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Merged
liuruibin merged 1 commit into from
Sep 8, 2025
Merged

feat: add MCP and tool configuration options in application settings #4001

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11 changes: 10 additions & 1 deletion apps/application/chat_pipeline/step/chat_step/i_chat_step.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,12 @@ class InstanceSerializer(serializers.Serializer):

model_params_setting = serializers.DictField(required=False, allow_null=True,
label=_("Model parameter settings"))
mcp_enable = serializers.BooleanField(label="MCP否启用", required=False, default=False)
mcp_tool_ids = serializers.JSONField(label="MCP工具ID列表", required=False, default=list)
mcp_servers = serializers.JSONField(label="MCP服务列表", required=False, default=dict)
mcp_source = serializers.CharField(label="MCP Source", required=False, default="referencing")
tool_enable = serializers.BooleanField(label="工具是否启用", required=False, default=False)
tool_ids = serializers.JSONField(label="工具ID列表", required=False, default=list)

def is_valid(self, *, raise_exception=False):
super().is_valid(raise_exception=True)
Expand All @@ -106,5 +112,8 @@ def execute(self, message_list: List[BaseMessage],
paragraph_list=None,
manage: PipelineManage = None,
padding_problem_text: str = None, stream: bool = True, chat_user_id=None, chat_user_type=None,
no_references_setting=None, model_params_setting=None, model_setting=None, **kwargs):
no_references_setting=None, model_params_setting=None, model_setting=None,
mcp_enable=False, mcp_tool_ids=None, mcp_servers='', mcp_source="referencing",
tool_enable=False, tool_ids=None,
**kwargs):
pass
130 changes: 116 additions & 14 deletions apps/application/chat_pipeline/step/chat_step/impl/base_chat_step.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,8 @@
@date：2024/1/9 18:25
@desc: 对话step Base实现
"""
import logging
import json
import os
import time
import traceback
import uuid_utils.compat as uuid
Expand All @@ -24,10 +25,14 @@
from application.chat_pipeline.I_base_chat_pipeline import ParagraphPipelineModel
from application.chat_pipeline.pipeline_manage import PipelineManage
from application.chat_pipeline.step.chat_step.i_chat_step import IChatStep, PostResponseHandler
from application.flow.tools import Reasoning
from application.flow.tools import Reasoning, mcp_response_generator
from application.models import ApplicationChatUserStats, ChatUserType
from common.utils.logger import maxkb_logger
from common.utils.rsa_util import rsa_long_decrypt
from common.utils.tool_code import ToolExecutor
from maxkb.const import CONFIG
from models_provider.tools import get_model_instance_by_model_workspace_id
from tools.models import Tool


def add_access_num(chat_user_id=None, chat_user_type=None, application_id=None):
Expand All @@ -54,6 +59,7 @@ def write_context(step, manage, request_token, response_token, all_text):
manage.context['answer_tokens'] = manage.context['answer_tokens'] + response_token



def event_content(response,
chat_id,
chat_record_id,
Expand Down Expand Up @@ -169,6 +175,12 @@ def execute(self, message_list: List[BaseMessage],
no_references_setting=None,
model_params_setting=None,
model_setting=None,
mcp_enable=False,
mcp_tool_ids=None,
mcp_servers='',
mcp_source="referencing",
tool_enable=False,
tool_ids=None,
**kwargs):
chat_model = get_model_instance_by_model_workspace_id(model_id, workspace_id,
**model_params_setting) if model_id is not None else None
Expand All @@ -177,14 +189,24 @@ def execute(self, message_list: List[BaseMessage],
paragraph_list,
manage, padding_problem_text, chat_user_id, chat_user_type,
no_references_setting,
model_setting)
model_setting,
mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids)
else:
return self.execute_block(message_list, chat_id, problem_text, post_response_handler, chat_model,
paragraph_list,
manage, padding_problem_text, chat_user_id, chat_user_type, no_references_setting,
model_setting)
model_setting,
mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids)

def get_details(self, manage, **kwargs):
# 删除临时生成的MCP代码文件
if self.context.get('execute_ids'):
executor = ToolExecutor(CONFIG.get('SANDBOX'))
# 清理工具代码文件，延时删除，避免文件被占用
for tool_id in self.context.get('execute_ids'):
code_path = f'{executor.sandbox_path}/execute/{tool_id}.py'
if os.path.exists(code_path):
os.remove(code_path)
return {
'step_type': 'chat_step',
'run_time': self.context['run_time'],
Expand All @@ -206,12 +228,63 @@ def reset_message_list(message_list: List[BaseMessage], answer_text):
result.append({'role': 'ai', 'content': answer_text})
return result

@staticmethod
def get_stream_result(message_list: List[BaseMessage],
def _handle_mcp_request(self, mcp_enable, tool_enable, mcp_source, mcp_servers, mcp_tool_ids, tool_ids,
chat_model, message_list):
if not mcp_enable and not tool_enable:
return None

mcp_servers_config = {}

# 迁移过来mcp_source是None
if mcp_source is None:
mcp_source = 'custom'
if mcp_enable:
# 兼容老数据
if not mcp_tool_ids:
mcp_tool_ids = []
if mcp_source == 'custom' and mcp_servers is not None and '"stdio"' not in mcp_servers:
mcp_servers_config = json.loads(mcp_servers)
elif mcp_tool_ids:
mcp_tools = QuerySet(Tool).filter(id__in=mcp_tool_ids).values()
for mcp_tool in mcp_tools:
if mcp_tool and mcp_tool['is_active']:
mcp_servers_config = {**mcp_servers_config, **json.loads(mcp_tool['code'])}

if tool_enable:
if tool_ids and len(tool_ids) > 0: # 如果有工具ID，则将其转换为MCP
self.context['tool_ids'] = tool_ids
self.context['execute_ids'] = []
for tool_id in tool_ids:
tool = QuerySet(Tool).filter(id=tool_id).first()
if not tool.is_active:
continue
executor = ToolExecutor(CONFIG.get('SANDBOX'))
if tool.init_params is not None:
params = json.loads(rsa_long_decrypt(tool.init_params))
else:
params = {}
_id, tool_config = executor.get_tool_mcp_config(tool.code, params)

self.context['execute_ids'].append(_id)
mcp_servers_config[str(tool.id)] = tool_config

if len(mcp_servers_config) > 0:
return mcp_response_generator(chat_model, message_list, json.dumps(mcp_servers_config))

return None


def get_stream_result(self, message_list: List[BaseMessage],
chat_model: BaseChatModel = None,
paragraph_list=None,
no_references_setting=None,
problem_text=None):
problem_text=None,
mcp_enable=False,
mcp_tool_ids=None,
mcp_servers='',
mcp_source="referencing",
tool_enable=False,
tool_ids=None):
if paragraph_list is None:
paragraph_list = []
directly_return_chunk_list = [AIMessageChunk(content=paragraph.content)
Expand All @@ -227,6 +300,12 @@ def get_stream_result(message_list: List[BaseMessage],
return iter([AIMessageChunk(
_('Sorry, the AI model is not configured. Please go to the application to set up the AI model first.'))]), False
else:
# 处理 MCP 请求
mcp_result = self._handle_mcp_request(
mcp_enable, tool_enable, mcp_source, mcp_servers, mcp_tool_ids, tool_ids, chat_model, message_list,
)
if mcp_result:
return mcp_result, True
return chat_model.stream(message_list), True

def execute_stream(self, message_list: List[BaseMessage],
Expand All @@ -239,9 +318,15 @@ def execute_stream(self, message_list: List[BaseMessage],
padding_problem_text: str = None,
chat_user_id=None, chat_user_type=None,
no_references_setting=None,
model_setting=None):
model_setting=None,
mcp_enable=False,
mcp_tool_ids=None,
mcp_servers='',
mcp_source="referencing",
tool_enable=False,
tool_ids=None):
chat_result, is_ai_chat = self.get_stream_result(message_list, chat_model, paragraph_list,
no_references_setting, problem_text)
no_references_setting, problem_text, mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids)
chat_record_id = uuid.uuid7()
r = StreamingHttpResponse(
streaming_content=event_content(chat_result, chat_id, chat_record_id, paragraph_list,
Expand All @@ -253,12 +338,17 @@ def execute_stream(self, message_list: List[BaseMessage],
r['Cache-Control'] = 'no-cache'
return r

@staticmethod
def get_block_result(message_list: List[BaseMessage],
def get_block_result(self, message_list: List[BaseMessage],
chat_model: BaseChatModel = None,
paragraph_list=None,
no_references_setting=None,
problem_text=None):
problem_text=None,
mcp_enable=False,
mcp_tool_ids=None,
mcp_servers='',
mcp_source="referencing",
tool_enable=False,
tool_ids=None):
if paragraph_list is None:
paragraph_list = []
directly_return_chunk_list = [AIMessageChunk(content=paragraph.content)
Expand All @@ -273,6 +363,12 @@ def get_block_result(message_list: List[BaseMessage],
return AIMessage(
_('Sorry, the AI model is not configured. Please go to the application to set up the AI model first.')), False
else:
# 处理 MCP 请求
mcp_result = self._handle_mcp_request(
mcp_enable, tool_enable, mcp_source, mcp_servers, mcp_tool_ids, tool_ids, chat_model, message_list,
)
if mcp_result:
return mcp_result, True
return chat_model.invoke(message_list), True

def execute_block(self, message_list: List[BaseMessage],
Expand All @@ -284,7 +380,13 @@ def execute_block(self, message_list: List[BaseMessage],
manage: PipelineManage = None,
padding_problem_text: str = None,
chat_user_id=None, chat_user_type=None, no_references_setting=None,
model_setting=None):
model_setting=None,
mcp_enable=False,
mcp_tool_ids=None,
mcp_servers='',
mcp_source="referencing",
tool_enable=False,
tool_ids=None):
reasoning_content_enable = model_setting.get('reasoning_content_enable', False)
reasoning_content_start = model_setting.get('reasoning_content_start', '<think>')
reasoning_content_end = model_setting.get('reasoning_content_end', '</think>')
Expand All @@ -294,7 +396,7 @@ def execute_block(self, message_list: List[BaseMessage],
# 调用模型
try:
chat_result, is_ai_chat = self.get_block_result(message_list, chat_model, paragraph_list,
no_references_setting, problem_text)
no_references_setting, problem_text, mcp_enable, mcp_tool_ids, mcp_servers, mcp_source, tool_enable, tool_ids)
if is_ai_chat:
request_token = chat_model.get_num_tokens_from_messages(message_list)
response_token = chat_model.get_num_tokens(chat_result.content)
Expand Down
70 changes: 3 additions & 67 deletions apps/application/flow/step_node/ai_chat_step_node/impl/base_chat_node.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,59 +6,28 @@
@date：2024/6/4 14:30
@desc:
"""
import asyncio
import json
import os
import re
import sys
import time
import traceback
from functools import reduce
from typing import List, Dict

import uuid_utils.compat as uuid
from django.db.models import QuerySet
from langchain.schema import HumanMessage, SystemMessage
from langchain_core.messages import BaseMessage, AIMessage, AIMessageChunk, ToolMessage
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import BaseMessage, AIMessage


from application.flow.i_step_node import NodeResult, INode
from application.flow.step_node.ai_chat_step_node.i_chat_node import IChatNode
from application.flow.tools import Reasoning
from common.utils.logger import maxkb_logger
from application.flow.tools import Reasoning, mcp_response_generator
from common.utils.rsa_util import rsa_long_decrypt
from common.utils.tool_code import ToolExecutor
from maxkb.const import CONFIG
from models_provider.models import Model
from models_provider.tools import get_model_credential, get_model_instance_by_model_workspace_id
from tools.models import Tool

tool_message_template = """
<details>
<summary>
<strong>Called MCP Tool: <em>%s</em></strong>
</summary>

%s

</details>

"""

tool_message_json_template = """
```json
%s
```
"""


def generate_tool_message_template(name, context):
if '```' in context:
return tool_message_template % (name, context)
else:
return tool_message_template % (name, tool_message_json_template % (context))


def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str,
reasoning_content: str):
Expand Down Expand Up @@ -122,39 +91,6 @@ def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INo
_write_context(node_variable, workflow_variable, node, workflow, answer, reasoning_content)


async def _yield_mcp_response(chat_model, message_list, mcp_servers):
client = MultiServerMCPClient(json.loads(mcp_servers))
tools = await client.get_tools()
agent = create_react_agent(chat_model, tools)
response = agent.astream({"messages": message_list}, stream_mode='messages')
async for chunk in response:
if isinstance(chunk[0], ToolMessage):
content = generate_tool_message_template(chunk[0].name, chunk[0].content)
chunk[0].content = content
yield chunk[0]
if isinstance(chunk[0], AIMessageChunk):
yield chunk[0]


def mcp_response_generator(chat_model, message_list, mcp_servers):
loop = asyncio.new_event_loop()
try:
async_gen = _yield_mcp_response(chat_model, message_list, mcp_servers)
while True:
try:
chunk = loop.run_until_complete(anext_async(async_gen))
yield chunk
except StopAsyncIteration:
break
except Exception as e:
maxkb_logger.error(f'Exception: {e}', traceback.format_exc())
finally:
loop.close()


async def anext_async(agen):
return await agen.__anext__()


def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow):
"""
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Contributor Author

@shaohuzhang1 shaohuzhang1 Sep 8, 2025

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This code snippet has several issues that need to be addressed:

  1. Import Order: The uuid_utils.compat as uuid import is not used anywhere in the code.
  2. Unused Imports: Unused imports like sys, time, and traceback should be removed without affecting functionality.
  3. Redundant Code: The _yield_mcp_response function creates an asynchronous generator, but mcp_response_generator is already wrapping it into a synchronous generator with its own event loop handling, which duplicates effort.
  4. Tool Message Generation: The generate_tool_message_template function is redundant due to the direct use of string concatenation in _yield_mcp_response.
  5. Synchronous Loop Handling: The _write_context_stream function uses multiple loops (while and try-except) unnecessarily. It's better if these can be combined or refactored for clarity and efficiency.

Here’s an optimized version of the code with improvements:

import asyncio
import json
import os
import re
from functools import reduce
from typing import List, Dict

from langchain.schema import HumanMessage, SystemMessage
from langchain_core.messages import BaseMessage, AIMessage
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent

from application.flow.i_step_node import NodeResult, INode
from application.flow.step_node.ai_chat_step_node.i_chat_node import IChatNode
from application.flow.tools import Reasoning
from common.utils.logger import maxkb_logger
from common.utils.rsa_util import rsa_long_decrypt
from common.utils.tool_code import ToolExecutor
from maxkb.const import CONFIG
from models_provider.models import Model
from models_provider.tools import get_model_credential, get_model_instance_by_model_workspace_id
from tools.models import Tool

tool_message_template = "<details>\n<summary><strong>Called MCP Tool: <em>{name}</em></strong></summary>\n{context}\n</details>"
tool_message_json_template = "```json\n{context}\n```"

async def _yield_mcp_response(chat_model, message_list, mcp_servers):
    client = MultiServerMCPClient(json.loads(mcp_servers))
    tools = await client.get_tools()
    agent = create_react_agent(chat_model, tools)
    response = agent.astream({"messages": message_list})
    
    async for chunk in response:
        # Assuming Agent API returns either messages or tool messages
        if isinstance(chunk, list) and all(isinstance(item, (BaseMessage)) for item in chunk):
            yield from chunk
        elif isinstance(chunk, ToolMessage):
            content = generate_tool_message_template(chunk.name, chunk.content)
            chunk.content = content
            yield chunk


def mcp_response_generator(chat_model, message_list, mcp_servers):
    return _yield_mcp_response(chat_model, message_list, mcp_servers)


async def anext_async(agen):
    return await agen.asend(None)


def write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workspace_id, answer: str, reasoning_content: str):
    """Write context logic here."""
    pass


def write_context_stream(node_variable: Dict, workflow_variable: Dict, node: INode, workspace_id, answer: str, reasoning_content: str):
    stream = mcp_response_generator(node.model_name, [HumanMessage(content=answer), SysstemMessage(content=reasoning_content)])
    async for chunk in asyncio.to_thread(stream.__anext__):  # Use asyncio.to_thread to run the async generator loop in another thread
        print(chunk.content)  # Simplified logging; replace with actual context handling.


@reduce
def update_context(acc, key, value):
    acc[key] = value
    return acc

# Example usage in main execution logic
result = await write_context_stream(..., ...)

Key Changes:

  • Removed Unnecessary Imports: Reduced the number of imports by removing unused ones.
  • Refactored generate_tool_message_template Functionality: Removed unnecessary checks and directly concatenated strings.
  • Simplified Synchronous Loops: Combined multiple loops into a single write_context_stream function using asyncio.to_thread to handle asynchronous processing synchronously.
  • Updated Comments: Improved comments for clarity.
  • Example Usage: Added example usage annotations in the function comment to show how it might be called within a larger process.

These changes should improve the readability and maintainability of the code while ensuring correctness and reliability.

Expand Down
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