adai/MemoryBear
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Merge branch 'develop-mark' into develop

Browse Source
This commit is contained in:
Mark
2025-12-18 19:47:06 +08:00
parent 334f20d7b3 0503b26232
commit 8d810af1d0
6 changed files with 256 additions and 118 deletions
Download Patch File Download Diff File Expand all files Collapse all files

13
api/app/controllers/app_controller.py
View File

@@ -574,7 +574,7 @@ async def draft_run(
# 3. 流式返回
if payload.stream:
logger.debug(
"开始多智能体流式试运行",
"开始工作流流式试运行",
extra={
"app_id": str(app_id),
"message_length": len(payload.message),
@@ -583,16 +583,13 @@ async def draft_run(
)
async def event_generator():
"""多智能体流式事件生成器"""
multiservice = MultiAgentService(db)
"""工作流事件生成器"""
# 调用多智能体服务的流式方法
async for event in multiservice.run_stream(
async for event in workflow_service.run_stream(
app_id=app_id,
request=multi_agent_request,
storage_type=storage_type,
user_rag_memory_id=user_rag_memory_id
payload=payload,
config=config
):
yield event

60
api/app/core/workflow/executor.py
View File

@@ -5,17 +5,15 @@
"""
import logging
import uuid
import datetime
from typing import Any
from langchain_core.messages import HumanMessage
from langgraph.graph import StateGraph, START, END
from langgraph.graph.state import CompiledStateGraph
from app.core.workflow.nodes import WorkflowState, NodeFactory
from app.core.workflow.expression_evaluator import evaluate_condition
from app.models.workflow_model import WorkflowExecution, WorkflowNodeExecution
from app.db import get_db
logger = logging.getLogger(__name__)
@@ -94,7 +92,7 @@ class WorkflowExecutor:
def build_graph(self) -> StateGraph:
def build_graph(self) -> CompiledStateGraph:
"""构建 LangGraph
Returns:
@@ -278,13 +276,24 @@ class WorkflowExecutor:
):
"""执行工作流（流式）
手动执行节点以支持细粒度的流式输出：
- workflow_start: 工作流开始
- node_start: 节点开始执行
- node_chunk: LLM 节点的流式输出片段（逐 token）
- node_complete: 节点执行完成
- workflow_complete: 工作流完成
Args:
input_data: 输入数据
Yields:
流式事件
"""
logger.info(f"开始执行工作流（流式）: execution_id={self.execution_id}")
#
logger.info(f"开始执行工作流: execution_id={self.execution_id}")
# 记录开始时间
start_time = datetime.datetime.now()
# 1. 构建图
graph = self.build_graph()
@@ -292,32 +301,29 @@ class WorkflowExecutor:
# 2. 初始化状态（自动注入系统变量）
initial_state = self._prepare_initial_state(input_data)
# 3. 流式执行工作流
# 3. 执行工作流
try:
# 使用 astream 获取节点级别的更新
async for event in graph.astream(initial_state, stream_mode="updates"):
for node_name, state_update in event.items():
yield {
"type": "node_complete",
"node": node_name,
"data": state_update,
"execution_id": self.execution_id
}
logger.info(f"工作流执行完成（流式）: execution_id={self.execution_id}")
# 发送完成事件
yield {
"type": "workflow_complete",
"execution_id": self.execution_id
}
async for chunk in graph.astream(
initial_state,
# subgraphs=True,
stream_mode="updates",
):
# print(chunk)
yield chunk
except Exception as e:
logger.error(f"工作流执行失败（流式）: execution_id={self.execution_id}, error={e}", exc_info=True)
# 计算耗时（即使失败也记录）
end_time = datetime.datetime.now()
elapsed_time = (end_time - start_time).total_seconds()
logger.error(f"工作流执行失败: execution_id={self.execution_id}, error={e}", exc_info=True)
yield {
"type": "workflow_error",
"execution_id": self.execution_id,
"error": str(e)
"status": "failed",
"error": str(e),
"output": None,
"node_outputs": {},
"elapsed_time": elapsed_time,
"token_usage": None
}

5
api/app/core/workflow/nodes/base_config.py
View File

@@ -50,6 +50,11 @@ class VariableDefinition(BaseModel):
description="变量描述"
)
max_length: int = Field(
default=200,
description="只对字符串类型生效"
)
class Config:
json_schema_extra = {
"examples": [

1
api/app/core/workflow/nodes/end/node.py
View File

@@ -5,7 +5,6 @@ End 节点实现
"""
import logging
from typing import Any
from app.core.workflow.nodes.base_node import BaseNode, WorkflowState

6
api/app/core/workflow/nodes/llm/node.py
View File

@@ -10,10 +10,8 @@ from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
from app.core.workflow.nodes.base_node import BaseNode, WorkflowState
from app.core.models import RedBearLLM, RedBearModelConfig
from app.models import ModelConfig
from app.db import get_db, get_db_context
from app.models.models_model import ModelApiKey
from app.services.model_service import ModelConfigService, ModelApiKeyService
from app.db import get_db_context
from app.services.model_service import ModelConfigService
from app.core.exceptions import BusinessException
from app.core.error_codes import BizCode

139
api/app/services/workflow_service.py
View File

@@ -1,7 +1,7 @@
"""
工作流服务层
"""
import json
import logging
import uuid
import datetime
@@ -530,6 +530,109 @@ class WorkflowService:
message=f"工作流执行失败: {str(e)}"
)
async def run_stream(
self,
app_id: uuid.UUID,
payload: DraftRunRequest,
config: WorkflowConfig
):
"""运行工作流（流式）
Args:
app_id: 应用 ID
payload: 请求对象（包含 message, variables, conversation_id 等）
config: 存储类型（可选）
Yields:
SSE 格式的流式事件
Raises:
BusinessException: 配置不存在或执行失败时抛出
"""
# 1. 获取工作流配置
if not config:
config = self.get_workflow_config(app_id)
if not config:
raise BusinessException(
code=BizCode.CONFIG_MISSING,
message=f"工作流配置不存在: app_id={app_id}"
)
input_data = {"message": payload.message, "variables": payload.variables,
"conversation_id": payload.conversation_id}
# 转换 user_id 为 UUID
triggered_by_uuid = None
if payload.user_id:
try:
triggered_by_uuid = uuid.UUID(payload.user_id)
except (ValueError, AttributeError):
logger.warning(f"无效的 user_id 格式: {payload.user_id}")
# 转换 conversation_id 为 UUID
conversation_id_uuid = None
if payload.conversation_id:
try:
conversation_id_uuid = uuid.UUID(payload.conversation_id)
except (ValueError, AttributeError):
logger.warning(f"无效的 conversation_id 格式: {payload.conversation_id}")
# 2. 创建执行记录
execution = self.create_execution(
workflow_config_id=config.id,
app_id=app_id,
trigger_type="manual",
triggered_by=triggered_by_uuid,
conversation_id=conversation_id_uuid,
input_data=input_data
)
# 3. 构建工作流配置字典
workflow_config_dict = {
"nodes": config.nodes,
"edges": config.edges,
"variables": config.variables,
"execution_config": config.execution_config
}
# 4. 获取工作空间 ID（从 app 获取）
from app.models import App
# 5. 流式执行工作流
from app.core.workflow.executor import execute_workflow, execute_workflow_stream
try:
# 更新状态为运行中
self.update_execution_status(execution.execution_id, "running")
# 发送开始事件
yield f"data: {json.dumps({'type': 'workflow_start', 'execution_id': execution.execution_id})}\n\n"
# 调用流式执行
async for event in self._run_workflow_stream(
workflow_config=workflow_config_dict,
input_data=input_data,
execution_id=execution.execution_id,
workspace_id="",
user_id=payload.user_id
):
# 清理事件数据，移除不可序列化的对象
cleaned_event = self._clean_event_for_json(event)
# 转换为 SSE 格式
yield f"data: {json.dumps(cleaned_event)}\n\n"
# 发送完成事件
yield f"data: {json.dumps({'type': 'workflow_end', 'execution_id': execution.execution_id})}\n\n"
except Exception as e:
logger.error(f"工作流流式执行失败: execution_id={execution.execution_id}, error={e}", exc_info=True)
self.update_execution_status(
execution.execution_id,
"failed",
error_message=str(e)
)
# 发送错误事件
yield f"data: {json.dumps({'type': 'error', 'execution_id': execution.execution_id, 'error': str(e)})}\n\n"
async def run_workflow(
self,
app_id: uuid.UUID,
@@ -651,14 +754,44 @@ class WorkflowService:
message=f"工作流执行失败: {str(e)}"
)
def _clean_event_for_json(self, event: dict[str, Any]) -> dict[str, Any]:
"""清理事件数据，移除不可序列化的对象
Args:
event: 原始事件数据
Returns:
可序列化的事件数据
"""
from langchain_core.messages import BaseMessage
def clean_value(value):
"""递归清理值"""
if isinstance(value, BaseMessage):
# 将 Message 对象转换为字典
return {
"type": value.__class__.__name__,
"content": value.content,
}
elif isinstance(value, dict):
return {k: clean_value(v) for k, v in value.items()}
elif isinstance(value, list):
return [clean_value(item) for item in value]
elif isinstance(value, (str, int, float, bool, type(None))):
return value
else:
# 其他不可序列化的对象转换为字符串
return str(value)
return clean_value(event)
async def _run_workflow_stream(
self,
workflow_config: dict[str, Any],
input_data: dict[str, Any],
execution_id: str,
workspace_id: str,
user_id: str
):
user_id: str):
"""运行工作流（流式，内部方法）
Args: