feat: 自进化经验记忆——任务成功后用模型把工具序列提炼成「操作配方」存入 agent_experience 表；下次相似任务（bigram 相似度检索 top2）自动注入 system prompt 参考，AI 越用越聪明（同类操作不再摸索）
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@@ -135,7 +135,7 @@ class Agent:
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# ---------- 主循环（流式） ----------
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async def run_stream(self, prompt: str, serial: str = "",
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history=None, on_delta=None, on_tool=None,
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should_stop=None):
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should_stop=None, extra_context=None):
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"""流式执行一轮指令，返回最终完整文本。
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history：上一轮的 [{"role": "user"|"assistant", "content": 文本}] 列表，
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@@ -143,6 +143,7 @@ class Agent:
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on_delta(text, kind)：content/reasoning 文本增量（实时推给前端）
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on_tool(step)：工具调用完成（实时显示 MCP 步骤）
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should_stop：可调用 fn() -> bool，每轮模型调用前检查（用户中断用）
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extra_context：附加文本（经验记忆注入，放在 system prompt 末尾）
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"""
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self.on_delta = on_delta
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self.on_tool = on_tool
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@@ -150,6 +151,8 @@ class Agent:
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sys_txt = SYSTEM_PROMPT
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if target:
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sys_txt += f"\n\n本次默认目标设备 serial：{target}（未指定设备时用它）。"
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if extra_context:
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sys_txt += f"\n\n## 过往成功经验参考（同类任务，可参考其中的操作套路，但要根据当前界面灵活调整）\n{extra_context}"
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self.messages = [{"role": "system", "content": sys_txt}]
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for h in (history or []):
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if h.get("role") in ("user", "assistant") and h.get("content"):
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+123
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@@ -60,6 +60,101 @@ 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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@@ -193,12 +288,21 @@ def _agent_thread(run_id, prompt, serial, cfg):
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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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@@ -211,13 +315,22 @@ def _agent_thread(run_id, prompt, serial, cfg):
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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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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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@@ -230,6 +343,15 @@ def _agent_thread(run_id, prompt, serial, cfg):
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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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