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session_id

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朱潮 2025-12-15 21:36:13 +08:00
parent d9ee1edf8a
commit 0d50cd8e9f
4 changed files with 75 additions and 14 deletions
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51
agent/deep_assistant.py
View File

@ -1,15 +1,22 @@
import json
import logging
import os
import sqlite3
from typing import Any, Dict, Optional
from langchain.chat_models import init_chat_model
# from deepagents import create_deep_agent
from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_core.callbacks import BaseCallbackHandler
from langgraph.checkpoint.memory import MemorySaver
from utils.fastapi_utils import detect_provider
from .guideline_middleware import GuidelineMiddleware
MAX_CONTEXT_TOKENS = int(os.getenv("MAX_CONTEXT_TOKENS", 65536))
MAX_OUTPUT_TOKENS = int(os.getenv("MAX_OUTPUT_TOKENS", 8000))
SUMMARIZATION_MAX_TOKENS = MAX_CONTEXT_TOKENS - MAX_OUTPUT_TOKENS - 1000
class LoggingCallbackHandler(BaseCallbackHandler):
"""自定义的 CallbackHandler，使用项目的 logger 来记录日志"""
@ -120,7 +127,24 @@ async def get_tools_from_mcp(mcp):
async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
model_server=None, generate_cfg=None,
system_prompt=None, mcp=None, robot_type=None, language="jp", user_identifier=None):
system_prompt=None, mcp=None, robot_type=None, language="jp", user_identifier=None,
session_id=None):
"""
初始化 Agent，支持持久化内存和对话摘要
Args:
bot_id: Bot 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
mcp: MCP配置
robot_type: 机器人类型
language: 语言
user_identifier: 用户标识
session_id: 会话ID（如果为None，则不启用持久化内存）
"""
system = system_prompt if system_prompt else read_system_prompt()
mcp = mcp if mcp else read_mcp_settings()
mcp_tools = await get_tools_from_mcp(mcp)
@ -143,14 +167,33 @@ async def init_agent(bot_id: str, model_name="qwen3-next", api_key=None,
# 创建自定义的日志处理器
logging_handler = LoggingCallbackHandler()
# 构建中间件列表
middleware = [GuidelineMiddleware(bot_id, llm_instance, system, robot_type, language, user_identifier)]
# 初始化 checkpointer 和中间件
checkpointer = None
if session_id:
checkpointer = MemorySaver()
summarization_middleware = SummarizationMiddleware(
model=llm_instance,
max_tokens_before_summary=SUMMARIZATION_MAX_TOKENS,
messages_to_keep=20, # 摘要后保留最近 20 条消息
summary_prompt="请简洁地总结以上对话的要点，包括重要的用户信息、讨论过的话题和关键结论。"
)
middleware.append(summarization_middleware)
agent = create_agent(
model=llm_instance,
system_prompt=system,
tools=mcp_tools,
middleware=[GuidelineMiddleware(bot_id, llm_instance, system, robot_type, language, user_identifier)]
middleware=middleware,
checkpointer=checkpointer # 传入 checkpointer 以启用持久化
)
# 将 handler 存储在 agent 的属性中，方便在调用时使用
# 将 handler 和 checkpointer 存储在 agent 的属性中，方便在调用时使用
agent.logging_handler = logging_handler
agent.checkpointer = checkpointer
agent.bot_id = bot_id
agent.session_id = session_id
return agent

4
agent/sharded_agent_manager.py
View File

@ -127,7 +127,8 @@ class ShardedAgentManager:
system_prompt: Optional[str] = None,
mcp_settings: Optional[List[Dict]] = None,
robot_type: Optional[str] = "general_agent",
user_identifier: Optional[str] = None):
user_identifier: Optional[str] = None,
session_id: Optional[str] = None):
"""获取或创建文件预加载的助手实例"""
# 更新请求统计
@ -201,6 +202,7 @@ class ShardedAgentManager:
robot_type=robot_type,
language=language,
user_identifier=user_identifier,
session_id=session_id
)
# 缓存实例

32
routes/chat.py
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@ -82,7 +82,8 @@ async def enhanced_generate_stream_response(
robot_type: str,
project_dir: Optional[str],
generate_cfg: Optional[dict],
user_identifier: Optional[str]
user_identifier: Optional[str],
session_id: Optional[str] = None,
):
"""增强的渐进式流式响应生成器 - 并发优化版本"""
try:
@ -133,7 +134,8 @@ async def enhanced_generate_stream_response(
system_prompt=system_prompt,
mcp_settings=mcp_settings,
robot_type=robot_type,
user_identifier=user_identifier
user_identifier=user_identifier,
session_id=session_id,
)
# 开始流式处理
@ -141,7 +143,11 @@ async def enhanced_generate_stream_response(
chunk_id = 0
message_tag = ""
config = {"callbacks": [agent.logging_handler]} if hasattr(agent, 'logging_handler') else {}
config = {}
if session_id:
config["configurable"] = {"thread_id": session_id}
if hasattr(agent, 'logging_handler'):
config["callbacks"] = [agent.logging_handler]
async for msg, metadata in agent.astream({"messages": messages}, stream_mode="messages", config=config):
new_content = ""
@ -265,7 +271,8 @@ async def create_agent_and_generate_response(
robot_type: str,
project_dir: Optional[str] = None,
generate_cfg: Optional[dict] = None,
user_identifier: Optional[str] = None
user_identifier: Optional[str] = None,
session_id: Optional[str] = None
) -> Union[ChatResponse, StreamingResponse]:
"""创建agent并生成响应的公共逻辑"""
if generate_cfg is None:
@ -288,7 +295,8 @@ async def create_agent_and_generate_response(
robot_type=robot_type,
project_dir=project_dir,
generate_cfg=generate_cfg,
user_identifier=user_identifier
user_identifier=user_identifier,
session_id=session_id
),
media_type="text/event-stream",
headers={"Cache-Control": "no-cache", "Connection": "keep-alive"}
@ -307,14 +315,19 @@ async def create_agent_and_generate_response(
system_prompt=system_prompt,
mcp_settings=mcp_settings,
robot_type=robot_type,
user_identifier=user_identifier
user_identifier=user_identifier,
session_id=session_id,
)
# 准备最终的消息
final_messages = messages.copy()
# 非流式响应
config = {"callbacks": [agent.logging_handler]} if hasattr(agent, 'logging_handler') else {}
config = {}
if session_id:
config["configurable"] = {"thread_id": session_id}
if hasattr(agent, 'logging_handler'):
config["callbacks"] = [agent.logging_handler]
agent_responses = await agent.ainvoke({"messages": final_messages}, config=config)
append_messages = agent_responses["messages"][len(final_messages):]
response_text = ""
@ -396,7 +409,7 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
project_dir = create_project_directory(request.dataset_ids, bot_id, request.robot_type)
# 收集额外参数作为 generate_cfg
exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier'}
exclude_fields = {'messages', 'model', 'model_server', 'dataset_ids', 'language', 'tool_response', 'system_prompt', 'mcp_settings' ,'stream', 'robot_type', 'bot_id', 'user_identifier', 'session_id'}
generate_cfg = {k: v for k, v in request.model_dump().items() if k not in exclude_fields}
# 处理消息
@ -417,7 +430,8 @@ async def chat_completions(request: ChatRequest, authorization: Optional[str] =
robot_type=request.robot_type,
project_dir=project_dir,
generate_cfg=generate_cfg,
user_identifier=request.user_identifier
user_identifier=request.user_identifier,
session_id=request.session_id
)
except Exception as e:

2
utils/api_models.py
View File

@ -52,6 +52,7 @@ class ChatRequest(BaseModel):
mcp_settings: Optional[List[Dict]] = None
robot_type: Optional[str] = "general_agent"
user_identifier: Optional[str] = ""
session_id: Optional[str] = None
class ChatRequestV2(BaseModel):
@ -61,6 +62,7 @@ class ChatRequestV2(BaseModel):
bot_id: str
language: Optional[str] = "zh"
user_identifier: Optional[str] = ""
session_id: Optional[str] = None
class FileProcessRequest(BaseModel):