365 lines
14 KiB
Python
365 lines
14 KiB
Python
"""AI 控制台 API：模型/Key 前端配置 + Agent 流式执行（SSE 事件流）。
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流程：
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POST /api/agent/run {prompt} → 启动 Agent 线程，返回 run_id
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GET /api/agent/stream?run_id= → SSE 事件流（EventSource 订阅）：
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event: delta {text, kind: content|reasoning} 流式文本增量
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event: step {tool, args, image?} 工具调用完成（MCP 步骤）
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event: done {answer} 完成
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event: error {message} 失败
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GET/POST /api/agent/config → 配置读写（key 打码回显）
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单实例：同时只允许一个 Agent 运行。
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"""
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import asyncio
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import base64
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import io
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import json
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import os
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import queue
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import sys
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import threading
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import uuid
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from flask import Blueprint, Response, jsonify, request
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from core.logger import get_logger
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from core.models import db
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from web.auth import admin_required
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_log = get_logger("web.agent")
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bp = Blueprint("agent", __name__)
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# ---------- 配置键（app_meta） ----------
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_CFG_KEYS = {"api_base": "agent_api_base",
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"model": "agent_model",
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"api_key": "agent_api_key",
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"default_serial": "agent_default_serial"}
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# ---------- 运行状态（单实例 + 事件队列） ----------
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_run = {"id": None, "state": "idle", "prompt": "", "serial": "",
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"answer": "", "error": "",
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"history": []} # 多轮对话历史 [{role: user|assistant, content}]
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_queues = {} # run_id -> queue.Queue（SSE 消费者读取）
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_stop_events = {} # run_id -> threading.Event（用户中断）
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_lock = threading.Lock()
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def _meta_get(key):
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return db.session.execute(
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db.text("SELECT value FROM app_meta WHERE key=:k"), {"k": key}).scalar() or ""
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def _meta_put(key, value):
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db.session.execute(
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db.text("INSERT OR REPLACE INTO app_meta(key,value) VALUES(:k,:v)"),
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{"k": key, "v": str(value)})
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def _read_cfg():
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return {k: _meta_get(v) for k, v in _CFG_KEYS.items()}
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# ================== 经验记忆（自进化） ==================
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# agent_experience：任务成功后的操作配方，下次相似任务检索注入 system prompt。
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# 原始 SQLite（CREATE IF NOT EXISTS 幂等），不进模型层迁移。
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_EXP_TABLE = """
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CREATE TABLE IF NOT EXISTS agent_experience (
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id INTEGER PRIMARY KEY AUTOINCREMENT,
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task_prompt TEXT DEFAULT '',
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recipe TEXT DEFAULT '',
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tool_seq TEXT DEFAULT '',
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hits INTEGER DEFAULT 0,
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created_at VARCHAR(20) DEFAULT '')"""
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def _ensure_exp_table():
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try:
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db.session.execute(db.text(_EXP_TABLE))
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db.session.commit()
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except Exception:
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pass
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def _bigrams(text):
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"""中文/英文文本 bigram 集合（无空格分词，粗粒度相似度）。"""
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t = "".join(c for c in (text or "").lower() if c.isalnum() or "\u4e00" <= c <= "\u9fff")
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return {t[i:i + 2] for i in range(len(t) - 1)}
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def _find_experiences(prompt, limit=2, threshold=0.10):
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"""按 bigram 重叠检索相似历史经验（prompt 与任务描述的字符相似度）。"""
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try:
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_ensure_exp_table()
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rows = db.session.execute(db.text(
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"SELECT task_prompt, recipe, hits FROM agent_experience "
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"WHERE recipe != '' ORDER BY hits DESC, id DESC LIMIT 50")).fetchall()
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except Exception:
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return ""
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if not rows:
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return ""
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cur = _bigrams(prompt)
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if not cur:
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return ""
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scored = []
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for task_prompt, recipe, hits in rows:
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sim = len(cur & _bigrams(task_prompt)) / len(cur)
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if sim >= threshold:
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scored.append((sim, hits or 0, recipe))
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scored.sort(key=lambda x: (-x[0], -x[1]))
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parts = []
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for sim, _hits, recipe in scored[:limit]:
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parts.append(f"- {recipe[:600]}")
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return "\n".join(parts)
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def _distill_experience(cfg, prompt, tool_seq):
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"""任务完成后用模型把操作序列提炼为可复用配方（失败静默，不阻塞）。"""
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try:
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import httpx
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body = {
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"model": cfg.get("model") or "deepseek-v4-flash-vision-exp",
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"messages": [{"role": "user",
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"content": "以下是一次成功的手机自动化操作记录。请提炼成简洁的"
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"「操作配方」（2-6 步，每步：目标 → 用哪个工具），"
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"供下次同类任务参考。不要解释，直接输出配方。\n"
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f"任务：{prompt[:300]}\n操作序列：{tool_seq[:800]}"}],
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"max_tokens": 600,
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}
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headers = {"Authorization": f"Bearer {cfg.get('api_key', '')}",
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"Content-Type": "application/json"}
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r = httpx.post(f"{(cfg.get('api_base') or 'https://api.deepseek.com').rstrip('/')}/chat/completions",
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json=body, headers=headers, timeout=60)
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if r.status_code != 200:
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return ""
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j = r.json()
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recipe = ((j.get("choices") or [{}])[0].get("message") or {}).get("content") or ""
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return recipe.strip()[:1500]
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except Exception as e:
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_log.warning(f"经验提炼失败: {e}")
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return ""
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def _save_experience(prompt, recipe, tool_seq):
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try:
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_ensure_exp_table()
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from datetime import datetime
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db.session.execute(db.text(
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"INSERT INTO agent_experience(task_prompt, recipe, tool_seq, hits, created_at) "
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"VALUES(:p, :r, :t, 0, :c)"),
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{"p": prompt[:500], "r": recipe, "t": tool_seq[:1000],
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"c": datetime.now().strftime("%Y-%m-%d %H:%M")})
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db.session.commit()
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_log.info("经验已保存（配方 %d 字符）", len(recipe))
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except Exception as e:
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_log.warning(f"经验保存失败: {e}")
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# ================== 配置 ==================
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@bp.route("/api/agent/config", methods=["GET"])
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@admin_required
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def agent_config_get():
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"""读 Agent 配置（key 打码返回）。"""
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cfg = _read_cfg()
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if cfg["api_key"]:
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k = cfg["api_key"]
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cfg["api_key_masked"] = k[:6] + "***" + k[-4:]
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return jsonify({"ok": True, **cfg})
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@bp.route("/api/agent/config", methods=["POST"])
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@admin_required
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def agent_config_save():
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"""保存 Agent 配置：{api_base?, model?, api_key?, default_serial?} 部分更新。"""
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data = request.json or {}
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for key, meta_key in _CFG_KEYS.items():
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if key in data and data[key] is not None:
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_meta_put(meta_key, str(data[key]).strip())
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db.session.commit()
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return jsonify({"ok": True, "msg": "已保存"})
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# ================== 运行 ==================
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@bp.route("/api/agent/run", methods=["POST"])
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@admin_required
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def agent_run():
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"""启动 Agent：{prompt, serial?}。运行中返回 409。"""
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data = request.json or {}
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prompt = (data.get("prompt") or "").strip()
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if not prompt:
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return jsonify({"ok": False, "error": "请输入指令"}), 400
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serial = (data.get("serial") or "").strip()
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cfg = _read_cfg()
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if not cfg.get("api_key"):
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return jsonify({"ok": False, "error": "请先在配置区填写 API Key"}), 400
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if not cfg.get("model"):
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return jsonify({"ok": False, "error": "请先填写模型名"}), 400
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with _lock:
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if _run["state"] == "running":
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return jsonify({"ok": False, "error": "已有 Agent 运行中，请等待完成"}), 409
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run_id = uuid.uuid4().hex[:8]
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_run.update(id=run_id, state="running", prompt=prompt,
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serial=(data.get("serial") or "").strip(),
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answer="", error="")
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# history 保留（同会话多轮对话），由前端「清空对话」调用 clear 重置
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_queues[run_id] = queue.Queue()
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_stop_events[run_id] = threading.Event()
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_log.info(f"Agent 启动: {prompt[:60]}")
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threading.Thread(target=_agent_thread,
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args=(run_id, prompt, serial, cfg),
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daemon=True).start()
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return jsonify({"ok": True, "run_id": run_id})
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@bp.route("/api/agent/stream")
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@admin_required
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def agent_stream():
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"""SSE 事件流（EventSource）：delta/step/done/error。"""
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run_id = request.args.get("run_id", "")
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with _lock:
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if run_id != _run["id"]:
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return jsonify({"ok": False, "error": "run_id 不存在"}), 404
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q = _queues.get(run_id)
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def gen():
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while True:
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try:
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evt = q.get(timeout=15)
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except queue.Empty:
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yield ": keepalive\n\n" # 心跳防超时
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continue
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if evt is None:
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break
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kind, payload = evt
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yield f"event: {kind}\ndata: {json.dumps(payload, ensure_ascii=False)}\n\n"
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if kind in ("done", "error"):
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break
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return Response(gen(), mimetype="text/event-stream",
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headers={"Cache-Control": "no-cache",
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"X-Accel-Buffering": "no"})
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@bp.route("/api/agent/stop", methods=["POST"])
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@admin_required
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def agent_stop():
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"""中断当前运行的 Agent（下一个检查点生效，通常在数秒内）。"""
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with _lock:
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if _run["state"] != "running":
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return jsonify({"ok": False, "error": "当前没有运行中的任务"}), 400
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evt = _stop_events.get(_run["id"])
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if evt:
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evt.set()
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_log.info("用户请求中断 Agent")
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return jsonify({"ok": True, "msg": "已请求停止"})
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@bp.route("/api/agent/clear", methods=["POST"])
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@admin_required
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def agent_clear():
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"""清空对话历史。"""
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with _lock:
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_run["history"] = []
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return jsonify({"ok": True, "msg": "已清空"})
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def _shrink_image(b64, width=220, quality=50):
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"""截图降采样（SSE step 事件用，控制传输体积）。失败原样返回。"""
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try:
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from PIL import Image
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img = Image.open(io.BytesIO(base64.b64decode(b64)))
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if img.width > width:
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img = img.resize((width, int(img.height * width / img.width)))
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buf = io.BytesIO()
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img.convert("RGB").save(buf, "JPEG", quality=quality)
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return base64.b64encode(buf.getvalue()).decode()
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except Exception:
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return b64
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def _agent_thread(run_id, prompt, serial, cfg):
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"""后台线程：Agent 流式执行，事件推入队列供 SSE 消费。"""
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q = _queues.get(run_id)
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try:
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# 平台进程 cwd=/app（含 mcp_agent 包），进程内 import
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from mcp_agent.agent import Agent
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def on_delta(text, kind):
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q.put(("delta", {"text": text, "kind": kind}))
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tool_seq = [] # 本轮工具序列（经验提炼用）
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def on_tool(step):
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rec = {"tool": step.get("tool"),
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"args": str(step.get("args"))[:200]}
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if step.get("image"):
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rec["image"] = _shrink_image(step["image"])
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q.put(("step", rec))
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# 记录精简工具序列
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try:
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args = step.get("args") or {}
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brief = {k: v for k, v in args.items() if k != "serial"}
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tool_seq.append(f"{step.get('tool')}({str(brief)[:60]})")
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except Exception:
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pass
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agent = Agent()
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agent.s.api_base = cfg.get("api_base") or agent.s.api_base
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agent.s.model = cfg.get("model") or agent.s.model
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agent.s.api_key = cfg.get("api_key") or agent.s.api_key
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agent.s.default_serial = cfg.get("default_serial") or agent.s.default_serial
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with _lock:
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history = list(_run.get("history") or [])
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target = serial or cfg.get("default_serial") or ""
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stop_evt = _stop_events.get(run_id)
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# 经验检索：相似历史任务的操作配方注入 system（自进化记忆）
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exp_ctx = _find_experiences(prompt)
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if exp_ctx:
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_log.info("命中历史经验，注入参考配方")
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q.put(("step", {"tool": "🧠 经验记忆",
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"args": f"命中 {exp_ctx.count(chr(10) + '- ')} 条同类历史经验，已注入参考",
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"image": None}))
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async def _execute():
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await agent._load_tools()
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return await agent.run_stream(prompt, target,
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history=history,
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on_delta=on_delta, on_tool=on_tool,
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should_stop=lambda: bool(
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stop_evt and stop_evt.is_set()),
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extra_context=exp_ctx)
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# 整体超时保护：卡死时结束，释放单实例
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answer = asyncio.run(asyncio.wait_for(_execute(), timeout=900))
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with _lock:
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_run["state"] = "done"
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_run["answer"] = answer
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# 追加本轮进历史（多轮连续性；上限 12 轮防 token 膨胀）
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hist = _run.setdefault("history", [])
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hist.append({"role": "user", "content": prompt[:2000]})
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hist.append({"role": "assistant", "content": (answer or "")[:4000]})
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_run["history"] = hist[-24:]
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q.put(("done", {"answer": answer}))
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# 自进化：成功后提炼操作配方存为经验（尽力而为，不阻塞/不影响结果）
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if tool_seq:
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try:
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recipe = _distill_experience(cfg, prompt, " -> ".join(tool_seq))
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if recipe and "配方" not in recipe[:50]:
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_save_experience(prompt, recipe, " -> ".join(tool_seq))
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except Exception as e:
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_log.warning(f"经验保存异常: {e}")
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except Exception as e:
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_log.warning(f"Agent 运行异常: {e}")
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with _lock:
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_run["state"] = "error"
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_run["error"] = f"{type(e).__name__}: {str(e)[:200]}"
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q.put(("error", {"message": str(e)[:200]}))
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finally:
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q.put(None) # 关闭 SSE
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_queues.pop(run_id, None)
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_stop_events.pop(run_id, None)
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