bugfix

skillagent/skillengine.py

@@ -2,21 +2,16 @@ import os
 import re
 import asyncio
 import yaml
-import logging
-import subprocess
 import hashlib
 from pathlib import Path
 from datetime import datetime
 from typing import List, Dict, Any
 form ahserver.serverenv import ServerEnv
 from appPublic.dictObject import DictObject
+from appPublic.log import info, debug, error, exception
+from appPublic.uniqueID import getID
 
 # 配置审计日志
-logging.basicConfig(
-    filename='skill_audit.log',
-    level=logging.INFO,
-    format='%(asctime)s [%(levelname)s] %(message)s'
-)
 
 class LLMHandler:
 	def __init__(self, request, llmid, apikey=None):
@@ -32,8 +27,11 @@ class LLMHandler:
 			"prompt": prompt
 		}
 		kw = DictObject(**kw)
-		r = await env.inference(self, request, params_kw=kw)
-		return r.content
+		txt = ''
+		async for d in env.inference_generator(self, request, params_kw=kw):
+			debug(f'{d=}, {type(d)=}')
+			txt += d.content
+		return txt
 
 async def run_subprocess(command, cwd, env, timeout=30.0):
 	try:
@@ -69,28 +67,29 @@ async def run_subprocess(command, cwd, env, timeout=30.0):
 		return None
 
 class IndustrialSkillEngine:
-    def __init__(self, request, skills_dir: str, llmid, apikey=None):
+    def __init__(self, request, llmid: str, apikey: str=None):
+		config = getConfig()
+		skills_dir = config.skills_dir
         self.root = Path(skills_dir).resolve()
         self.llm = LLMHandler(request, llmid, apikey=apikey)
         self.registry = {}
 		self.task_queue = asyncio.Queue(maxsize=20)
-        self.session_id = hashlib.md5(str(datetime.now()).encode()).hexdigest()[:8]
         # 状态机：记录当前任务执行到的步骤
         self.state = {"current_skill": None, "history": [], "pending_params": []}
 
 	async def write_output(self, data=None):
 		await self.task_queue.put(data)
 	
-	async def gen_output(self):
-		while True:
-			data = await self.task_queue.get()
-			if not data:
-				break;
-			yield data
-			asyncio.sleep(0.1)
-
     # --- 1. 工业级初始化：依赖检查与索引 ---
-    def boot(self):
+    def boot(self, refresh=False):
+		env = self.request._run_ns
+		userid = await env.get_user()
+		key = f'skillregister_{userid}'
+		skills = await env.session_getvalue(key)
+		if not refresh and skills:
+			self.self.registry = skills
+			return
+
         for skill_md in self.root.glob("**/SKILL.md"):
             with open(skill_md, 'r', encoding='utf-8') as f:
                 content = f.read()
@@ -107,7 +106,7 @@ class IndustrialSkillEngine:
                     "content": content,
                     "has_deps": has_deps
                 }
-        logging.info(f"Engine Booted. Session: {self.session_id}. Skills: {list(self.registry.keys())}")
+		await session_setvalue(key, self.registry)
 
 	# --- 2. 自动化依赖环境隔离 (venv 思想) ---
 	async def _ensure_dependencies(self, skill_name: str):
@@ -128,12 +127,7 @@ class IndustrialSkillEngine:
         if any(f in command for f in forbidden):
             return "🚫 安全风险：检测到非法指令，执行被拦截。"
 
-        # 权限确认 (Y/N)
-        # print(f"\n\033[1;33m[Audit ID: {self.session_id}]\033[0m 请求执行: {command}")
-        # confirm = input("是否授权执行？(y/n/skip): ").lower()
-        # if confirm != 'y': return "Execution skipped by user."
-
-        logging.info(f"Executing: {command} in {skill_name}")
+        info(f"Executing: {command} in {skill_name}")
         
         try:
             env = os.environ.copy()
@@ -143,20 +137,25 @@ class IndustrialSkillEngine:
             
             if res.return_code != 0:
                 # 工业级特性：自动将错误回传给 LLM 进行自愈 (Self-healing)
-                logging.error(f"Command failed: {res.stderr}")
+                error(f"Command failed: {res.stderr}")
                 if retry_count > 0:
                     print(f"⚠️ 执行失败，尝试让 AI 自愈修复参数...")
                     new_prompt = f"命令 '{command}' 失败，错误信息: {res.stderr}。请根据错误重新生成正确的命令，或提示用户补全参数。"
                     # 这里会递归调用逻辑进行修复
                     return await self.run(new_prompt, is_retry=True)
-                return f"Error: {res.stderr}"
+				
+                await self.write_output({
+					"status": "FAILED",
+					"error": f"Error: {res.stderr}"
+				})
+				raise Exception(f"Error: {res.stderr}")
             
             return res.stdout
         except Exception as e:
             return str(e)
 
     # --- 4. 递归文档注入与意图路由 ---
-    async def _get_expanded_context(self, skill_name: str, user_prompt: str):
+    async def _get_expanded_context(self, skill_name: str, user_prompt: str, context=None):
         skill = self.registry[skill_name]
         base_content = skill["content"]
         
@@ -170,7 +169,7 @@ class IndustrialSkillEngine:
         
         if found_docs:
             # 仅在模型认为有必要时，“点餐式”加载
-            choice = await self.llm(f"用户问题: {user_prompt}\n可选深入文档: {found_docs}\n需要读取哪个？(仅返回路径，不需则返回 None)")
+            choice = await self.llm(f"上下文：{context or ''}\n用户问题: {user_prompt}\n可选深入文档: {found_docs}\n需要读取哪个？(仅返回路径，不需则返回 None)")
             if choice != "None" and any(choice in doc for doc in found_docs):
                 with open(skill["root"] / choice, 'r') as f:
                     await self.write_output({
@@ -181,9 +180,21 @@ class IndustrialSkillEngine:
         
         return base_content
 
+	async def reference(self, user_prompt:str, context: str=None, is_retry: boolean=False):
+		f = partial(self.run, user_prompt, context=context, is_retry=is_retry)
+		background_coro(f)
+		while True:
+			data = await self.task_queue.get()
+			if not data:
+				break;
+			yield data
+			asyncio.sleep(0.1)
+
+
     # --- 5. 主运行接口 ---
-    async def run(self, user_prompt: str, is_retry=False):
+    async def run(self, user_prompt: str, context=None, is_retry=False):
         # 如果是重试，跳过技能选择
+		self.boot()
         if not is_retry:
 			await self.write_output({
 				"status": "PROCESSING",
@@ -203,26 +214,38 @@ class IndustrialSkillEngine:
 				"status": "FAILED",
 				"error": f"技能名{skill_name}未注册"
 			})
-			return "Skill not found."
+			raise Exception("Skill not found.")
 
         # 获取递归上下文
-        context = await self._get_expanded_context(skill_name, user_prompt)
+        context = await self._get_expanded_context(skill_name, user_prompt, context=context)
         
         # 决策：是直接回答还是执行脚本
         decision = await self.llm(f"上下文: {context}\n问题: {user_prompt}\n决定动作：EXEC: <command> 或 ANSWER: <text> 或 REPLY: <question>")
         
 		if "REPLY" in decision:
+			sessionkey = getID()
 			await self.write_output({
 				"status": "REPLY",
-				"quest": decision.split("REPLY:")[1].strip()
+				"reply": {
+					"questionkey": sessionkey(),
+					"question": decision.split("REPLY:")[1].strip()
+				}
 			})
-        if "EXEC:" in decision:
+			env = self.request._run_ns
+			user_reply = await env.session_getvalue(sessionkey)
+			while not user_reply:
+				asyncio.sleep(0.5)
+				user_reply = await env.session_getvalue(sessionkey)
+			prompt = f"{user_prompt}\n补充输入：{user_reply}"
+			await self.run(prompt, context=context, is_retry=True)
+        	return
+		if "EXEC:" in decision:
             cmd = decision.split("EXEC:")[1].strip()
             output =  await self._execute_with_retry(cmd, skill_name)
-			if not is_retry: await self.write_output(output)
+			await self.write_output(output)
+        	return
+		if  "ANSWER:" in decision
+			output = decision.replace("ANSWER:", "").strip()
+			await self.write_output(output)
 			return output
-        
-        output = decision.replace("ANSWER:", "").strip()
-		await self.write_output(output)
-		return output
-
+		debug(f' undefined decision:{decision}')

wwwroot/inference.dspy

@@ -0,0 +1,5 @@
+prompt = params_kw.prompt
+llmid = params_kw.llmid
+engine = IndustrialSkillEngine(request, llmid)
+f = partial(engine.reference, prompt)
+return await env.stream_response(request, f)

wwwroot/question_answered.dspy

@@ -0,0 +1,6 @@
+questionkey = params_kw.questionkey
+answer = params_kw.answer
+await session_setvalue(questionkey, answer)
+return {
+	"status":"OK"
+}