feat: restore risk assessment workspace and improve page scrolling
This commit is contained in:
NathanAlex7
+81
-12
@@ -19,12 +19,17 @@ class AgentManager:
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self._base_dir: Path | None = None
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self._tools: list = []
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self._llm = None
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self._raw_logs_dir: Path | None = None
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def initialize(self, base_dir: Path) -> None:
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"""Initialize LLM (DeepSeek) and tools. Called once at startup."""
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self._base_dir = base_dir
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self._tools = get_all_tools(base_dir)
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# 初始化原始报文日志目录 (base_dir 已是 backend/ 目录)
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self._raw_logs_dir = base_dir / "raw_logs"
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self._raw_logs_dir.mkdir(exist_ok=True, parents=True)
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# Use langchain-deepseek for native DeepSeek support
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from langchain_deepseek import ChatDeepSeek
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@@ -70,7 +75,7 @@ class AgentManager:
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return messages
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async def astream(
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self, message: str, history: list[dict[str, Any]]
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self, message: str, history: list[dict[str, Any]], session_id: str = "default"
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) -> AsyncGenerator[dict[str, Any], None]:
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"""Stream agent response with token-level and node-level events.
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@@ -80,6 +85,8 @@ class AgentManager:
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{"type": "tool_start", "tool": "...", "input": "..."}
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{"type": "tool_end", "tool": "...", "output": "..."}
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{"type": "done", "content": "..."}
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Also persists complete message flow to {session}.msgs.jsonl for trajectory.
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"""
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# RAG retrieval: inject memory context if enabled
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rag_mode = get_rag_mode()
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@@ -101,6 +108,31 @@ class AgentManager:
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)
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rag_context = f"[记忆检索结果]\n{snippets}"
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# 为当前 session 创建原始报文回调
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from graph.raw_trace import RawTraceCallback
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raw_callback = RawTraceCallback(session_id, self._raw_logs_dir)
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# 消息流日志文件（记录完整的 human/assistant/tool 消息序列）
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import json
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import time
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msgs_log_file = self._raw_logs_dir / f"{session_id}.msgs.jsonl"
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# 记录 human 消息
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def _append_msg(role: str, content: str, **kwargs):
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"""追加一条消息到 msgs.jsonl"""
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msg_record = {
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"role": role,
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"content": content,
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"timestamp": time.time(),
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**kwargs
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}
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with open(msgs_log_file, "a", encoding="utf-8") as f:
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f.write(json.dumps(msg_record, ensure_ascii=False) + "\n")
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_append_msg("human", message)
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# 构建 agent 时传入 callback
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agent = self._build_agent()
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# Build messages with optional RAG context appended to history
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@@ -113,10 +145,13 @@ class AgentManager:
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full_response = ""
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tools_just_finished = False
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current_assistant_content = "" # 当前 assistant 消息累积
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current_tool_calls = [] # 当前 assistant 的 tool_calls
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async for event in agent.astream(
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{"messages": messages},
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stream_mode=["messages", "updates"],
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config={"callbacks": [raw_callback]},
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):
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# event is a tuple of (stream_mode, data) when using multiple modes
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if isinstance(event, tuple):
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@@ -136,31 +171,65 @@ class AgentManager:
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yield {"type": "new_response"}
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tools_just_finished = False
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full_response += msg.content
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current_assistant_content += msg.content
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yield {"type": "token", "content": msg.content}
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elif mode == "updates":
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if isinstance(data, dict):
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for node_name, node_data in data.items():
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if node_name == "tools" and "messages" in node_data:
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for tool_msg in node_data["messages"]:
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if hasattr(tool_msg, "name"):
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yield {
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"type": "tool_end",
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"tool": tool_msg.name,
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"output": str(tool_msg.content)[:2000],
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}
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# After all tool results, mark that tools finished
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tools_just_finished = True
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elif node_name == "model" and "messages" in node_data:
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if node_name == "model" and "messages" in node_data:
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# 模型节点：记录 assistant 消息和 tool_calls
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for agent_msg in node_data["messages"]:
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if hasattr(agent_msg, "tool_calls") and agent_msg.tool_calls:
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# 记录 assistant 消息（带 tool_calls）
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tool_calls_data = []
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for tc in agent_msg.tool_calls:
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tool_call_info = {
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"name": tc["name"],
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"args": tc.get("args", {})
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}
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tool_calls_data.append(tool_call_info)
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current_tool_calls.append(tool_call_info)
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yield {
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"type": "tool_start",
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"tool": tc["name"],
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"input": str(tc.get("args", ""))[:1000],
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}
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# 记录带 tool_calls 的 assistant 消息
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_append_msg(
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"assistant",
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agent_msg.content if hasattr(agent_msg, "content") else "",
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tool_calls=tool_calls_data
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)
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elif node_name == "tools" and "messages" in node_data:
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# 工具节点：记录每个 ToolMessage 的返回内容
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for tool_msg in node_data["messages"]:
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if hasattr(tool_msg, "name"):
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tool_output = str(tool_msg.content)
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# 记录 tool 消息（完整 content）
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_append_msg(
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"tool",
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tool_output,
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name=tool_msg.name,
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tool_call_id=getattr(tool_msg, "tool_call_id", None)
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)
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yield {
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"type": "tool_end",
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"tool": tool_msg.name,
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"output": tool_output[:2000], # yield 时截断，但完整内容已记录
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}
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# After all tool results, mark that tools finished
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tools_just_finished = True
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# 记录最终的 assistant 消息（纯回复，无 tool_calls）
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if current_assistant_content and not current_tool_calls:
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_append_msg("assistant", current_assistant_content)
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elif current_assistant_content and current_tool_calls:
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# 如果有 tool_calls，记录最后一轮的纯文本回复
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_append_msg("assistant", current_assistant_content)
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yield {"type": "done", "content": full_response}
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async def ainvoke(self, message: str, session_id: str) -> str:
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