Show-Me is an open-source application designed to provide a visual and transparent alternative to traditional Large Language Model (LLM) interactions. It breaks down complex questions into a series of reasoned sub-tasks, allowing users to understand the LLM's step-by-step thought process. The application uses LangChain to interact with LLMs and visualizes the reasoning process using a dynamic graph interface.

https://x.com/i/status/1838590157265539307

• Self-Healing Reasoning: The system iteratively refines its answers based on automated checks, improving accuracy and demonstrating a form of "self-healing" behavior.
• Visual Reasoning Graph: Provides a dynamic graph visualization of the LLM's reasoning process, making it transparent and understandable.
• Task Decomposition: Breaks down complex questions into smaller, more manageable sub-tasks, facilitating more accurate and efficient problem-solving.
• Code Generation and Execution (Limited): Can generate and execute basic Python Pandas code for data manipulation and analysis tasks within specific contexts. (Further code execution capabilities are a planned feature).
• Software Reasoning (Experimental): Includes an experimental mode (/software route) designed to handle software-related reasoning tasks, showcasing the framework's adaptability to different domains.
• Interactive Chat Interface: Allows users to ask questions and receive answers in a conversational format.
• Real-time Updates: Uses SocketIO to provide real-time updates to the visualization graph as the LLM processes the question.

Show-Me consists of two main components:

1. Frontend (React): Built using React, React Flow, Material UI, and Framer Motion. Responsible for:

   • User Interface: Presents the chat interface, question input area, and the reasoning graph visualization.
   • User Interaction: Handles user input, submits questions to the backend, and manages the display of chat messages.
   • Graph Rendering: Dynamically renders the reasoning graph based on real-time updates from the backend.
   • Component Breakdown:
     • src/App.js: The main application component. Handles routing and renders either the general reasoning or software reasoning interface.
     • src/SoftwareAgent.js: Renders the interface for software-related questions.
     • src/components/ImageNode.js: Renders image nodes in the graph (currently not actively used, but the infrastructure is present).
     • src/components/DisplayNode.js: Displays text-based answers to sub-tasks and the final answer.
     • src/components/CodeAnswerNode.js: Displays code-based answers from the Python interpreter.
     • src/components/TextUpdaterNode.js: Represents sub-tasks in the graph, displaying the task description.
     • src/components/MainTaskNode.js, src/components/SubTaskNode.js, src/components/TestNode.js: Specialized node components to visually distinguish between different task types.
     • src/Devtools.js: Provides developer tools, including a change logger for debugging React Flow.
     • src/components/QuestionBar.js: (Not currently used but could be implemented) A component for a dedicated question input area.
     • src/components/ui/Card.js, src/components/ui/Input.js: Basic UI components for styling.

2. Backend (Flask): Built using Flask, Flask-SocketIO, and Flask-CORS. Responsible for:

   • API Endpoints: Provides API endpoints for receiving questions and sending responses.
   • LLM Interaction: Uses LangChain to interact with the gpt-4o-mini LLM. Implements the Reasoning, Refinement, and Update (RRU) algorithm.
   • Task Complexity Analysis: Evaluates the complexity of a task to determine if it needs further decomposition.
   • Task Generation: Generates sub-tasks based on the main question and automated checks.
   • Task Ordering: Orders sub-tasks logically to mimic human problem-solving.
   • Answer Aggregation: Combines the answers from sub-tasks to form the final answer.
   • Answer Checking: Generates checks to validate the accuracy of answers.
   • Answer Fixing: Refines answers that fail the checks using feedback from the LLM.
   • Answer Shortening: Concisely presents the final answer.
   • SocketIO Communication: Sends real-time updates about the reasoning process to the frontend using SocketIO.
   • File Breakdown:
     • backend/app.py: The main Flask application file. Handles routing, SocketIO connections, and API requests.
     • backend/llm_stuff.py: Contains the core LangChain logic for general reasoning tasks.
     • backend/software_llm_stuff.py: Contains similar LangChain logic for software-related reasoning tasks.

The backend uses the RRU algorithm to process questions:

1. Reasoning/Decomposition: The LLM assesses the complexity of the task. If complex, it is broken down into sub-tasks.
2. LLM Interaction: The LLM generates answers for each sub-task (or the main task if it's not decomposed). Code generation and execution are handled for Python-related sub-tasks.
3. Refinement (Self-Healing): The system automatically generates checks for each answer. If an answer fails a check, the LLM is used to refine the answer based on the failed check. This process repeats until the answer passes all checks or a retry limit is reached.
4. Update/Aggregation: Results of sub-tasks are aggregated. The backend sends updates to the frontend via SocketIO throughout the process, allowing for dynamic visualization of the reasoning steps.

1. Clone the Repository: git clone [repository url]
2. Backend Setup:
   • cd backend
   • python3 -m venv .venv
   • source .venv/bin/activate
   • pip install -r requirements.txt
   • Create a .env file and add your OpenAI API Key: OPENAI_API_KEY=[your key]
3. Frontend Setup:
   • cd .. (go to project root)
   • npm install (or yarn install)
4. Run the Application:
   • Backend: python app.py (or flask run) in the backend directory.
   • Frontend: npm start (or yarn start) in the project root directory.

• Enhanced Code Execution: Expand code generation and execution capabilities beyond basic Python Pandas.
• Improved Visualization: Refine the graph layout and add more interactive elements.
• Pluggable LLMs: Allow users to select different LLMs for greater flexibility.
• More Robust Checking: Develop more sophisticated and context-aware answer checking mechanisms.
• User Authentication and Data Persistence: Add user accounts and allow users to save their reasoning graphs.

Open a Github issue please.

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