"""LangChain orchestration agent backed by ZhipuAI (OpenAI-compatible API). Uses LangChain 1.x tool-calling pattern: bind_tools + manual agentic loop. """ from __future__ import annotations import asyncio import json import logging import re from collections import defaultdict from typing import Dict, List, Optional from langchain_core.messages import ( AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage, ) from langchain_openai import ChatOpenAI from agent.manager import manager from config import OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL, WORKING_DIR from orchestrator.tools import TOOLS, set_current_user logger = logging.getLogger(__name__) SYSTEM_PROMPT_TEMPLATE = """You are PhoneWork, an AI assistant that helps users control Claude Code \ from their phone via Feishu (飞书). You manage Claude Code sessions. Each session has a conv_id and runs in a project directory. Base working directory: {working_dir} Users refer to projects by subfolder name (e.g. "todo_app") or relative path. \ Pass these names directly to `create_conversation` — the tool resolves them automatically. {active_session_line} Your responsibilities: 1. NEW session: call `create_conversation` with the project name/path. \ If the user's message also contains a task, pass it as `initial_message` too. 2. Follow-up to ACTIVE session: call `send_to_conversation` with the active conv_id shown above. 3. List sessions: call `list_conversations`. 4. Close session: call `close_conversation`. 5. GENERAL QUESTIONS: If the user asks a general question (not about a specific project or file), \ answer directly using your own knowledge. Do NOT create a session for simple Q&A. Guidelines: - Relay Claude Code's output verbatim. - If no active session and the user sends a task without naming a directory, ask them which project. - For general knowledge questions (e.g., "what is a Python generator?", "explain async/await"), \ answer directly without creating a session. - Keep your own words brief — let Claude Code's output speak. - Reply in the same language the user uses (Chinese or English). """ MAX_ITERATIONS = 10 _TOOL_MAP = {t.name: t for t in TOOLS} QUESTION_PATTERNS = [ r'\?$', # ends with ? r'？$', # ends with Chinese ? r'\b(what|how|why|when|where|who|which|explain|describe|tell me|can you|could you|is there|are there|do you know)\b', r'(什么|怎么|为什么|何时|哪里|谁|哪个|解释|描述|告诉我|能否|可以|有没有|是不是)', ] def _is_general_question(text: str) -> bool: """Check if text looks like a general knowledge question (not a project task).""" text_lower = text.lower().strip() project_indicators = [ 'create', 'make', 'build', 'fix', 'update', 'delete', 'remove', 'add', 'implement', 'refactor', 'test', 'run', 'execute', 'start', 'stop', 'project', 'folder', 'directory', 'file', 'code', 'session', '创建', '制作', '构建', '修复', '更新', '删除', '添加', '实现', '重构', '测试', '运行', '项目', '文件夹', '文件', '代码', ] for indicator in project_indicators: if indicator in text_lower: return False for pattern in QUESTION_PATTERNS: if re.search(pattern, text_lower, re.IGNORECASE): return True return False class OrchestrationAgent: """Per-user agent with conversation history and active session tracking.""" def __init__(self) -> None: llm = ChatOpenAI( base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY, model=OPENAI_MODEL, temperature=0.0, ) self._llm_with_tools = llm.bind_tools(TOOLS) # user_id -> list[BaseMessage] self._history: Dict[str, List[BaseMessage]] = defaultdict(list) # user_id -> most recently active conv_id self._active_conv: Dict[str, Optional[str]] = defaultdict(lambda: None) # user_id -> asyncio.Lock (prevents concurrent processing per user) self._user_locks: Dict[str, asyncio.Lock] = defaultdict(asyncio.Lock) # user_id -> passthrough mode enabled self._passthrough: Dict[str, bool] = defaultdict(lambda: False) def _build_system_prompt(self, user_id: str) -> str: conv_id = self._active_conv[user_id] if conv_id: active_line = f"ACTIVE SESSION: conv_id={conv_id!r} ← use this for all follow-up messages" else: active_line = "ACTIVE SESSION: none" return SYSTEM_PROMPT_TEMPLATE.format( working_dir=WORKING_DIR, active_session_line=active_line, ) def get_active_conv(self, user_id: str) -> Optional[str]: return self._active_conv.get(user_id) def get_passthrough(self, user_id: str) -> bool: return self._passthrough.get(user_id, False) def set_passthrough(self, user_id: str, enabled: bool) -> None: self._passthrough[user_id] = enabled async def run(self, user_id: str, text: str) -> str: """Process a user message and return the agent's reply.""" async with self._user_locks[user_id]: return await self._run_locked(user_id, text) async def _run_locked(self, user_id: str, text: str) -> str: """Internal implementation, must be called with user lock held.""" set_current_user(user_id) active_conv = self._active_conv[user_id] short_uid = user_id[-8:] logger.info(">>> user=...%s conv=%s msg=%r", short_uid, active_conv, text[:80]) logger.debug(" history_len=%d", len(self._history[user_id])) # Passthrough mode: if enabled and active session, bypass LLM if self._passthrough[user_id] and active_conv: try: reply = await manager.send(active_conv, text, user_id=user_id) logger.info("<<< [passthrough] reply: %r", reply[:120]) return reply except KeyError: logger.warning("Session %s no longer exists, clearing active_conv", active_conv) self._active_conv[user_id] = None except Exception as exc: logger.exception("Passthrough error for user=%s", user_id) return f"[Error] {exc}" # Direct Q&A: if no active session and message looks like a general question, answer directly if not active_conv and _is_general_question(text): logger.debug(" → direct Q&A (no tools)") llm_no_tools = ChatOpenAI( base_url=OPENAI_BASE_URL, api_key=OPENAI_API_KEY, model=OPENAI_MODEL, temperature=0.7, ) qa_prompt = ( "You are a helpful assistant. Answer the user's question concisely and accurately. " "Reply in the same language the user uses.\n\n" f"Question: {text}" ) response = await llm_no_tools.ainvoke([HumanMessage(content=qa_prompt)]) return response.content or "" messages: List[BaseMessage] = ( [SystemMessage(content=self._build_system_prompt(user_id))] + self._history[user_id] + [HumanMessage(content=text)] ) reply = "" try: for iteration in range(MAX_ITERATIONS): logger.debug(" LLM call #%d", iteration) ai_msg: AIMessage = await self._llm_with_tools.ainvoke(messages) messages.append(ai_msg) if not ai_msg.tool_calls: reply = ai_msg.content or "" logger.debug(" → done (no tool call)") break for tc in ai_msg.tool_calls: tool_name = tc["name"] tool_args = tc["args"] tool_id = tc["id"] args_summary = ", ".join( f"{k}={str(v)[:50]!r}" for k, v in tool_args.items() ) logger.info(" ⚙ %s(%s)", tool_name, args_summary) tool_obj = _TOOL_MAP.get(tool_name) if tool_obj is None: result = f"Unknown tool: {tool_name}" logger.warning(" unknown tool: %s", tool_name) else: try: result = await tool_obj.arun(tool_args) except Exception as exc: result = f"Tool error: {exc}" logger.error(" tool %s error: %s", tool_name, exc) logger.debug(" ← %s: %r", tool_name, str(result)[:120]) if tool_name == "create_conversation": try: data = json.loads(result) if "conv_id" in data: self._active_conv[user_id] = data["conv_id"] logger.info(" ✓ active session → %s", data["conv_id"]) except Exception: pass messages.append( ToolMessage(content=str(result), tool_call_id=tool_id) ) else: reply = "[Max iterations reached]" logger.warning(" max iterations reached") except Exception as exc: logger.exception("agent error for user=%s", user_id) reply = f"[Error] {exc}" logger.info("<<< reply: %r", reply[:120]) # Update history self._history[user_id].append(HumanMessage(content=text)) self._history[user_id].append(AIMessage(content=reply)) if len(self._history[user_id]) > 40: self._history[user_id] = self._history[user_id][-40:] return reply agent = OrchestrationAgent()