From 720db80ae9a0ac5e14e31cc738990bbfb583fc72 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=9C=B1=E6=BD=AE?= Date: Fri, 12 Dec 2025 18:41:52 +0800 Subject: [PATCH] add deep_agent --- .vscode/launch.json | 15 + .vscode/settings.json | 9 + CLAUDE.md | 27 +- agent/custom_mcp_manager.py | 502 ---------- agent/deep_assistant.py | 63 ++ agent/file_loaded_agent_manager.py | 285 ------ agent/modified_assistant.py | 287 ------ agent/sharded_agent_manager.py | 17 +- poetry.lock | 1371 +++++++++++++++------------- pyproject.toml | 6 +- requirements.txt | 233 ++--- routes/chat.py | 90 +- routes/system.py | 2 +- utils/__init__.py | 19 - utils/fastapi_utils.py | 198 ++-- utils/project_manager.py | 9 - 16 files changed, 1164 insertions(+), 1969 deletions(-) create mode 100644 .vscode/launch.json create mode 100644 .vscode/settings.json delete mode 100644 agent/custom_mcp_manager.py create mode 100644 agent/deep_assistant.py delete mode 100644 agent/file_loaded_agent_manager.py delete mode 100644 agent/modified_assistant.py diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..82f997a --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,15 @@ +{ + "version": "0.2.0", + "configurations": [ + { + "name": "FastAPI: Debug", + "type": "python", + "request": "launch", + "program": "fastapi_app.py", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "envFile": "${workspaceFolder}/.env", + "python": "${workspaceFolder}/.venv/bin/python" + } + ] +} diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..b80c1e2 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,9 @@ +{ + "python.languageServer": "Pylance", + "python.analysis.indexing": true, + "python.analysis.autoSearchPaths": true, + "python.analysis.diagnosticMode": "workspace", + "python.analysis.extraPaths": [ + "${workspaceFolder}/.venv" + ] +} \ No newline at end of file diff --git a/CLAUDE.md b/CLAUDE.md index 1dd78bf..f67e87d 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -1,3 +1,28 @@ # python环境 本项目的python环境是基于 poetry创建的，如果需要运行 py文件，需要执行poetry run python xxx.py 来执行。 - +启动脚本： +``` +poetry run uvicorn fastapi_app:app --host 0.0.0.0 --port 8001 +``` +测试脚本： +``` +curl --request POST \ + --url http://localhost:8001/api/v2/chat/completions \ + --header 'authorization: Bearer a21c99620a8ef61d69563afe05ccce89' \ + --header 'content-type: application/json' \ + --header 'x-trace-id: 123123123' \ + --data '{ + "messages": [ + { + "role": "user", + "content": "咖啡多少钱一杯" + } + ], + "stream": true, + "model": "whatever", + "language": "ja", + "bot_id": "63069654-7750-409d-9a58-a0960d899a20", + "tool_response": true, + "user_identifier": "及川" +}' +``` diff --git a/agent/custom_mcp_manager.py b/agent/custom_mcp_manager.py deleted file mode 100644 index 371caa9..0000000 --- a/agent/custom_mcp_manager.py +++ /dev/null @@ -1,502 +0,0 @@ -# Copyright 2023 The Qwen team, Alibaba Group. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import asyncio -import atexit -import datetime -import json -import threading -import time -import uuid -from contextlib import AsyncExitStack -from typing import Dict, Optional, Union - -from dotenv import load_dotenv - -import logging - -logger = logging.getLogger('app') -from qwen_agent.tools.base import BaseTool - - -class CustomMCPManager: - _instance = None # Private class variable to store the unique instance - - def __new__(cls, *args, **kwargs): - if cls._instance is None: - cls._instance = super(CustomMCPManager, cls).__new__(cls, *args, **kwargs) - return cls._instance - - def __init__(self): - if not hasattr(self, 'clients'): # The singleton should only be inited once - """Set a new event loop in a separate thread""" - try: - import mcp # noqa - except ImportError as e: - raise ImportError('Could not import mcp. Please install mcp with `pip install -U mcp`.') from e - - load_dotenv() # Load environment variables from .env file - self.clients: dict = {} - self.loop = asyncio.new_event_loop() - self.loop_thread = threading.Thread(target=self.start_loop, daemon=True) - self.loop_thread.start() - - # A fallback way to terminate MCP tool processes after Qwen-Agent exits - self.processes = [] - self.monkey_patch_mcp_create_platform_compatible_process() - - def monkey_patch_mcp_create_platform_compatible_process(self): - try: - import mcp.client.stdio - target = mcp.client.stdio._create_platform_compatible_process - except (ModuleNotFoundError, AttributeError) as e: - raise ImportError('Qwen-Agent needs to monkey patch MCP for process cleanup. ' - 'Please upgrade MCP to a higher version with `pip install -U mcp`.') from e - - async def _monkey_patched_create_platform_compatible_process(*args, **kwargs): - process = await target(*args, **kwargs) - self.processes.append(process) - return process - - mcp.client.stdio._create_platform_compatible_process = _monkey_patched_create_platform_compatible_process - - def start_loop(self): - asyncio.set_event_loop(self.loop) - - # Set a global exception handler to silently handle cross-task exceptions from MCP SSE connections - def exception_handler(loop, context): - exception = context.get('exception') - if exception: - # Silently handle cross-task exceptions from MCP SSE connections - if (isinstance(exception, RuntimeError) and - 'Attempted to exit cancel scope in a different task' in str(exception)): - return # Silently ignore this type of exception - if (isinstance(exception, BaseExceptionGroup) and # noqa - 'Attempted to exit cancel scope in a different task' in str(exception)): # noqa - return # Silently ignore this type of exception - - # Other exceptions are handled normally - loop.default_exception_handler(context) - - self.loop.set_exception_handler(exception_handler) - self.loop.run_forever() - - def is_valid_mcp_servers(self, config: dict): - """Example of mcp servers configuration: - { - "mcpServers": { - "memory": { - "command": "npx", - "args": ["-y", "@modelcontextprotocol/server-memory"] - }, - "filesystem": { - "command": "npx", - "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/allowed/files"] - } - } - } - """ - - # Check if the top-level key "mcpServers" exists and its value is a dictionary - if not isinstance(config, dict) or 'mcpServers' not in config or not isinstance(config['mcpServers'], dict): - return False - mcp_servers = config['mcpServers'] - # Check each sub-item under "mcpServers" - for key in mcp_servers: - server = mcp_servers[key] - # Each sub-item must be a dictionary - if not isinstance(server, dict): - return False - if 'command' in server: - # "command" must be a string - if not isinstance(server['command'], str): - return False - # "args" must be a list - if 'args' not in server or not isinstance(server['args'], list): - return False - if 'url' in server: - # "url" must be a string - if not isinstance(server['url'], str): - return False - # "headers" must be a dictionary - if 'headers' in server and not isinstance(server['headers'], dict): - return False - # If the "env" key exists, it must be a dictionary - if 'env' in server and not isinstance(server['env'], dict): - return False - return True - - def initConfig(self, config: Dict): - if not self.is_valid_mcp_servers(config): - raise ValueError('Config of mcpservers is not valid') - logger.info(f'Initializing MCP tools from mcp servers: {list(config["mcpServers"].keys())}') - # Submit coroutine to the event loop and wait for the result - future = asyncio.run_coroutine_threadsafe(self.init_config_async(config), self.loop) - try: - result = future.result() # You can specify a timeout if desired - return result - except Exception as e: - logger.info(f'Failed in initializing MCP tools: {e}') - raise e - - async def init_config_async(self, config: Dict): - tools: list = [] - mcp_servers = config['mcpServers'] - - # 并发连接所有MCP服务器 - connection_tasks = [] - for server_name in mcp_servers: - client = CustomMCPClient() - server = mcp_servers[server_name] - # 创建连接任务 - task = self._connect_and_store_client(client, server_name, server) - connection_tasks.append(task) - - # 并发执行所有连接任务 - connected_clients = await asyncio.gather(*connection_tasks, return_exceptions=True) - - # 处理连接结果并为每个客户端创建工具 - for result in connected_clients: - if isinstance(result, Exception): - logger.error(f'Failed to connect MCP server: {result}') - continue - - client, server_name = result - client_id = client.client_id - for tool in client.tools: - """MCP tool example: - { - "name": "read_query", - "description": "Execute a SELECT query on the SQLite database", - "inputSchema": { - "type": "object", - "properties": { - "query": { - "type": "string", - "description": "SELECT SQL query to execute" - } - }, - "required": ["query"] - } - """ - parameters = tool.inputSchema - # The required field in inputSchema may be empty and needs to be initialized. - if 'required' not in parameters: - parameters['required'] = [] - # Remove keys from parameters that do not conform to the standard OpenAI schema - # Check if the required fields exist - required_fields = {'type', 'properties', 'required'} - missing_fields = required_fields - parameters.keys() - if missing_fields: - raise ValueError(f'Missing required fields in schema: {missing_fields}') - - # Keep only the necessary fields - cleaned_parameters = { - 'type': parameters['type'], - 'properties': parameters['properties'], - 'required': parameters['required'] - } - register_name = server_name + '-' + tool.name - agent_tool = self.create_tool_class(register_name=register_name, - register_client_id=client_id, - tool_name=tool.name, - tool_desc=tool.description, - tool_parameters=cleaned_parameters) - tools.append(agent_tool) - - if client.resources: - """MCP resource example: - { - uri: string; // Unique identifier for the resource - name: string; // Human-readable name - description?: string; // Optional description - mimeType?: string; // Optional MIME type - } - """ - # List resources - list_resources_tool_name = server_name + '-' + 'list_resources' - list_resources_params = {'type': 'object', 'properties': {}, 'required': []} - list_resources_agent_tool = self.create_tool_class( - register_name=list_resources_tool_name, - register_client_id=client_id, - tool_name='list_resources', - tool_desc='Servers expose a list of concrete resources through this tool. ' - 'By invoking it, you can discover the available resources and obtain resource templates, which help clients understand how to construct valid URIs. ' - 'These URI formats will be used as input parameters for the read_resource function. ', - tool_parameters=list_resources_params) - tools.append(list_resources_agent_tool) - - # Read resource - resources_template_str = '' # Check if there are resource templates - try: - list_resource_templates = await client.session.list_resource_templates( - ) # Check if the server has resources tesmplate - if list_resource_templates.resourceTemplates: - resources_template_str = '\n'.join( - str(template) for template in list_resource_templates.resourceTemplates) - - except Exception as e: - logger.info(f'Failed in listing MCP resource templates: {e}') - - read_resource_tool_name = server_name + '-' + 'read_resource' - read_resource_params = { - 'type': 'object', - 'properties': { - 'uri': { - 'type': 'string', - 'description': 'The URI identifying the specific resource to access' - } - }, - 'required': ['uri'] - } - original_tool_desc = 'Request to access a resource provided by a connected MCP server. Resources represent data sources that can be used as context, such as files, API responses, or system information.' - if resources_template_str: - tool_desc = original_tool_desc + '\nResource Templates:\n' + resources_template_str - else: - tool_desc = original_tool_desc - read_resource_agent_tool = self.create_tool_class(register_name=read_resource_tool_name, - register_client_id=client_id, - tool_name='read_resource', - tool_desc=tool_desc, - tool_parameters=read_resource_params) - tools.append(read_resource_agent_tool) - - return tools - - async def _connect_and_store_client(self, client, server_name, server): - """辅助方法：连接MCP服务器并存储客户端""" - try: - await client.connection_server(mcp_server_name=server_name, - mcp_server=server) # Attempt to connect to the server - - client_id = server_name + '_' + str( - uuid.uuid4()) # To allow the same server name be used across different running agents - client.client_id = client_id # Ensure client_id is set on the client instance - self.clients[client_id] = client # Add to clients dict after successful connection - return client, server_name - except Exception as e: - logger.error(f'Failed to connect MCP server {server_name}: {e}') - raise e - - def create_tool_class(self, register_name, register_client_id, tool_name, tool_desc, tool_parameters): - - class ToolClass(BaseTool): - name = register_name - description = tool_desc - parameters = tool_parameters - client_id = register_client_id - - def call(self, params: Union[str, dict], **kwargs) -> str: - tool_args = json.loads(params) - # Submit coroutine to the event loop and wait for the result - manager = CustomMCPManager() - client = manager.clients[self.client_id] - future = asyncio.run_coroutine_threadsafe(client.execute_function(tool_name, tool_args), manager.loop) - try: - result = future.result() - return result - except Exception as e: - logger.info(f'Failed in executing MCP tool: {e}') - raise e - - ToolClass.__name__ = f'{register_name}_Class' - return ToolClass() - - def shutdown(self): - futures = [] - for client_id in list(self.clients.keys()): - client: CustomMCPClient = self.clients[client_id] - future = asyncio.run_coroutine_threadsafe(client.cleanup(), self.loop) - futures.append(future) - del self.clients[client_id] - time.sleep(1) # Wait for the graceful cleanups, otherwise fall back - - # fallback - if asyncio.all_tasks(self.loop): - logger.info( - 'There are still tasks in `CustomMCPManager().loop`, force terminating the MCP tool processes. There may be some exceptions.' - ) - for process in self.processes: - try: - process.terminate() - except ProcessLookupError: - pass # it's ok, the process may exit earlier - - self.loop.call_soon_threadsafe(self.loop.stop) - self.loop_thread.join() - - -class CustomMCPClient: - - def __init__(self): - from mcp import ClientSession - self.session: Optional[ClientSession] = None - self.tools: list = None - self.exit_stack = AsyncExitStack() - self.resources: bool = False - self._last_mcp_server_name = None - self._last_mcp_server = None - self.client_id = None # For replacing in MCPManager.clients - - async def connection_server(self, mcp_server_name, mcp_server): - from mcp import ClientSession, StdioServerParameters - from mcp.client.sse import sse_client - from mcp.client.stdio import stdio_client - from mcp.client.streamable_http import streamablehttp_client - """Connect to an MCP server and retrieve the available tools.""" - # Save parameters - self._last_mcp_server_name = mcp_server_name - self._last_mcp_server = mcp_server - - try: - if 'url' in mcp_server: - url = mcp_server.get('url') - sse_read_timeout = mcp_server.get('sse_read_timeout', 300) - logger.info(f'{mcp_server_name} sse_read_timeout: {sse_read_timeout}s') - if mcp_server.get('type', 'sse') == 'streamable-http': - # streamable-http mode - """streamable-http mode mcp example: - {"mcpServers": { - "streamable-mcp-server": { - "type": "streamable-http", - "url":"http://0.0.0.0:8000/mcp" - } - } - } - """ - headers = mcp_server.get('headers', {}) - self._streams_context = streamablehttp_client( - url=url, headers=headers, sse_read_timeout=datetime.timedelta(seconds=sse_read_timeout)) - read_stream, write_stream, get_session_id = await self.exit_stack.enter_async_context( - self._streams_context) - self._session_context = ClientSession(read_stream, write_stream) - self.session = await self.exit_stack.enter_async_context(self._session_context) - else: - # sse mode - headers = mcp_server.get('headers', {'Accept': 'text/event-stream'}) - self._streams_context = sse_client(url, headers, sse_read_timeout=sse_read_timeout) - streams = await self.exit_stack.enter_async_context(self._streams_context) - self._session_context = ClientSession(*streams) - self.session = await self.exit_stack.enter_async_context(self._session_context) - else: - server_params = StdioServerParameters(command=mcp_server['command'], - args=mcp_server['args'], - env=mcp_server.get('env', None)) - stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params)) - self.stdio, self.write = stdio_transport - self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write)) - logger.info( - f'Initializing a MCP stdio_client, if this takes forever, please check the config of this mcp server: {mcp_server_name}' - ) - - await self.session.initialize() - list_tools = await self.session.list_tools() - self.tools = list_tools.tools - try: - list_resources = await self.session.list_resources() # Check if the server has resources - if list_resources.resources: - self.resources = True - except Exception: - # logger.info(f"No list resources: {e}") - pass - except Exception as e: - logger.warning(f'Failed in connecting to MCP server: {e}') - raise e - - async def reconnect(self): - # Create a new MCPClient and connect - if self.client_id is None: - raise RuntimeError( - 'Cannot reconnect: client_id is None. This usually means the client was not properly registered in MCPManager.' - ) - new_client = CustomMCPClient() - new_client.client_id = self.client_id - await new_client.connection_server(self._last_mcp_server_name, self._last_mcp_server) - return new_client - - async def execute_function(self, tool_name, tool_args: dict): - from mcp.types import TextResourceContents - - # Check if session is alive - try: - await self.session.send_ping() - except Exception as e: - logger.info(f"Session is not alive, please increase 'sse_read_timeout' in the config, try reconnect: {e}") - # Auto reconnect - try: - manager = CustomMCPManager() - if self.client_id is not None: - manager.clients[self.client_id] = await self.reconnect() - return await manager.clients[self.client_id].execute_function(tool_name, tool_args) - else: - logger.info('Reconnect failed: client_id is None') - return 'Session reconnect (client creation) exception: client_id is None' - except Exception as e3: - logger.info(f'Reconnect (client creation) exception type: {type(e3)}, value: {repr(e3)}') - return f'Session reconnect (client creation) exception: {e3}' - if tool_name == 'list_resources': - try: - list_resources = await self.session.list_resources() - if list_resources.resources: - resources_str = '\n\n'.join(str(resource) for resource in list_resources.resources) - else: - resources_str = 'No resources found' - return resources_str - except Exception as e: - logger.info(f'No list resources: {e}') - return f'Error: {e}' - elif tool_name == 'read_resource': - try: - uri = tool_args.get('uri') - if not uri: - raise ValueError('URI is required for read_resource') - read_resource = await self.session.read_resource(uri) - texts = [] - for resource in read_resource.contents: - if isinstance(resource, TextResourceContents): - texts.append(resource.text) - # if isinstance(resource, BlobResourceContents): - # texts.append(resource.blob) - if texts: - return '\n\n'.join(texts) - else: - return 'Failed to read resource' - except Exception as e: - logger.info(f'Failed to read resource: {e}') - return f'Error: {e}' - else: - response = await self.session.call_tool(tool_name, tool_args) - texts = [] - for content in response.content: - if content.type == 'text': - texts.append(content.text) - if texts: - return '\n\n'.join(texts) - else: - return 'execute error' - - async def cleanup(self): - await self.exit_stack.aclose() - - -def _cleanup_mcp(_sig_num=None, _frame=None): - if CustomMCPManager._instance is None: - return - manager = CustomMCPManager() - manager.shutdown() - - -# Make sure all subprocesses are terminated even if killed abnormally -if threading.current_thread() is threading.main_thread(): - atexit.register(_cleanup_mcp) diff --git a/agent/deep_assistant.py b/agent/deep_assistant.py new file mode 100644 index 0000000..f44b754 --- /dev/null +++ b/agent/deep_assistant.py @@ -0,0 +1,63 @@ +import json +from langchain.chat_models import init_chat_model +from deepagents import create_deep_agent +from langchain.agents import create_agent +from langchain_mcp_adapters.client import MultiServerMCPClient +from utils.fastapi_utils import detect_provider + +# Utility functions +def read_system_prompt(): + """读取通用的无状态系统prompt""" + with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f: + return f.read().strip() + + +def read_mcp_settings(): + """读取MCP工具配置""" + with open("./mcp/mcp_settings.json", "r") as f: + mcp_settings_json = json.load(f) + return mcp_settings_json + +async def init_agent(model_name="qwen3-next", api_key=None, + model_server=None, generate_cfg=None, + system_prompt=None, mcp=None): + system = system_prompt if system_prompt else read_system_prompt() + mcp = mcp if mcp else read_mcp_settings() + # 修改mcp[0]["mcpServers"]列表，把 type 字段改成 transport ，如果没有的话，就默认transport:stdio + if mcp and len(mcp) > 0 and "mcpServers" in mcp[0]: + for server_name, server_config in mcp[0]["mcpServers"].items(): + if isinstance(server_config, dict): + if "type" in server_config and "transport" not in server_config: + # 如果有 type 字段但没有 transport 字段，将 type 改为 transport + type_value = server_config.pop("type") + # 特殊处理：'streamable-http' 改为 'http' + if type_value == "streamable-http": + server_config["transport"] = "http" + else: + server_config["transport"] = type_value + elif "transport" not in server_config: + # 如果既没有 type 也没有 transport，添加默认的 transport: stdio + server_config["transport"] = "stdio" + + mcp_client = MultiServerMCPClient(mcp[0]["mcpServers"]) + mcp_tools = await mcp_client.get_tools() + + # 检测或使用指定的提供商 + model_provider,base_url = detect_provider(model_name,model_server) + + # 构建模型参数 + model_kwargs = { + "model": model_name, + "model_provider": model_provider, + "temperature": 0.8, + "base_url":base_url, + "api_key":api_key + } + llm_instance = init_chat_model(**model_kwargs) + + agent = create_agent( + model=llm_instance, + system_prompt=system, + tools=mcp_tools + ) + return agent diff --git a/agent/file_loaded_agent_manager.py b/agent/file_loaded_agent_manager.py deleted file mode 100644 index 5a40cd6..0000000 --- a/agent/file_loaded_agent_manager.py +++ /dev/null @@ -1,285 +0,0 @@ -# Copyright 2023 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""文件预加载助手管理器 - 管理基于unique_id的助手实例缓存""" - -import hashlib -import time -import json -import asyncio -from typing import Dict, List, Optional - -from qwen_agent.agents import Assistant -import logging - -logger = logging.getLogger('app') - -from agent.modified_assistant import init_modified_agent_service_with_files, update_agent_llm -from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_async - - -class FileLoadedAgentManager: - """文件预加载助手管理器 - - 基于 unique_id 缓存助手实例，避免重复创建和文件解析 - """ - - def __init__(self, max_cached_agents: int = 20): - self.agents: Dict[str, Assistant] = {} # {cache_key: assistant_instance} - self.unique_ids: Dict[str, str] = {} # {cache_key: unique_id} - self.access_times: Dict[str, float] = {} # LRU 访问时间管理 - self.creation_times: Dict[str, float] = {} # 创建时间记录 - self.max_cached_agents = max_cached_agents - self._creation_locks: Dict[str, asyncio.Lock] = {} # 防止并发创建相同agent的锁 - - def _get_cache_key(self, bot_id: str, model_name: str = None, api_key: str = None, - model_server: str = None, generate_cfg: Dict = None, - system_prompt: str = None, mcp_settings: List[Dict] = None) -> str: - """获取包含所有相关参数的哈希值作为缓存键 - - Args: - bot_id: 机器人项目ID - model_name: 模型名称 - api_key: API密钥 - model_server: 模型服务器地址 - generate_cfg: 生成配置 - system_prompt: 系统提示词 - mcp_settings: MCP设置列表 - - Returns: - str: 缓存键的哈希值 - """ - # 构建包含所有相关参数的字符串 - cache_data = { - 'bot_id': bot_id, - 'model_name': model_name or '', - 'api_key': api_key or '', - 'model_server': model_server or '', - 'generate_cfg': json.dumps(generate_cfg or {}, sort_keys=True), - 'system_prompt': system_prompt or '', - 'mcp_settings': json.dumps(mcp_settings or [], sort_keys=True) - } - - # 将字典转换为JSON字符串并计算哈希值 - cache_str = json.dumps(cache_data, sort_keys=True) - return hashlib.md5(cache_str.encode('utf-8')).hexdigest()[:16] - - def _update_access_time(self, cache_key: str): - """更新访问时间（LRU 管理）""" - self.access_times[cache_key] = time.time() - - def _cleanup_old_agents(self): - """清理旧的助手实例，基于 LRU 策略""" - if len(self.agents) <= self.max_cached_agents: - return - - # 按 LRU 顺序排序，删除最久未访问的实例 - sorted_keys = sorted(self.access_times.keys(), key=lambda k: self.access_times[k]) - - keys_to_remove = sorted_keys[:-self.max_cached_agents] - removed_count = 0 - - for cache_key in keys_to_remove: - try: - del self.agents[cache_key] - del self.unique_ids[cache_key] - del self.access_times[cache_key] - del self.creation_times[cache_key] - removed_count += 1 - logger.info(f"清理过期的助手实例缓存: {cache_key}") - except KeyError: - continue - - if removed_count > 0: - logger.info(f"已清理 {removed_count} 个过期的助手实例缓存") - - async def get_or_create_agent(self, - bot_id: str, - project_dir: Optional[str], - model_name: str = "qwen3-next", - api_key: Optional[str] = None, - model_server: Optional[str] = None, - generate_cfg: Optional[Dict] = None, - language: Optional[str] = None, - system_prompt: Optional[str] = None, - mcp_settings: Optional[List[Dict]] = None, - robot_type: Optional[str] = "general_agent", - user_identifier: Optional[str] = None) -> Assistant: - """获取或创建文件预加载的助手实例 - - Args: - bot_id: 项目的唯一标识符 - project_dir: 项目目录路径，用于读取README.md，可以为None - model_name: 模型名称 - api_key: API 密钥 - model_server: 模型服务器地址 - generate_cfg: 生成配置 - language: 语言代码，用于选择对应的系统提示词 - system_prompt: 可选的系统提示词，优先级高于项目配置 - mcp_settings: 可选的MCP设置，优先级高于项目配置 - robot_type: 机器人类型，取值 agent/catalog_agent - user_identifier: 用户标识符 - - Returns: - Assistant: 配置好的助手实例 - """ - import os - - # 使用异步加载配置文件（带缓存） - final_system_prompt = await load_system_prompt_async( - project_dir, language, system_prompt, robot_type, bot_id, user_identifier - ) - final_mcp_settings = await load_mcp_settings_async( - project_dir, mcp_settings, bot_id, robot_type - ) - - cache_key = self._get_cache_key(bot_id, model_name, api_key, model_server, - generate_cfg, final_system_prompt, final_mcp_settings) - - # 使用异步锁防止并发创建相同的agent - creation_lock = self._creation_locks.setdefault(cache_key, asyncio.Lock()) - async with creation_lock: - # 再次检查是否已存在该助手实例（获取锁后可能有其他请求已创建） - if cache_key in self.agents: - self._update_access_time(cache_key) - agent = self.agents[cache_key] - - # 动态更新 LLM 配置和系统设置（如果参数有变化） - update_agent_llm(agent, model_name, api_key, model_server, generate_cfg) - - logger.info(f"复用现有的助手实例缓存: {cache_key} (bot_id: {bot_id})") - return agent - - # 清理过期实例 - self._cleanup_old_agents() - - # 创建新的助手实例，预加载文件 - logger.info(f"创建新的助手实例缓存: {cache_key}, bot_id: {bot_id}") - current_time = time.time() - - agent = init_modified_agent_service_with_files( - model_name=model_name, - api_key=api_key, - model_server=model_server, - generate_cfg=generate_cfg, - system_prompt=final_system_prompt, - mcp=final_mcp_settings - ) - - # 缓存实例 - self.agents[cache_key] = agent - self.unique_ids[cache_key] = bot_id - self.access_times[cache_key] = current_time - self.creation_times[cache_key] = current_time - - # 清理创建锁 - self._creation_locks.pop(cache_key, None) - - logger.info(f"助手实例缓存创建完成: {cache_key}") - return agent - - def get_cache_stats(self) -> Dict: - """获取缓存统计信息""" - current_time = time.time() - stats = { - "total_cached_agents": len(self.agents), - "max_cached_agents": self.max_cached_agents, - "agents": {} - } - - for cache_key, agent in self.agents.items(): - stats["agents"][cache_key] = { - "unique_id": self.unique_ids.get(cache_key, "unknown"), - "created_at": self.creation_times.get(cache_key, 0), - "last_accessed": self.access_times.get(cache_key, 0), - "age_seconds": int(current_time - self.creation_times.get(cache_key, current_time)), - "idle_seconds": int(current_time - self.access_times.get(cache_key, current_time)) - } - - return stats - - def clear_cache(self) -> int: - """清空所有缓存 - - Returns: - int: 清理的实例数量 - """ - cache_count = len(self.agents) - - self.agents.clear() - self.unique_ids.clear() - self.access_times.clear() - self.creation_times.clear() - - logger.info(f"已清空所有助手实例缓存，共清理 {cache_count} 个实例") - return cache_count - - def remove_cache_by_unique_id(self, unique_id: str) -> int: - """根据 unique_id 移除所有相关的缓存 - - 由于缓存key现在包含 system_prompt 和 mcp_settings， - 一个 unique_id 可能对应多个缓存实例。 - - Args: - unique_id: 项目的唯一标识符 - - Returns: - int: 成功移除的实例数量 - """ - keys_to_remove = [] - - # 找到所有匹配的 unique_id 的缓存键 - for cache_key, stored_unique_id in self.unique_ids.items(): - if stored_unique_id == unique_id: - keys_to_remove.append(cache_key) - - # 移除找到的缓存 - removed_count = 0 - for cache_key in keys_to_remove: - try: - del self.agents[cache_key] - del self.unique_ids[cache_key] - del self.access_times[cache_key] - del self.creation_times[cache_key] - self._creation_locks.pop(cache_key, None) # 清理创建锁 - removed_count += 1 - logger.info(f"已移除助手实例缓存: {cache_key} (unique_id: {unique_id})") - except KeyError: - continue - - if removed_count > 0: - logger.info(f"已移除 unique_id={unique_id} 的 {removed_count} 个助手实例缓存") - else: - logger.warning(f"未找到 unique_id={unique_id} 的缓存实例") - - return removed_count - - -# 全局文件预加载助手管理器实例 -_global_agent_manager: Optional[FileLoadedAgentManager] = None - - -def get_global_agent_manager() -> FileLoadedAgentManager: - """获取全局文件预加载助手管理器实例""" - global _global_agent_manager - if _global_agent_manager is None: - _global_agent_manager = FileLoadedAgentManager() - return _global_agent_manager - - -def init_global_agent_manager(max_cached_agents: int = 20): - """初始化全局文件预加载助手管理器""" - global _global_agent_manager - _global_agent_manager = FileLoadedAgentManager(max_cached_agents) - return _global_agent_manager diff --git a/agent/modified_assistant.py b/agent/modified_assistant.py deleted file mode 100644 index b57b8d8..0000000 --- a/agent/modified_assistant.py +++ /dev/null @@ -1,287 +0,0 @@ -# Copyright 2023 The Qwen team, Alibaba Group. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import copy -import json -import logging -import os -import time -from typing import Dict, Iterator, List, Literal, Optional, Union - -from qwen_agent.agents import Assistant -from qwen_agent.llm.schema import ASSISTANT, FUNCTION, Message -from qwen_agent.llm.oai import TextChatAtOAI -from qwen_agent.tools import BaseTool -from agent.custom_mcp_manager import CustomMCPManager -import logging - -logger = logging.getLogger('app') -# 设置工具日志记录器 -tool_logger = logging.getLogger('app') - -class ModifiedAssistant(Assistant): - """ - 修改后的 Assistant 子类，改变循环判断逻辑： - - 原始逻辑：如果没有使用工具，立即退出循环 - - 修改后逻辑：如果没有使用工具，调用模型判断回答是否完整，如果不完整则继续循环 - """ - - def _is_retryable_error(self, error: Exception) -> bool: - """判断错误是否可重试 - - Args: - error: 异常对象 - - Returns: - bool: 是否可重试 - """ - error_str = str(error).lower() - retryable_indicators = [ - '502', '500', '503', '504', # HTTP错误代码 - 'internal server error', # 内部服务器错误 - 'timeout', # 超时 - 'connection', # 连接错误 - 'network', # 网络错误 - 'rate', # 速率限制和相关错误 - 'quota', # 配额限制 - 'service unavailable', # 服务不可用 - 'provider returned error', # Provider错误 - 'model service error', # 模型服务错误 - 'temporary', # 临时错误 - 'retry' # 明确提示重试 - ] - return any(indicator in error_str for indicator in retryable_indicators) - - def _call_tool(self, tool_name: str, tool_args: Union[str, dict] = '{}', **kwargs) -> str: - """重写工具调用方法，添加调试信息""" - if tool_name not in self.function_map: - error_msg = f'Tool {tool_name} does not exist. Available tools: {list(self.function_map.keys())}' - tool_logger.error(error_msg) - return error_msg - - tool = self.function_map[tool_name] - - try: - tool_logger.info(f"开始调用工具: {tool_name} {tool_args}") - start_time = time.time() - - # 调用父类的_call_tool方法 - tool_result = super()._call_tool(tool_name, tool_args, **kwargs) - - end_time = time.time() - tool_logger.info(f"工具 {tool_name} 执行完成，耗时: {end_time - start_time:.2f}秒 结果长度: {len(tool_result) if tool_result else 0}") - - # 打印部分结果内容（避免过长） - if tool_result and len(tool_result) > 0: - preview = tool_result[:200] if len(tool_result) > 200 else tool_result - tool_logger.debug(f"工具 {tool_name} 结果预览: {preview}...") - - return tool_result - - except Exception as ex: - end_time = time.time() - tool_logger.error(f"工具调用异常，耗时: {end_time - start_time:.2f}秒 异常类型: {type(ex).__name__} {str(ex)}") - - # 打印完整的堆栈跟踪 - import traceback - tool_logger.error(f"堆栈跟踪:\n{traceback.format_exc()}") - - # 返回详细的错误信息 - error_message = f'An error occurred when calling tool {tool_name}: {type(ex).__name__}: {str(ex)}' - return error_message - - def _init_tool(self, tool: Union[str, Dict, BaseTool]): - """重写工具初始化方法，使用CustomMCPManager处理MCP服务器配置""" - if isinstance(tool, BaseTool): - # 处理BaseTool实例 - tool_name = tool.name - if tool_name in self.function_map: - tool_logger.warning(f'Repeatedly adding tool {tool_name}, will use the newest tool in function list') - self.function_map[tool_name] = tool - elif isinstance(tool, dict) and 'mcpServers' in tool: - # 使用CustomMCPManager处理MCP服务器配置，支持headers - tools = CustomMCPManager().initConfig(tool) - for tool in tools: - tool_name = tool.name - if tool_name in self.function_map: - tool_logger.warning(f'Repeatedly adding tool {tool_name}, will use the newest tool in function list') - self.function_map[tool_name] = tool - else: - # 调用父类的处理方法 - super()._init_tool(tool) - - def _call_llm_with_retry(self, messages: List[Message], functions=None, extra_generate_cfg=None, max_retries: int = 5) -> Iterator: - """带重试机制的LLM调用 - - Args: - messages: 消息列表 - functions: 函数列表 - extra_generate_cfg: 额外生成配置 - max_retries: 最大重试次数 - - Returns: - LLM响应流 - - Raises: - Exception: 重试次数耗尽后重新抛出原始异常 - """ - for attempt in range(max_retries): - try: - return self._call_llm(messages=messages, functions=functions, extra_generate_cfg=extra_generate_cfg) - except Exception as e: - # 检查是否为可重试的错误 - if self._is_retryable_error(e) and attempt < max_retries - 1: - delay = 2 ** attempt # 指数退避: 1s, 2s, 4s - tool_logger.warning(f"LLM调用失败 (尝试 {attempt + 1}/{max_retries})，{delay}秒后重试: {str(e)}") - time.sleep(delay) - continue - else: - # 不可重试的错误或已达到最大重试次数 - if attempt > 0: - tool_logger.error(f"LLM调用重试失败，已达到最大重试次数 {max_retries}") - raise - - def _run(self, messages: List[Message], lang: Literal['en', 'zh', 'ja'] = 'en', **kwargs) -> Iterator[List[Message]]: - - message_list = copy.deepcopy(messages) - response = [] - - # 保持原有的最大调用次数限制 - total_num_llm_calls_available = self.MAX_LLM_CALL_PER_RUN if hasattr(self, 'MAX_LLM_CALL_PER_RUN') else 100 - num_llm_calls_available = total_num_llm_calls_available - while num_llm_calls_available > 0: - num_llm_calls_available -= 1 - extra_generate_cfg = {'lang': lang} - if kwargs.get('seed') is not None: - extra_generate_cfg['seed'] = kwargs['seed'] - - output_stream = self._call_llm_with_retry(messages=message_list, - functions=[func.function for func in self.function_map.values()], - extra_generate_cfg=extra_generate_cfg) - output: List[Message] = [] - for output in output_stream: - if output: - yield response + output - - if output: - response.extend(output) - message_list.extend(output) - - # 处理工具调用 - used_any_tool = False - for out in output: - use_tool, tool_name, tool_args, _ = self._detect_tool(out) - if use_tool: - tool_result = self._call_tool(tool_name, tool_args, messages=message_list, **kwargs) - - # 验证工具结果 - if not tool_result: - tool_logger.warning(f"工具 {tool_name} 返回空结果") - tool_result = f"Tool {tool_name} completed execution but returned empty result" - elif tool_result.startswith('An error occurred when calling tool') or tool_result.startswith('工具调用失败'): - tool_logger.error(f"工具 {tool_name} 调用失败: {tool_result}") - - fn_msg = Message(role=FUNCTION, - name=tool_name, - content=tool_result, - extra={'function_id': out.extra.get('function_id', '1')}) - message_list.append(fn_msg) - response.append(fn_msg) - yield response - used_any_tool = True - - # 如果使用了工具，继续循环 - if not used_any_tool: - break - - # 检查是否因为调用次数用完而退出循环 - if num_llm_calls_available == 0: - # 根据语言选择错误消息 - if lang == 'zh': - error_message = "工具调用超出限制" - elif lang == 'ja': - error_message = "ツール呼び出しが制限を超えました。" - else: - error_message = "Tool calls exceeded limit" - tool_logger.error(error_message) - - error_msg = Message( - role=ASSISTANT, - content=error_message, - ) - response.append(error_msg) - - yield response - - -# Utility functions -def read_system_prompt(): - """读取通用的无状态系统prompt""" - with open("./prompt/system_prompt_default.md", "r", encoding="utf-8") as f: - return f.read().strip() - - -def read_mcp_settings(): - """读取MCP工具配置""" - with open("./mcp/mcp_settings.json", "r") as f: - mcp_settings_json = json.load(f) - return mcp_settings_json - - -def update_agent_llm(agent, model_name: str, api_key: str = None, model_server: str = None, generate_cfg: Dict = None): - """动态更新助手实例的LLM和配置，支持从接口传入参数""" - # 获取基础配置 - llm_config = { - "model": model_name, - "api_key": api_key, - "model_server": model_server, - "generate_cfg": generate_cfg if generate_cfg else {} - } - - # 创建LLM实例 - llm_instance = TextChatAtOAI(llm_config) - - # 动态设置LLM - agent.llm = llm_instance - return agent - - -# 向后兼容：保持原有的初始化函数接口 -def init_modified_agent_service_with_files(rag_cfg=None, - model_name="qwen3-next", api_key=None, - model_server=None, generate_cfg=None, - system_prompt=None, mcp=None): - """创建支持预加载文件的修改版助手实例""" - system = system_prompt if system_prompt else read_system_prompt() - tools = mcp if mcp else read_mcp_settings() - - llm_config = { - "model": model_name, - "api_key": api_key, - "model_server": model_server, - "generate_cfg": generate_cfg if generate_cfg else {} - } - - # 创建LLM实例 - llm_instance = TextChatAtOAI(llm_config) - - bot = ModifiedAssistant( - llm=llm_instance, - name="修改版数据检索助手", - description="基于智能判断循环终止的助手", - system_message=system, - function_list=tools, - ) - - return bot diff --git a/agent/sharded_agent_manager.py b/agent/sharded_agent_manager.py index da9eb77..17ae552 100644 --- a/agent/sharded_agent_manager.py +++ b/agent/sharded_agent_manager.py @@ -18,17 +18,13 @@ import hashlib import time import json import asyncio -from typing import Dict, List, Optional, Tuple -from concurrent.futures import ThreadPoolExecutor +from typing import Dict, List, Optional import threading -from collections import defaultdict - -from qwen_agent.agents import Assistant import logging logger = logging.getLogger('app') -from agent.modified_assistant import init_modified_agent_service_with_files, update_agent_llm +from agent.deep_assistant import init_agent from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_async @@ -131,7 +127,7 @@ class ShardedAgentManager: system_prompt: Optional[str] = None, mcp_settings: Optional[List[Dict]] = None, robot_type: Optional[str] = "general_agent", - user_identifier: Optional[str] = None) -> Assistant: + user_identifier: Optional[str] = None): """获取或创建文件预加载的助手实例""" # 更新请求统计 @@ -159,10 +155,6 @@ class ShardedAgentManager: if cache_key in shard['agents']: self._update_access_time(shard, cache_key) agent = shard['agents'][cache_key] - - # 动态更新 LLM 配置和系统设置 - update_agent_llm(agent, model_name, api_key, model_server, generate_cfg) - # 更新缓存命中统计 with self._stats_lock: self._global_stats['cache_hits'] += 1 @@ -184,7 +176,6 @@ class ShardedAgentManager: if cache_key in shard['agents']: self._update_access_time(shard, cache_key) agent = shard['agents'][cache_key] - update_agent_llm(agent, model_name, api_key, model_server, generate_cfg) with self._stats_lock: self._global_stats['cache_hits'] += 1 @@ -199,7 +190,7 @@ class ShardedAgentManager: logger.info(f"分片创建新的助手实例缓存: {cache_key}, bot_id: {bot_id}, shard: {shard_index}") current_time = time.time() - agent = init_modified_agent_service_with_files( + agent = await init_agent( model_name=model_name, api_key=api_key, model_server=model_server, diff --git a/poetry.lock b/poetry.lock index 0e16aae..f052c70 100644 --- a/poetry.lock +++ b/poetry.lock @@ -194,6 +194,33 @@ files = [ {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, ] +[[package]] +name = "anthropic" +version = "0.75.0" +description = "The official Python library for the anthropic API" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "anthropic-0.75.0-py3-none-any.whl", hash = "sha256:ea8317271b6c15d80225a9f3c670152746e88805a7a61e14d4a374577164965b"}, + {file = "anthropic-0.75.0.tar.gz", hash = "sha256:e8607422f4ab616db2ea5baacc215dd5f028da99ce2f022e33c7c535b29f3dfb"}, +] + +[package.dependencies] +anyio = ">=3.5.0,<5" +distro = ">=1.7.0,<2" +docstring-parser = ">=0.15,<1" +httpx = ">=0.25.0,<1" +jiter = ">=0.4.0,<1" +pydantic = ">=1.9.0,<3" +sniffio = "*" +typing-extensions = ">=4.10,<5" + +[package.extras] +aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.9)"] +bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"] +vertex = ["google-auth[requests] (>=2,<3)"] + [[package]] name = "anyio" version = "4.11.0" @@ -227,28 +254,17 @@ files = [ ] [[package]] -name = "beautifulsoup4" -version = "4.14.2" -description = "Screen-scraping library" +name = "bracex" +version = "2.6" +description = "Bash style brace expander." optional = false -python-versions = ">=3.7.0" +python-versions = ">=3.9" groups = ["main"] files = [ - 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"3.12" and python_version < "4.0" +jinja2==3.1.6 ; python_version >= "3.12" and python_version < "4.0" +jiter==0.11.1 ; python_version >= "3.12" and python_version < "4.0" +joblib==1.5.2 ; python_version >= "3.12" and python_version < "4.0" +jsonpatch==1.33 ; python_version >= "3.12" and python_version < "4.0" +jsonpointer==3.0.0 ; python_version >= "3.12" and python_version < "4.0" +jsonschema-specifications==2025.9.1 ; python_version >= "3.12" and python_version < "4.0" +jsonschema==4.25.1 ; python_version >= "3.12" and python_version < "4.0" +langchain-anthropic==1.2.0 ; python_version >= "3.12" and python_version < "4.0" +langchain-core==1.1.3 ; python_version >= "3.12" and python_version < "4.0" +langchain-mcp-adapters==0.2.1 ; python_version >= "3.12" and python_version < "4.0" +langchain-openai==1.1.1 ; python_version >= "3.12" and python_version < "4.0" +langchain==1.1.3 ; python_version >= "3.12" and python_version < "4.0" +langgraph-checkpoint==3.0.1 ; python_version >= "3.12" and python_version < "4.0" +langgraph-prebuilt==1.0.5 ; python_version >= "3.12" and python_version < "4.0" +langgraph-sdk==0.2.15 ; python_version >= "3.12" and python_version < "4.0" +langgraph==1.0.4 ; python_version >= "3.12" and python_version < "4.0" +langsmith==0.4.59 ; python_version >= "3.12" and python_version < "4.0" +markupsafe==3.0.3 ; python_version >= "3.12" and python_version < "4.0" +mcp==1.12.4 ; python_version >= "3.12" and python_version < "4.0" +mpmath==1.3.0 ; python_version >= "3.12" and python_version < "4.0" +multidict==6.7.0 ; python_version >= "3.12" and python_version < "4.0" +networkx==3.5 ; python_version >= "3.12" and python_version < "4.0" +numpy==1.26.4 ; python_version >= "3.12" and python_version < "4.0" +nvidia-cublas-cu12==12.1.3.1 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cuda-cupti-cu12==12.1.105 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cuda-nvrtc-cu12==12.1.105 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cuda-runtime-cu12==12.1.105 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cudnn-cu12==8.9.2.26 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cufft-cu12==11.0.2.54 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-curand-cu12==10.3.2.106 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cusolver-cu12==11.4.5.107 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-cusparse-cu12==12.1.0.106 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-nccl-cu12==2.19.3 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-nvjitlink-cu12==12.9.86 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +nvidia-nvtx-cu12==12.1.105 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +openai==2.5.0 ; python_version >= "3.12" and python_version < "4.0" +openpyxl==3.1.5 ; python_version >= "3.12" and python_version < "4.0" +orjson==3.11.5 ; python_version >= "3.12" and python_version < "4.0" +ormsgpack==1.12.0 ; python_version >= "3.12" and python_version < "4.0" +packaging==25.0 ; python_version >= "3.12" and python_version < "4.0" +pandas==2.3.3 ; python_version >= "3.12" and python_version < "4.0" +pillow==12.0.0 ; python_version >= "3.12" and python_version < "4.0" +propcache==0.4.1 ; python_version >= "3.12" and python_version < "4.0" +psutil==7.1.3 ; python_version >= "3.12" and python_version < "4.0" +pydantic-core==2.27.2 ; python_version >= "3.12" and python_version < "4.0" +pydantic-settings==2.11.0 ; python_version >= "3.12" and python_version < "4.0" +pydantic==2.10.5 ; python_version >= "3.12" and python_version < "4.0" +python-dateutil==2.8.2 ; python_version >= "3.12" and python_version < "4.0" +python-dotenv==1.1.1 ; python_version >= "3.12" and python_version < "4.0" +python-multipart==0.0.20 ; python_version >= "3.12" and python_version < "4.0" +pytz==2025.2 ; python_version >= "3.12" and python_version < "4.0" +pywin32==311 ; python_version >= "3.12" and python_version < "4.0" and sys_platform == "win32" +pyyaml==6.0.3 ; python_version >= "3.12" and python_version < "4.0" +referencing==0.37.0 ; python_version >= "3.12" and python_version < "4.0" +regex==2025.9.18 ; python_version >= "3.12" and python_version < "4.0" +requests-toolbelt==1.0.0 ; python_version >= "3.12" and python_version < "4.0" +requests==2.32.5 ; python_version >= "3.12" and python_version < "4.0" +rpds-py==0.27.1 ; python_version >= "3.12" and python_version < "4.0" +safetensors==0.6.2 ; python_version >= "3.12" and python_version < "4.0" +scikit-learn==1.7.2 ; python_version >= "3.12" and python_version < "4.0" +scipy==1.16.2 ; python_version >= "3.12" and python_version < "4.0" +sentence-transformers==5.1.1 ; python_version >= "3.12" and python_version < "4.0" +six==1.17.0 ; python_version >= "3.12" and python_version < "4.0" +sniffio==1.3.1 ; python_version >= "3.12" and python_version < "4.0" +sse-starlette==3.0.2 ; python_version >= "3.12" and python_version < "4.0" +starlette==0.47.3 ; python_version >= "3.12" and python_version < "4.0" +sympy==1.14.0 ; python_version >= "3.12" and python_version < "4.0" +tenacity==9.1.2 ; python_version >= "3.12" and python_version < "4.0" +threadpoolctl==3.6.0 ; python_version >= "3.12" and python_version < "4.0" +tiktoken==0.12.0 ; python_version >= "3.12" and python_version < "4.0" +tokenizers==0.22.1 ; python_version >= "3.12" and python_version < "4.0" +torch==2.2.0 ; python_version >= "3.12" and python_version < "4.0" +tqdm==4.67.1 ; python_version >= "3.12" and python_version < "4.0" +transformers==4.57.1 ; python_version >= "3.12" and python_version < "4.0" +triton==2.2.0 ; python_version >= "3.12" and python_version < "4.0" and platform_system == "Linux" and platform_machine == "x86_64" +typing-extensions==4.15.0 ; python_version >= "3.12" and python_version < "4.0" +typing-inspection==0.4.2 ; python_version >= "3.12" and python_version < "4.0" +tzdata==2025.2 ; python_version >= "3.12" and python_version < "4.0" +urllib3==2.5.0 ; python_version >= "3.12" and python_version < "4.0" +uuid-utils==0.12.0 ; python_version >= "3.12" and python_version < "4.0" +uvicorn==0.35.0 ; python_version >= "3.12" and python_version < "4.0" +uvloop==0.22.1 ; python_version >= "3.12" and python_version < "4.0" +wcmatch==10.1 ; python_version >= "3.12" and python_version < "4.0" +xlrd==2.0.2 ; python_version >= "3.12" and python_version < "4.0" +xxhash==3.6.0 ; python_version >= "3.12" and python_version < "4.0" +yarl==1.22.0 ; python_version >= "3.12" and python_version < "4.0" +zstandard==0.25.0 ; python_version >= "3.12" and python_version < "4.0" diff --git a/routes/chat.py b/routes/chat.py index f79a79d..1b031f4 100644 --- a/routes/chat.py +++ b/routes/chat.py @@ -17,9 +17,10 @@ from agent.prompt_loader import load_guideline_prompt from utils.fastapi_utils import ( process_messages, extract_block_from_system_prompt, format_messages_to_chat_history, create_project_directory, extract_api_key_from_auth, generate_v2_auth_token, fetch_bot_config, - _get_optimal_batch_size, process_guideline, get_content_from_messages, call_preamble_llm, get_preamble_text, get_language_text, + process_guideline, call_preamble_llm, get_preamble_text, get_language_text, create_stream_chunk ) +from langchain_core.messages import AIMessageChunk, HumanMessage, ToolMessage, AIMessage router = APIRouter() @@ -180,9 +181,17 @@ Action: Provide concise, friendly, and personified natural responses. all_results = await asyncio.gather(*tasks, return_exceptions=True) # 处理结果 - agent = all_results[0] if len(all_results) >0 else None # agent创建的结果 + agent = all_results[0] if len(all_results) > 0 else None # agent创建的结果 - guideline_reasoning = all_results[1] if len(all_results) >1 else "" + # 检查agent是否为异常对象 + if isinstance(agent, Exception): + logger.error(f"Error creating agent: {agent}") + raise agent + + guideline_reasoning = all_results[1] if len(all_results) > 1 else "" + if isinstance(guideline_reasoning, Exception): + logger.error(f"Error in guideline processing: {guideline_reasoning}") + guideline_reasoning = "" if guideline_prompt or guideline_reasoning: logger.info("Guideline Prompt: %s, Reasoning: %s", guideline_prompt.replace('\n', '\\n') if guideline_prompt else "None", @@ -243,7 +252,7 @@ async def enhanced_generate_stream_response( preamble_text = await preamble_task # 只有当preamble_text不为空且不为""时才输出 if preamble_text and preamble_text.strip() and preamble_text != "": - preamble_content = get_content_from_messages([{"role": "preamble","content": preamble_text + "\n"}], tool_response=tool_response) + preamble_content = f"[PREAMBLE]\n{preamble_text}\n" chunk_data = create_stream_chunk(f"chatcmpl-preamble", model_name, preamble_content) yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n" logger.info(f"Stream mode: Generated preamble text ({len(preamble_text)} chars)") @@ -257,7 +266,7 @@ async def enhanced_generate_stream_response( # 立即发送guideline_reasoning if guideline_reasoning: - guideline_content = get_content_from_messages([{"role": "assistant","reasoning_content": guideline_reasoning+ "\n"}], tool_response=tool_response) + guideline_content = f"[THINK]\n{guideline_reasoning}\n" chunk_data = create_stream_chunk(f"chatcmpl-guideline", model_name, guideline_content) yield f"data: {json.dumps(chunk_data, ensure_ascii=False)}\n\n" @@ -270,20 +279,41 @@ async def enhanced_generate_stream_response( else: final_messages = append_assistant_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}") + logger.debug(f"Final messages for agent (showing first 2): {final_messages[:2]}") + # 第三阶段：agent响应流式传输 logger.info(f"Starting agent stream response") - accumulated_content = "" chunk_id = 0 - for response in agent.run(messages=final_messages): - previous_content = accumulated_content - accumulated_content = get_content_from_messages(response, tool_response=tool_response) - - # 计算新增的内容 - if accumulated_content.startswith(previous_content): - new_content = accumulated_content[len(previous_content):] - else: - new_content = accumulated_content - previous_content = "" + message_tag = "" + function_name = "" + tool_args = "" + async for msg,metadata in agent.astream({"messages": final_messages}, stream_mode="messages"): + new_content = "" + if isinstance(msg, AIMessageChunk): + # 判断是否有工具调用 + if msg.tool_call_chunks: # 检查工具调用块 + if message_tag != "TOOL_CALL": + message_tag = "TOOL_CALL" + if msg.tool_call_chunks[0]["name"]: + function_name = msg.tool_call_chunks[0]["name"] + if msg.tool_call_chunks[0]["args"]: + tool_args += msg.tool_call_chunks[0]["args"] + elif len(msg.content)>0: + if message_tag != "ANSWER": + message_tag = "ANSWER" + new_content = f"[{message_tag}]\n{msg.text}" + elif message_tag == "ANSWER": + new_content = msg.text + elif message_tag == "TOOL_CALL" and \ + ( + ("finish_reason" in msg.response_metadata and msg.response_metadata["finish_reason"] == "tool_calls") or \ + ("stop_reason" in msg.response_metadata and msg.response_metadata["stop_reason"] == "tool_use") + ): + new_content = f"[{message_tag}] {function_name}\n{tool_args}" + message_tag = "TOOL_CALL" + elif isinstance(msg, ToolMessage) and len(msg.content)>0: + message_tag = "TOOL_RESPONSE" + new_content = f"[{message_tag}] {msg.name}\n{msg.text}" # 只有当有新内容时才发送chunk if new_content: @@ -379,36 +409,44 @@ async def create_agent_and_generate_response( # 准备最终的消息 final_messages = messages.copy() - pre_message_list = [] if guideline_reasoning: # 用###分割guideline_reasoning，取最后一段作为Guidelines guidelines_text = guideline_reasoning.split('###')[-1].strip() if guideline_reasoning else "" final_messages = append_assistant_last_message(final_messages, f"language:{get_language_text(language)}\n\nGuidelines:\n{guidelines_text}\n I will follow these guidelines step by step.") - pre_message_list.append({"role": "assistant","reasoning_content": guideline_reasoning+ "\n"}) else: final_messages = append_assistant_last_message(final_messages, f"\n\nlanguage:{get_language_text(language)}") # 非流式响应 - agent_responses = agent.run_nonstream(final_messages) - final_responses = pre_message_list + agent_responses - if final_responses and len(final_responses) > 0: - # 使用 get_content_from_messages 处理响应，支持 tool_response 参数 - content = get_content_from_messages(final_responses, tool_response=tool_response) + agent_responses = await agent.ainvoke({"messages": final_messages}) + append_messages = agent_responses["messages"][len(final_messages):] + # agent_responses = agent.run_nonstream(final_messages) + response_text = "" + if guideline_reasoning: + response_text += "[THINK]\n"+guideline_reasoning+ "\n" + for msg in append_messages: + if isinstance(msg,AIMessage): + if len(msg.text)>0: + response_text += "[ANSWER]\n"+msg.text+ "\n" + if len(msg.tool_calls)>0: + response_text += "".join([f"[TOOL_CALL] {tool['name']}\n{json.dumps(tool["args"]) if isinstance(tool["args"],dict) else tool["args"]}\n" for tool in msg.tool_calls]) + elif isinstance(msg,ToolMessage) and tool_response: + response_text += f"[TOOL_RESPONSE] {msg.name}\n{msg.text}\n" + if len(response_text) > 0: # 构造OpenAI格式的响应 return ChatResponse( choices=[{ "index": 0, "message": { "role": "assistant", - "content": content + "content": response_text }, "finish_reason": "stop" }], usage={ "prompt_tokens": sum(len(msg.get("content", "")) for msg in messages), - "completion_tokens": len(content), - "total_tokens": sum(len(msg.get("content", "")) for msg in messages) + len(content) + "completion_tokens": len(response_text), + "total_tokens": sum(len(msg.get("content", "")) for msg in messages) + len(response_text) } ) else: diff --git a/routes/system.py b/routes/system.py index 30ef999..70e927c 100644 --- a/routes/system.py +++ b/routes/system.py @@ -16,7 +16,7 @@ try: except ImportError: def apply_optimization_profile(profile): return {"profile": profile, "status": "system_optimizer not available"} -from utils.fastapi_utils import get_content_from_messages + from embedding import get_model_manager from pydantic import BaseModel import logging diff --git a/utils/__init__.py b/utils/__init__.py index 220e1c9..7c1037f 100644 --- a/utils/__init__.py +++ b/utils/__init__.py @@ -22,7 +22,6 @@ from .dataset_manager import ( ) from .project_manager import ( - get_content_from_messages, generate_project_readme, save_project_readme, get_project_status, @@ -31,20 +30,7 @@ from .project_manager import ( get_project_stats ) -# Import agent management modules -# Note: These have been moved to agent package -# from .file_loaded_agent_manager import ( -# get_global_agent_manager, -# init_global_agent_manager -# ) -# Import optimized modules -# Note: These have been moved to agent package -# from .sharded_agent_manager import ( -# ShardedAgentManager, -# get_global_sharded_agent_manager, -# init_global_sharded_agent_manager -# ) from .connection_pool import ( HTTPConnectionPool, @@ -153,7 +139,6 @@ __all__ = [ 'remove_dataset_directory_by_key', # project_manager - 'get_content_from_messages', 'generate_project_readme', 'save_project_readme', 'get_project_status', @@ -161,10 +146,6 @@ __all__ = [ 'list_projects', 'get_project_stats', - # file_loaded_agent_manager (moved to agent package) - # 'get_global_agent_manager', - # 'init_global_agent_manager', - # agent_pool 'AgentPool', 'get_agent_pool', diff --git a/utils/fastapi_utils.py b/utils/fastapi_utils.py index 9cbc1b3..f9b0f78 100644 --- a/utils/fastapi_utils.py +++ b/utils/fastapi_utils.py @@ -6,10 +6,14 @@ import asyncio from concurrent.futures import ThreadPoolExecutor from typing import List, Dict, Optional, Union, Any import aiohttp -from qwen_agent.llm.schema import ASSISTANT, FUNCTION -from qwen_agent.llm.oai import TextChatAtOAI from fastapi import HTTPException import logging +from langchain_core.messages import HumanMessage, AIMessage, SystemMessage +from langchain.chat_models import init_chat_model + +USER = "user" +ASSISTANT = "assistant" +TOOL = "tool" logger = logging.getLogger('app') @@ -19,6 +23,16 @@ thread_pool = ThreadPoolExecutor(max_workers=10) # 创建并发信号量，限制同时进行的API调用数量 api_semaphore = asyncio.Semaphore(8) # 最多同时进行8个API调用 +def detect_provider(model_name,model_server): + """根据模型名称检测提供商类型""" + model_name_lower = model_name.lower() + if any(claude_model in model_name_lower for claude_model in ["claude", "anthropic"]): + return "anthropic",model_server.replace("/v1","") + elif any(openai_model in model_name_lower for openai_model in ["gpt", "openai", "o1"]): + return "openai",model_server + else: + # 默认使用 openai 兼容格式 + return "openai",model_server def get_versioned_filename(upload_dir: str, name_without_ext: str, file_extension: str) -> tuple[str, int]: """ @@ -93,50 +107,50 @@ def create_stream_chunk(chunk_id: str, model_name: str, content: str = None, fin } return chunk_data -def get_content_from_messages(messages: List[dict], tool_response: bool = True) -> str: - """Extract content from qwen-agent messages with special formatting""" - full_text = '' - content = [] - TOOL_CALL_S = '[TOOL_CALL]' - TOOL_RESULT_S = '[TOOL_RESPONSE]' - THOUGHT_S = '[THINK]' - ANSWER_S = '[ANSWER]' - PREAMBLE_S = '[PREAMBLE]' +# def get_content_from_messages(messages: List[dict], tool_response: bool = True) -> str: +# """Extract content from qwen-agent messages with special formatting""" +# full_text = '' +# content = [] +# TOOL_CALL_S = '[TOOL_CALL]' +# TOOL_RESULT_S = '[TOOL_RESPONSE]' +# THOUGHT_S = '[THINK]' +# ANSWER_S = '[ANSWER]' +# PREAMBLE_S = '[PREAMBLE]' - for msg in messages: - if msg['role'] == ASSISTANT: - if msg.get('reasoning_content'): - assert isinstance(msg['reasoning_content'], str), 'Now only supports text messages' - content.append(f'{THOUGHT_S}\n{msg["reasoning_content"]}') - if msg.get('content'): - assert isinstance(msg['content'], str), 'Now only supports text messages' - # 过滤掉流式输出中的不完整 tool_call 文本 - content_text = msg["content"] +# for msg in messages: +# if msg['role'] == ASSISTANT: +# if msg.get('reasoning_content'): +# assert isinstance(msg['reasoning_content'], str), 'Now only supports text messages' +# content.append(f'{THOUGHT_S}\n{msg["reasoning_content"]}') +# if msg.get('content'): +# assert isinstance(msg['content'], str), 'Now only supports text messages' +# # 过滤掉流式输出中的不完整 tool_call 文本 +# content_text = msg["content"] - # 使用正则表达式替换不完整的 tool_call 模式为空字符串 +# # 使用正则表达式替换不完整的 tool_call 模式为空字符串 - # 匹配并替换不完整的 tool_call 模式 - content_text = re.sub(r' List[Dict[str, str]]: @@ -156,13 +170,13 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li processed_messages = [] # 收集所有ASSISTANT消息的索引 - assistant_indices = [i for i, msg in enumerate(messages) if msg.role == "assistant"] + assistant_indices = [i for i, msg in enumerate(messages) if msg.role == ASSISTANT] total_assistant_messages = len(assistant_indices) cutoff_point = max(0, total_assistant_messages - 5) # 处理每条消息 for i, msg in enumerate(messages): - if msg.role == "assistant": + if msg.role == ASSISTANT: # 确定当前ASSISTANT消息在所有ASSISTANT消息中的位置（从0开始） assistant_position = assistant_indices.index(i) @@ -236,7 +250,7 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li assistant_content = "" function_calls = [] tool_responses = [] - + tool_id = "" for i in range(0, len(parts)): if i % 2 == 0: # 文本内容 text = parts[i].strip() @@ -259,8 +273,10 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li break if should_include: + # 将 TOOL_RESPONSE 包装成 tool_result 消息，紧跟对应的 tool_use final_messages.append({ - "role": FUNCTION, + "role": TOOL, + "tool_call_id": tool_id, # 与前面 tool_use 的 id 保持一致 "name": function_name, "content": response_content }) @@ -279,13 +295,17 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li break if should_include: + tool_id = f"tool_id_{i}" final_messages.append({ "role": ASSISTANT, "content": "", - "function_call": { - "name": function_name, - "arguments": arguments - } + "tool_calls": [{ + "id":tool_id, + "function": { + "name": function_name, + "arguments": arguments + } + }] }) elif current_tag != "THINK" and current_tag != "PREAMBLE": final_messages.append({ @@ -297,7 +317,6 @@ def process_messages(messages: List[Dict], language: Optional[str] = None) -> Li else: # 非 assistant 消息或不包含 [TOOL_RESPONSE] 的消息直接添加 final_messages.append(msg) - print(final_messages) return final_messages @@ -317,16 +336,19 @@ def format_messages_to_chat_history(messages: List[Dict[str, str]]) -> str: for message in messages: role = message.get('role', '') content = message.get('content', '') - if role == 'user': + name = message.get('name', '') + if role == USER: chat_history.append(f"user: {content}") - elif role == FUNCTION: - chat_history.append(f"function_response: {content}") + elif role == TOOL: + chat_history.append(f"{name} response: {content}") elif role == ASSISTANT: if len(content) >0: chat_history.append(f"assistant: {content}") - if message.get('function_call'): - chat_history.append(f"function_call: {message.get('function_call').get('name')} ") - chat_history.append(f"{message.get('function_call').get('arguments')}") + if message.get('tool_calls'): + for tool_call in message.get('tool_calls'): + function_name = tool_call.get('function').get('name') + arguments = tool_call.get('function').get('arguments') + chat_history.append(f"{function_name} call: {arguments}") recent_chat_history = chat_history[-15:] if len(chat_history) > 15 else chat_history print(f"recent_chat_history:{recent_chat_history}") @@ -411,30 +433,44 @@ async def fetch_bot_config(bot_id: str) -> Dict[str, Any]: ) -def _sync_call_llm(llm_config, messages) -> str: - """同步调用LLM的辅助函数，在线程池中执行""" - llm_instance = TextChatAtOAI(llm_config) +async def _sync_call_llm(llm_config, messages) -> str: + """同步调用LLM的辅助函数，在线程池中执行 - 使用LangChain""" try: - # 设置stream=False来获取非流式响应 - response = llm_instance.chat(messages=messages, stream=False) + # 创建LangChain LLM实例 + model_name = llm_config.get('model') + model_server = llm_config.get('model_server') + api_key = llm_config.get('api_key') + # 检测或使用指定的提供商 + model_provider,base_url = detect_provider(model_name,model_server) + + # 构建模型参数 + model_kwargs = { + "model": model_name, + "model_provider": model_provider, + "temperature": 0.8, + "base_url":base_url, + "api_key":api_key + } + llm_instance = init_chat_model(**model_kwargs) - # 处理响应 - if isinstance(response, list) and response: - # 如果返回的是Message列表，提取内容 - if hasattr(response[0], 'content'): - return response[0].content - elif isinstance(response[0], dict) and 'content' in response[0]: - return response[0]['content'] - - # 如果是字符串，直接返回 - if isinstance(response, str): - return response - - # 处理其他类型 - return str(response) if response else "" + # 转换消息格式为LangChain格式 + langchain_messages = [] + for msg in messages: + if msg['role'] == 'system': + langchain_messages.append(SystemMessage(content=msg['content'])) + elif msg['role'] == 'user': + langchain_messages.append(HumanMessage(content=msg['content'])) + elif msg['role'] == 'assistant': + langchain_messages.append(AIMessage(content=msg['content'])) + + # 调用LangChain模型 + response = await llm_instance.ainvoke(langchain_messages) + + # 返回响应内容 + return response.content if response.content else "" except Exception as e: - logger.error(f"Error calling guideline LLM: {e}") + logger.error(f"Error calling guideline LLM with LangChain: {e}") return "" def get_language_text(language: str): @@ -550,9 +586,8 @@ async def call_preamble_llm(chat_history: str, last_message: str, preamble_choic try: # 使用信号量控制并发API调用数量 async with api_semaphore: - # 使用线程池执行同步HTTP调用，避免阻塞事件循环 - loop = asyncio.get_event_loop() - response = await loop.run_in_executor(thread_pool, _sync_call_llm, llm_config, messages) + # 直接调用异步LLM函数 + response = await _sync_call_llm(llm_config, messages) # 从响应中提取 ```json 和 ``` 包裹的内容 json_pattern = r'```json\s*\n(.*?)\n```' @@ -605,9 +640,8 @@ async def call_guideline_llm(chat_history: str, guidelines_prompt: str, model_na try: # 使用信号量控制并发API调用数量 async with api_semaphore: - # 使用线程池执行同步HTTP调用，避免阻塞事件循环 - loop = asyncio.get_event_loop() - response = await loop.run_in_executor(thread_pool, _sync_call_llm, llm_config, messages) + # 直接调用异步LLM函数 + response = await _sync_call_llm(llm_config, messages) return response except Exception as e: diff --git a/utils/project_manager.py b/utils/project_manager.py index e9e4f8d..614d3aa 100644 --- a/utils/project_manager.py +++ b/utils/project_manager.py @@ -15,15 +15,6 @@ logger = logging.getLogger('app') from utils.file_utils import get_document_preview, load_processed_files_log -def get_content_from_messages(messages: List[dict]) -> str: - """Extract content from messages list""" - content = "" - for message in messages: - if message.get("role") == "user": - content += message.get("content", "") - return content - - def generate_directory_tree(project_dir: str, unique_id: str, max_depth: int = 3) -> str: """Generate dataset directory tree structure for the project""" def _build_tree(path: str, prefix: str = "", is_last: bool = True, depth: int = 0) -> List[str]: