feat: 为智能体操作实现人工审批机制
- 新增 HumanApprovalModal 组件，用于处理用户对关键操作的审批。 - 引入 useApproval 可组合项，用于管理审批状态和逻辑。 - 更新 AgentChatComponent 以显示审批模态框并处理审批操作。 - 增强消息处理功能，支持工具调用合并并改进对 AI 消息块的处理。 - 重构各种组件和 API，以整合新的审批流程，确保代理交互期间的流畅用户体验。
This commit is contained in:
Wenjie Zhang
parent
127c73e180
commit
181341db48
@ -314,7 +314,10 @@ def upload(
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directory: pathlib.Path = typer.Option(
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..., help="The directory containing files to upload.", exists=True, file_okay=False
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),
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pattern: list[str] = typer.Option(["*.md"], help="The glob patterns for files to upload (e.g., '*.pdf', '**/*.txt'). Can be specified multiple times."),
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pattern: list[str] = typer.Option(
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["*.md"],
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help="The glob patterns for files to upload (e.g., '*.pdf', '**/*.txt'). Can be specified multiple times.",
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),
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base_url: str = typer.Option("http://127.0.0.1:5050/api", help="The base URL of the API server."),
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username: str = typer.Option(..., help="Admin username for login."),
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password: str = typer.Option(..., help="Admin password for login."),
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@ -354,7 +357,9 @@ def upload(
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if not all_files:
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patterns_str = "', '".join(pattern)
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console.print(f"[bold yellow]No files found in '{directory}' matching patterns: '{patterns_str}'. Aborting.[/bold yellow]")
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console.print(
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f"[bold yellow]No files found in '{directory}' matching patterns: '{patterns_str}'. Aborting.[/bold yellow]"
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)
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raise typer.Exit()
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# 过滤掉macos的隐藏文件
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@ -398,11 +403,13 @@ def upload(
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# Split all files into batches
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for batch_num in range(0, len(files_to_upload), batch_size):
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batch_files = files_to_upload[batch_num:batch_num + batch_size]
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batch_files = files_to_upload[batch_num : batch_num + batch_size]
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batch_start = batch_num + 1
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batch_end = min(batch_num + batch_size, len(files_to_upload))
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console.print(f"\n[bold yellow]=== Batch {batch_start}-{batch_end} of {len(files_to_upload)} ===[/bold yellow]")
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console.print(
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f"\n[bold yellow]=== Batch {batch_start}-{batch_end} of {len(files_to_upload)} ===[/bold yellow]"
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)
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# Step 1: Upload this batch of files sequentially
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console.print(f"[blue]Step 1: Uploading {len(batch_files)} files...[/blue]")
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@ -420,7 +427,9 @@ def upload(
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console=console,
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transient=True,
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) as progress:
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upload_task_id = progress.add_task(f"Uploading batch {batch_start}-{batch_end}...", total=len(batch_files), postfix="")
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upload_task_id = progress.add_task(
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f"Uploading batch {batch_start}-{batch_end}...", total=len(batch_files), postfix=""
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)
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for file_path, file_hash in batch_files:
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server_file_path = await upload_single_file(
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@ -458,7 +467,9 @@ def upload(
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# Step 3: Wait for this batch to complete
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if wait_for_completion and task_id:
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console.print(f"[cyan]Step 3: Waiting for batch {batch_start}-{batch_end} to complete...[/cyan]")
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console.print(
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f"[cyan]Step 3: Waiting for batch {batch_start}-{batch_end} to complete...[/cyan]"
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)
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await wait_for_tasks_completion(client, base_url, [task_id], poll_interval)
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console.print(f"[green]Batch {batch_start}-{batch_end} completed![/green]")
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else:
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@ -8,6 +8,7 @@ from pathlib import Path
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from fastapi import APIRouter, Body, Depends, HTTPException
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from fastapi.responses import StreamingResponse
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from langchain.messages import AIMessageChunk, HumanMessage
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from langgraph.types import Command
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from pydantic import BaseModel
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from sqlalchemy.orm import Session
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@ -81,6 +82,200 @@ async def set_default_agent(request_data: dict = Body(...), current_user=Depends
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# =============================================================================
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async def _get_langgraph_messages(agent_instance, config_dict):
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"""获取LangGraph中的消息"""
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graph = await agent_instance.get_graph()
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state = await graph.aget_state(config_dict)
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if not state or not state.values:
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logger.warning("No state found in LangGraph")
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return None
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return state.values.get("messages", [])
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def _get_existing_message_ids(conv_mgr, thread_id):
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"""获取已保存的消息ID集合"""
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existing_messages = conv_mgr.get_messages_by_thread_id(thread_id)
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return {msg.extra_metadata["id"] for msg in existing_messages if msg.extra_metadata and "id" in msg.extra_metadata}
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async def _save_ai_message(conv_mgr, thread_id, msg_dict):
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"""保存AI消息和相关的工具调用"""
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content = msg_dict.get("content", "")
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tool_calls_data = msg_dict.get("tool_calls", [])
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# 保存AI消息
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ai_msg = conv_mgr.add_message_by_thread_id(
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thread_id=thread_id,
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role="assistant",
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content=content,
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message_type="text",
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extra_metadata=msg_dict,
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)
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# 保存工具调用
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if tool_calls_data:
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logger.debug(f"Saving {len(tool_calls_data)} tool calls from AI message")
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for tc in tool_calls_data:
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conv_mgr.add_tool_call(
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message_id=ai_msg.id,
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tool_name=tc.get("name", "unknown"),
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tool_input=tc.get("args", {}),
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status="pending",
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langgraph_tool_call_id=tc.get("id"),
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)
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logger.debug(f"Saved AI message {ai_msg.id} with {len(tool_calls_data)} tool calls")
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def _save_tool_message(conv_mgr, msg_dict):
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"""保存工具执行结果"""
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tool_call_id = msg_dict.get("tool_call_id")
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content = msg_dict.get("content", "")
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name = msg_dict.get("name", "")
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if not tool_call_id:
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return
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# 确保tool_output是字符串类型
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if isinstance(content, list):
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tool_output = json.dumps(content) if content else ""
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else:
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tool_output = str(content)
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# 更新工具调用结果
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updated_tc = conv_mgr.update_tool_call_output(
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langgraph_tool_call_id=tool_call_id,
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tool_output=tool_output,
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status="success",
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)
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if updated_tc:
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logger.debug(f"Updated tool_call {tool_call_id} ({name}) with output")
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else:
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logger.warning(f"Tool call {tool_call_id} not found for update")
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async def save_messages_from_langgraph_state(
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agent_instance,
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thread_id,
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conv_mgr,
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config_dict,
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):
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"""
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从 LangGraph state 中读取完整消息并保存到数据库
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这样可以获得完整的 tool_calls 参数
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"""
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try:
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messages = await _get_langgraph_messages(agent_instance, config_dict)
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if messages is None:
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return
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logger.debug(f"Retrieved {len(messages)} messages from LangGraph state")
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existing_ids = _get_existing_message_ids(conv_mgr, thread_id)
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for msg in messages:
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msg_dict = msg.model_dump() if hasattr(msg, "model_dump") else {}
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msg_type = msg_dict.get("type", "unknown")
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if msg_type == "human" or msg.id in existing_ids:
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continue
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if msg_type == "ai":
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await _save_ai_message(conv_mgr, thread_id, msg_dict)
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elif msg_type == "tool":
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_save_tool_message(conv_mgr, msg_dict)
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else:
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logger.warning(f"Unknown message type: {msg_type}, skipping")
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continue
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logger.debug(f"Processed message type={msg_type}")
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logger.info("Saved messages from LangGraph state")
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except Exception as e:
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logger.error(f"Error saving messages from LangGraph state: {e}")
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logger.error(traceback.format_exc())
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async def check_and_handle_interrupts(agent, langgraph_config, make_chunk, meta, thread_id):
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"""检查并处理 LangGraph 中断状态，发送人工审批请求到前端"""
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try:
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# 获取 agent 的 graph 对象
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graph = await agent.get_graph()
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# 获取当前状态，检查是否有中断
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state = await graph.aget_state(langgraph_config)
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if not state or not state.values:
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logger.debug("No state found when checking for interrupts")
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return
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# 检查是否有中断信息
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# LangGraph 中断信息通常在 state.tasks 或 __interrupt__ 字段中
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interrupt_info = None
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# 方法1: 检查 state.tasks 中的中断
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if hasattr(state, "tasks") and state.tasks:
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for task in state.tasks:
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if hasattr(task, "interrupts") and task.interrupts:
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interrupt_info = task.interrupts[0] # 取第一个中断
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break
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# 方法2: 检查 state.values 中的 __interrupt__ 字段
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if not interrupt_info and state.values:
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interrupt_data = state.values.get("__interrupt__")
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if interrupt_data and isinstance(interrupt_data, list) and len(interrupt_data) > 0:
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interrupt_info = interrupt_data[0]
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# 方法3: 检查 state.next 字段，如果指向中断节点
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if not interrupt_info and hasattr(state, "next") and state.next:
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# 如果 next 指向某个需要审批的节点，可能需要额外处理
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logger.debug(f"State next nodes: {state.next}")
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if interrupt_info:
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logger.info(f"Human approval interrupt detected: {interrupt_info}")
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# 提取中断信息
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question = "是否批准以下操作？"
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operation = "需要人工审批的操作"
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if isinstance(interrupt_info, dict):
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question = interrupt_info.get("question", question)
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operation = interrupt_info.get("operation", operation)
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elif isinstance(interrupt_info, (list, tuple)) and len(interrupt_info) > 0:
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# 有些情况下中断信息可能是元组形式
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first_interrupt = interrupt_info[0]
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if isinstance(first_interrupt, dict):
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question = first_interrupt.get("question", question)
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operation = first_interrupt.get("operation", operation)
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else:
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operation = str(first_interrupt)
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else:
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operation = str(interrupt_info)
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# 发送人工审批请求到前端
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logger.info(f"Sending human approval request - question: {question}, operation: {operation}")
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yield make_chunk(
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status="human_approval_required",
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thread_id=thread_id,
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interrupt_info={"question": question, "operation": operation},
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)
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else:
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logger.debug("No human approval interrupt detected")
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except Exception as e:
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logger.error(f"Error checking for interrupts: {e}")
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logger.error(traceback.format_exc())
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# 不抛出异常，避免影响主流程
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# =============================================================================
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@chat.post("/call")
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async def call(query: str = Body(...), meta: dict = Body(None), current_user: User = Depends(get_required_user)):
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"""调用模型进行简单问答（需要登录）"""
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@ -147,114 +342,6 @@ async def chat_agent(
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+ b"\n"
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)
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async def save_messages_from_langgraph_state(
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agent_instance,
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thread_id,
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conv_mgr,
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config_dict,
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):
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"""
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从 LangGraph state 中读取完整消息并保存到数据库
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这样可以获得完整的 tool_calls 参数
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"""
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try:
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graph = await agent_instance.get_graph()
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state = await graph.aget_state(config_dict)
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if not state or not state.values:
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logger.warning("No state found in LangGraph")
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return
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messages = state.values.get("messages", [])
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logger.debug(f"Retrieved {len(messages)} messages from LangGraph state")
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# 获取已保存的消息数量，避免重复保存
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existing_messages = conv_mgr.get_messages_by_thread_id(thread_id)
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existing_ids = {
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msg.extra_metadata["id"]
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for msg in existing_messages
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if msg.extra_metadata and "id" in msg.extra_metadata
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}
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for msg in messages:
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msg_dict = msg.model_dump() if hasattr(msg, "model_dump") else {}
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msg_type = msg_dict.get("type", "unknown")
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if msg_type == "human" or msg.id in existing_ids:
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continue
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elif msg_type == "ai":
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# AI 消息
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content = msg_dict.get("content", "")
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tool_calls_data = msg_dict.get("tool_calls", [])
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# 格式清洗
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if finish_reason := msg_dict.get("response_metadata", {}).get("finish_reason"):
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if "tool_call" in finish_reason and len(finish_reason) > len("tool_call"):
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model_name = msg_dict.get("response_metadata", {}).get("model_name", "")
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repeat_count = len(finish_reason) // len("tool_call")
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msg_dict["response_metadata"]["finish_reason"] = "tool_call"
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msg_dict["response_metadata"]["model_name"] = model_name[: len(model_name) // repeat_count]
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# 保存 AI 消息
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ai_msg = conv_mgr.add_message_by_thread_id(
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thread_id=thread_id,
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role="assistant",
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content=content,
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message_type="text",
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extra_metadata=msg_dict, # 保存原始 model_dump
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)
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# 保存 tool_calls（如果有）- 使用 LangGraph 的 tool_call_id
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if tool_calls_data:
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logger.debug(f"Saving {len(tool_calls_data)} tool calls from AI message")
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for tc in tool_calls_data:
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conv_mgr.add_tool_call(
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message_id=ai_msg.id,
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tool_name=tc.get("name", "unknown"),
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tool_input=tc.get("args", {}), # 完整的参数
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status="pending", # 工具还未执行
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langgraph_tool_call_id=tc.get("id"), # 保存 LangGraph tool_call_id
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)
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logger.debug(f"Saved AI message {ai_msg.id} with {len(tool_calls_data)} tool calls")
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elif msg_type == "tool":
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# 工具执行结果消息 - 使用 tool_call_id 精确匹配
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tool_call_id = msg_dict.get("tool_call_id")
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content = msg_dict.get("content", "")
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name = msg_dict.get("name", "")
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if tool_call_id:
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# 确保tool_output是字符串类型，避免SQLite不支持列表类型
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if isinstance(content, list):
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tool_output = json.dumps(content) if content else ""
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else:
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tool_output = str(content)
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# 通过 LangGraph tool_call_id 精确匹配并更新
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updated_tc = conv_mgr.update_tool_call_output(
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langgraph_tool_call_id=tool_call_id,
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tool_output=tool_output,
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status="success",
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)
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if updated_tc:
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logger.debug(f"Updated tool_call {tool_call_id} ({name}) with output")
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else:
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logger.warning(f"Tool call {tool_call_id} not found for update")
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else:
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logger.warning(f"Unknown message type: {msg_type}, skipping")
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continue
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logger.debug(f"Processed message type={msg_type}")
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logger.info("Saved messages from LangGraph state")
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except Exception as e:
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logger.error(f"Error saving messages from LangGraph state: {e}")
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logger.error(traceback.format_exc())
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# TODO:[功能建议]针对需要人工审批后再执行的工具，
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# 可以使用langgraph的interrupt方法中断对话，等待用户输入后再使用command跳转回去
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async def stream_messages():
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@ -323,11 +410,17 @@ async def chat_agent(
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yield make_chunk(message="检测到敏感内容，已中断输出", status="error")
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return
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# After streaming finished, check for interrupts and save messages
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langgraph_config = {"configurable": input_context}
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# Check for human approval interrupts
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async for chunk in check_and_handle_interrupts(agent, langgraph_config, make_chunk, meta, thread_id):
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yield chunk
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meta["time_cost"] = asyncio.get_event_loop().time() - start_time
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yield make_chunk(status="finished", meta=meta)
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# After streaming finished, save all messages from LangGraph state
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langgraph_config = {"configurable": input_context}
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# Save all messages from LangGraph state
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await save_messages_from_langgraph_state(
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agent_instance=agent,
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thread_id=thread_id,
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@ -336,8 +429,17 @@ async def chat_agent(
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)
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except (asyncio.CancelledError, ConnectionError) as e:
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# 客户端主动中断连接，尝试保存已生成的部分内容
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# 客户端主动中断连接，检查中断并保存已生成的部分内容
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logger.warning(f"Client disconnected, cancelling stream: {e}")
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|
||||
# 即使在断开连接时也检查中断，确保状态一致性
|
||||
langgraph_config = {"configurable": input_context}
|
||||
try:
|
||||
async for chunk in check_and_handle_interrupts(agent, langgraph_config, make_chunk, meta, thread_id):
|
||||
yield chunk
|
||||
except Exception as interrupt_error:
|
||||
logger.error(f"Error checking interrupts during disconnect: {interrupt_error}")
|
||||
|
||||
if full_msg:
|
||||
# 创建新的 db session，因为原 session 可能已关闭
|
||||
new_db = db_manager.get_session()
|
||||
@ -360,6 +462,15 @@ async def chat_agent(
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error streaming messages: {e}, {traceback.format_exc()}")
|
||||
|
||||
# 即使在异常情况下也检查中断，确保状态一致性
|
||||
langgraph_config = {"configurable": input_context}
|
||||
try:
|
||||
async for chunk in check_and_handle_interrupts(agent, langgraph_config, make_chunk, meta, thread_id):
|
||||
yield chunk
|
||||
except Exception as interrupt_error:
|
||||
logger.error(f"Error checking interrupts during exception: {interrupt_error}")
|
||||
|
||||
if full_msg:
|
||||
# 创建新的 db session，因为原 session 可能已关闭
|
||||
new_db = db_manager.get_session()
|
||||
@ -420,6 +531,91 @@ async def get_tools(agent_id: str, current_user: User = Depends(get_required_use
|
||||
return {"tools": {tool["id"]: tool for tool in tools_info}}
|
||||
|
||||
|
||||
@chat.post("/agent/{agent_id}/resume")
|
||||
async def resume_agent_chat(
|
||||
agent_id: str,
|
||||
thread_id: str = Body(...),
|
||||
approved: bool = Body(...),
|
||||
current_user: User = Depends(get_required_user),
|
||||
db: Session = Depends(get_db),
|
||||
):
|
||||
"""恢复被人工审批中断的对话（需要登录）"""
|
||||
start_time = asyncio.get_event_loop().time()
|
||||
logger.info(f"Resuming agent_id: {agent_id}, thread_id: {thread_id}, approved: {approved}")
|
||||
|
||||
meta = {
|
||||
"agent_id": agent_id,
|
||||
"thread_id": thread_id,
|
||||
"user_id": current_user.id,
|
||||
"approved": approved,
|
||||
}
|
||||
if "request_id" not in meta or not meta.get("request_id"):
|
||||
meta["request_id"] = str(uuid.uuid4())
|
||||
|
||||
async def stream_resume():
|
||||
# 定义resume专用的make_chunk函数，与主聊天端点保持一致
|
||||
def make_resume_chunk(content=None, **kwargs):
|
||||
return (
|
||||
json.dumps(
|
||||
{"request_id": meta.get("request_id"), "response": content, **kwargs}, ensure_ascii=False
|
||||
).encode("utf-8")
|
||||
+ b"\n"
|
||||
)
|
||||
|
||||
try:
|
||||
agent = agent_manager.get_agent(agent_id)
|
||||
except Exception as e:
|
||||
logger.error(f"Error getting agent {agent_id}: {e}, {traceback.format_exc()}")
|
||||
yield (
|
||||
f'{{"request_id": "{meta.get("request_id")}", "message": '
|
||||
f'"Error getting agent {agent_id}: {e}", "status": "error"}}\n'
|
||||
)
|
||||
return
|
||||
|
||||
# 发送init状态块，与主聊天端点保持一致
|
||||
init_msg = {"type": "system", "content": f"Resume with approved: {approved}"}
|
||||
yield make_resume_chunk(status="init", meta=meta, msg=init_msg)
|
||||
|
||||
# 使用 Command(resume=approved) 恢复执行
|
||||
resume_command = Command(resume=approved)
|
||||
graph = await agent.get_graph()
|
||||
|
||||
# 加载 context（包含 tools, model 等配置）
|
||||
input_context = {"user_id": str(current_user.id), "thread_id": thread_id}
|
||||
context = agent.context_schema.from_file(module_name=agent.module_name, input_context=input_context)
|
||||
logger.debug(f"Resume with context: {context}")
|
||||
|
||||
# 创建流式数据源
|
||||
stream_source = graph.astream(
|
||||
resume_command, context=context, config={"configurable": input_context}, stream_mode="messages"
|
||||
)
|
||||
|
||||
async for msg, metadata in stream_source:
|
||||
# 确保msg有正确的ID结构
|
||||
msg_dict = msg.model_dump()
|
||||
if "id" not in msg_dict:
|
||||
msg_dict["id"] = str(uuid.uuid4())
|
||||
|
||||
yield make_resume_chunk(
|
||||
content=getattr(msg, "content", ""), msg=msg_dict, metadata=metadata, status="loading"
|
||||
)
|
||||
|
||||
meta["time_cost"] = asyncio.get_event_loop().time() - start_time
|
||||
yield make_resume_chunk(status="finished", meta=meta)
|
||||
|
||||
# 保存消息到数据库
|
||||
langgraph_config = {"configurable": input_context}
|
||||
conv_manager = ConversationManager(db)
|
||||
await save_messages_from_langgraph_state(
|
||||
agent_instance=agent,
|
||||
thread_id=thread_id,
|
||||
conv_mgr=conv_manager,
|
||||
config_dict=langgraph_config,
|
||||
)
|
||||
|
||||
return StreamingResponse(stream_resume(), media_type="application/json")
|
||||
|
||||
|
||||
@chat.post("/agent/{agent_id}/config")
|
||||
async def save_agent_config(agent_id: str, config: dict = Body(...), current_user: User = Depends(get_required_user)):
|
||||
"""保存智能体配置到YAML文件（需要登录）"""
|
||||
|
||||
@ -88,6 +88,7 @@ async def get_mcp_tools(server_name: str, additional_servers: dict[str, dict] =
|
||||
logger.error(f"Failed to load tools from MCP server '{server_name}': {e}")
|
||||
return []
|
||||
|
||||
|
||||
async def get_all_mcp_tools() -> list[Callable[..., Any]]:
|
||||
"""Get all tools from all configured MCP servers."""
|
||||
all_tools = []
|
||||
|
||||
@ -10,8 +10,9 @@ from src.agents.common.mcp import get_mcp_tools
|
||||
from src.agents.common.models import load_chat_model
|
||||
from src.utils import logger
|
||||
|
||||
from .state import BaseState
|
||||
from .context import BaseContext
|
||||
from .state import BaseState
|
||||
|
||||
|
||||
class ToolAgent(BaseAgent):
|
||||
name = "ToolAgent"
|
||||
@ -24,7 +25,6 @@ class ToolAgent(BaseAgent):
|
||||
self.context_schema = BaseContext
|
||||
self.agent_tools = None
|
||||
|
||||
|
||||
# TODO:[修改建议] _get_invoke_tools,llm_call,dynamic_tools_node这类针对工具调用的功能大多数Agent都能用得到
|
||||
# 可以通过一个ToolAgent类继承BaseAgent,通过重写抽象方法获取tools,通过继承BaseState和BaseContext获取配置
|
||||
# 必要时可通过重写以下方法实现其他逻辑
|
||||
@ -33,7 +33,6 @@ class ToolAgent(BaseAgent):
|
||||
logger.error(f"get_tools() is not implemented in {self.__class__.__name__}")
|
||||
return []
|
||||
|
||||
|
||||
async def _get_invoke_tools(self, selected_tools: list[str], selected_mcps: list[str]):
|
||||
"""根据配置获取工具。
|
||||
默认不使用任何工具。
|
||||
|
||||
@ -11,6 +11,7 @@ from pydantic import BaseModel, Field
|
||||
from src import config, graph_base, knowledge_base
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
# TODO[修改建议]:前端需要通过interrupt进行交互，点击是或否来批准执行
|
||||
# 返回中断点：
|
||||
# is_approved : bool = True 或者 False
|
||||
@ -20,7 +21,7 @@ from src.utils import logger
|
||||
@tool(name_or_callable="人工审批工具", description="请求人工审批工具，用于在执行重要操作前获得人类确认。")
|
||||
def get_approved_user_goal(
|
||||
operation_description: str,
|
||||
)->dict:
|
||||
) -> dict:
|
||||
"""
|
||||
请求人工审批，在执行重要操作前获得人类确认。
|
||||
|
||||
@ -54,6 +55,7 @@ def get_approved_user_goal(
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@tool(name_or_callable="查询知识图谱", description="使用这个工具可以查询知识图谱中包含的三元组信息。")
|
||||
def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge graph."]) -> Any:
|
||||
"""Use this to query knowledge graph, which include some food domain knowledge."""
|
||||
@ -72,10 +74,7 @@ def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge
|
||||
|
||||
def get_static_tools() -> list:
|
||||
"""注册静态工具"""
|
||||
static_tools = [
|
||||
query_knowledge_graph,
|
||||
get_approved_user_goal
|
||||
]
|
||||
static_tools = [query_knowledge_graph, get_approved_user_goal]
|
||||
|
||||
# 检查是否启用网页搜索
|
||||
if config.enable_web_search:
|
||||
|
||||
@ -1,4 +1,3 @@
|
||||
|
||||
from langchain.agents import create_agent
|
||||
from langchain.agents.middleware import ModelRequest, ModelResponse, dynamic_prompt, wrap_model_call
|
||||
|
||||
|
||||
@ -13,12 +13,12 @@ class SampleMultiAgent(ToolAgent):
|
||||
description = "Supervisor智能体，具有调用其他子智能体的能力(在工具中添加)"
|
||||
|
||||
# TODO[已完成]: 通过将其他agent封装为工具的方式添加了多智能体调度
|
||||
'''
|
||||
"""
|
||||
你是一个多智能体核心，通过多智能体调用的方式帮助用户完成一系列任务：
|
||||
|
||||
1.当你需要知识库问答功能时，请调用对话聊天智能体实现
|
||||
2.当你需要加密计算的时候，请调用加密计算智能体实现
|
||||
'''
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
@ -7,6 +7,7 @@ from src.agents import agent_manager
|
||||
from src.agents.common.tools import get_buildin_tools
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
# TODO[修改建议]:能不能通过前端直接指定子智能体？
|
||||
# 调用子智能体后的日志是输出到tool_calls的
|
||||
@tool(name_or_callable="对话聊天智能体", description="调用指定智能体进行对话聊天的功能")
|
||||
@ -23,20 +24,21 @@ async def call_chatbot(query: str, config: RunnableConfig) -> str:
|
||||
try:
|
||||
input = [{"role": "user", "content": query}]
|
||||
chatbot = agent_manager.get_agent("ChatbotAgent")
|
||||
configurable = config.get("configurable",{})
|
||||
configurable = config.get("configurable", {})
|
||||
input_context = {
|
||||
"thread_id":configurable.get("thread_id"),
|
||||
"thread_id": configurable.get("thread_id"),
|
||||
"user_id": configurable.get("user_id"),
|
||||
}
|
||||
message = await chatbot.invoke_messages(input,input_context=input_context)
|
||||
message = await chatbot.invoke_messages(input, input_context=input_context)
|
||||
# 直接获取最后一个消息的内容
|
||||
final_answer = message.get('messages', [])[-1].content
|
||||
final_answer = message.get("messages", [])[-1].content
|
||||
logger.info(f"ChatbotAgent: {final_answer}")
|
||||
return final_answer
|
||||
except Exception as e:
|
||||
logger.error(f"CallAgent error: {e}")
|
||||
raise
|
||||
|
||||
|
||||
@tool(name_or_callable="加密计算智能体", description="调用指定智能体进行加密计算的功能")
|
||||
async def call_react_agent(query: str, config: RunnableConfig) -> str:
|
||||
"""
|
||||
@ -51,14 +53,14 @@ async def call_react_agent(query: str, config: RunnableConfig) -> str:
|
||||
try:
|
||||
input = [{"role": "user", "content": query}]
|
||||
chatbot = agent_manager.get_agent("ReActAgent")
|
||||
configurable = config.get("configurable",{})
|
||||
configurable = config.get("configurable", {})
|
||||
input_context = {
|
||||
"thread_id":configurable.get("thread_id"),
|
||||
"thread_id": configurable.get("thread_id"),
|
||||
"user_id": configurable.get("user_id"),
|
||||
}
|
||||
message = await chatbot.invoke_messages(input,input_context=input_context)
|
||||
message = await chatbot.invoke_messages(input, input_context=input_context)
|
||||
# 直接获取最后一个消息的内容
|
||||
final_answer = message.get('messages', [])[-1].content
|
||||
final_answer = message.get("messages", [])[-1].content
|
||||
logger.info(f"ReActAgent: {final_answer}")
|
||||
return final_answer
|
||||
except Exception as e:
|
||||
|
||||
@ -3,10 +3,11 @@ from langgraph.graph import StateGraph
|
||||
|
||||
from src.agents.common.toolagent import ToolAgent
|
||||
|
||||
from .state import State
|
||||
from .context import Context
|
||||
from .state import State
|
||||
from .tools import get_tools
|
||||
|
||||
|
||||
def tools_branch_continue(state: State):
|
||||
messages = state.messages
|
||||
last_message = messages[-1]
|
||||
@ -21,14 +22,14 @@ class ReActAgent(ToolAgent):
|
||||
name = "ReActAgent"
|
||||
description = "符合 ReAct 范式的智能体，可以通过调用工具来完成复杂任务。"
|
||||
|
||||
'''
|
||||
"""
|
||||
提示词示例：
|
||||
你是一个智能体助手
|
||||
|
||||
你的所有需要计算的工作不能自己计算，只能通过调用加密计算机工具进行,你只能通过调用该工具的方式进行计算才能得到正确答案。计算顺序是正常的。
|
||||
|
||||
例如 2+3+2，你必须调用两次加密计算机工具进行运算，最终结果为9。
|
||||
'''
|
||||
"""
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
@ -64,4 +65,3 @@ class ReActAgent(ToolAgent):
|
||||
graph = builder.compile(checkpointer=self.checkpointer, name=self.name)
|
||||
self.graph = graph
|
||||
return graph
|
||||
|
||||
|
||||
@ -6,6 +6,7 @@ from src.agents.common.toolkits.mysql import get_mysql_tools
|
||||
from src.agents.common.tools import get_buildin_tools
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
@tool(name_or_callable="加密计算器", description="可以对给定的2个数字选择进行加减乘除四种加密计算")
|
||||
def calculator(a: float, b: float, operation: str) -> float:
|
||||
"""
|
||||
|
||||
@ -4,8 +4,8 @@ from langchain.agents import create_agent
|
||||
from langchain.agents.middleware import ModelRequest, ModelResponse, dynamic_prompt, wrap_model_call
|
||||
|
||||
from src.agents.common.base import BaseAgent
|
||||
from src.agents.common.models import load_chat_model
|
||||
from src.agents.common.mcp import get_mcp_tools
|
||||
from src.agents.common.models import load_chat_model
|
||||
from src.agents.common.toolkits.mysql import get_mysql_tools
|
||||
from src.utils import logger
|
||||
|
||||
@ -16,6 +16,7 @@ _mcp_servers = {
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@dynamic_prompt
|
||||
def context_aware_prompt(request: ModelRequest) -> str:
|
||||
user_prompt = request.runtime.context.system_prompt
|
||||
|
||||
@ -136,9 +136,8 @@ class KnowledgeBase(ABC):
|
||||
"""
|
||||
from src.utils import hashstr
|
||||
|
||||
|
||||
# 从 kwargs 中获取 is_private 配置
|
||||
is_private = kwargs.get('is_private', False)
|
||||
is_private = kwargs.get("is_private", False)
|
||||
prefix = "kb_private_" if is_private else "kb_"
|
||||
db_id = f"{prefix}{hashstr(database_name, with_salt=True)}"
|
||||
|
||||
|
||||
@ -137,7 +137,33 @@ export const agentApi = {
|
||||
* 获取所有可用工具的信息
|
||||
* @returns {Promise} - 工具信息列表
|
||||
*/
|
||||
getTools: (agentId) => apiGet(`/api/chat/tools?agent_id=${agentId}`)
|
||||
getTools: (agentId) => apiGet(`/api/chat/tools?agent_id=${agentId}`),
|
||||
|
||||
/**
|
||||
* 恢复被人工审批中断的对话（流式响应）
|
||||
* @param {string} agentId - 智能体ID
|
||||
* @param {Object} data - 恢复数据 { thread_id, approved }
|
||||
* @param {Object} options - 可选参数（signal, headers等）
|
||||
* @returns {Promise} - 恢复响应流
|
||||
*/
|
||||
resumeAgentChat: (agentId, data, options = {}) => {
|
||||
const { signal, headers: extraHeaders, ...restOptions } = options || {};
|
||||
const baseHeaders = {
|
||||
'Content-Type': 'application/json',
|
||||
...useUserStore().getAuthHeaders()
|
||||
};
|
||||
|
||||
return fetch(`/api/chat/agent/${agentId}/resume`, {
|
||||
method: 'POST',
|
||||
body: JSON.stringify(data),
|
||||
signal,
|
||||
headers: {
|
||||
...baseHeaders,
|
||||
...(extraHeaders || {})
|
||||
},
|
||||
...restOptions
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
@ -29,7 +29,7 @@
|
||||
<div type="button" class="agent-nav-btn" v-if="!uiState.isSidebarOpen" @click="toggleSidebar">
|
||||
<PanelLeftOpen class="nav-btn-icon" size="18"/>
|
||||
</div>
|
||||
<div type="button" class="agent-nav-btn" v-if="!uiState.isSidebarOpen" @click="createNewChat" :disabled="isProcessing">
|
||||
<div type="button" class="agent-nav-btn" v-if="!uiState.isSidebarOpen" @click="createNewChat" :disabled="chatState.creatingNewChat">
|
||||
<MessageCirclePlus class="nav-btn-icon" size="18"/>
|
||||
<span class="text" :class="{'hide-text': isMediumContainer}">新对话</span>
|
||||
</div>
|
||||
@ -97,14 +97,14 @@
|
||||
</AgentMessageComponent>
|
||||
<!-- 显示对话最后一个消息使用的模型 -->
|
||||
<RefsComponent
|
||||
v-if="getLastMessage(conv) && conv.status !== 'streaming'"
|
||||
v-if="shouldShowRefs(conv)"
|
||||
:message="getLastMessage(conv)"
|
||||
:show-refs="['model', 'copy']"
|
||||
:is-latest-message="false"
|
||||
/>
|
||||
</div>
|
||||
|
||||
<!-- 生成中的加载状态 -->
|
||||
<!-- 生成中的加载状态 - 增强条件支持主聊天和resume流程 -->
|
||||
<div class="generating-status" v-if="isProcessing && conversations.length > 0">
|
||||
<div class="generating-indicator">
|
||||
<div class="loading-dots">
|
||||
@ -117,6 +117,15 @@
|
||||
</div>
|
||||
</div>
|
||||
<div class="bottom">
|
||||
<!-- 人工审批弹窗 - 放在输入框上方 -->
|
||||
<HumanApprovalModal
|
||||
:visible="approvalState.showModal"
|
||||
:question="approvalState.question"
|
||||
:operation="approvalState.operation"
|
||||
@approve="handleApprove"
|
||||
@reject="handleReject"
|
||||
/>
|
||||
|
||||
<div class="message-input-wrapper" v-if="conversations.length > 0">
|
||||
<MessageInputComponent
|
||||
v-model="userInput"
|
||||
@ -152,6 +161,8 @@ import { useAgentStore } from '@/stores/agent';
|
||||
import { storeToRefs } from 'pinia';
|
||||
import { MessageProcessor } from '@/utils/messageProcessor';
|
||||
import { agentApi, threadApi } from '@/apis';
|
||||
import HumanApprovalModal from '@/components/HumanApprovalModal.vue';
|
||||
import { useApproval } from '@/composables/useApproval';
|
||||
|
||||
// ==================== PROPS & EMITS ====================
|
||||
const props = defineProps({
|
||||
@ -181,6 +192,14 @@ const exampleQuestions = computed(() => {
|
||||
}));
|
||||
});
|
||||
|
||||
// Keep per-thread streaming scratch data in a consistent shape.
|
||||
const createOnGoingConvState = () => ({
|
||||
msgChunks: {},
|
||||
currentRequestKey: null,
|
||||
currentAssistantKey: null,
|
||||
toolCallBuffers: {}
|
||||
});
|
||||
|
||||
const chatState = reactive({
|
||||
currentThreadId: null,
|
||||
isLoadingThreads: false,
|
||||
@ -227,6 +246,18 @@ const currentThread = computed(() => {
|
||||
|
||||
const currentThreadMessages = computed(() => threadMessages.value[currentChatId.value] || []);
|
||||
|
||||
// 计算是否显示Refs组件的条件
|
||||
const shouldShowRefs = computed(() => {
|
||||
return (conv) => {
|
||||
return getLastMessage(conv) &&
|
||||
conv.status !== 'streaming' &&
|
||||
!approvalState.showModal &&
|
||||
!(approvalState.threadId &&
|
||||
chatState.currentThreadId === approvalState.threadId &&
|
||||
isProcessing.value);
|
||||
};
|
||||
});
|
||||
|
||||
// 当前线程状态的computed属性
|
||||
const currentThreadState = computed(() => {
|
||||
return getThreadState(currentChatId.value);
|
||||
@ -316,7 +347,7 @@ const getThreadState = (threadId) => {
|
||||
chatState.threadStates[threadId] = {
|
||||
isStreaming: false,
|
||||
streamAbortController: null,
|
||||
onGoingConv: { msgChunks: {} }
|
||||
onGoingConv: createOnGoingConvState()
|
||||
};
|
||||
}
|
||||
return chatState.threadStates[threadId];
|
||||
@ -349,11 +380,11 @@ const resetOnGoingConv = (threadId = null, preserveMessages = false) => {
|
||||
// 延迟清空消息，给历史记录加载足够时间
|
||||
setTimeout(() => {
|
||||
if (threadState.onGoingConv) {
|
||||
threadState.onGoingConv = { msgChunks: {} };
|
||||
threadState.onGoingConv = createOnGoingConvState();
|
||||
}
|
||||
}, 100);
|
||||
} else {
|
||||
threadState.onGoingConv = { msgChunks: {} };
|
||||
threadState.onGoingConv = createOnGoingConvState();
|
||||
}
|
||||
}
|
||||
} else {
|
||||
@ -369,11 +400,11 @@ const resetOnGoingConv = (threadId = null, preserveMessages = false) => {
|
||||
if (preserveMessages) {
|
||||
setTimeout(() => {
|
||||
if (threadState.onGoingConv) {
|
||||
threadState.onGoingConv = { msgChunks: {} };
|
||||
threadState.onGoingConv = createOnGoingConvState();
|
||||
}
|
||||
}, 100);
|
||||
} else {
|
||||
threadState.onGoingConv = { msgChunks: {} };
|
||||
threadState.onGoingConv = createOnGoingConvState();
|
||||
}
|
||||
}
|
||||
} else {
|
||||
@ -388,6 +419,7 @@ const resetOnGoingConv = (threadId = null, preserveMessages = false) => {
|
||||
const _processStreamChunk = (chunk, threadId) => {
|
||||
const { status, msg, request_id, message } = chunk;
|
||||
const threadState = getThreadState(threadId);
|
||||
// console.log('Processing stream chunk:', chunk, 'for thread:', threadId);
|
||||
|
||||
if (!threadState) return false;
|
||||
|
||||
@ -399,10 +431,10 @@ const _processStreamChunk = (chunk, threadId) => {
|
||||
if (msg.id) {
|
||||
if (!threadState.onGoingConv.msgChunks[msg.id]) {
|
||||
threadState.onGoingConv.msgChunks[msg.id] = [];
|
||||
}
|
||||
}
|
||||
threadState.onGoingConv.msgChunks[msg.id].push(msg);
|
||||
}
|
||||
return false;
|
||||
return false;
|
||||
case 'error':
|
||||
handleChatError({ message }, 'stream');
|
||||
// Stop the loading indicator
|
||||
@ -420,6 +452,9 @@ const _processStreamChunk = (chunk, threadId) => {
|
||||
fetchThreadMessages({ agentId: currentAgentId.value, threadId: threadId });
|
||||
resetOnGoingConv(threadId);
|
||||
return true;
|
||||
case 'human_approval_required':
|
||||
// 使用审批 composable 处理审批请求
|
||||
return processApprovalInStream(chunk, threadId, currentAgentId.value);
|
||||
case 'finished':
|
||||
// 先标记流式结束，但保持消息显示直到历史记录加载完成
|
||||
if (threadState) {
|
||||
@ -542,6 +577,13 @@ const fetchThreadMessages = async ({ agentId, threadId }) => {
|
||||
}
|
||||
};
|
||||
|
||||
// ==================== 审批功能管理 ====================
|
||||
const { approvalState, handleApproval, processApprovalInStream } = useApproval({
|
||||
getThreadState,
|
||||
resetOnGoingConv,
|
||||
fetchThreadMessages
|
||||
});
|
||||
|
||||
// 发送消息并处理流式响应
|
||||
const sendMessage = async ({ agentId, threadId, text, signal = undefined }) => {
|
||||
if (!agentId || !threadId || !text) {
|
||||
@ -590,7 +632,7 @@ const switchToFirstChatIfEmpty = async () => {
|
||||
};
|
||||
|
||||
const createNewChat = async () => {
|
||||
if (!AgentValidator.validateAgentId(currentAgentId.value, '创建对话') || isProcessing.value) return;
|
||||
if (!AgentValidator.validateAgentId(currentAgentId.value, '创建对话') || chatState.creatingNewChat) return;
|
||||
|
||||
// 如果第一个对话为空，直接切换到第一个对话而不是创建新对话
|
||||
if (await switchToFirstChatIfEmpty()) return;
|
||||
@ -603,6 +645,17 @@ const createNewChat = async () => {
|
||||
try {
|
||||
const newThread = await createThread(currentAgentId.value, '新的对话');
|
||||
if (newThread) {
|
||||
// 中断之前线程的流式输出（如果存在）
|
||||
const previousThreadId = chatState.currentThreadId;
|
||||
if (previousThreadId) {
|
||||
const previousThreadState = getThreadState(previousThreadId);
|
||||
if (previousThreadState?.isStreaming && previousThreadState.streamAbortController) {
|
||||
previousThreadState.streamAbortController.abort();
|
||||
previousThreadState.isStreaming = false;
|
||||
previousThreadState.streamAbortController = null;
|
||||
}
|
||||
}
|
||||
|
||||
chatState.currentThreadId = newThread.id;
|
||||
}
|
||||
} catch (error) {
|
||||
@ -615,8 +668,17 @@ const createNewChat = async () => {
|
||||
const selectChat = async (chatId) => {
|
||||
if (!AgentValidator.validateAgentIdWithError(currentAgentId.value, '选择对话', handleValidationError)) return;
|
||||
|
||||
// 切换线程时，不再中断上一个线程的流式输出
|
||||
// resetOnGoingConv(chatState.currentThreadId);
|
||||
// 中断之前线程的流式输出（如果存在）
|
||||
const previousThreadId = chatState.currentThreadId;
|
||||
if (previousThreadId && previousThreadId !== chatId) {
|
||||
const previousThreadState = getThreadState(previousThreadId);
|
||||
if (previousThreadState?.isStreaming && previousThreadState.streamAbortController) {
|
||||
previousThreadState.streamAbortController.abort();
|
||||
previousThreadState.isStreaming = false;
|
||||
previousThreadState.streamAbortController = null;
|
||||
}
|
||||
}
|
||||
|
||||
chatState.currentThreadId = chatId;
|
||||
chatState.isLoadingMessages = true;
|
||||
try {
|
||||
@ -763,6 +825,106 @@ const handleSendOrStop = async () => {
|
||||
await handleSendMessage();
|
||||
};
|
||||
|
||||
// ==================== 人工审批处理 ====================
|
||||
const handleApprovalWithStream = async (approved) => {
|
||||
console.log('🔄 [STREAM] Starting resume stream processing');
|
||||
|
||||
const threadId = approvalState.threadId;
|
||||
if (!threadId) {
|
||||
message.error('无效的审批请求');
|
||||
approvalState.showModal = false;
|
||||
return;
|
||||
}
|
||||
|
||||
const threadState = getThreadState(threadId);
|
||||
if (!threadState) {
|
||||
message.error('无法找到对应的对话线程');
|
||||
approvalState.showModal = false;
|
||||
return;
|
||||
}
|
||||
|
||||
try {
|
||||
// 使用审批 composable 处理审批
|
||||
const response = await handleApproval(approved, currentAgentId.value);
|
||||
|
||||
if (!response) return; // 如果 handleApproval 抛出错误，这里不会执行
|
||||
|
||||
console.log('🔄 [STREAM] Processing resume streaming response');
|
||||
|
||||
// 处理流式响应
|
||||
const reader = response.body.getReader();
|
||||
const decoder = new TextDecoder();
|
||||
let buffer = '';
|
||||
let stopReading = false;
|
||||
|
||||
while (!stopReading) {
|
||||
const { done, value } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
buffer += decoder.decode(value, { stream: true });
|
||||
const lines = buffer.split('\n');
|
||||
buffer = lines.pop() || '';
|
||||
|
||||
for (const line of lines) {
|
||||
const trimmedLine = line.trim();
|
||||
if (trimmedLine) {
|
||||
try {
|
||||
const chunk = JSON.parse(trimmedLine);
|
||||
console.log('🔄 [STREAM] Processing chunk:', chunk);
|
||||
|
||||
// 处理chunk并更新对话 - _processStreamChunk 已经处理了所有必要的逻辑
|
||||
if (_processStreamChunk(chunk, threadId)) {
|
||||
stopReading = true;
|
||||
break;
|
||||
}
|
||||
|
||||
} catch (e) {
|
||||
console.warn('Failed to parse stream chunk JSON:', e, 'Line:', trimmedLine);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (!stopReading && buffer.trim()) {
|
||||
try {
|
||||
const chunk = JSON.parse(buffer.trim());
|
||||
console.log('🔄 [STREAM] Processing final chunk:', chunk);
|
||||
|
||||
// 处理最终chunk - _processStreamChunk 已经处理了所有必要的逻辑
|
||||
if (_processStreamChunk(chunk, threadId)) {
|
||||
stopReading = true;
|
||||
}
|
||||
|
||||
} catch (e) {
|
||||
console.warn('Failed to parse final stream chunk JSON:', e);
|
||||
}
|
||||
}
|
||||
|
||||
console.log('🔄 [STREAM] Resume stream processing completed');
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ [STREAM] Resume stream failed:', error);
|
||||
if (error.name !== 'AbortError') {
|
||||
console.error('Resume approval error:', error);
|
||||
// handleChatError 已在 useApproval 中调用
|
||||
}
|
||||
} finally {
|
||||
console.log('🔄 [STREAM] Cleaning up streaming state');
|
||||
if (threadState) {
|
||||
threadState.isStreaming = false;
|
||||
threadState.streamAbortController = null;
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
const handleApprove = () => {
|
||||
handleApprovalWithStream(true);
|
||||
};
|
||||
|
||||
const handleReject = () => {
|
||||
handleApprovalWithStream(false);
|
||||
};
|
||||
|
||||
// ==================== UI HANDLERS ====================
|
||||
const handleKeyDown = (e) => {
|
||||
if (e.key === 'Enter' && !e.shiftKey) {
|
||||
@ -795,7 +957,6 @@ defineExpose({
|
||||
getExportPayload: buildExportPayload
|
||||
});
|
||||
|
||||
const retryMessage = (msg) => { /* TODO */ };
|
||||
const toggleSidebar = () => {
|
||||
uiState.isSidebarOpen = !uiState.isSidebarOpen;
|
||||
localStorage.setItem('chat_sidebar_open', uiState.isSidebarOpen);
|
||||
@ -812,7 +973,24 @@ const getLastMessage = (conv) => {
|
||||
};
|
||||
|
||||
const showMsgRefs = (msg) => {
|
||||
if (msg.isLast) return ['copy'];
|
||||
// 如果正在审批中，不显示 refs
|
||||
if (approvalState.showModal) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// 如果当前线程ID与审批线程ID匹配，但审批框已关闭（说明刚刚处理完审批）
|
||||
// 且当前有新的流式处理正在进行，则不显示之前被中断的消息的 refs
|
||||
if (approvalState.threadId &&
|
||||
chatState.currentThreadId === approvalState.threadId &&
|
||||
!approvalState.showModal &&
|
||||
isProcessing) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// 只有真正完成的消息才显示 refs
|
||||
if (msg.isLast && msg.status === 'finished') {
|
||||
return ['copy'];
|
||||
}
|
||||
return false;
|
||||
};
|
||||
|
||||
@ -875,6 +1053,7 @@ watch(currentAgentId, async (newAgentId, oldAgentId) => {
|
||||
}
|
||||
}, { immediate: true });
|
||||
|
||||
|
||||
watch(conversations, () => {
|
||||
if (isProcessing.value) {
|
||||
scrollController.scrollToBottom();
|
||||
|
||||
@ -3,6 +3,8 @@
|
||||
<!-- 用户消息 -->
|
||||
<p v-if="message.type === 'human'" class="message-text">{{ message.content }}</p>
|
||||
|
||||
<p v-else-if="message.type === 'system'" class="message-text-system">{{ message.content }}</p>
|
||||
|
||||
<!-- 助手消息 -->
|
||||
<div v-else-if="message.type === 'ai'" class="assistant-message">
|
||||
<div v-if="parsedData.reasoning_content" class="reasoning-box">
|
||||
@ -232,6 +234,19 @@ const toggleToolCall = (toolCallId) => {
|
||||
white-space: pre-line;
|
||||
}
|
||||
|
||||
.message-text-system {
|
||||
max-width: 100%;
|
||||
margin-bottom: 0;
|
||||
white-space: pre-line;
|
||||
color: var(--gray-600);
|
||||
font-style: italic;
|
||||
font-size: 14px;
|
||||
padding: 8px 12px;
|
||||
background-color: var(--gray-50);
|
||||
border-left: 3px solid var(--gray-300);
|
||||
border-radius: 4px;
|
||||
}
|
||||
|
||||
.err-msg {
|
||||
color: #d15252;
|
||||
border: 1px solid #f19999;
|
||||
|
||||
238
web/src/components/HumanApprovalModal.vue
Normal file
238
web/src/components/HumanApprovalModal.vue
Normal file
@ -0,0 +1,238 @@
|
||||
<template>
|
||||
<transition name="slide-up">
|
||||
<div v-if="visible" class="approval-modal">
|
||||
<div class="approval-content">
|
||||
<div class="approval-header">
|
||||
<h4>{{ question }}</h4>
|
||||
</div>
|
||||
|
||||
<div class="approval-operation">
|
||||
<span class="label">操作：</span>
|
||||
<span class="operation-text">{{ operation }}</span>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="approval-actions">
|
||||
<button class="btn btn-reject" @click="handleReject" :disabled="isProcessing">
|
||||
✕ 拒绝
|
||||
</button>
|
||||
<button class="btn btn-approve" @click="handleApprove" :disabled="isProcessing">
|
||||
✓ 批准
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div v-if="isProcessing" class="approval-processing">
|
||||
<span class="processing-spinner"></span>
|
||||
处理中...
|
||||
</div>
|
||||
</div>
|
||||
</transition>
|
||||
</template>
|
||||
|
||||
<script setup>
|
||||
import { ref, watch } from 'vue';
|
||||
|
||||
const props = defineProps({
|
||||
visible: {
|
||||
type: Boolean,
|
||||
default: false
|
||||
},
|
||||
question: {
|
||||
type: String,
|
||||
default: '是否批准此操作？'
|
||||
},
|
||||
operation: {
|
||||
type: String,
|
||||
default: ''
|
||||
}
|
||||
});
|
||||
|
||||
const emit = defineEmits(['approve', 'reject']);
|
||||
|
||||
const isProcessing = ref(false);
|
||||
|
||||
// 监听弹窗关闭，重置处理状态
|
||||
watch(() => props.visible, (newVal) => {
|
||||
if (!newVal) {
|
||||
isProcessing.value = false;
|
||||
}
|
||||
});
|
||||
|
||||
const handleApprove = () => {
|
||||
if (isProcessing.value) return;
|
||||
isProcessing.value = true;
|
||||
emit('approve');
|
||||
};
|
||||
|
||||
const handleReject = () => {
|
||||
if (isProcessing.value) return;
|
||||
isProcessing.value = true;
|
||||
emit('reject');
|
||||
};
|
||||
|
||||
</script>
|
||||
|
||||
<style scoped>
|
||||
.approval-modal {
|
||||
background: white;
|
||||
border-radius: 12px 12px;
|
||||
box-shadow: 0 -4px 16px rgba(0, 0, 0, 0.12);
|
||||
margin: 0 auto 8px;
|
||||
max-width: 800px;
|
||||
width: 100%;
|
||||
border: 1px solid var(--gray-200);
|
||||
}
|
||||
|
||||
.approval-content {
|
||||
padding: 16px 20px;
|
||||
}
|
||||
|
||||
.approval-header {
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
margin-bottom: 12px;
|
||||
}
|
||||
|
||||
.approval-header h4 {
|
||||
margin: 0;
|
||||
font-size: 15px;
|
||||
font-weight: 500;
|
||||
color: var(--gray-800);
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.approval-operation {
|
||||
background: var(--gray-50);
|
||||
padding: 10px 12px;
|
||||
border-radius: 6px;
|
||||
font-size: 13px;
|
||||
line-height: 1.5;
|
||||
display: flex;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.approval-operation .label {
|
||||
color: var(--gray-600);
|
||||
font-weight: 500;
|
||||
flex-shrink: 0;
|
||||
}
|
||||
|
||||
.approval-operation .operation-text {
|
||||
color: var(--gray-800);
|
||||
word-break: break-word;
|
||||
}
|
||||
|
||||
.approval-actions {
|
||||
display: flex;
|
||||
gap: 10px;
|
||||
padding: 12px 20px 16px;
|
||||
}
|
||||
|
||||
.btn {
|
||||
flex: 1;
|
||||
padding: 10px 20px;
|
||||
border: none;
|
||||
border-radius: 6px;
|
||||
font-size: 14px;
|
||||
font-weight: 500;
|
||||
cursor: pointer;
|
||||
transition: all 0.2s ease;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 6px;
|
||||
}
|
||||
|
||||
.btn:disabled {
|
||||
opacity: 0.5;
|
||||
cursor: not-allowed;
|
||||
}
|
||||
|
||||
.btn-reject {
|
||||
background: var(--gray-100);
|
||||
color: var(--gray-700);
|
||||
}
|
||||
|
||||
.btn-reject:hover:not(:disabled) {
|
||||
background: var(--gray-200);
|
||||
}
|
||||
|
||||
.btn-approve {
|
||||
background: var(--main-color);
|
||||
color: white;
|
||||
}
|
||||
|
||||
.btn-approve:hover:not(:disabled) {
|
||||
background: var(--main-700);
|
||||
box-shadow: 0 2px 6px rgba(59, 130, 246, 0.25);
|
||||
}
|
||||
|
||||
.approval-processing {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 8px;
|
||||
padding: 10px;
|
||||
color: var(--gray-600);
|
||||
font-size: 13px;
|
||||
background: var(--gray-25);
|
||||
border-top: 1px solid var(--gray-100);
|
||||
}
|
||||
|
||||
.processing-spinner {
|
||||
width: 14px;
|
||||
height: 14px;
|
||||
border: 2px solid var(--gray-300);
|
||||
border-top-color: var(--main-color);
|
||||
border-radius: 50%;
|
||||
animation: spin 0.8s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes spin {
|
||||
to {
|
||||
transform: rotate(360deg);
|
||||
}
|
||||
}
|
||||
|
||||
/* 滑入滑出动画 */
|
||||
.slide-up-enter-active,
|
||||
.slide-up-leave-active {
|
||||
transition: all 0.25s ease;
|
||||
}
|
||||
|
||||
.slide-up-enter-from {
|
||||
opacity: 0;
|
||||
transform: translateY(20px);
|
||||
}
|
||||
|
||||
.slide-up-leave-to {
|
||||
opacity: 0;
|
||||
transform: translateY(20px);
|
||||
}
|
||||
|
||||
@media (max-width: 520px) {
|
||||
.approval-content {
|
||||
padding: 12px 16px;
|
||||
}
|
||||
|
||||
.approval-header h4 {
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
.approval-operation {
|
||||
font-size: 12px;
|
||||
padding: 8px 10px;
|
||||
}
|
||||
|
||||
.approval-actions {
|
||||
padding: 10px 16px 12px;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.btn {
|
||||
padding: 8px 16px;
|
||||
font-size: 13px;
|
||||
}
|
||||
}
|
||||
</style>
|
||||
128
web/src/composables/useApproval.js
Normal file
128
web/src/composables/useApproval.js
Normal file
@ -0,0 +1,128 @@
|
||||
import { reactive } from 'vue';
|
||||
import { message } from 'ant-design-vue';
|
||||
import { handleChatError } from '@/utils/errorHandler';
|
||||
import { agentApi } from '@/apis';
|
||||
|
||||
export function useApproval({ getThreadState, resetOnGoingConv, fetchThreadMessages }) {
|
||||
// 审批状态
|
||||
const approvalState = reactive({
|
||||
showModal: false,
|
||||
question: '',
|
||||
operation: '',
|
||||
threadId: null,
|
||||
interruptInfo: null
|
||||
});
|
||||
|
||||
// 处理审批逻辑
|
||||
const handleApproval = async (approved, currentAgentId) => {
|
||||
const threadId = approvalState.threadId;
|
||||
if (!threadId) {
|
||||
message.error('无效的审批请求');
|
||||
approvalState.showModal = false;
|
||||
return;
|
||||
}
|
||||
|
||||
const threadState = getThreadState(threadId);
|
||||
if (!threadState) {
|
||||
message.error('无法找到对应的对话线程');
|
||||
approvalState.showModal = false;
|
||||
return;
|
||||
}
|
||||
|
||||
// 关闭弹窗
|
||||
approvalState.showModal = false;
|
||||
|
||||
// 清理旧的流式控制器（如果存在）
|
||||
if (threadState.streamAbortController) {
|
||||
threadState.streamAbortController.abort();
|
||||
threadState.streamAbortController = null;
|
||||
}
|
||||
|
||||
// 标记为处理中
|
||||
threadState.isStreaming = true;
|
||||
resetOnGoingConv(threadId);
|
||||
threadState.streamAbortController = new AbortController();
|
||||
|
||||
console.log('🔄 [APPROVAL] Starting resume process:', { approved, threadId, currentAgentId });
|
||||
|
||||
try {
|
||||
// 调用恢复接口
|
||||
const response = await agentApi.resumeAgentChat(
|
||||
currentAgentId,
|
||||
{
|
||||
thread_id: threadId,
|
||||
approved: approved
|
||||
},
|
||||
{
|
||||
signal: threadState.streamAbortController?.signal
|
||||
}
|
||||
);
|
||||
|
||||
console.log('🔄 [APPROVAL] Resume API response received');
|
||||
|
||||
if (!response.ok) {
|
||||
const errorText = await response.text();
|
||||
console.error('Resume API error:', response.status, errorText);
|
||||
throw new Error(`HTTP error! status: ${response.status}, details: ${errorText}`);
|
||||
}
|
||||
|
||||
console.log('🔄 [APPROVAL] Resume API successful, returning response for stream processing');
|
||||
return response; // 返回响应供调用方处理流式数据
|
||||
|
||||
} catch (error) {
|
||||
console.error('❌ [APPROVAL] Resume failed:', error);
|
||||
if (error.name !== 'AbortError') {
|
||||
handleChatError(error, 'resume');
|
||||
message.error(`恢复对话失败: ${error.message || '未知错误'}`);
|
||||
}
|
||||
// 重置状态 - 只在错误时重置
|
||||
threadState.isStreaming = false;
|
||||
threadState.streamAbortController = null;
|
||||
throw error; // 重新抛出错误让调用方处理
|
||||
}
|
||||
// 移除 finally 块 - 让组件管理流式状态的生命周期
|
||||
};
|
||||
|
||||
// 在流式处理中处理审批请求
|
||||
const processApprovalInStream = (chunk, threadId, currentAgentId) => {
|
||||
if (chunk.status !== 'human_approval_required') {
|
||||
return false;
|
||||
}
|
||||
|
||||
const { interrupt_info } = chunk;
|
||||
const threadState = getThreadState(threadId);
|
||||
|
||||
if (!threadState) return false;
|
||||
|
||||
// 停止显示"处理中"状态，让用户可以看到并操作审批弹窗
|
||||
threadState.isStreaming = false;
|
||||
|
||||
// 显示审批弹窗
|
||||
approvalState.showModal = true;
|
||||
approvalState.question = interrupt_info?.question || '是否批准以下操作？';
|
||||
approvalState.operation = interrupt_info?.operation || '未知操作';
|
||||
approvalState.threadId = chunk.thread_id || threadId;
|
||||
approvalState.interruptInfo = interrupt_info;
|
||||
|
||||
// 刷新消息历史显示已执行的部分
|
||||
fetchThreadMessages({ agentId: currentAgentId, threadId: threadId });
|
||||
|
||||
return true; // 表示已处理审批请求，应停止流式处理
|
||||
};
|
||||
|
||||
// 重置审批状态
|
||||
const resetApprovalState = () => {
|
||||
approvalState.showModal = false;
|
||||
approvalState.question = '';
|
||||
approvalState.operation = '';
|
||||
approvalState.threadId = null;
|
||||
approvalState.interruptInfo = null;
|
||||
};
|
||||
|
||||
return {
|
||||
approvalState,
|
||||
handleApproval,
|
||||
processApprovalInStream,
|
||||
resetApprovalState
|
||||
};
|
||||
}
|
||||
@ -134,9 +134,6 @@ export class MessageProcessor {
|
||||
// 处理AIMessageChunk类型
|
||||
if (result.type === 'AIMessageChunk') {
|
||||
result.type = 'ai';
|
||||
if (result.additional_kwargs?.tool_calls) {
|
||||
result.tool_calls = result.additional_kwargs.tool_calls;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
@ -149,23 +146,47 @@ export class MessageProcessor {
|
||||
* @param {Object} chunk - 当前块
|
||||
*/
|
||||
static _mergeToolCalls(result, chunk) {
|
||||
if (chunk.additional_kwargs?.tool_calls) {
|
||||
if (!result.additional_kwargs) result.additional_kwargs = {};
|
||||
if (!result.additional_kwargs.tool_calls) result.additional_kwargs.tool_calls = [];
|
||||
if (chunk.tool_call_chunks && chunk.tool_call_chunks.length > 0) {
|
||||
// 确保 result 有 tool_calls 数组
|
||||
if (!result.tool_calls) result.tool_calls = [];
|
||||
|
||||
for (const toolCall of chunk.additional_kwargs.tool_calls) {
|
||||
const existingToolCall = result.additional_kwargs.tool_calls.find(
|
||||
t => (t.id === toolCall.id || t.index === toolCall.index)
|
||||
for (const toolCallChunk of chunk.tool_call_chunks) {
|
||||
// 使用 index 来标识工具调用（因为可能有多个工具调用）
|
||||
const existingToolCallIndex = result.tool_calls.findIndex(
|
||||
t => t.index === toolCallChunk.index
|
||||
);
|
||||
|
||||
if (existingToolCall) {
|
||||
// 合并相同ID的tool call
|
||||
if (existingToolCall.function && toolCall.function) {
|
||||
existingToolCall.function.arguments += toolCall.function.arguments;
|
||||
if (existingToolCallIndex !== -1) {
|
||||
// 合并相同index的tool call
|
||||
const existingToolCall = result.tool_calls[existingToolCallIndex];
|
||||
|
||||
// 更新名称和ID（如果存在）
|
||||
if (toolCallChunk.name && !existingToolCall.function?.name) {
|
||||
if (!existingToolCall.function) existingToolCall.function = {};
|
||||
existingToolCall.function.name = toolCallChunk.name;
|
||||
}
|
||||
|
||||
if (toolCallChunk.id && !existingToolCall.id) {
|
||||
existingToolCall.id = toolCallChunk.id;
|
||||
}
|
||||
|
||||
// 合并参数
|
||||
if (toolCallChunk.args) {
|
||||
if (!existingToolCall.function) existingToolCall.function = {};
|
||||
if (!existingToolCall.function.arguments) existingToolCall.function.arguments = '';
|
||||
existingToolCall.function.arguments += toolCallChunk.args;
|
||||
}
|
||||
} else {
|
||||
// 添加新的tool call
|
||||
result.additional_kwargs.tool_calls.push(JSON.parse(JSON.stringify(toolCall)));
|
||||
const newToolCall = {
|
||||
index: toolCallChunk.index,
|
||||
id: toolCallChunk.id,
|
||||
function: {
|
||||
name: toolCallChunk.name || null,
|
||||
arguments: toolCallChunk.args || ''
|
||||
}
|
||||
};
|
||||
result.tool_calls.push(newToolCall);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user