09b63341cd
- 将 Dict、List 等传统类型注解替换为 dict、list 等现代类型注解 - 更新类型注解以更精确地反映变量类型 - 修复部分类型注解与实际使用不匹配的问题 - 优化部分代码逻辑以提高类型安全性
250 lines
9.8 KiB
Python
250 lines
9.8 KiB
Python
"""LangChain orchestration agent backed by ZhipuAI (OpenAI-compatible API).
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Uses LangChain 1.x tool-calling pattern: bind_tools + manual agentic loop.
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"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from collections import defaultdict
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from typing import Dict, List, Optional
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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HumanMessage,
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SystemMessage,
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ToolMessage,
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)
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from langchain_openai import ChatOpenAI
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from agent.manager import manager
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from config import OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL, WORKING_DIR
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from orchestrator.tools import TOOLS, set_current_user
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logger = logging.getLogger(__name__)
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SYSTEM_PROMPT_TEMPLATE = """You are PhoneWork, an AI assistant that helps users control Claude Code \
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from their phone via Feishu (飞书).
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You manage Claude Code sessions. Each session has a conv_id and runs in a project directory.
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Base working directory: {working_dir}
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Users refer to projects by subfolder name (e.g. "todo_app") or relative path. \
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Pass these names directly to `create_conversation` — the tool resolves them automatically.
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{active_session_line}
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Your responsibilities:
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1. NEW session: call `create_conversation` with the project name/path. \
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If the user's message also contains a task, pass it as `initial_message` too.
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2. Follow-up to ACTIVE session: call `send_to_conversation` with the active conv_id shown above.
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3. List sessions: call `list_conversations`.
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4. Close session: call `close_conversation`.
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5. GENERAL QUESTIONS: If the user asks a general question (not about a specific project or file), \
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answer directly using your own knowledge. Do NOT create a session for simple Q&A.
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Guidelines:
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- Relay Claude Code's output verbatim.
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- If no active session and the user sends a task without naming a directory, ask them which project.
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- For general knowledge questions (e.g., "what is a Python generator?", "explain async/await"), \
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answer directly without creating a session.
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- Keep your own words brief — let Claude Code's output speak.
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- Reply in the same language the user uses (Chinese or English).
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"""
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MAX_ITERATIONS = 10
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_TOOL_MAP = {t.name: t for t in TOOLS}
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QUESTION_PATTERNS = [
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r'\?$', # ends with ?
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r'？$', # ends with Chinese ?
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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',
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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)',
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]
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def _is_general_question(text: str) -> bool:
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"""Check if text looks like a general knowledge question (not a project task)."""
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text_lower = text.lower().strip()
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project_indicators = [
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'create', 'make', 'build', 'fix', 'update', 'delete', 'remove', 'add',
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'implement', 'refactor', 'test', 'run', 'execute', 'start', 'stop',
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'project', 'folder', 'directory', 'file', 'code', 'session',
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'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',
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]
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for indicator in project_indicators:
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if indicator in text_lower:
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return False
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for pattern in QUESTION_PATTERNS:
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if re.search(pattern, text_lower, re.IGNORECASE):
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return True
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return False
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class OrchestrationAgent:
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"""Per-user agent with conversation history and active session tracking."""
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def __init__(self) -> None:
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llm = ChatOpenAI(
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base_url=OPENAI_BASE_URL,
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api_key=OPENAI_API_KEY,
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model=OPENAI_MODEL,
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temperature=0.1,
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)
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self._llm_with_tools = llm.bind_tools(TOOLS)
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# user_id -> list[BaseMessage]
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self._history: dict[str, list[BaseMessage]] = defaultdict(list)
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# user_id -> most recently active conv_id
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self._active_conv: dict[str, Optional[str]] = defaultdict(lambda: None)
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# user_id -> asyncio.Lock (prevents concurrent processing per user)
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self._user_locks: dict[str, asyncio.Lock] = defaultdict(asyncio.Lock)
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# user_id -> passthrough mode enabled
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self._passthrough: dict[str, bool] = defaultdict(lambda: False)
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def _build_system_prompt(self, user_id: str) -> str:
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conv_id = self._active_conv[user_id]
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if conv_id:
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active_line = f"ACTIVE SESSION: conv_id={conv_id!r} ← use this for all follow-up messages"
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else:
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active_line = "ACTIVE SESSION: none"
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return SYSTEM_PROMPT_TEMPLATE.format(
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working_dir=WORKING_DIR,
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active_session_line=active_line,
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)
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def get_active_conv(self, user_id: str) -> Optional[str]:
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return self._active_conv.get(user_id)
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def get_passthrough(self, user_id: str) -> bool:
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return self._passthrough.get(user_id, False)
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def set_passthrough(self, user_id: str, enabled: bool) -> None:
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self._passthrough[user_id] = enabled
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async def run(self, user_id: str, text: str) -> str:
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"""Process a user message and return the agent's reply."""
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async with self._user_locks[user_id]:
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return await self._run_locked(user_id, text)
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async def _run_locked(self, user_id: str, text: str) -> str:
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"""Internal implementation, must be called with user lock held."""
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set_current_user(user_id)
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active_conv = self._active_conv[user_id]
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short_uid = user_id[-8:]
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logger.info(">>> user=...%s conv=%s msg=%r", short_uid, active_conv, text[:80])
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logger.debug(" history_len=%d", len(self._history[user_id]))
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# Passthrough mode: if enabled and active session, bypass LLM
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if self._passthrough[user_id] and active_conv:
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try:
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reply = await manager.send(active_conv, text, user_id=user_id)
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logger.info("<<< [passthrough] reply: %r", reply[:120])
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return reply
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except KeyError:
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logger.warning("Session %s no longer exists, clearing active_conv", active_conv)
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self._active_conv[user_id] = None
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except Exception as exc:
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logger.exception("Passthrough error for user=%s", user_id)
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return f"[Error] {exc}"
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# Direct Q&A: if no active session and message looks like a general question, answer directly
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if not active_conv and _is_general_question(text):
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logger.debug(" → direct Q&A (no tools)")
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llm_no_tools = ChatOpenAI(
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base_url=OPENAI_BASE_URL,
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api_key=OPENAI_API_KEY,
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model=OPENAI_MODEL,
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temperature=0.7,
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)
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qa_prompt = (
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"You are a helpful assistant. Answer the user's question concisely and accurately. "
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"Reply in the same language the user uses.\n\n"
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f"Question: {text}"
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)
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response = await llm_no_tools.ainvoke([HumanMessage(content=qa_prompt)])
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return response.content or ""
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messages: list[BaseMessage] = (
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[SystemMessage(content=self._build_system_prompt(user_id))]
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+ self._history[user_id]
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+ [HumanMessage(content=text)]
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)
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reply = ""
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try:
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for iteration in range(MAX_ITERATIONS):
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logger.debug(" LLM call #%d", iteration)
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ai_msg: AIMessage = await self._llm_with_tools.ainvoke(messages)
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messages.append(ai_msg)
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if not ai_msg.tool_calls:
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reply = ai_msg.content or ""
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logger.debug(" → done (no tool call)")
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break
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for tc in ai_msg.tool_calls:
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tool_name = tc["name"]
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tool_args = tc["args"]
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tool_id = tc["id"]
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args_summary = ", ".join(
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f"{k}={str(v)[:50]!r}" for k, v in tool_args.items()
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)
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logger.info(" ⚙ %s(%s)", tool_name, args_summary)
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tool_obj = _TOOL_MAP.get(tool_name)
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if tool_obj is None:
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result = f"Unknown tool: {tool_name}"
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logger.warning(" unknown tool: %s", tool_name)
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else:
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try:
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result = await tool_obj.arun(tool_args)
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except Exception as exc:
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result = f"Tool error: {exc}"
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logger.error(" tool %s error: %s", tool_name, exc)
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logger.debug(" ← %s: %r", tool_name, str(result)[:120])
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if tool_name == "create_conversation":
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try:
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data = json.loads(result)
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if "conv_id" in data:
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self._active_conv[user_id] = data["conv_id"]
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logger.info(" ✓ active session → %s", data["conv_id"])
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except Exception:
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pass
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messages.append(
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ToolMessage(content=str(result), tool_call_id=tool_id)
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)
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else:
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reply = "[Max iterations reached]"
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logger.warning(" max iterations reached")
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except Exception as exc:
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logger.exception("agent error for user=%s", user_id)
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reply = f"[Error] {exc}"
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logger.info("<<< reply: %r", reply[:120])
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# Update history
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self._history[user_id].append(HumanMessage(content=text))
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self._history[user_id].append(AIMessage(content=reply))
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if len(self._history[user_id]) > 40:
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self._history[user_id] = self._history[user_id][-40:]
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return reply
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agent = OrchestrationAgent()
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