update

.gitignore

@@ -1,6 +1,7 @@
 # ---> Python
+.gitignore
+backend/evaluate/eval_rag_result
 backend/psk-graphrag
-backend/evaluate/eval_rag_dataset/*
 backend/history/*
 # Byte-compiled / optimized / DLL files
 __pycache__/

README.md

@@ -1,85 +1,104 @@
-Ubuntu20.04 + python3.11.9 + django
-#1、安装python3.11.9
-sudo apt install build-essential zlib1g-dev libncurses5-dev libgdbm-dev libnss3-dev libssl-dev libreadline-dev libffi-dev libsqlite3-dev wget
-wget https://www.python.org/ftp/python/3.11.9/Python-3.11.9.tgz
-tar zxvf Python-3.11.9.tgz
-cd Python-3.11.9
-./configure --enable-optimizations
-make -j$(nproc)
-sudo make altinstall
-python3.11 -m ensurepip --upgrade
-wget https://bootstrap.pypa.io/get-pip.py
-python3.11 get-pip.py
-#2、安装python虚拟环境和依赖
-cd /opt/matagent
-python3.11 -m venv pyautogen
-source pyautogen/bin/activate
-#安装pyautogen
-pip install pyautogen==0.2.34
-pip install flaml[automl]
-pip install chromadb
-pip install Image
-#安装django
-pip install django
-pip install djangorestframework
-pip install requests
-#安装 websocket支持  channels+asgi
-pip install channels
-pip install asgiref  # Django Channels 依赖的包
-#运行服务，支持http+websocket  websocket接口：  ws://192.168.42.130:8000/matagent/chat
-DJANGO_SETTINGS_MODULE=matagent.settings uvicorn matagent.asgi:application --host 0.0.0.0 --port 8000
-#安装nginx
-sudo apt-get update
-sudo apt-get install nginx
---查看状态
-sudo systemctl status nginx
---修改配置
-/etc/nginx/default
-nginx配置在default
+# Multi-Agent & Robotic System (MARS) for Material Science Research
 
+This project is a comprehensive platform for material science research, consisting of a backend API server, a frontend web application, and a middleware desktop application. The platform integrates various AI agents for planning, scientific analysis, engineering tasks, and data analysis to assist in material science research.
 
+## Project Structure
 
+The project is divided into three main components:
 
-#pip install autogen-agentchat[websockets]~=0.2 fastapi uvicorn
+1. Backend (Python/FastAPI/Autogen)
+2. Frontend (Vue.js)
+3. Middleware (C#/WPF)
 
+## Backend
 
-# 完整agent的console版本
-matagent_main.py是入口点。
-安装requirements.txt里面的pyautogen，然后运行即可。
-.coding目录不要删除，里面包含的是一个配置好的，AGENT执行python代码的环境，否则会报错。
-如果存在问题，检查.coding/pyvenv.cfg文件配置
-data目录记录了实验数据，PL和UV数据，不要删除，否则会报错。
+The backend is built with Python using the FastAPI framework. It manages the core logic of the multi-agent system, handles WebSocket connections for real-time communication, and provides API endpoints for the frontend.
 
-代码中，agent之间的状态流转逻辑通常为每个group的state_transition函数中，其中return 'auto'表示自动选择合适的agent。
+### Setup and Running
 
-# 简单agent的ui版本
-cd ui-simple
-chainlit run --port 8989 appUI.py
+1. Navigate to the backend directory:
+   ```
+   cd backend
+   ```
 
-chainlit继承了pyautogen的AssistantAgent，在这个基础上实现了chainlit的接口。
-class ChainlitAssistantAgent(AssistantAgent):
-class ChainlitUserProxyAgent(UserProxyAgent):
-同时重载了两个方法以发送数据到前端。
+2. Install dependencies (it's recommended to use a virtual environment):
+   ```
+   pip install -r requirements.txt
+   ```
 
-    """
-    Wrapper for AutoGens Assistant Agent
-    """
-    def send(
-        self,
-        message: Union[Dict, str],
-        recipient: Agent,
-        request_reply: Optional[bool] = None,
-        silent: Optional[bool] = False,
-    ) -> bool:
-        cl.run_sync(
-            cl.Message(
-                content=f'**{self.name}** Sending message to "{recipient.name}":\n\n{message}',
-                author=self.name,
-            ).send()
-        )
-        super(ChainlitAssistantAgent, self).send(
-            message=message,
-            recipient=recipient,
-            request_reply=request_reply,
-            silent=silent,
-        )
+3. Set up environment variables:
+   - Copy `.env.example` to `.env`
+   - Fill in the required API keys and configuration settings
+
+4. Run the backend server:
+   ```
+   uvicorn api:app --host 0.0.0.0 --port 8000
+   ```
+
+The backend will be available at `http://localhost:8000`.
+
+## Frontend
+
+The frontend is a Vue.js application that provides the user interface for interacting with the multi-agent system.
+
+### Setup and Running
+
+1. Navigate to the frontend directory:
+   ```
+   cd frontend
+   ```
+
+2. Install dependencies:
+   ```
+   npm install
+   ```
+
+3. Run the development server:
+   ```
+   npm run dev
+   ```
+
+4. For production build:
+   ```
+   npm run build
+   ```
+
+Before building for production, ensure that the following environment variables in the `.env` file are correctly set:
+- `VITE_API_URL`: The backend API URL
+- `VITE_API_URL_PREFIX`: The API prefix
+- `VITE_WB_BASE_URL`: The WebSocket base URL for real-time communication
+
+## Middleware
+
+The middleware is a C# WPF desktop application that provides additional functionality and integration with local systems.
+
+### Setup and Running
+
+1. Open the solution file `middleware/zdhsys.sln` in Visual Studio.
+
+2. Build the solution in Visual Studio.
+
+3. Run the application from Visual Studio or navigate to the build output directory and run the executable.
+
+## Usage
+
+1. Start the backend server.
+2. Launch the frontend application (either in development mode or by serving the production build).
+3. If required, run the middleware desktop application.
+4. Access the web interface through your browser and begin interacting with the multi-agent material science research platform.
+
+## Features
+
+- Real-time communication using WebSockets
+- Multi-agent system for complex task planning and execution
+- Integration of scientific, engineering, and data analysis teams
+- Video streaming capabilities for remote monitoring
+- Customizable UI for different aspects of material science research
+
+## Contributing
+
+Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE.md file for details.

backend/agent_config/planning.yaml

@@ -1,57 +0,0 @@
-PlanningAgent: >
-    You are a planning agent.
-    Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
-    Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
-    Your sub-teams are:
-        1. User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
-        2. Scientist: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
-            - The scientist team has the following members: 
-            2.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
-            2.2 Structure Scientist: focusing on agents of structural topics in materials science.
-            2.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
-            2.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.
-        3. Engineer: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
-            - The engineer team has the following members: 
-            3.1 Structural engineer: A professional structural engineer who focus on converting natural language synthesis schemes to JSON or XML formated scheme, and then upload this JSON to S3 Storage.
-            3.2 Software engineer: A professional software engineers will coding with Python.
-            3.3 Code reviewer: A professional code reviewer will review the code written by software engineers and execute it.
-            3.4 Scheme Plotter: An agent responsible for converting a expriment scheme into a Mermaid flowchart.
-        4. Executor: A robotic platform is responsible for performing automated synthesis experiments, automated characterization experiments, and collecting experimental datas.
-            - The Executor team has the following members: 
-            4.1 MobileRobot_Agent: This agent controls the mobile robot by calling the funciton sendScheme2MobileRobot to place the experimental container into the robot workstation. This agent called before RobotWorkstation_Agent.
-            4.2 RobotWorkstation_Agent: This agent is called by the mobile robot agent, do not plan it alone.
-            4.3 DataCollector_Agent: This agent collects experimental data and experimental logs from the characterization device in the robot platform and stores them.
-        5. Analyst: A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.
-            - The Data Analysis team has the following members:
-            5.1 Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
-            5.2 Expriment_Optimizer: The agent optimizes the experimental scheme by means of component regulation and so on to make the experimental result close to the desired goal of the user.
-            5.3 Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.
-
-    You only plan and delegate tasks - you do not execute them yourself.
-    
-    回答时你需要初始化/更新如下任务分配表和Mermaid流程图，并按顺序执行，使用如下格式并利用：
-    | Team_name   | Member_name   | sub-task                             |
-    | ----------- | ------------- | ------------------------------------ |
-    | <team_name> | <member_name> | <status: brief sub-task description> |
-    
-    ```mermaid
-    graph TD
-    User[User]
-    subgraph <team_name>
-        A1[<member_name>]
-    end
-    style xxx # 推荐多样的风格
-    ...
-    User --> A1
-    ...
-    ```
-
-    每次回答时，你需要清晰明确的指出已经完成的子任务下一步子任务，使用如下格式：
-    **已完成子任务：**
-    1. <team> : <subtask>
-    **Next sub-task:**
-    n. <team> : <subtask>
-    
-    You can end with "HUMAN" if you need to, which means you need human approval or other advice or instructions;
-    After plan and delegate tasks are complete, end with "START";
-    Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".

backend/evaluate/eval_rag_dataset/dataset_dict.json

@@ -0,0 +1 @@
+{"splits": ["train"]}

backend/evaluate/eval_rag_dataset/dataset_info.json

@@ -0,0 +1,58 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "context": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "question": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "answer": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "topic": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "source_doc": {
+      "dataset_id": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "document_id": {
+        "dtype": "string",
+        "_type": "Value"
+      }
+    },
+    "groundedness_score": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "groundedness_eval": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "relevance_score": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "relevance_eval": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "standalone_score": {
+      "dtype": "float64",
+      "_type": "Value"
+    },
+    "standalone_eval": {
+      "dtype": "string",
+      "_type": "Value"
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

backend/evaluate/eval_rag_dataset/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "0748dad1d6b34503",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": "train"
+}

backend/evaluate/eval_rag_dataset/train/dataset_info.json

@@ -0,0 +1,30 @@
+{
+  "citation": "",
+  "description": "",
+  "features": {
+    "context": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "question": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "answer": {
+      "dtype": "string",
+      "_type": "Value"
+    },
+    "source_doc": {
+      "dataset_id": {
+        "dtype": "string",
+        "_type": "Value"
+      },
+      "document_id": {
+        "dtype": "string",
+        "_type": "Value"
+      }
+    }
+  },
+  "homepage": "",
+  "license": ""
+}

backend/evaluate/eval_rag_dataset/train/state.json

@@ -0,0 +1,13 @@
+{
+  "_data_files": [
+    {
+      "filename": "data-00000-of-00001.arrow"
+    }
+  ],
+  "_fingerprint": "bcd109aa52b21899",
+  "_format_columns": null,
+  "_format_kwargs": {},
+  "_format_type": null,
+  "_output_all_columns": false,
+  "_split": null
+}

backend/rtsp2webrtc/config.json

@@ -1,41 +0,0 @@
-{
-    "server": {
-        "debug": true,
-        "log_level": "info",
-        "http_demo": true,
-        "http_debug": false,
-        "http_login": "demo",
-        "http_password": "demo",
-        "http_port": ":8083",
-        "ice_servers": [
-            "stun:stun.xten.com:3478"
-        ],
-        "rtsp_port": ":5541"
-    },
-    "streams": {
-        "demo1": {
-            "name": "test video stream 1",
-            "channels": {
-                "0": {
-                    "name": "ch1",
-                    "url": "rtsp://admin:@192.168.1.13:554/live",
-                    "on_demand": true,
-                    "debug": true,
-                    "audio": true,
-                    "status": 0
-                },
-                "1": {
-                    "name": "ch2",
-                    "url": "rtsp://admin:@192.168.1.10:554/live",
-                    "on_demand": true,
-                    "debug": true,
-                    "audio": true,
-                    "status": 0
-                }
-            }
-        }
-    },
-    "channel_defaults": {
-        "on_demand": true
-    }
-}

backend/tools.py

@@ -372,55 +372,6 @@ def upload_to_s3(json_data: str):
         # print(f"JSON解析错误: {e}")
         return f"Error: {str(e)}, Request human/user intervention."
 
-def get_latest_exp_log():
-    def get_uv_latest_file():
-        import os
-        import glob
-        # UV数据缓存文件夹路径 (请将此路径修改为实际的文件夹路径)
-        current_folder = os.path.dirname(os.path.abspath(__file__))
-        folder_path = os.path.join(current_folder, 'data/UV/')
-
-        # 查找文件夹中的所有 .wls 文件
-        uv_files = sorted(glob.glob(os.path.join(folder_path, '*.[Tt][Xx][Tt]')))
-
-        if not uv_files:
-            res = f"ERROR: 缓存文件夹{current_folder}中没有找到任何UV文件"
-            return res
-
-        # 找到最新修改的文件
-        latest_file = uv_files[-1]
-        res = f"找到最新的UV数据文件: {latest_file}"
-        
-        return res
-    
-    def get_pl_latest_file():
-        import os
-        import glob
-
-        current_folder = os.path.dirname(os.path.abspath(__file__))
-        folder_path = os.path.join(current_folder, 'data/PL/')
-
-        # 查找文件夹中的所有 .txt 或 .TXT 文件
-        pl_files = sorted(glob.glob(os.path.join(folder_path, '*.[Tt][Xx][Tt]')))
-
-        if not pl_files:
-            res = f"ERROR: 缓存文件夹{current_folder}中没有找到任何PL文件"
-            return res
-
-        # 找到最新修改的文件
-        latest_file = pl_files[-1]
-        res = f"找到最新的PL数据文件: {latest_file}"
-        return res
-
-    pl_latest = get_pl_latest_file()
-    uv_latest = get_uv_latest_file()
-
-    return pl_latest + "\n" + uv_latest
-
-
-def read_data():
-    get_latest_exp_log()
-
 
 def default_func():
     return "Approved. Proceed as planned!"

manage.py

@@ -1,22 +0,0 @@
-#!/usr/bin/env python
-"""Django's command-line utility for administrative tasks."""
-import os
-import sys
-
-
-def main():
-    """Run administrative tasks."""
-    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'backend.settings')
-    try:
-        from django.core.management import execute_from_command_line
-    except ImportError as exc:
-        raise ImportError(
-            "Couldn't import Django. Are you sure it's installed and "
-            "available on your PYTHONPATH environment variable? Did you "
-            "forget to activate a virtual environment?"
-        ) from exc
-    execute_from_command_line(sys.argv)
-
-
-if __name__ == '__main__':
-    main()

package-lock.json

@@ -1,6 +0,0 @@
-{
-  "name": "matagent",
-  "lockfileVersion": 3,
-  "requires": true,
-  "packages": {}
-}