feat:调试对话

src/mindpilot/app/api/conversation_routes.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 from fastapi import APIRouter, Request
-from ..conversation.conversation_api import add_conversation, list_conversations, get_conversation, delete_conversation, send_messages
+from ..conversation.conversation_api import add_conversation, list_conversations, get_conversation, delete_conversation, send_messages, debug_messages
 
 conversation_router = APIRouter(prefix="/api/conversation", tags=["对话接口"])
 
@@ -28,3 +28,8 @@ conversation_router.post(
     "/{conversation_id}/messages",
     summary="发送消息"
 )(send_messages)
+
+conversation_router.post(
+    "/debug",
+    summary="调试对话",
+)(debug_messages)

src/mindpilot/app/chat/chat.py

@@ -1,7 +1,7 @@
 import asyncio
 import json
 import uuid
-from typing import AsyncIterable, List
+from typing import AsyncIterable, List, Dict, Any
 
 from fastapi import Body
 from langchain.chains import LLMChain
@@ -378,3 +378,127 @@ async def chat_online(
             ret.append(data)
 
     return ret
+
+
+async def debug_chat_online(
+        content: str,
+        history: List[History],
+        chat_model_config: dict,
+        tool_config: List[str],
+        agent_config: Dict[str, Any]
+):
+    async def chat_iterator() -> AsyncIterable[OpenAIChatOutput]:
+        callback = AgentExecutorAsyncIteratorCallbackHandler()
+        callbacks = [callback]
+
+        model, prompt = create_models_from_config(
+            callbacks=callbacks, configs=chat_model_config, stream=False
+        )
+
+        all_tools = get_tool().values()
+        tool_configs = tool_config
+
+        agent_enable = agent_config["agent_enable"]
+        if agent_enable:
+            agent_name = agent_config["agent_name"]
+            agent_abstract = agent_config["agent_abstract"]
+            agent_info = agent_config["agent_info"]
+            if not tool_config:
+                tool_configs = agent_config["tool_config"]
+            agent_prompt_pre = "Your name is " + agent_name + "." + agent_abstract + ". Below is your detailed information:" + agent_info + "."
+            agent_prompt_after = "DO NOT forget " + agent_prompt_pre
+            prompt = agent_prompt_pre + prompt + agent_prompt_after
+            # TODO 处理知识库
+
+        tool_configs = tool_configs or TOOL_CONFIG
+        tools = [tool for tool in all_tools if tool.name in tool_configs]
+        tools = [t.copy(update={"callbacks": callbacks}) for t in tools]
+
+        full_chain = create_models_chains(
+            prompts=prompt,
+            models=model,
+            tools=tools,
+            callbacks=callbacks,
+            history=history,
+            agent_enable=agent_enable
+        )
+
+        _history = [History.from_data(h) for h in history]
+        chat_history = [h.to_msg_tuple() for h in _history]
+
+        history_message = convert_to_messages(chat_history)
+
+        task = asyncio.create_task(
+            wrap_done(
+                full_chain.ainvoke(
+                    {
+                        "input": content,
+                        "chat_history": history_message,
+                    }
+                ),
+                callback.done,
+            )
+        )
+
+        last_tool = {}
+        async for chunk in callback.aiter():
+            data = json.loads(chunk)
+            data["tool_calls"] = []
+            data["message_type"] = MsgType.TEXT
+
+            if data["status"] == AgentStatus.tool_start:
+                last_tool = {
+                    "index": 0,
+                    "id": data["run_id"],
+                    "type": "function",
+                    "function": {
+                        "name": data["tool"],
+                        "arguments": data["tool_input"],
+                    },
+                    "tool_output": None,
+                    "is_error": False,
+                }
+                data["tool_calls"].append(last_tool)
+            if data["status"] in [AgentStatus.tool_end]:
+                last_tool.update(
+                    tool_output=data["tool_output"],
+                    is_error=data.get("is_error", False),
+                )
+                data["tool_calls"] = [last_tool]
+                last_tool = {}
+                try:
+                    tool_output = json.loads(data["tool_output"])
+                    if message_type := tool_output.get("message_type"):
+                        data["message_type"] = message_type
+                except:
+                    ...
+            elif data["status"] == AgentStatus.agent_finish:
+                try:
+                    tool_output = json.loads(data["text"])
+                    if message_type := tool_output.get("message_type"):
+                        data["message_type"] = message_type
+                except:
+                    ...
+
+            ret = OpenAIChatOutput(
+                id=f"chat{uuid.uuid4()}",
+                object="chat.completion.chunk",
+                content=data.get("text", ""),
+                role="assistant",
+                tool_calls=data["tool_calls"],
+                model=model.model_name,
+                status=data["status"],
+                message_type=data["message_type"],
+            )
+            yield ret.model_dump_json()
+
+        await task
+
+    ret = []
+
+    async for chunk in chat_iterator():
+        data = json.loads(chunk)
+        if data["status"] != AgentStatus.llm_start and data["status"] != AgentStatus.llm_new_token:
+            ret.append(data)
+
+    return ret

src/mindpilot/app/conversation/conversation_api.py

@@ -6,11 +6,13 @@ from fastapi import Body
 from uuid import uuid4
 from datetime import datetime
 import sqlite3
+
+from ..chat.utils import History
 from ..utils.system_utils import BaseResponse, ListResponse, get_mindpilot_db_connection
 from .message import init_messages_table, insert_message, split_message_content
 from ..model_configs.utils import get_config_from_id
 from ..agent.utils import get_agent_from_id
-from ..chat.chat import chat_online
+from ..chat.chat import chat_online, debug_chat_online
 
 
 def init_conversations_table():
@@ -185,6 +187,7 @@ async def send_messages(
         max_tokens: int = Body(..., description="模型输出最大长度", examples=[4096]),
 
 ):
+    # TODO 缺少知识库部分
     init_conversations_table()
     init_messages_table()
     conn = get_mindpilot_db_connection()
@@ -316,3 +319,83 @@ async def send_messages(
     conn.close()
 
     return BaseResponse(code=200, msg="success", data=response_messages)
+
+
+async def debug_messages(
+        query: str = Body(..., description="用户输入", examples=[""]),
+        history: List[History] = Body(
+            [],
+            description="历史对话",
+            examples=[
+                [
+                    {"role": "user", "content": "你好"},
+                    {"role": "assistant", "content": "您好，我是智能Agent桌面助手MindPilot，请问有什么可以帮您？"},
+                ]
+            ],
+        ),
+        config_id: int = Body("0", description="模型配置", examples=[1]),
+        agent_config: dict = Body(..., description="agent配置", examples=[
+            {
+                "agent_name": "调试助手",
+                "agent_abstract": "",
+                "agent_info": "这是一个用于调试的AI助手",
+                "agent_enable": True,
+                "temperature": 0.7,
+                "max_tokens": 150,
+                "tool_config": ["search_internet", "calculator"]
+            }
+        ])
+):
+    # TODO 缺少知识库部分
+
+    if not agent_config["agent_enable"]:
+        temp_agent_name = agent_config["agent_name"]
+        temp_agent_abstract = agent_config["agent_abstract"]
+        temp_agent_info = agent_config["agent_info"]
+        agent_prompt = "Your name is " + temp_agent_name + "." + temp_agent_abstract + ". Below is your detailed information:" + temp_agent_info + "."
+        history.append({"role": "user", "content": agent_prompt})
+
+    # 获取模型配置
+    chat_model_config = get_config_from_id(config_id=config_id)
+    model_key = next(iter(chat_model_config["llm_model"]))
+    chat_model_config["llm_model"][model_key]["temperature"] = agent_config['temperature']
+    chat_model_config["llm_model"][model_key]["max_tokens"] = agent_config['max_tokens']
+
+    # 获取模型输出
+    ret = await debug_chat_online(content=query, history=history, chat_model_config=chat_model_config,
+                                  tool_config=agent_config['tool_config'], agent_config=agent_config)
+
+    response_messages = []
+    for message in ret:
+        if message['status'] == 7:
+            message_role = message['choices'][0]['role']
+            message_content = "Observation:\n" + message['choices'][0]['delta']['tool_calls'][0]['tool_output']
+            timestamp = datetime.now().isoformat()
+            message_dict = {
+                "message_id": 0,
+                "agent_status": 7,
+                "text": message_content,
+                "files": [],
+                "timestamp": timestamp
+            }
+            response_messages.append(message_dict)
+
+        if message['status'] == 3:
+            message_role = message['choices'][0]['role']
+            message_content = message['choices'][0]['delta']['content']
+            message_list = split_message_content(message_content)
+            for m in message_list:
+                timestamp = datetime.now().isoformat()
+                message_dict = {
+                    "message_id": 0,
+                    "agent_status": 3,
+                    "text": m,
+                    "files": [],
+                    "timestamp": timestamp
+                }
+
+                response_messages.append(message_dict)
+
+        # TODO 这里考虑处理一下message['status']是4但之前一个message['status']不是3的，即agent无法解析的内容
+
+    return BaseResponse(code=200, msg="success", data=response_messages)