refactor: 改用 LangGraph 原生 create_react_agent + astream_events
Some checks failed
构建并部署 AI Agent 服务 / deploy (push) Failing after 6m43s
Some checks failed
构建并部署 AI Agent 服务 / deploy (push) Failing after 6m43s
This commit is contained in:
@@ -1,10 +1,9 @@
|
||||
"""
|
||||
AI Agent 服务类 - 极简 LangGraph Agent 架构
|
||||
AI Agent 服务类 - 用 LangGraph 原生 astream_events
|
||||
接收外部传入的 checkpointer，不负责管理连接生命周期
|
||||
"""
|
||||
|
||||
import json
|
||||
import asyncio
|
||||
from typing import AsyncGenerator, Dict, Any, Optional, Tuple
|
||||
|
||||
# LangGraph 序列化器（修复 checkpoint 反序列化警告）
|
||||
@@ -15,7 +14,6 @@ from ..model_services import get_cached_chat_services
|
||||
from ..main_graph.main_graph_builder import build_agent_graph
|
||||
from backend.app.logger import debug, info, warning, error
|
||||
from ..main_graph.state import AgentState
|
||||
from .stream_context import token_queue_var
|
||||
|
||||
|
||||
class AIAgentService:
|
||||
@@ -120,125 +118,10 @@ class AIAgentService:
|
||||
"model_used": resolved_model
|
||||
}
|
||||
|
||||
def _serialize_value(self, value):
|
||||
"""递归将 LangChain 对象转换为可 JSON 序列化的格式"""
|
||||
if hasattr(value, 'content'):
|
||||
msg_type = getattr(value, 'type', 'message')
|
||||
return {
|
||||
"role": msg_type,
|
||||
"content": getattr(value, 'content', ''),
|
||||
"additional_kwargs": getattr(value, 'additional_kwargs', {}),
|
||||
"tool_calls": getattr(value, 'tool_calls', [])
|
||||
}
|
||||
elif isinstance(value, dict):
|
||||
return {k: self._serialize_value(v) for k, v in value.items()}
|
||||
elif isinstance(value, (list, tuple)):
|
||||
return [self._serialize_value(item) for item in value]
|
||||
else:
|
||||
try:
|
||||
json.dumps(value)
|
||||
return value
|
||||
except (TypeError, ValueError):
|
||||
return str(value)
|
||||
|
||||
async def _handle_message_chunk(
|
||||
self, chunk: Dict[str, Any], current_node: Optional[str], tool_calls_in_progress: Dict[str, Any]
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
"""处理 messages 类型的 chunk"""
|
||||
message_chunk, metadata = chunk["data"]
|
||||
node_name = metadata.get("langgraph_node", "unknown")
|
||||
new_current_node = current_node
|
||||
|
||||
# 检测节点变化，发送节点开始事件
|
||||
if node_name != current_node:
|
||||
if current_node:
|
||||
yield {"type": "node_end", "node": current_node}
|
||||
yield {"type": "node_start", "node": node_name}
|
||||
new_current_node = node_name
|
||||
|
||||
# 处理消息内容
|
||||
token_content = getattr(message_chunk, 'content', str(message_chunk))
|
||||
reasoning_token = ""
|
||||
if hasattr(message_chunk, 'additional_kwargs'):
|
||||
reasoning_token = message_chunk.additional_kwargs.get("reasoning_content", "")
|
||||
|
||||
# 处理思考过程
|
||||
if reasoning_token:
|
||||
yield {
|
||||
"type": "llm_token",
|
||||
"node": node_name,
|
||||
"reasoning_token": reasoning_token
|
||||
}
|
||||
# 处理工具调用
|
||||
elif hasattr(message_chunk, 'tool_calls') and message_chunk.tool_calls:
|
||||
for tool_call in message_chunk.tool_calls:
|
||||
tool_call_id = tool_call.get("id", "")
|
||||
tool_name = tool_call.get("name", "")
|
||||
tool_args = tool_call.get("args", {})
|
||||
|
||||
# 记录工具调用开始，避免重复
|
||||
if tool_call_id and tool_call_id not in tool_calls_in_progress:
|
||||
tool_calls_in_progress[tool_call_id] = {
|
||||
"name": tool_name,
|
||||
"args": tool_args
|
||||
}
|
||||
yield {
|
||||
"type": "tool_call_start",
|
||||
"tool": tool_name,
|
||||
"args": tool_args,
|
||||
"id": tool_call_id
|
||||
}
|
||||
# 处理普通 token
|
||||
elif token_content:
|
||||
yield {
|
||||
"type": "llm_token",
|
||||
"node": node_name,
|
||||
"token": token_content,
|
||||
"reasoning_token": reasoning_token
|
||||
}
|
||||
|
||||
# 返回更新后的 current_node
|
||||
yield {"type": "_update_state", "current_node": new_current_node}
|
||||
|
||||
async def _handle_updates_chunk(
|
||||
self, chunk: Dict[str, Any], tool_calls_in_progress: Dict[str, Any], actual_model_used: str
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
"""处理 updates 类型的 chunk"""
|
||||
updates_data = chunk["data"]
|
||||
new_actual_model = actual_model_used
|
||||
|
||||
serialized_data = self._serialize_value(updates_data)
|
||||
|
||||
# 检查是否有工具结果
|
||||
if "messages" in serialized_data:
|
||||
for msg in serialized_data["messages"]:
|
||||
# 检测工具结果消息
|
||||
if msg.get("role") == "tool":
|
||||
tool_call_id = msg.get("tool_call_id", "")
|
||||
tool_name = msg.get("name", "")
|
||||
tool_result = msg.get("content", "")
|
||||
|
||||
if tool_call_id and tool_call_id in tool_calls_in_progress:
|
||||
yield {
|
||||
"type": "tool_call_end",
|
||||
"tool": tool_name,
|
||||
"id": tool_call_id,
|
||||
"result": tool_result
|
||||
}
|
||||
del tool_calls_in_progress[tool_call_id]
|
||||
|
||||
yield {
|
||||
"type": "state_update",
|
||||
"data": serialized_data
|
||||
}
|
||||
|
||||
# 返回更新后的模型
|
||||
yield {"type": "_update_state", "actual_model_used": new_actual_model}
|
||||
|
||||
async def process_message_stream(
|
||||
self, message: str, thread_id: str, model: str = "", user_id: str = "default_user"
|
||||
) -> AsyncGenerator[Dict[str, Any], None]:
|
||||
"""流式处理消息，返回异步生成器"""
|
||||
"""流式处理消息，用 astream_events 原生支持"""
|
||||
# 解析模型名称
|
||||
resolved_model = self._resolve_model(model)
|
||||
|
||||
@@ -246,123 +129,86 @@ class AIAgentService:
|
||||
config, input_state = self._build_invocation(message, thread_id, resolved_model, user_id)
|
||||
|
||||
info(f"🚀 开始执行 Agent 图，指定模型: {resolved_model}")
|
||||
current_node = None
|
||||
tool_calls_in_progress: Dict[str, Any] = {}
|
||||
actual_model_used = resolved_model
|
||||
chunk_count = 0
|
||||
full_message_content = ""
|
||||
|
||||
# 创建 token 队列
|
||||
token_queue = asyncio.Queue()
|
||||
|
||||
# 设置上下文变量
|
||||
token_queue_var.set(token_queue)
|
||||
|
||||
# 事件：graph 执行完成
|
||||
graph_done = asyncio.Event()
|
||||
graph_error = None
|
||||
|
||||
async def run_graph():
|
||||
"""在后台运行 graph，并把 chunk 放进队列，同时也处理 events"""
|
||||
nonlocal chunk_count, full_message_content, graph_error
|
||||
try:
|
||||
info(f"📡 开始调用 graph.astream()...")
|
||||
|
||||
event_count = 0
|
||||
|
||||
async for chunk in self.graph.astream(
|
||||
input_state,
|
||||
config=config,
|
||||
stream_mode=["messages", "updates"],
|
||||
version="v2",
|
||||
subgraphs=True
|
||||
):
|
||||
chunk_count += 1
|
||||
chunk_type = chunk["type"]
|
||||
|
||||
# 记录原始 chunk 信息（前 10 个和后 10 个）
|
||||
if chunk_count <= 10 or chunk_count % 50 == 0:
|
||||
info(f" [{chunk_count}] chunk_type={chunk_type}, data={type(chunk.get('data'))}")
|
||||
|
||||
if chunk_type == "messages":
|
||||
async for event in self._handle_message_chunk(
|
||||
chunk, current_node, tool_calls_in_progress
|
||||
):
|
||||
if event.get("type") == "_update_state":
|
||||
nonlocal current_node
|
||||
current_node = event.get("current_node", current_node)
|
||||
else:
|
||||
event_count += 1
|
||||
# 记录前 10 个事件
|
||||
if event_count <= 10:
|
||||
info(f" → yield event #{event_count}: {event.get('type')}")
|
||||
|
||||
# 如果是 agent 节点的 token，收集完整消息
|
||||
if (
|
||||
event.get("type") == "llm_token"
|
||||
and event.get("node") == "agent"
|
||||
and "token" in event
|
||||
):
|
||||
full_message_content += event["token"]
|
||||
await token_queue.put(event)
|
||||
|
||||
elif chunk_type == "updates":
|
||||
async for event in self._handle_updates_chunk(
|
||||
chunk, tool_calls_in_progress, actual_model_used
|
||||
):
|
||||
if event.get("type") == "_update_state":
|
||||
nonlocal actual_model_used
|
||||
actual_model_used = event.get("actual_model_used", actual_model_used)
|
||||
else:
|
||||
event_count += 1
|
||||
if event_count <= 10:
|
||||
info(f" → yield event #{event_count}: {event.get('type')}")
|
||||
await token_queue.put(event)
|
||||
|
||||
# 完整消息集合完成后，一次性打印
|
||||
info(f"✅ graph.astream() 完成，共 {chunk_count} 个 chunks, {event_count} 个 events")
|
||||
if full_message_content:
|
||||
info(f"📄 完整消息内容: {repr(full_message_content)}")
|
||||
|
||||
except Exception as e:
|
||||
error(f"❌ 执行图时出错: {e}")
|
||||
import traceback
|
||||
error(f"📋 堆栈: {traceback.format_exc()}")
|
||||
graph_error = e
|
||||
await token_queue.put({
|
||||
"type": "error",
|
||||
"message": str(e)
|
||||
})
|
||||
finally:
|
||||
graph_done.set()
|
||||
|
||||
# 启动后台任务运行 graph
|
||||
graph_task = asyncio.create_task(run_graph())
|
||||
|
||||
try:
|
||||
# 从队列里取事件并 yield
|
||||
while True:
|
||||
# 尝试从队列取事件，超时检查 graph 是否完成
|
||||
try:
|
||||
event = await asyncio.wait_for(token_queue.get(), timeout=0.1)
|
||||
yield event
|
||||
except asyncio.TimeoutError:
|
||||
# 检查 graph 是否完成
|
||||
if graph_done.is_set():
|
||||
break
|
||||
|
||||
# 如果 graph 有错误，已经在 run_graph 里 yield error 了
|
||||
|
||||
info(f"📡 开始调用 graph.astream_events()...")
|
||||
|
||||
async for event in self.graph.astream_events(input_state, config=config, version="v2"):
|
||||
kind = event["event"]
|
||||
# info(f"[Stream Event] {kind}") # 调试用
|
||||
|
||||
if kind == "on_chat_model_stream":
|
||||
# 流式 token
|
||||
chunk = event["data"]["chunk"]
|
||||
content = chunk.content if chunk.content else ""
|
||||
reasoning_content = ""
|
||||
if hasattr(chunk, 'additional_kwargs') and chunk.additional_kwargs:
|
||||
reasoning_content = chunk.additional_kwargs.get("reasoning_content", "")
|
||||
|
||||
if content:
|
||||
full_message_content += content
|
||||
|
||||
yield {
|
||||
"type": "llm_token",
|
||||
"node": "agent",
|
||||
"token": content,
|
||||
"reasoning_token": reasoning_content
|
||||
}
|
||||
|
||||
elif kind == "on_tool_start":
|
||||
# 工具调用开始
|
||||
tool_name = event["name"]
|
||||
tool_args = event["data"].get("input", {})
|
||||
yield {
|
||||
"type": "tool_call_start",
|
||||
"tool": tool_name,
|
||||
"args": tool_args,
|
||||
"id": event.get("run_id", "")
|
||||
}
|
||||
|
||||
elif kind == "on_tool_end":
|
||||
# 工具调用结束
|
||||
tool_name = event["name"]
|
||||
tool_output = event["data"].get("output", "")
|
||||
yield {
|
||||
"type": "tool_call_end",
|
||||
"tool": tool_name,
|
||||
"id": event.get("run_id", ""),
|
||||
"result": str(tool_output)
|
||||
}
|
||||
|
||||
elif kind == "on_chain_start":
|
||||
# 节点开始
|
||||
node_name = event.get("name", "unknown")
|
||||
yield {
|
||||
"type": "node_start",
|
||||
"node": node_name
|
||||
}
|
||||
|
||||
elif kind == "on_chain_end":
|
||||
# 节点结束
|
||||
node_name = event.get("name", "unknown")
|
||||
yield {
|
||||
"type": "node_end",
|
||||
"node": node_name
|
||||
}
|
||||
|
||||
info(f"✅ graph.astream_events() 完成")
|
||||
if full_message_content:
|
||||
info(f"📄 完整消息内容: {repr(full_message_content)}")
|
||||
|
||||
except Exception as e:
|
||||
error(f"❌ 执行图时出错: {e}")
|
||||
import traceback
|
||||
error(f"📋 堆栈: {traceback.format_exc()}")
|
||||
yield {
|
||||
"type": "error",
|
||||
"message": str(e)
|
||||
}
|
||||
finally:
|
||||
# 无论成功或失败，都发送结束事件，保证前端平稳关闭
|
||||
if current_node:
|
||||
yield {
|
||||
"type": "node_end",
|
||||
"node": current_node
|
||||
}
|
||||
yield {
|
||||
"type": "done",
|
||||
"model_used": actual_model_used
|
||||
}
|
||||
# 取消任务
|
||||
graph_task.cancel()
|
||||
|
||||
Reference in New Issue
Block a user