600 lines
26 KiB
Python
Executable File
600 lines
26 KiB
Python
Executable File
from rest_framework.views import APIView
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from rest_framework.response import Response
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from rest_framework import status
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import requests # 用于调用 AutoGen API
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from django.http import StreamingHttpResponse
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from contextlib import asynccontextmanager
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from autogen.code_utils import create_virtual_env
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from autogen.coding import LocalCommandLineCodeExecutor
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from autogen.agentchat.contrib.capabilities.teachability import Teachability
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from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability
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from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
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from pathlib import Path
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import websockets
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import autogen
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import os
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from .constant import config_list, STREAM, SILENT, WORK_DIR
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from .utils import load_agent_configs
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from .retrieval_group import init_retrieval_group
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from .generate_group import init_generate_group
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from .converter_group import init_converter_group
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from .executor_group import init_executor_group
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from .optimize_group import init_optimize_group
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import io
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import sys
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from contextlib import redirect_stdout
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from websockets.sync.client import connect as ws_connect
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from autogen.io.websockets import IOWebsockets
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import json
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import asyncio
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from channels.generic.websocket import AsyncWebsocketConsumer
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venv_context = create_virtual_env(WORK_DIR)
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llm_config = {"config_list": config_list, "stream": True}
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import threading
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class ChatConsumer(AsyncWebsocketConsumer):
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async def connect(self):
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await self.accept()
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async def disconnect(self, close_code):
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pass
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async def receive(self, text_data):
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# 在这里处理接收到的消息
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print(f"Received message: {text_data}")
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# 异步启动 WebSocket 连接和处理过程
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await self.handle_websocket_connection(text_data)
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async def handle_websocket_connection(self, text_data):
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# 启动 WebSocket 服务器的同步部分在后台线程中
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threading.Thread(target=self.run_websocket_server, args=(text_data,), daemon=True).start()
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def run_websocket_server(self, text_data):
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"""
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启动 IOWebsocket 服务器，处理消息并与客户端进行交互
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"""
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uri = "ws://localhost:8765" # 你要连接的 WebSocket 服务器 URI
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# 运行 WebSocket 服务器并处理消息
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print(f"Starting WebSocket server at {uri}")
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with IOWebsockets.run_server_in_thread(on_connect=self.handle_message, port=8765) as uri:
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asyncio.run(self.connect_to_server(uri, text_data))
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async def connect_to_server(self, uri, text_data):
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# async with ws_connect(uri) as websocket:
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async with websockets.connect(uri) as websocket:
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print(f" - Connected to server on {uri}", flush=True)
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print(" - Sending message to server.", flush=True)
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print(text_data)
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json_data = json.loads(json.loads(text_data))
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chat_id = json_data['chat_id']
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await websocket.send(json_data['message'])
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import re
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current_agent = "User"
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while True:
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message = await websocket.recv()
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message = message.decode("utf-8") if isinstance(message, bytes) else message
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print(message, end="", flush=True)
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cleaned_string = re.sub(r'\x1b\[[0-?]*[ -/]*[@-~]', '', message)
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cleaned_string = cleaned_string.replace('\n>>>>>>>> USING AUTO REPLY...', '').replace('\n>>>>>>>> ', '')
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if "Next speaker: User" in cleaned_string:
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print('wcnm')
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if "Next speaker" in cleaned_string:
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match = re.search(r"Next\s+speaker:\s+(\w+)", cleaned_string)
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current_agent = match.group(1)
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else:
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if cleaned_string == "\n--------------------------------------------------------------------------------\n":
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continue
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if cleaned_string == "\n********************************************************************************\n":
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continue
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if cleaned_string == "Starting a new chat....\n":
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continue
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if cleaned_string == "\n>>>>>>>> USING AUTO REPLY...\n":
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continue
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match = re.findall(r"(\w+)\s*\(to\s+(\w+)\)", cleaned_string)
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if len(match)==1:
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continue
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if current_agent in ['Outer_Retrieval_Admin', 'Outer_Generate_Admin', 'Outer_Converter_Admin']:
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current_agent = current_agent.replace('Outer_', '')
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if current_agent in ['vector_code_executor']:
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continue
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if current_agent == 'User':
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group_name = 'Planner'
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if current_agent in ['vector_searcher','vector_code_executor', 'graphrag_searcher', 'graphrag_code_executor', 'web_searcher', 'web_summary', 'Outer_Retrieval_Admin']:
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group_name = 'Retrieval'
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if current_agent in ['structure_scientist','property_scientist', 'application_scientist', 'synthesis_scientist', 'scheme_critic', 'Outer_Generate_Admin']:
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group_name = 'Generator'
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if current_agent in ['scheme_converter','converter_critic', 'mergrid_ploter', 'scheme_code_writer', 'scheme_code_critic', 'Outer_Converter_Admin']:
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group_name = 'Converter'
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if current_agent in ['experiment_executor','expriment_code_writer', 'data_collector', 'collector_code_writer', 'Outer_Executor_Admin']:
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group_name = 'Executor'
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if current_agent in ['analysis_executor','analysis_pl_uv', 'analysis_picturer', 'Experiment_Optimizer', 'optimizer_critic', 'Outer_Analysis_Admin']:
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group_name = 'Optimizer'
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if 'TERMINATE' in cleaned_string or (group_name != 'Retrieval' and len(cleaned_string) > 100):
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continue
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content = {"group_name": group_name, "agent_name": current_agent.replace("_", " ").title(), "content": cleaned_string}
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await self.send(text_data=json.dumps(content))
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def handle_message(self, iostream: IOWebsockets):
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initial_msg = iostream.input()
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agent_configs = load_agent_configs(
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os.path.join(os.path.dirname(os.path.abspath(__file__)), "config/plan_group.yaml"))
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user = autogen.UserProxyAgent(
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name="User",
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chat_messages=None,
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human_input_mode="ALWAYS",
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code_execution_config={
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"work_dir": WORK_DIR,
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"use_docker": False,
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},
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is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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description="User",
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)
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inner_retrieval_admin, outer_retrieval_agent = init_retrieval_group(WORK_DIR, venv_context)
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inner_generate_admin, outer_generate_agent = init_generate_group(outer_retrieval_agent, inner_retrieval_admin)
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inner_converter_admin, outer_converter_agent = init_converter_group()
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inner_executor_admin, outer_executor_agent = init_executor_group(WORK_DIR, venv_context)
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inner_analysis_admin, outer_analysis_agent, optimizer = init_optimize_group(WORK_DIR, venv_context)
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def state_transition(last_speaker, groupchat):
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messages = groupchat.messages
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if last_speaker is user:
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if len(messages) <= 1:
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return outer_generate_agent
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else:
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return "auto"
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elif last_speaker is outer_generate_agent:
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if "synthesis" in messages[-1]["content"].lower():
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return outer_converter_agent
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else:
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return user
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elif last_speaker is outer_converter_agent:
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return outer_executor_agent
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elif last_speaker is outer_executor_agent:
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return outer_analysis_agent
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elif last_speaker is outer_analysis_agent:
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return optimizer
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else:
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return user
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matagent_group = autogen.GroupChat(
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agents=[user, outer_generate_agent, outer_converter_agent, outer_executor_agent, outer_analysis_agent,
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optimizer],
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messages=[],
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speaker_selection_method=state_transition,
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max_round=60,
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)
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matagent_admin_name = "Planer"
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matagent_admin = autogen.GroupChatManager(
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name=matagent_admin_name,
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groupchat=matagent_group,
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# is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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llm_config=llm_config,
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system_message=agent_configs[matagent_admin_name]['system_message'],
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description=matagent_admin_name
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)
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outer_generate_agent.register_nested_chats(
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[
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{"recipient": inner_generate_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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],
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trigger=matagent_admin,
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#reply_func_from_nested_chats =
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)
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outer_converter_agent.register_nested_chats(
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[
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{"recipient": inner_converter_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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],
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trigger=matagent_admin,
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)
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outer_executor_agent.register_nested_chats(
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[
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{"recipient": inner_executor_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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],
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trigger=matagent_admin,
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)
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outer_analysis_agent.register_nested_chats(
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[
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{"recipient": inner_analysis_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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],
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trigger=matagent_admin,
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)
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user.initiate_chat(
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matagent_admin,
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message=initial_msg, # "如何在常温条件下制备CsPbBr3纳米立方体",
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cache=False
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)
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return iostream
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# class ChatConsumer(AsyncWebsocketConsumer):
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# async def connect(self):
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# await self.accept()
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# async def disconnect(self, close_code):
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# pass
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# async def receive(self, text_data):
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# # 在这里处理接收到的消息
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# print(f"Received message: {text_data}")
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# # 异步启动 WebSocket 连接和处理过程
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# await self.handle_websocket_connection(text_data)
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# async def handle_websocket_connection(self, text_data):
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# # 启动独立的 WebSocket 服务器，使用异步线程避免阻塞
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# uri = "ws://localhost:8765" # 示例 URI，可以替换为你的 URI
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# # 启动 WebSocket 服务器在后台线程中运行
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# await asyncio.to_thread(self.run_websocket_server, uri, text_data)
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# def run_websocket_server(self, uri, text_data):
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# """
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# 这是一个同步方法，负责启动 IOWebsockets 的服务器并处理消息
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# 使用 asyncio.to_thread() 将其异步化
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# """
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# with IOWebsockets.run_server_in_thread(on_connect=self.handle_message, port=8765) as uri:
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# print(f" - test_setup() with websocket server running on {uri}.", flush=True)
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# with ws_connect(uri) as websocket:
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# print(f" - Connected to server on {uri}", flush=True)
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# print(" - Sending message to server.", flush=True)
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# print(text_data)
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# json_data = json.loads(json.loads(text_data))
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# chat_id = json_data['chat_id']
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# websocket.send(json_data['message'])
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# import re
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# current_agent = "User"
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# while True:
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# message = websocket.recv()
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# message = message.decode("utf-8") if isinstance(message, bytes) else message
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# print(message, end="", flush=True)
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# cleaned_string = re.sub(r'\x1b\[[0-?]*[ -/]*[@-~]', '', message)
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# cleaned_string = cleaned_string.replace('\n>>>>>>>> USING AUTO REPLY...', '').replace('\n>>>>>>>> ', '')
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# if "Next speaker" in cleaned_string:
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# match = re.search(r"Next\s+speaker:\s+(\w+)", cleaned_string)
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# current_agent = match.group(1)
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# else:
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# if cleaned_string == "\n--------------------------------------------------------------------------------\n":
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# continue
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# if cleaned_string == "\n********************************************************************************\n":
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# continue
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# if cleaned_string == "Starting a new chat....\n":
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# continue
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# if cleaned_string == "\n>>>>>>>> USING AUTO REPLY...\n":
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# continue
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# match = re.findall(r"(\w+)\s*\(to\s+(\w+)\)", cleaned_string)
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# if len(match)==1:
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# continue
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# if current_agent in ['Outer_Retrieval_Admin', 'Outer_Generate_Admin', 'Outer_Converter_Admin']:
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# current_agent = current_agent.replace('Outer_', '')
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# if current_agent in ['vector_code_executor']:
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# continue
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# if current_agent == 'User':
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# group_name = 'Planner'
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# if current_agent in ['vector_searcher','vector_code_executor', 'graphrag_searcher', 'graphrag_code_executor', 'web_searcher', 'web_summary', 'Outer_Retrieval_Admin']:
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# group_name = 'Retrieval'
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# if current_agent in ['structure_scientist','property_scientist', 'application_scientist', 'synthesis_scientist', 'scheme_critic', 'Outer_Generate_Admin']:
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# group_name = 'Generator'
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# if current_agent in ['scheme_converter','converter_critic', 'mergrid_ploter', 'scheme_code_writer', 'scheme_code_critic', 'Outer_Converter_Admin']:
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# group_name = 'Converter'
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# if current_agent in ['experiment_executor','expriment_code_writer', 'data_collector', 'collector_code_writer', 'Outer_Executor_Admin']:
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# group_name = 'Executor'
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# if current_agent in ['analysis_executor','analysis_pl_uv', 'analysis_picturer', 'Experiment_Optimizer', 'optimizer_critic', 'Outer_Analysis_Admin']:
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# group_name = 'Optimizer'
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# if 'TERMINATE' in cleaned_string or (group_name != 'Retrieval' and len(cleaned_string) > 100):
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# continue
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# content = {"group_name": group_name, "agent_name": current_agent.replace("_", " ").title(), "content": cleaned_string}
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# # 通过 WebSocket 消费者发送消息
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# asyncio.run(self.send(text_data=json.dumps(content)))
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# # if "TERMINATE" in message:
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# # print(" - Received TERMINATE message. Exiting.", flush=True)
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# # break
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# def handle_message(self, iostream: IOWebsockets):
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# initial_msg = iostream.input()
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# agent_configs = load_agent_configs(
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# os.path.join(os.path.dirname(os.path.abspath(__file__)), "config/plan_group.yaml"))
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# user = autogen.UserProxyAgent(
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# name="User",
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# chat_messages=None,
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# human_input_mode="NEVER",
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# code_execution_config={
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# "work_dir": WORK_DIR,
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# "use_docker": False,
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# },
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# is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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# description="User",
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# )
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# inner_retrieval_admin, outer_retrieval_agent = init_retrieval_group(WORK_DIR, venv_context)
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# inner_generate_admin, outer_generate_agent = init_generate_group(outer_retrieval_agent, inner_retrieval_admin)
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# inner_converter_admin, outer_converter_agent = init_converter_group()
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# inner_executor_admin, outer_executor_agent = init_executor_group(WORK_DIR, venv_context)
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# inner_analysis_admin, outer_analysis_agent, optimizer = init_optimize_group(WORK_DIR, venv_context)
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# def state_transition(last_speaker, groupchat):
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# messages = groupchat.messages
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# if last_speaker is user:
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# if len(messages) <= 1:
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# return outer_generate_agent
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# else:
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# return "auto"
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# elif last_speaker is outer_generate_agent:
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# if "synthesis" in messages[-1]["content"].lower():
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# return outer_converter_agent
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# else:
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# return user
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# elif last_speaker is outer_converter_agent:
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# return outer_executor_agent
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# elif last_speaker is outer_executor_agent:
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# return outer_analysis_agent
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# elif last_speaker is outer_analysis_agent:
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# return optimizer
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# else:
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# return user
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# matagent_group = autogen.GroupChat(
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# agents=[user, outer_generate_agent, outer_converter_agent, outer_executor_agent, outer_analysis_agent,
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# optimizer],
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# messages=[],
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# speaker_selection_method=state_transition,
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# max_round=50,
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# )
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# matagent_admin_name = "Planer"
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# matagent_admin = autogen.GroupChatManager(
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# name=matagent_admin_name,
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# groupchat=matagent_group,
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# # is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
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# llm_config=llm_config,
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# system_message=agent_configs[matagent_admin_name]['system_message'],
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# description=matagent_admin_name
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# )
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# outer_generate_agent.register_nested_chats(
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# [
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# {"recipient": inner_generate_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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# ],
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# trigger=matagent_admin,
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# #reply_func_from_nested_chats =
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# )
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# outer_converter_agent.register_nested_chats(
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# [
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# {"recipient": inner_converter_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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# ],
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# trigger=matagent_admin,
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# )
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# outer_executor_agent.register_nested_chats(
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# [
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# {"recipient": inner_executor_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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# ],
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# trigger=matagent_admin,
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# )
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# outer_analysis_agent.register_nested_chats(
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# [
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# {"recipient": inner_analysis_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
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# ],
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# trigger=matagent_admin,
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# )
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# user.initiate_chat(
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# matagent_admin,
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# message=initial_msg, # "如何在常温条件下制备CsPbBr3纳米立方体",
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# cache=False
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# )
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# return iostream
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# class ChatConsumer(AsyncWebsocketConsumer):
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# async def connect(self):
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# await self.accept()
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# async def disconnect(self, close_code):
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# pass
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# async def receive(self, text_data):
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# #text_data_json = json.loads(text_data)
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# #message = text_data_json['message']
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# # 在这里处理接收到的消息
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# print(f"Received message: {text_data}")
|
||
|
||
# agent_configs = load_agent_configs(
|
||
# os.path.join(os.path.dirname(os.path.abspath(__file__)), "config/plan_group.yaml"))
|
||
# user = autogen.UserProxyAgent(
|
||
# name="User",
|
||
# chat_messages=None,
|
||
# human_input_mode="NEVER",
|
||
# code_execution_config={
|
||
# "work_dir": WORK_DIR,
|
||
# "use_docker": False,
|
||
# },
|
||
# is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
|
||
# description="User",
|
||
# )
|
||
|
||
# inner_retrieval_admin, outer_retrieval_agent = init_retrieval_group(WORK_DIR, venv_context)
|
||
# inner_generate_admin, outer_generate_agent = init_generate_group(outer_retrieval_agent, inner_retrieval_admin)
|
||
# inner_converter_admin, outer_converter_agent = init_converter_group()
|
||
# inner_executor_admin, outer_executor_agent = init_executor_group(WORK_DIR, venv_context)
|
||
# inner_analysis_admin, outer_analysis_agent, optimizer = init_optimize_group(WORK_DIR, venv_context)
|
||
|
||
# def state_transition(last_speaker, groupchat):
|
||
# messages = groupchat.messages
|
||
|
||
# if last_speaker is user:
|
||
# if len(messages) <= 1:
|
||
# return outer_generate_agent
|
||
# else:
|
||
# return "auto"
|
||
# elif last_speaker is outer_generate_agent:
|
||
# if "synthesis" in messages[-1]["content"].lower():
|
||
# return outer_converter_agent
|
||
# else:
|
||
# return user
|
||
# elif last_speaker is outer_converter_agent:
|
||
# return outer_executor_agent
|
||
# elif last_speaker is outer_executor_agent:
|
||
# return outer_analysis_agent
|
||
# elif last_speaker is outer_analysis_agent:
|
||
# return optimizer
|
||
# else:
|
||
# return user
|
||
|
||
# matagent_group = autogen.GroupChat(
|
||
# agents=[user, outer_generate_agent, outer_converter_agent, outer_executor_agent, outer_analysis_agent,
|
||
# optimizer],
|
||
# messages=[],
|
||
# speaker_selection_method=state_transition,
|
||
# max_round=50,
|
||
# )
|
||
|
||
# matagent_admin_name = "Planer"
|
||
# matagent_admin = autogen.GroupChatManager(
|
||
# name=matagent_admin_name,
|
||
# groupchat=matagent_group,
|
||
# # is_termination_msg=lambda x: x.get("content", "").find("TERMINATE") >= 0,
|
||
# llm_config=llm_config,
|
||
# system_message=agent_configs[matagent_admin_name]['system_message'],
|
||
# description=matagent_admin_name
|
||
# )
|
||
|
||
# outer_generate_agent.register_nested_chats(
|
||
# [
|
||
# {"recipient": inner_generate_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
|
||
# ],
|
||
# trigger=matagent_admin,
|
||
# #reply_func_from_nested_chats =
|
||
# )
|
||
|
||
# outer_converter_agent.register_nested_chats(
|
||
# [
|
||
# {"recipient": inner_converter_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
|
||
# ],
|
||
# trigger=matagent_admin,
|
||
# )
|
||
|
||
# outer_executor_agent.register_nested_chats(
|
||
# [
|
||
# {"recipient": inner_executor_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
|
||
# ],
|
||
# trigger=matagent_admin,
|
||
# )
|
||
|
||
# outer_analysis_agent.register_nested_chats(
|
||
# [
|
||
# {"recipient": inner_analysis_admin, "max_turn": 1, "summary_method": "last_msg", "silent": SILENT},
|
||
# ],
|
||
# trigger=matagent_admin,
|
||
# )
|
||
|
||
# def print_messages(recipient, messages, sender, config):
|
||
# # if "callback" in config and config["callback"] is not None:
|
||
# # callback = config["callback"]
|
||
# # callback(sender, recipient, messages[-1])
|
||
# print(f"~~~~~~~~~~~~~~~~: {recipient.name} | num messages: {len(messages)}")
|
||
# self.send(text_data=json.dumps(messages))
|
||
# return False, None # required to ensure the agent communication flow continues
|
||
|
||
# # user.register_reply(
|
||
# # [autogen.Agent, None],
|
||
# # reply_func=print_messages,
|
||
# # config={"callback": None},
|
||
# # )
|
||
|
||
# user.initiate_chat(
|
||
# matagent_admin,
|
||
# message=text_data # "如何在常温条件下制备CsPbBr3纳米立方体",
|
||
# )
|
||
|
||
|
||
# #json.dump(user.chat_messages[matagent_admin], open(output.txt, "w"), indent=2)
|
||
# response_text = outer_generate_agent.last_message(matagent_admin)
|
||
# await self.send(text_data=json.dumps(response_text))
|
||
|
||
|
||
|
||
# with IOWebsockets.run_server_in_thread(on_connect=on_connect, host='192.168.42.130', port=8765) as uri:
|
||
# print(f" - test_setup() with websocket server running on {uri}.", flush=True)
|
||
# with ws_connect(uri) as websocket:
|
||
# print(f" - Connected to server on {uri}", flush=True)
|
||
#
|
||
# print(" - Sending message to server.", flush=True)
|
||
# websocket.send("开始聊天吧")
|
||
#
|
||
# while True:
|
||
# message = websocket.recv()
|
||
# message = message.decode("utf-8") if isinstance(message, bytes) else message
|
||
#
|
||
# print(message, end="", flush=True)
|
||
# if "TERMINATE" in message:
|
||
# print()
|
||
# print(" - Received TERMINATE message. Exiting.", flush=True)
|
||
# break
|
||
|
||
class UserView(APIView):
|
||
def post(self, request, *args, **kwargs):
|
||
# 从请求中获取用户输入
|
||
user_name = request.data.get('user_name')
|
||
pass_word = request.data.get('pass_word')
|
||
|
||
# 验证用户名和密码
|
||
if user_name=='test' and pass_word=='111111':
|
||
return Response({"token": "token_test"},status=status.HTTP_200_OK)
|
||
else:
|
||
return Response({"error": "用户名或密码错误"},status=status.HTTP_401_UNAUTHORIZED)
|
||
|
||
class ModelView(APIView):
|
||
def get(self, request, *args, **kwargs):
|
||
token = request.headers.get("token", None)
|
||
if not token:
|
||
return Response({"error": "请登录以后再重试"}, status=status.HTTP_203_NON_AUTHORITATIVE_INFORMATION)
|
||
model_lists = {
|
||
"count":"1",
|
||
"data":
|
||
[
|
||
{"model_name":"model1","model_des":"model1"},
|
||
{"model_name":"model1","model_des":"model2"}
|
||
]
|
||
}
|
||
return Response(model_lists,status=status.HTTP_200_OK)
|
||
|
||
|