as BackendClient (l1Summarize, l15Judge, l2Generate)
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* but calls the LLM directly via AI SDK instead of routing through a remote backend.
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*
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* Used when `offload.model` is configured and `offload.backendUrl` is not set.
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*/
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import { callLlm, type LlmCallerConfig } from "./llm-caller.js";
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import { L1_SYSTEM_PROMPT, buildL1UserPrompt, type L1ToolPair } from "./prompts/l1-prompt.js";
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import { L15_SYSTEM_PROMPT, buildL15UserPrompt, type L15CurrentMmd, type L15MmdMeta } from "./prompts/l15-prompt.js";
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import { L2_SYSTEM_PROMPT, buildL2UserPrompt, type L2NewEntry } from "./prompts/l2-prompt.js";
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import { parseL1Response } from "./parsers/l1-parser.js";
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import { parseL15Response } from "./parsers/l15-parser.js";
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import { parseL2Response, type L2ParsedResponse } from "./parsers/l2-parser.js";
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import type { OffloadEntry, TaskJudgment, PluginLogger } from "../types.js";
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import type { L1Request, L1Response, L15Request, L15Response, L2Request, L2Response } from "../backend-client.js";
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const TAG = "[context-offload] [local-llm]";
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export interface LocalLlmClientConfig {
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baseUrl: string;
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apiKey: string;
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model: string;
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temperature?: number;
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timeoutMs?: number;
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}
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export class LocalLlmClient {
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private config: LlmCallerConfig;
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private logger?: PluginLogger;
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constructor(cfg: LocalLlmClientConfig, logger?: PluginLogger) {
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this.config = {
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baseUrl: cfg.baseUrl,
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apiKey: cfg.apiKey,
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model: cfg.model,
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temperature: cfg.temperature ?? 0.2,
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timeoutMs: cfg.timeoutMs ?? 120_000,
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};
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this.logger = logger;
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logger?.info?.(`${TAG} Initialized: model=${cfg.model}, baseUrl=${cfg.baseUrl}`);
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}
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// ─── L1 Summarize ──────────────────────────────────────────────────────────
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async l1Summarize(req: L1Request): Promise<L1Response> {
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const pairs: L1ToolPair[] = req.toolPairs.map((p) => ({
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toolName: p.toolName,
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toolCallId: p.toolCallId,
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params: p.params,
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result: p.result,
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timestamp: p.timestamp,
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}));
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const userPrompt = buildL1UserPrompt(req.recentMessages, pairs);
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const raw = await callLlm(this.config, {
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systemPrompt: L1_SYSTEM_PROMPT,
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userPrompt,
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label: "L1",
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}, this.logger);
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const entries = parseL1Response(raw);
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if (entries.length === 0) {
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this.logger?.warn?.(`${TAG} L1: parsed 0 entries from LLM response (${raw.length} chars)`);
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}
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return { entries };
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}
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// ─── L1.5 Judge ────────────────────────────────────────────────────────────
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async l15Judge(req: L15Request): Promise<L15Response> {
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const currentMmd: L15CurrentMmd | null = req.currentMmd
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? { filename: req.currentMmd.filename, content: req.currentMmd.content, path: req.currentMmd.path }
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: null;
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const metas: L15MmdMeta[] = req.availableMmdMetas.map((m) => ({
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filename: m.filename,
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path: m.path,
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taskGoal: m.taskGoal,
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doneCount: m.doneCount,
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doingCount: m.doingCount,
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todoCount: m.todoCount,
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updatedTime: m.updatedTime,
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nodeSummaries: m.nodeSummaries?.map((n) => ({
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nodeId: n.nodeId,
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status: n.status,
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summary: n.summary,
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})),
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}));
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const userPrompt = buildL15UserPrompt(req.recentMessages, currentMmd, metas);
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const raw = await callLlm(this.config, {
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systemPrompt: L15_SYSTEM_PROMPT,
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userPrompt,
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label: "L1.5",
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}, this.logger);
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const result = parseL15Response(raw);
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if (!result) {
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this.logger?.warn?.(`${TAG} L1.5: failed to parse judgment from LLM response (${raw.length} chars)`);
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// Return all-null to trigger normalizeJudgment's "LLM unavailable" path
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return {
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taskCompleted: false,
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isContinuation: false,
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isLongTask: false,
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} as L15Response;
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}
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return result as L15Response;
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}
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// ─── L2 Generate ───────────────────────────────────────────────────────────
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async l2Generate(req: L2Request): Promise<L2Response> {
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const entries: L2NewEntry[] = req.newEntries.map((e) => ({
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toolCallId: e.tool_call_id,
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toolCall: e.tool_call,
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summary: e.summary,
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timestamp: e.timestamp,
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}));
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const userPrompt = buildL2UserPrompt({
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existingMmd: req.existingMmd,
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entries,
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recentHistory: req.recentHistory,
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currentTurn: req.currentTurn,
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taskLabel: req.taskLabel,
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mmdPrefix: req.mmdPrefix,
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charCount: req.mmdCharCount,
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});
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const raw = await callLlm(this.config, {
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systemPrompt: L2_SYSTEM_PROMPT,
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userPrompt,
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label: "L2",
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timeoutMs: 120_000, // L2 may take longer due to complex prompts
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}, this.logger);
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const result = parseL2Response(raw);
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if (!result) {
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this.logger?.error?.(`${TAG} L2: failed to parse response (${raw.length} chars)`);
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throw new Error("L2 response parsing failed");
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}
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return {
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fileAction: result.fileAction,
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mmdContent: result.mmdContent,
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replaceBlocks: result.replaceBlocks?.map((b) => ({
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startLine: b.startLine,
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endLine: b.endLine,
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content: b.content,
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})),
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nodeMapping: result.nodeMapping,
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};
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}
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// ─── Stubs (not applicable in local mode) ──────────────────────────────────
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/** No-op in local mode — state reporting requires a remote backend. */
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async storeState(_payload: unknown): Promise<void> {}
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/** L4 Skill generation is not supported in local mode. */
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async l4Generate(_req: unknown): Promise<unknown> {
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return null;
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}
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}
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@@ -0,0 +1,80 @@
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/**
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* Unified LLM caller for offload local mode.
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*
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* Uses Vercel AI SDK (`ai` + `@ai-sdk/openai`) with "compatible" mode
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* to support any OpenAI-compatible backend.
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*/
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import { generateText } from "ai";
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import { createOpenAI } from "@ai-sdk/openai";
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import type { PluginLogger } from "../types.js";
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const TAG = "[context-offload] [local-llm]";
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export interface LlmCallerConfig {
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baseUrl: string;
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apiKey: string;
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model: string;
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temperature: number;
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timeoutMs: number;
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}
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export interface CallLlmOpts {
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systemPrompt: string;
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userPrompt: string;
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/** Override temperature for this call */
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temperature?: number;
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/** Override timeout for this call */
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timeoutMs?: number;
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/** Label for logging (e.g. "L1", "L1.5", "L2") */
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label?: string;
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}
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/**
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* Call LLM with the given prompts and return the text response.
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* Throws on timeout or API errors.
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*/
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export async function callLlm(
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config: LlmCallerConfig,
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opts: CallLlmOpts,
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logger?: PluginLogger,
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): Promise<string> {
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const startMs = Date.now();
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const label = opts.label ?? "call";
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const temperature = opts.temperature ?? config.temperature;
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const timeoutMs = opts.timeoutMs ?? config.timeoutMs;
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logger?.info?.(
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`${TAG} ${label} >>> model=${config.model}, temp=${temperature}, timeout=${timeoutMs}ms, ` +
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`systemLen=${opts.systemPrompt.length}, userLen=${opts.userPrompt.length}`,
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);
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const provider = createOpenAI({
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baseURL: config.baseUrl,
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apiKey: config.apiKey,
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compatibility: "compatible",
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});
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try {
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const result = await generateText({
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model: provider.chat(config.model),
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system: opts.systemPrompt,
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prompt: opts.userPrompt,
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temperature,
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abortSignal: AbortSignal.timeout(timeoutMs),
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});
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const text = result.text.trim();
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const elapsedMs = Date.now() - startMs;
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logger?.info?.(
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`${TAG} ${label} <<< ${elapsedMs}ms, output=${text.length} chars`,
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);
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return text;
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} catch (err) {
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const elapsedMs = Date.now() - startMs;
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const errMsg = err instanceof Error ? err.message : String(err);
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logger?.error?.(`${TAG} ${label} FAILED (${elapsedMs}ms): ${errMsg}`);
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throw err;
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}
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}
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@@ -0,0 +1,85 @@
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/**
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* Tolerant JSON parsing utilities for LLM responses.
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*
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* LLMs often wrap JSON in markdown code fences, include trailing commas,
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* or prepend explanatory text. These utilities handle common deviations.
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*/
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/**
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* Extract JSON from LLM output — handles code fences, prefix text, etc.
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* Returns the parsed object/array, or null if parsing fails.
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*/
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export function extractJson<T = unknown>(raw: string): T | null {
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if (!raw || typeof raw !== "string") return null;
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const trimmed = raw.trim();
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// Strategy 1: Direct parse (ideal case)
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const direct = tryParse<T>(trimmed);
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if (direct !== null) return direct;
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// Strategy 2: Extract from markdown code fence (```json ... ``` or ``` ... ```)
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const fenceMatch = trimmed.match(/```(?:json)?\s*\n?([\s\S]*?)```/);
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if (fenceMatch) {
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const inner = fenceMatch[1].trim();
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const parsed = tryParse<T>(inner);
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if (parsed !== null) return parsed;
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}
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// Strategy 3: Find first { to last } (or first [ to last ])
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const firstBrace = trimmed.indexOf("{");
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const lastBrace = trimmed.lastIndexOf("}");
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if (firstBrace >= 0 && lastBrace > firstBrace) {
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const candidate = trimmed.slice(firstBrace, lastBrace + 1);
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const parsed = tryParse<T>(candidate);
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if (parsed !== null) return parsed;
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// Try with trailing comma fix
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const fixed = fixTrailingCommas(candidate);
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const parsedFixed = tryParse<T>(fixed);
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if (parsedFixed !== null) return parsedFixed;
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}
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const firstBracket = trimmed.indexOf("[");
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const lastBracket = trimmed.lastIndexOf("]");
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if (firstBracket >= 0 && lastBracket > firstBracket) {
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const candidate = trimmed.slice(firstBracket, lastBracket + 1);
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const parsed = tryParse<T>(candidate);
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if (parsed !== null) return parsed;
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}
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// Strategy 4: Try fixing the entire string
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const fixed = fixTrailingCommas(trimmed);
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const parsedFixed = tryParse<T>(fixed);
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if (parsedFixed !== null) return parsedFixed;
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return null;
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}
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/**
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* Extract mermaid content from a code fence.
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* Returns the raw mermaid text (without fence markers).
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*/
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export function extractMermaidFromFence(text: string): string | null {
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if (!text) return null;
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const match = text.match(/```mermaid\s*\n?([\s\S]*?)```/);
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if (match) return match[1].trim();
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// Fallback: if no fence, return as-is (might already be raw mermaid)
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if (text.includes("flowchart") || text.includes("graph")) return text.trim();
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return null;
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}
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// ─── Internal Helpers ────────────────────────────────────────────────────────
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function tryParse<T>(s: string): T | null {
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try {
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return JSON.parse(s) as T;
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} catch {
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return null;
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}
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}
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function fixTrailingCommas(s: string): string {
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// Remove trailing commas before } or ]
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return s.replace(/,\s*([}\]])/g, "$1");
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}
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@@ -0,0 +1,41 @@
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/**
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* L1 Response Parser — extracts summarization results from LLM output.
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*/
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import { extractJson } from "./json-utils.js";
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import type { OffloadEntry } from "../../types.js";
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interface RawL1Entry {
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tool_call?: string;
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summary?: string;
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tool_call_id?: string;
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timestamp?: string;
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score?: number;
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}
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/**
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* Parse L1 LLM response into OffloadEntry array.
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* Tolerant of markdown wrapping, missing fields, etc.
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*/
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export function parseL1Response(raw: string): OffloadEntry[] {
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const parsed = extractJson<RawL1Entry[]>(raw);
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if (!parsed || !Array.isArray(parsed)) return [];
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const entries: OffloadEntry[] = [];
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for (const item of parsed) {
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if (!item || typeof item !== "object") continue;
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const toolCallId = item.tool_call_id ?? "";
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if (!toolCallId) continue; // tool_call_id is required
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entries.push({
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tool_call_id: toolCallId,
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tool_call: item.tool_call ?? "",
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summary: item.summary ?? "",
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timestamp: item.timestamp ?? "",
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score: typeof item.score === "number" ? item.score : 5,
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node_id: null,
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});
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}
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return entries;
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}
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@@ -0,0 +1,37 @@
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/**
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* L1.5 Response Parser — extracts task judgment from LLM output.
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*/
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import { extractJson } from "./json-utils.js";
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import type { TaskJudgment } from "../../types.js";
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interface RawL15Response {
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taskCompleted?: boolean | null;
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isContinuation?: boolean | null;
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isLongTask?: boolean | null;
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continuationMmdFile?: string | null;
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newTaskLabel?: string | null;
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}
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/**
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* Parse L1.5 LLM response into TaskJudgment.
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* Returns null if the response is completely unparseable or all-null (backend unavailable).
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*/
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export function parseL15Response(raw: string): TaskJudgment | null {
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const parsed = extractJson<RawL15Response>(raw);
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if (!parsed || typeof parsed !== "object") return null;
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// All-null check (mirrors normalizeJudgment logic)
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if (parsed.taskCompleted == null && parsed.isContinuation == null && parsed.isLongTask == null) {
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return null;
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}
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return {
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taskCompleted: Boolean(parsed.taskCompleted),
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isContinuation: Boolean(parsed.isContinuation),
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isLongTask: Boolean(parsed.isLongTask),
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continuationMmdFile:
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typeof parsed.continuationMmdFile === "string" ? parsed.continuationMmdFile : undefined,
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newTaskLabel:
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typeof parsed.newTaskLabel === "string" ? parsed.newTaskLabel : undefined,
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};
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}
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@@ -0,0 +1,92 @@
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/**
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* L2 Response Parser — extracts MMD generation results from LLM output.
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*/
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import { extractJson, extractMermaidFromFence } from "./json-utils.js";
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export interface L2ParsedResponse {
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fileAction: "write" | "replace";
|
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mmdContent?: string;
|
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replaceBlocks?: Array<{
|
||||
startLine: number;
|
||||
endLine: number;
|
||||
content: string;
|
||||
}>;
|
||||
nodeMapping: Record<string, string>;
|
||||
}
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|
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interface RawL2Response {
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file_action?: string;
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mmd_content?: string | null;
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||||
replace_blocks?: Array<{
|
||||
start_line?: number | string;
|
||||
end_line?: number | string;
|
||||
content?: string;
|
||||
}> | null;
|
||||
node_mapping?: Record<string, string>;
|
||||
}
|
||||
|
||||
/**
|
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* Parse L2 LLM response into structured L2 result.
|
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* Returns null if parsing fails completely.
|
||||
*/
|
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export function parseL2Response(raw: string): L2ParsedResponse | null {
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const parsed = extractJson<RawL2Response>(raw);
|
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if (!parsed || typeof parsed !== "object") {
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// Fallback: try extracting ```mermaid ... ``` code block (same as Go backend)
|
||||
const mmd = extractMermaidFromFence(raw);
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if (mmd) {
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return { fileAction: "write", mmdContent: mmd, nodeMapping: {} };
|
||||
}
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||||
return null;
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||||
}
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||||
|
||||
const fileAction = parsed.file_action === "replace" ? "replace" : "write";
|
||||
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||||
// Extract mmd_content (may be wrapped in code fence)
|
||||
let mmdContent: string | undefined;
|
||||
if (fileAction === "write") {
|
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if (parsed.mmd_content) {
|
||||
mmdContent = extractMermaidFromFence(parsed.mmd_content) ?? parsed.mmd_content;
|
||||
} else {
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||||
// mmd_content missing in write mode — try extracting from raw response
|
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const fallbackMmd = extractMermaidFromFence(raw);
|
||||
if (fallbackMmd) mmdContent = fallbackMmd;
|
||||
}
|
||||
}
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||||
|
||||
// Parse replace_blocks
|
||||
let replaceBlocks: L2ParsedResponse["replaceBlocks"] | undefined;
|
||||
if (fileAction === "replace" && Array.isArray(parsed.replace_blocks)) {
|
||||
replaceBlocks = [];
|
||||
for (const block of parsed.replace_blocks) {
|
||||
if (!block || typeof block !== "object") continue;
|
||||
const startLine = Number(block.start_line);
|
||||
const endLine = Number(block.end_line);
|
||||
if (isNaN(startLine) || isNaN(endLine)) continue;
|
||||
|
||||
let content = block.content ?? "";
|
||||
// Extract mermaid from fence if present
|
||||
const extracted = extractMermaidFromFence(content);
|
||||
if (extracted) content = extracted;
|
||||
|
||||
replaceBlocks.push({ startLine, endLine, content });
|
||||
}
|
||||
}
|
||||
|
||||
// Parse node_mapping
|
||||
const nodeMapping: Record<string, string> = {};
|
||||
if (parsed.node_mapping && typeof parsed.node_mapping === "object") {
|
||||
for (const [key, value] of Object.entries(parsed.node_mapping)) {
|
||||
if (typeof value === "string") {
|
||||
nodeMapping[key] = value;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return {
|
||||
fileAction,
|
||||
mmdContent,
|
||||
replaceBlocks,
|
||||
nodeMapping,
|
||||
};
|
||||
}
|
||||
@@ -0,0 +1,98 @@
|
||||
/**
|
||||
* L1 Summarization Prompt — migrated from context-offload-server.
|
||||
*
|
||||
* Converts tool call/result pairs into high-density JSON summaries.
|
||||
*/
|
||||
|
||||
// ─── System Prompt ───────────────────────────────────────────────────────────
|
||||
|
||||
export const L1_SYSTEM_PROMPT = `你是一个专为 AI 编码助手提供支持的"工具结果摘要器"。你的核心任务是深度理解当前的对话上下文，并将繁杂的工具调用与执行结果（一对toolcall和tool result整合成一条summary输出），提炼为高信息密度的 JSON 数组。
|
||||
|
||||
在生成摘要前，请务必进行以下内部思考：
|
||||
1. 任务对齐：结合最近的对话记录，识别用户当前的核心目标和最新意图。若上下文存在冲突，始终以最新的用户意图为准。
|
||||
2. 价值过滤：忽略工具如何工作的冗余细节，直接提取"发现了什么关键线索"、"做了什么关键动作"、"修改了什么具体内容"或"遇到了什么具体报错"。
|
||||
3. 影响评估：判断该结果对当前任务的实质性影响（例如：证实了某个假设、推进了哪一步、做出了什么决策，或因为什么报错导致了阻塞）。
|
||||
|
||||
【输出格式要求】
|
||||
你必须且只能输出一个合法的 JSON 对象数组 [{...}]，每个对象**必须**包含以下字段：
|
||||
- "tool_call": 工具调用的简洁描述。处理规则如下：
|
||||
· 如果输入中该 tool pair 标记了 [NEEDS_COMPRESS]，你必须将工具名+关键参数压缩为一句简洁的描述（≤150字符），保留工具名、操作目标（如文件路径、命令意图），省略内联脚本/大段内容的细节。
|
||||
示例：exec({"command":"python3 -c 'import csv; ...200行脚本...'"}) → "exec: 运行 Python （xx/xx/xx.sh，标明具体路径和文件）脚本分析 sales_channels.csv 数据质量"
|
||||
示例：write_file({"path":"/root/app.py","content":"...5000字符..."}) → "write_file: 写入 /root/app.py (Flask 应用主文件)，大致内容是……"
|
||||
· 如果未标记 [NEEDS_COMPRESS]，直接简述工具与参数即可（系统会用原始值覆盖）。
|
||||
- "summary": 融合上述思考的精炼总结（≤200个字符）。必须一针见血地说清楚结果的业务价值，以及它对任务的推进/阻塞作用。
|
||||
- "tool_call_id": 原始的 tool_call_id（必须原样透传）。
|
||||
- "timestamp": 原始的中国标准时间（+08:00）ISO 8601 时间戳（必须原样透传）。
|
||||
- "score"（**必填**）: 结合信息密度和任务目的分析summary对于原文的可替代性，范围在0-10之间，越接近10表示summary越能替代原文。
|
||||
|
||||
【严格规则】
|
||||
只允许输出纯 JSON 数组，严禁输出思考过程或其他解释性文本。`;
|
||||
|
||||
// ─── Constants ───────────────────────────────────────────────────────────────
|
||||
|
||||
const PARAMS_MAX_LEN = 500;
|
||||
const RESULT_MAX_LEN = 2000;
|
||||
const COMPRESS_THRESHOLD = 200;
|
||||
|
||||
// ─── Types ───────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface L1ToolPair {
|
||||
toolName: string;
|
||||
toolCallId: string;
|
||||
params: unknown;
|
||||
result: unknown;
|
||||
timestamp: string;
|
||||
}
|
||||
|
||||
// ─── User Prompt Builder ─────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Build the L1 user prompt for summarization.
|
||||
* Mirrors context-offload-server/internal/service/prompt/BuildL1UserPrompt.
|
||||
*/
|
||||
export function buildL1UserPrompt(recentMessages: string, pairs: L1ToolPair[]): string {
|
||||
const parts: string[] = [];
|
||||
|
||||
parts.push("## 最近的对话上下文（用于理解当前任务）：");
|
||||
parts.push(recentMessages);
|
||||
parts.push("\n## Tool call/result pairs to summarize:");
|
||||
|
||||
for (let i = 0; i < pairs.length; i++) {
|
||||
const p = pairs[i];
|
||||
const paramsStr = truncate(stringify(p.params), PARAMS_MAX_LEN);
|
||||
const resultStr = truncate(stringify(p.result), RESULT_MAX_LEN);
|
||||
const canonical = `${p.toolName}(${stringify(p.params)})`;
|
||||
const needsCompress = canonical.length > COMPRESS_THRESHOLD;
|
||||
|
||||
parts.push(`--- Tool Pair ${i + 1} ---`);
|
||||
parts.push(`tool_call_id: ${p.toolCallId}`);
|
||||
parts.push(`timestamp: ${p.timestamp}`);
|
||||
if (needsCompress) {
|
||||
parts.push(`Tool: ${p.toolName} [NEEDS_COMPRESS]`);
|
||||
} else {
|
||||
parts.push(`Tool: ${p.toolName}`);
|
||||
}
|
||||
parts.push(`Params: ${paramsStr}`);
|
||||
parts.push(`Result: ${resultStr}\n`);
|
||||
}
|
||||
|
||||
parts.push("Summarize each pair into the JSON array format described.");
|
||||
return parts.join("\n");
|
||||
}
|
||||
|
||||
// ─── Helpers ─────────────────────────────────────────────────────────────────
|
||||
|
||||
function stringify(value: unknown): string {
|
||||
if (value == null) return "";
|
||||
if (typeof value === "string") return value;
|
||||
try {
|
||||
return JSON.stringify(value);
|
||||
} catch {
|
||||
return String(value);
|
||||
}
|
||||
}
|
||||
|
||||
function truncate(s: string, maxLen: number): string {
|
||||
if (s.length <= maxLen) return s;
|
||||
return s.slice(0, maxLen) + "...";
|
||||
}
|
||||
@@ -0,0 +1,101 @@
|
||||
/**
|
||||
* L1.5 Task Judgment Prompt — migrated from context-offload-server.
|
||||
*
|
||||
* Determines task lifecycle: completion, continuation, new task detection.
|
||||
*/
|
||||
|
||||
// ─── System Prompt ───────────────────────────────────────────────────────────
|
||||
|
||||
export const L15_SYSTEM_PROMPT = `你是一个面向 AI 编码助手的"任务生命周期门神"。
|
||||
你的职责是交叉分析提供的三个输入源，精准研判任务状态，并输出纯 JSON 对象。
|
||||
|
||||
【输入数据利用指南（必须遵循的思考链路）】
|
||||
1. 第一步 - 剖析 recentMessages（识别意图）：根据当前和历史对话，提取用户最新回复的核心诉求。判断是"继续排查"、"宣布完工（如：跑通了）"、"单轮闲聊问答"还是"开启全新需求"。
|
||||
2. 第二步 - 对齐 currentMmd（评估当前基线）：将用户的最新意图与 currentMmd 的完整 Mermaid 内容进行比对——关注 taskGoal、各节点的 status（done/doing/todo）以及 summary。如果诉求完全超出了当前图表的范畴或目标已实现（所有节点 done 且无后续），则 taskCompleted 为 true。若仍在解决图表中的子问题（包括 doing 节点或修 bug），则为 false。(如果没有currentMmd，就只根据当前对话和历史对话来判断是否继续任务)
|
||||
3. 第三步 - 检索 availableMmds（判断是否延续）：如果判定要开启新任务（isLongTask=true 且 taskCompleted=true/当前无任务），必须扫描 availableMmds 的 taskGoal 和时间信息。若新诉求与列表中某个旧任务高度重合（如回到昨天没做完的模块），则是延续（isContinuation=true）。
|
||||
|
||||
【严格 JSON 输出格式】
|
||||
务必输出合法的纯 JSON 对象，格式如下：
|
||||
{
|
||||
"taskCompleted": boolean, // 当前任务是否已结束（如果 currentMmd 为 none，这里必须填 true）
|
||||
"isLongTask": boolean, // 最新诉求是否是需要多步操作的复杂工程（普通技术问答、闲聊填 false）
|
||||
"isContinuation": boolean, // 是否在延续 availableMmds 中的历史任务
|
||||
"continuationMmdFile": "string|null", // 若延续旧任务，精确填入 availableMmds 中的文件名（不含路径前缀），否则为 null
|
||||
"newTaskLabel": "string|null" // 若是全新长任务，生成简短标签（≤30字符，kebab-case，如 "refactor-api"），否则为 null
|
||||
}
|
||||
|
||||
只输出纯 JSON 对象，绝不允许包含解释文字。`;
|
||||
|
||||
// ─── Types ───────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface L15CurrentMmd {
|
||||
filename: string;
|
||||
content: string;
|
||||
path: string;
|
||||
}
|
||||
|
||||
export interface L15MmdMeta {
|
||||
filename: string;
|
||||
path: string;
|
||||
taskGoal: string;
|
||||
doneCount: number;
|
||||
doingCount: number;
|
||||
todoCount: number;
|
||||
updatedTime?: string | null;
|
||||
nodeSummaries?: Array<{ nodeId: string; status: string; summary: string }>;
|
||||
}
|
||||
|
||||
// ─── User Prompt Builder ─────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Build the L1.5 user prompt for task judgment.
|
||||
* Mirrors context-offload-server/internal/service/prompt/BuildL15UserPrompt.
|
||||
*/
|
||||
export function buildL15UserPrompt(
|
||||
recentMessages: string,
|
||||
currentMmd: L15CurrentMmd | null,
|
||||
metas: L15MmdMeta[],
|
||||
): string {
|
||||
const parts: string[] = [];
|
||||
|
||||
parts.push("## 1. 最近的对话上下文 (Recent 6 messages):");
|
||||
parts.push(recentMessages);
|
||||
parts.push("\n## 2. 当前挂载的任务图 (Active Mermaid — 完整内容):");
|
||||
|
||||
if (currentMmd && currentMmd.filename) {
|
||||
parts.push(`**File:** ${currentMmd.filename}`);
|
||||
if (currentMmd.path) {
|
||||
parts.push(`**Path:** \`${currentMmd.path}\``);
|
||||
}
|
||||
parts.push(`\n\`\`\`mermaid\n${currentMmd.content}\n\`\`\``);
|
||||
} else {
|
||||
parts.push("(none - 当前处于闲置状态，无活跃任务)");
|
||||
}
|
||||
|
||||
parts.push("\n## 3. 历史可用的任务图 (Available Mermaid task files):");
|
||||
|
||||
if (metas.length === 0) {
|
||||
parts.push("(none - 暂无历史长任务)");
|
||||
} else {
|
||||
for (const m of metas) {
|
||||
parts.push(`- **${m.filename}**`);
|
||||
parts.push(` path: \`${m.path}\``);
|
||||
parts.push(` taskGoal: ${m.taskGoal}`);
|
||||
const total = m.doneCount + m.doingCount + m.todoCount;
|
||||
parts.push(` progress: ${m.doneCount}/${total} done, ${m.doingCount} doing, ${m.todoCount} todo`);
|
||||
if (m.updatedTime) {
|
||||
parts.push(` lastUpdated: ${m.updatedTime}`);
|
||||
}
|
||||
if (m.nodeSummaries && m.nodeSummaries.length > 0) {
|
||||
parts.push(" recentNodes:");
|
||||
for (const n of m.nodeSummaries) {
|
||||
parts.push(` - [${n.nodeId}] (${n.status}) ${n.summary}`);
|
||||
}
|
||||
}
|
||||
parts.push("");
|
||||
}
|
||||
}
|
||||
|
||||
parts.push("请严格根据系统指令的【三步思考链路】进行研判，并输出合法的 JSON 对象。");
|
||||
return parts.join("\n");
|
||||
}
|
||||
@@ -0,0 +1,127 @@
|
||||
/**
|
||||
* L2 MMD Generation Prompt — migrated from context-offload-server.
|
||||
*
|
||||
* Generates/updates Mermaid flowchart diagrams from offload entries.
|
||||
*/
|
||||
|
||||
// ─── System Prompt ───────────────────────────────────────────────────────────
|
||||
|
||||
export const L2_SYSTEM_PROMPT = `你是一个究极实用主义的 AI 任务拓扑架构师与视觉叙事者。
|
||||
你的核心逻辑是用尽量少的字符表达尽量多的信息，让LLM模型能看懂，不是为人类服务，尽量减少无用的视觉符号。任务是将底层工具调用记录，升维映射为一张高度语义化、表现力丰富且极度克制的 Mermaid (flowchart TD) 认知状态机。你要根据当前任务和意图，归纳"过去"，要思考"未来"如何用这些已有的信息（你只需要记录已有信息，不需要写下一步规划）并标记"雷区"。保持图表的高度概括性。
|
||||
|
||||
【高阶认知与拓扑指南（你的自主权与极简原则）】
|
||||
1. 弹性聚合：你拥有决定节点拆合的完全自主权。对于连续的、意图相同的常规动作（如连续查看多个文件以了解上下文），建议合并为一个宏观节点；，但保留关键转折点或重大发现为独立节点。图表必须保持宏观和克制，绝不事无巨细地记流水账。
|
||||
2. 认知墓碑 (防重蹈覆辙)：遇到彻底走不通的死胡同或引发严重报错的废弃方案，可以建立警示节点（status: blocked）（如果是价值不高的fail信息则不需要记录）。
|
||||
3. 结论导向的摘要：节点的 summary（注意：尽量小于150字）应聚焦于"得出了什么结论"或"发生了什么实质改变"，而非罗列琐碎的数据或参数，记得保持极简原则。
|
||||
4. 要实事求是，你的任务是记录并归纳已经发生的事情，不是规划未来的具体操作，未发生的节点不要写，记录的已发生节点要有对应的消息来源（对应标注node_id）。
|
||||
【符号即语义：高维认知字典（你的核心武器）】为了极致压缩 Token 并为你下一步推理提供"认知锚点"，请自由使用不同的mmd形状来代表不同的节点逻辑。让形状替你说话，省略冗余的文字描述。
|
||||
|
||||
【高度自由的拓扑与极简法则】
|
||||
1. 语义浓缩：既然形状已经表达了"领域"，你的 summary 必须极其精简（≤150字），如"发现死锁"、"依赖冲突"、"已修复"。
|
||||
2. 弹性拓扑：自主使用带标签的连线（-->|测试失败|）和虚线（-.->|参考|）来构建"依赖树"和"假设验证环"。不要记流水账。
|
||||
3. 动态更新 (Token 极简)：
|
||||
- replace (增量微调)：仅修改现有节点的状态、时间戳、短文本或追加极少节点时。
|
||||
- write (全量重写)：逻辑大洗牌、重构图表或初始化时。
|
||||
注意：Existing Mermaid content 中每行开头都带有行号标记（如 "L1: ..."），这些行号仅供你在 replace 模式中引用，不是 MMD 内容的一部分。
|
||||
|
||||
【严格的工程底线】
|
||||
1.节点标准格式：NodeID["阶段名: 宏观动作简述<br/>status: done|doing|paused|blocked <br/>summary: 核心结论摘要<br/>Timestamp: ISO8601"]
|
||||
2. 全员归宿映射：输入的每一个新 tool_call_id，都必须在 node_mapping 中被分配到一个 Node ID；MMD里的每一个node都应该有源头的tool_call消息来源，不能乱编，绝对不允许遗漏！（Node_id和tool_call_id是一对多的关系）
|
||||
3. 你可以通过各种整合方法，尽量把更新后mmd文件大小控制在4000字以内
|
||||
|
||||
【严格时间戳与元数据规则】
|
||||
1. 顶部元数据（必填）：%%{ "taskGoal": "一句话总结此次任务的目标（可动态更新）", "progress（0-100）": "进度百分比（严格点，几乎确认完成再打到90+)", createdTime": "ISO时间", "updatedTime": "ISO时间" }%%（updatedTime为node中的最新时间）。
|
||||
2. 节点内时间：如果合并了多个新条目，节点内的 Timestamp 必须取其中最新的 ISO 时间。
|
||||
|
||||
【严格 JSON 输出格式】
|
||||
务必正确转义双引号。所有 Mermaid 代码（无论是 mmd_content 还是 replace_blocks 中的 content）都必须用 \`\`\`mermaid ... \`\`\` 代码块包裹起来。必须输出如下 JSON 结构：
|
||||
{
|
||||
"file_action": "replace 或 write",
|
||||
"mmd_content": "完整的、带转义的 .mmd 代码，必须用 \`\`\`mermaid ... \`\`\` 包裹。（仅在 file_action 为 write 时填写，否则必须设为 null）",
|
||||
"replace_blocks": [
|
||||
{
|
||||
"start_line": "需要更新范围的起始行号（整数，对应 Existing Mermaid content 中的 L 标号）",
|
||||
"end_line": "需要更新范围的结束行号（整数，包含该行）。要在某行之前插入新内容而不删除任何行，将 start_line 设为该行号，end_line 设为 start_line - 1",
|
||||
"content": "替换后的新内容（不需要带行号前缀），必须用 \`\`\`mermaid ... \`\`\` 包裹"
|
||||
}
|
||||
],
|
||||
"node_mapping": {
|
||||
"tool_call_id_1": "N1",
|
||||
"tool_call_id_2": "N1"
|
||||
}
|
||||
}
|
||||
|
||||
仅输出纯 JSON 对象，绝不允许包含任何解释。`;
|
||||
|
||||
// ─── Types ───────────────────────────────────────────────────────────────────
|
||||
|
||||
export interface L2NewEntry {
|
||||
toolCallId: string;
|
||||
toolCall: string;
|
||||
summary: string;
|
||||
timestamp: string;
|
||||
}
|
||||
|
||||
// ─── User Prompt Builder ─────────────────────────────────────────────────────
|
||||
|
||||
/**
|
||||
* Build the L2 user prompt for MMD generation.
|
||||
* Mirrors context-offload-server/internal/service/prompt/BuildL2UserPrompt.
|
||||
*/
|
||||
export function buildL2UserPrompt(opts: {
|
||||
existingMmd: string | null;
|
||||
entries: L2NewEntry[];
|
||||
recentHistory: string | null;
|
||||
currentTurn: string | null;
|
||||
taskLabel: string;
|
||||
mmdPrefix: string;
|
||||
charCount: number;
|
||||
}): string {
|
||||
const { existingMmd, entries, recentHistory, currentTurn, taskLabel, mmdPrefix, charCount } = opts;
|
||||
const parts: string[] = [];
|
||||
|
||||
// History section
|
||||
if (recentHistory) {
|
||||
parts.push(`## 近期对话历史：\n${recentHistory}`);
|
||||
} else {
|
||||
parts.push("## 近期对话历史：\n(无可用历史)");
|
||||
}
|
||||
|
||||
if (currentTurn) {
|
||||
parts.push(`\n## 当前最新一轮：\n${currentTurn}`);
|
||||
}
|
||||
|
||||
parts.push(`\n## MMD prefix: ${mmdPrefix}`);
|
||||
parts.push(`（所有节点 ID 必须以此前缀开头，如 ${mmdPrefix}-N1, ${mmdPrefix}-N2...）`);
|
||||
parts.push(`\n## Current task label: ${taskLabel}`);
|
||||
|
||||
// Char count warning
|
||||
if (charCount > 2500) {
|
||||
parts.push(`\n## Current MMD size: ${charCount} chars (budget: 4000 chars)`);
|
||||
parts.push("⚠ 接近上限，请积极合并节点、精简 summary，优先使用 replace 模式微调而非 write 全量重写。");
|
||||
} else if (charCount > 2000) {
|
||||
parts.push(`\n## Current MMD size: ${charCount} chars (budget: 4000 chars)`);
|
||||
parts.push("注意控制增长，合并同类节点。");
|
||||
}
|
||||
|
||||
// Existing MMD with line numbers
|
||||
parts.push("\n## Existing Mermaid content:");
|
||||
if (existingMmd) {
|
||||
const lines = existingMmd.split("\n");
|
||||
for (let i = 0; i < lines.length; i++) {
|
||||
parts.push(`L${i + 1}: ${lines[i]}`);
|
||||
}
|
||||
} else {
|
||||
parts.push("(empty — create new)");
|
||||
}
|
||||
|
||||
// New entries
|
||||
parts.push("\n## New offload entries to incorporate:");
|
||||
for (let i = 0; i < entries.length; i++) {
|
||||
const e = entries[i];
|
||||
parts.push(`${i + 1}. [${e.toolCallId}] ${e.toolCall} → ${e.summary} (${e.timestamp})`);
|
||||
}
|
||||
|
||||
parts.push("\n请根据系统指令生成/更新 Mermaid 流程图，并输出合法的 JSON 对象（含 node_mapping）。");
|
||||
return parts.join("\n");
|
||||
}
|
||||
Reference in New Issue
Block a user