Prod

.github/dependabot.yml

@@ -0,0 +1,14 @@
+version: 2
+updates:
+  - package-ecosystem: "pip"
+    directory: "/"
+    schedule:
+      interval: "weekly"
+      timezone: "Asia/Shanghai"
+      day: "friday"
+    target-branch: "v2"
+    groups:
+      python-dependencies:
+        patterns:
+          - "*"
+

.github/workflows/build-and-push.yml

@@ -7,7 +7,7 @@ on:
     inputs:
       dockerImageTag:
         description: 'Image Tag'
-        default: 'v1.10.3-dev'
+        default: 'v1.10.7-dev'
         required: true
       dockerImageTagWithLatest:
         description: '是否发布latest tag（正式发版时选择，测试版本切勿选择）'
@@ -36,7 +36,7 @@ on:
 jobs:
   build-and-push-to-fit2cloud-registry:
     if: ${{ contains(github.event.inputs.registry, 'fit2cloud') }}
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - name: Check Disk Space
         run: df -h
@@ -52,10 +52,6 @@ jobs:
           swap-storage: true
       - name: Check Disk Space
         run: df -h
-      - name: Set Swap Space
-        uses: pierotofy/set-swap-space@master
-        with:
-          swap-size-gb: 8
       - name: Checkout
         uses: actions/checkout@v4
         with:
@@ -73,19 +69,12 @@ jobs:
             DOCKER_IMAGE_TAGS="--tag ${DOCKER_IMAGE}:${TAG_NAME}"
           fi
           echo ::set-output name=buildx_args::--platform ${DOCKER_PLATFORMS} --memory-swap -1 \
-            --build-arg DOCKER_IMAGE_TAG=${{ github.event.inputs.dockerImageTag }} --build-arg BUILD_AT=$(TZ=Asia/Shanghai date +'%Y-%m-%dT%H:%M') --build-arg GITHUB_COMMIT=${GITHUB_SHA::8} --no-cache \
+            --build-arg DOCKER_IMAGE_TAG=${{ github.event.inputs.dockerImageTag }} --build-arg BUILD_AT=$(TZ=Asia/Shanghai date +'%Y-%m-%dT%H:%M') --build-arg GITHUB_COMMIT=`git rev-parse --short HEAD` --no-cache \
             ${DOCKER_IMAGE_TAGS} .
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
-        with:
-          # Until https://github.com/tonistiigi/binfmt/issues/215
-          image: tonistiigi/binfmt:qemu-v7.0.0-28
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
-        with:
-          buildkitd-config-inline: |
-            [worker.oci]
-              max-parallelism = 1
       - name: Login to GitHub Container Registry
         uses: docker/login-action@v3
         with:
@@ -100,11 +89,12 @@ jobs:
           password: ${{ secrets.FIT2CLOUD_REGISTRY_PASSWORD }}
       - name: Docker Buildx (build-and-push)
         run: |
+          sudo sync && echo 3 | sudo tee /proc/sys/vm/drop_caches && free -m
           docker buildx build --output "type=image,push=true" ${{ steps.prepare.outputs.buildx_args }} -f installer/Dockerfile
 
   build-and-push-to-dockerhub:
     if: ${{ contains(github.event.inputs.registry, 'dockerhub') }}
-    runs-on: ubuntu-22.04
+    runs-on: ubuntu-latest
     steps:
       - name: Check Disk Space
         run: df -h
@@ -120,10 +110,6 @@ jobs:
           swap-storage: true
       - name: Check Disk Space
         run: df -h
-      - name: Set Swap Space
-        uses: pierotofy/set-swap-space@master
-        with:
-          swap-size-gb: 8
       - name: Checkout
         uses: actions/checkout@v4
         with:
@@ -141,19 +127,12 @@ jobs:
             DOCKER_IMAGE_TAGS="--tag ${DOCKER_IMAGE}:${TAG_NAME}"
           fi
           echo ::set-output name=buildx_args::--platform ${DOCKER_PLATFORMS} --memory-swap -1 \
-            --build-arg DOCKER_IMAGE_TAG=${{ github.event.inputs.dockerImageTag }} --build-arg BUILD_AT=$(TZ=Asia/Shanghai date +'%Y-%m-%dT%H:%M') --build-arg GITHUB_COMMIT=${GITHUB_SHA::8} --no-cache \
+            --build-arg DOCKER_IMAGE_TAG=${{ github.event.inputs.dockerImageTag }} --build-arg BUILD_AT=$(TZ=Asia/Shanghai date +'%Y-%m-%dT%H:%M') --build-arg GITHUB_COMMIT=`git rev-parse --short HEAD` --no-cache \
             ${DOCKER_IMAGE_TAGS} .
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v3
-        with:
-          # Until https://github.com/tonistiigi/binfmt/issues/215
-          image: tonistiigi/binfmt:qemu-v7.0.0-28
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
-        with:
-          buildkitd-config-inline: |
-            [worker.oci]
-              max-parallelism = 1
       - name: Login to GitHub Container Registry
         uses: docker/login-action@v3
         with:
@@ -167,4 +146,5 @@ jobs:
           password: ${{ secrets.DOCKERHUB_TOKEN }}
       - name: Docker Buildx (build-and-push)
         run: |
+          sudo sync && echo 3 | sudo tee /proc/sys/vm/drop_caches && free -m
           docker buildx build --output "type=image,push=true" ${{ steps.prepare.outputs.buildx_args }} -f installer/Dockerfile

README.md

@@ -1,5 +1,5 @@
 <p align="center"><img src= "https://github.com/1Panel-dev/maxkb/assets/52996290/c0694996-0eed-40d8-b369-322bf2a380bf" alt="MaxKB" width="300" /></p>
-<h3 align="center">Ready-to-use AI Chatbot</h3>
+<h3 align="center">An Open-Source AI Assistant for Enterprise</h3>
 <p align="center"><a href="https://trendshift.io/repositories/9113" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9113" alt="1Panel-dev%2FMaxKB | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a></p>
 <p align="center">
   <a href="https://www.gnu.org/licenses/gpl-3.0.html#license-text"><img src="https://img.shields.io/github/license/1Panel-dev/maxkb?color=%231890FF" alt="License: GPL v3"></a>
@@ -10,10 +10,10 @@
 </p>
 <hr/>
 
-MaxKB = Max Knowledge Base, it is a ready-to-use AI chatbot that integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities. MaxKB is widely applied in scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education.
+MaxKB = Max Knowledge Brain, it is a powerful and easy-to-use AI assistant that integrates Retrieval-Augmented Generation (RAG) pipelines, supports robust workflows, and provides advanced MCP tool-use capabilities. MaxKB is widely applied in scenarios such as intelligent customer service, corporate internal knowledge bases, academic research, and education.
 
-- **RAG Pipeline**: Supports direct uploading of documents / automatic crawling of online documents, with features for automatic text splitting, vectorization, and RAG (Retrieval-Augmented Generation). This effectively reduces hallucinations in large models, providing a superior smart Q&A interaction experience.
-- **Flexible Orchestration**: Equipped with a powerful workflow engine, function library and MCP tool-use, enabling the orchestration of AI processes to meet the needs of complex business scenarios. 
+- **RAG Pipeline**: Supports direct uploading of documents / automatic crawling of online documents, with features for automatic text splitting, vectorization. This effectively reduces hallucinations in large models, providing a superior smart Q&A interaction experience.
+- **Agentic Workflow**: Equipped with a powerful workflow engine, function library and MCP tool-use, enabling the orchestration of AI processes to meet the needs of complex business scenarios. 
 - **Seamless Integration**: Facilitates zero-coding rapid integration into third-party business systems, quickly equipping existing systems with intelligent Q&A capabilities to enhance user satisfaction.
 - **Model-Agnostic**: Supports various large models, including private models (such as DeepSeek, Llama, Qwen, etc.) and public models (like OpenAI, Claude, Gemini, etc.).
 - **Multi Modal**: Native support for input and output text, image, audio and video.

README_CN.md

@@ -1,6 +1,5 @@
 <p align="center"><img src= "https://github.com/1Panel-dev/maxkb/assets/52996290/c0694996-0eed-40d8-b369-322bf2a380bf" alt="MaxKB" width="300" /></p>
-<h3 align="center">基于大模型和 RAG 的知识库问答系统</h3>
-<h4 align="center">Ready-to-use, flexible RAG Chatbot</h4>
+<h3 align="center">强大易用的企业级 AI 助手</h3>
 <p align="center">
     <a href="https://trendshift.io/repositories/9113" target="_blank"><img src="https://trendshift.io/api/badge/repositories/9113" alt="1Panel-dev%2FMaxKB | Trendshift" style="width: 250px; height: auto;" /></a>
     <a href="https://market.aliyun.com/products/53690006/cmjj00067609.html?userCode=kmemb8jp" target="_blank"><img src="https://img.alicdn.com/imgextra/i2/O1CN01H5JIwY1rZ0OobDjnJ_!!6000000005644-2-tps-1000-216.png" alt="1Panel-dev%2FMaxKB | Aliyun" style="width: 250px; height: auto;" /></a>
@@ -14,12 +13,12 @@
 </p>
 <hr/>
 
-MaxKB = Max Knowledge Base，是一款开箱即用的 RAG Chatbot，具备强大的工作流和 MCP 工具调用能力。它支持对接各种主流大语言模型（LLMs），广泛应用于智能客服、企业内部知识库、学术研究与教育等场景。
+MaxKB = Max Knowledge Brain，是一款强大易用的企业级 AI 助手，支持 RAG 检索增强生成、工作流编排、MCP 工具调用能力。MaxKB 支持对接各种主流大语言模型，广泛应用于智能客服、企业内部知识库问答、员工助手、学术研究与教育等场景。
 
-- **开箱即用**：支持直接上传文档 / 自动爬取在线文档，支持文本自动拆分、向量化和 RAG（检索增强生成），有效减少大模型幻觉，智能问答交互体验好；
-- **模型中立**：支持对接各种大模型，包括本地私有大模型（DeepSeek R1 / Llama 3 / Qwen 2 等）、国内公共大模型（通义千问 / 腾讯混元 / 字节豆包 / 百度千帆 / 智谱 AI / Kimi 等）和国外公共大模型（OpenAI / Claude / Gemini 等）；
+- **RAG 检索增强生成**：高效搭建本地 AI 知识库，支持直接上传文档 / 自动爬取在线文档，支持文本自动拆分、向量化，有效减少大模型幻觉，提升问答效果；
 - **灵活编排**：内置强大的工作流引擎、函数库和 MCP 工具调用能力，支持编排 AI 工作过程，满足复杂业务场景下的需求；
-- **无缝嵌入**：支持零编码快速嵌入到第三方业务系统，让已有系统快速拥有智能问答能力，提高用户满意度。
+- **无缝嵌入**：支持零编码快速嵌入到第三方业务系统，让已有系统快速拥有智能问答能力，提高用户满意度；
+- **模型中立**：支持对接各种大模型，包括本地私有大模型（DeepSeek R1 / Llama 3 / Qwen 2 等）、国内公共大模型（通义千问 / 腾讯混元 / 字节豆包 / 百度千帆 / 智谱 AI / Kimi 等）和国外公共大模型（OpenAI / Claude / Gemini 等）。
 
 MaxKB 三分钟视频介绍：https://www.bilibili.com/video/BV18JypYeEkj/
 

apps/application/flow/step_node/ai_chat_step_node/impl/base_chat_node.py

@@ -40,6 +40,7 @@
 
 """
 
+
 def _write_context(node_variable: Dict, workflow_variable: Dict, node: INode, workflow, answer: str,
                    reasoning_content: str):
     chat_model = node_variable.get('chat_model')
@@ -102,7 +103,6 @@ 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):
     async with MultiServerMCPClient(json.loads(mcp_servers)) as client:
         agent = create_react_agent(chat_model, client.get_tools())
@@ -115,6 +115,7 @@ async def _yield_mcp_response(chat_model, message_list, mcp_servers):
             if isinstance(chunk[0], AIMessageChunk):
                 yield chunk[0]
 
+
 def mcp_response_generator(chat_model, message_list, mcp_servers):
     loop = asyncio.new_event_loop()
     try:
@@ -130,6 +131,7 @@ def mcp_response_generator(chat_model, message_list, mcp_servers):
     finally:
         loop.close()
 
+
 async def anext_async(agen):
     return await agen.__anext__()
 
@@ -186,7 +188,8 @@ def save_context(self, details, workflow_manage):
         self.context['answer'] = details.get('answer')
         self.context['question'] = details.get('question')
         self.context['reasoning_content'] = details.get('reasoning_content')
-        self.answer_text = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
 
     def execute(self, model_id, system, prompt, dialogue_number, history_chat_record, stream, chat_id, chat_record_id,
                 model_params_setting=None,

apps/application/flow/step_node/application_node/impl/base_application_node.py

@@ -168,7 +168,8 @@ def save_context(self, details, workflow_manage):
         self.context['question'] = details.get('question')
         self.context['type'] = details.get('type')
         self.context['reasoning_content'] = details.get('reasoning_content')
-        self.answer_text = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
 
     def execute(self, application_id, message, chat_id, chat_record_id, stream, re_chat, client_id, client_type,
                 app_document_list=None, app_image_list=None, app_audio_list=None, child_node=None, node_data=None,
@@ -178,7 +179,8 @@ def execute(self, application_id, message, chat_id, chat_record_id, stream, re_c
         current_chat_id = string_to_uuid(chat_id + application_id)
         Chat.objects.get_or_create(id=current_chat_id, defaults={
             'application_id': application_id,
-            'abstract': message[0:1024]
+            'abstract': message[0:1024],
+            'client_id': client_id,
         })
         if app_document_list is None:
             app_document_list = []

apps/application/flow/step_node/direct_reply_node/impl/base_reply_node.py

@@ -15,7 +15,9 @@
 class BaseReplyNode(IReplyNode):
     def save_context(self, details, workflow_manage):
         self.context['answer'] = details.get('answer')
-        self.answer_text = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
+
     def execute(self, reply_type, stream, fields=None, content=None, **kwargs) -> NodeResult:
         if reply_type == 'referencing':
             result = self.get_reference_content(fields)

apps/application/flow/step_node/form_node/impl/base_form_node.py

@@ -38,7 +38,8 @@ def save_context(self, details, workflow_manage):
         self.context['start_time'] = details.get('start_time')
         self.context['form_data'] = form_data
         self.context['is_submit'] = details.get('is_submit')
-        self.answer_text = details.get('result')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('result')
         if form_data is not None:
             for key in form_data:
                 self.context[key] = form_data[key]
@@ -70,7 +71,7 @@ def get_answer_list(self) -> List[Answer] | None:
                         "chat_record_id": self.flow_params_serializer.data.get("chat_record_id"),
                         'form_data': self.context.get('form_data', {}),
                         "is_submit": self.context.get("is_submit", False)}
-        form = f'<form_rander>{json.dumps(form_setting,ensure_ascii=False)}</form_rander>'
+        form = f'<form_rander>{json.dumps(form_setting, ensure_ascii=False)}</form_rander>'
         context = self.workflow_manage.get_workflow_content()
         form_content_format = self.workflow_manage.reset_prompt(form_content_format)
         prompt_template = PromptTemplate.from_template(form_content_format, template_format='jinja2')
@@ -85,7 +86,7 @@ def get_details(self, index: int, **kwargs):
                         "chat_record_id": self.flow_params_serializer.data.get("chat_record_id"),
                         'form_data': self.context.get('form_data', {}),
                         "is_submit": self.context.get("is_submit", False)}
-        form = f'<form_rander>{json.dumps(form_setting,ensure_ascii=False)}</form_rander>'
+        form = f'<form_rander>{json.dumps(form_setting, ensure_ascii=False)}</form_rander>'
         context = self.workflow_manage.get_workflow_content()
         form_content_format = self.workflow_manage.reset_prompt(form_content_format)
         prompt_template = PromptTemplate.from_template(form_content_format, template_format='jinja2')

apps/application/flow/step_node/function_lib_node/impl/base_function_lib_node.py

@@ -65,7 +65,7 @@ def valid_reference_value(_type, value, name):
 
 
 def convert_value(name: str, value, _type, is_required, source, node):
-    if not is_required and value is None:
+    if not is_required and (value is None or (isinstance(value, str) and len(value) == 0)):
         return None
     if not is_required and source == 'reference' and (value is None or len(value) == 0):
         return None
@@ -113,7 +113,8 @@ def valid_function(function_lib, user_id):
 class BaseFunctionLibNodeNode(IFunctionLibNode):
     def save_context(self, details, workflow_manage):
         self.context['result'] = details.get('result')
-        self.answer_text = str(details.get('result'))
+        if self.node_params.get('is_result'):
+            self.answer_text = str(details.get('result'))
 
     def execute(self, function_lib_id, input_field_list, **kwargs) -> NodeResult:
         function_lib = QuerySet(FunctionLib).filter(id=function_lib_id).first()

apps/application/flow/step_node/function_node/impl/base_function_node.py

@@ -49,7 +49,7 @@ def valid_reference_value(_type, value, name):
 
 
 def convert_value(name: str, value, _type, is_required, source, node):
-    if not is_required and value is None:
+    if not is_required and (value is None or (isinstance(value, str) and len(value) == 0)):
         return None
     if source == 'reference':
         value = node.workflow_manage.get_reference_field(
@@ -84,7 +84,8 @@ def convert_value(name: str, value, _type, is_required, source, node):
 class BaseFunctionNodeNode(IFunctionNode):
     def save_context(self, details, workflow_manage):
         self.context['result'] = details.get('result')
-        self.answer_text = str(details.get('result'))
+        if self.node_params.get('is_result', False):
+            self.answer_text = str(details.get('result'))
 
     def execute(self, input_field_list, code, **kwargs) -> NodeResult:
         params = {field.get('name'): convert_value(field.get('name'), field.get('value'), field.get('type'),

apps/application/flow/step_node/image_generate_step_node/impl/base_image_generate_node.py

@@ -16,15 +16,17 @@ class BaseImageGenerateNode(IImageGenerateNode):
     def save_context(self, details, workflow_manage):
         self.context['answer'] = details.get('answer')
         self.context['question'] = details.get('question')
-        self.answer_text = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
 
     def execute(self, model_id, prompt, negative_prompt, dialogue_number, dialogue_type, history_chat_record, chat_id,
                 model_params_setting,
                 chat_record_id,
                 **kwargs) -> NodeResult:
         print(model_params_setting)
         application = self.workflow_manage.work_flow_post_handler.chat_info.application
-        tti_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'), **model_params_setting)
+        tti_model = get_model_instance_by_model_user_id(model_id, self.flow_params_serializer.data.get('user_id'),
+                                                        **model_params_setting)
         history_message = self.get_history_message(history_chat_record, dialogue_number)
         self.context['history_message'] = history_message
         question = self.generate_prompt_question(prompt)

apps/application/flow/step_node/image_understand_step_node/impl/base_image_understand_node.py

@@ -69,7 +69,8 @@ class BaseImageUnderstandNode(IImageUnderstandNode):
     def save_context(self, details, workflow_manage):
         self.context['answer'] = details.get('answer')
         self.context['question'] = details.get('question')
-        self.answer_text = details.get('answer')
+        if self.node_params.get('is_result', False):
+            self.answer_text = details.get('answer')
 
     def execute(self, model_id, system, prompt, dialogue_number, dialogue_type, history_chat_record, stream, chat_id,
                 model_params_setting,