Calling MCP server: https://mcp.deepwiki.com/mcp

function: ask_question with args: {"repoName": "gwdg/chat-ai", "question": "Explain what this GitHub Repo is about"}

MCP response received of type text

Okay, let’s see. The user asked about the GitHub repo https://github.com/gwdg/chat-ai. I called the ask_question function with the repo name and the question. The response came back with a detailed explanation.

First, I need to parse the tool response. The repo is called chat-ai, part of a larger system for LLMs on HPC. The main components are frontend (React/Vite) and backend (Node.js/Express). They mention it’s a web interface that wraps OpenAI-compatible APIs. Key features include memory system, tools integration (web search, image gen, etc.), customizable models, and Slurm-native for HPC.

Wait, the user just wants an explanation. The tool response already gives a thorough answer. My job is to present this in a clear, concise way without adding extra info. Let me check if all the important points are covered.

The response mentions Docker services, configuration files, and some notes about the project structure. The user might not need all the technical details, but since they asked for an explanation, including the main points is good.

I should structure the answer to highlight the purpose, key components, features, and maybe a note on deployment. Avoid jargon where possible, but since it’s a technical repo, some terms are necessary.

Wait, the tool response says it’s part of a larger architecture with server and HPC repos. So the chat-ai repo is specifically the web interface. Need to clarify that it’s not the entire system but the frontend/backend for the web part.

Also, the memory system stores locally in the browser, which is a nice feature. Tools like web search and image generation are integrated. Slurm-native integration is important for HPC, so that’s a key point for the target audience (HPC users).

Check if the answer should mention the Docker setup, but maybe briefly. The user might be a developer looking to deploy it, so configuration via Docker and JSON files is relevant.

Make sure to mention it’s a standalone app that can wrap any OpenAI-compatible API, so it’s flexible.

Avoid including the DeepWiki link or the citation file details unless they’re crucial. The main explanation should focus on what the repo does, not the supplementary files.

Putting it all together: Start with the main purpose, then components, key features, and deployment. Keep it to a few paragraphs. Let me draft that.

The GitHub repository gwdg/chat-ai is a web-based interface for interacting with Large Language Models (LLMs), designed specifically for deployment on High-Performance Computing (HPC) systems. It serves as the frontend and backend layer for a larger architecture (with server/HPC components in separate repositories) and functions as a wrapper for OpenAI-compatible API endpoints.

🔑 Core Components #

1. Frontend (front)

   • Built with React + Vite, runs in the browser.
   • Handles user interactions, UI features (e.g., collapsible sidebars, model selector, audio input), and local memory storage for conversation context.

2. Backend (back)

   • A Node.js/Express proxy that routes API requests to LLM providers (e.g., OpenAI, custom endpoints).
   • Manages document processing, memory updates, and tool integrations.

💡 Key Features #

• Memory System: Stores conversation context locally in the browser (via generateMemory API).
• Tool Integration: Supports web search, image generation, speech synthesis, and RAG (Retrieval-Augmented Generation) via Arcana.
• Customization: Adjustable LLM parameters (e.g., temperature, top_p), model selection, and default settings via secrets/front.json/back.json.
• HPC Integration: Designed as a Slurm-native platform for deploying LLM services on HPC infrastructure.
• Standalone Deployment: Works as a self-contained Dockerized app (frontend + backend) with configuration via JSON secrets.

🛠️ Deployment #

• Uses Docker and Docker Compose for containerization.
• Configuration is managed through environment variables injected from secrets/*.json files.
• The frontend reads front.json to set VITE_* variables (e.g., API endpoints, model defaults).

📌 Why It Matters #

This repo enables researchers and developers to access LLMs via a user-friendly web interface directly on HPC systems (e.g., university clusters), avoiding the need for manual API calls. It’s part of the SAIA software suite, which emphasizes Slurm-based orchestration for HPC workloads.

For details, check the Wiki or the CITATION.cff for academic reference.

This GitHub repository, gwdg/chat-ai, contains the standalone web interface for a privacy-focused web application called Chat AI . This application allows users to interact with Large Language Models (LLMs) through OpenAI-compatible APIs . It is designed to be deployed independently but also functions as the web interface component of a larger SAIA (Slurm-Native AI Architecture) ecosystem .

Purpose and Architecture #

The primary purpose of this repository is to provide the frontend and a proxy backend for the Chat AI system . The system is composed of two main services:

• Frontend (front): A React-based single-page application built with Vite . This part of the application handles the user interface, state management using Redux, and local data storage in IndexedDB .
• Backend (back): A Node.js/Express proxy service . This service acts as a wrapper for API requests, preventing CORS errors and providing more control over requests to the OpenAI-compatible API endpoint . It also integrates with docling for document processing, such as PDF conversion .

The architecture follows a three-tier design: a Browser Layer, a Backend Proxy Layer, and an External Services Layer . All user data is stored locally in the browser using IndexedDB, ensuring privacy .

Key Features #

Chat AI offers several features to enhance user interaction with LLMs :

• Multiple Conversations: Users can manage an unlimited number of conversations, which are stored locally .
• Streaming Responses: Provides real-time message streaming from the LLM .
• User Memory System: Allows the AI to learn and remember relevant details across conversations, with options for “Recall” and “Learn” modes . Memories are stored locally in the browser .
• File Attachments: Supports various file types including images, audio, video, PDFs, and CSV files .
• GWDG Tools Integration: Extends LLM capabilities with tools like web search, image/audio generation, and RAG (Retrieval Augmented Generation) .
• Markdown & LaTeX Rendering: Full support for formatted text and mathematical notation in responses .
• Personas: Users can import pre-configured system prompts from GitHub to define the AI’s persona . The ImportPersonaModal.jsx component handles fetching and processing these persona files from a GitHub repository .
• Internationalization: The interface is available in English and German .

Deployment and Configuration #

The application uses Docker Compose for orchestration, with both frontend and backend services running in separate containers . Configuration is managed through two JSON files: secrets/front.json for frontend settings and secrets/back.json for backend settings . These files allow customization of ports, API endpoints, API keys, and default conversation settings .

Notes #

The CITATION.cff file indicates that this software is part of a larger project named SAIA, which focuses on providing a Slurm-native solution for HPC-based services . The README.md further clarifies that this repository specifically contains the standalone web interface, while other components of the SAIA architecture (server components and HPC components) are located in separate GitHub repositories . The front/package.json file provides details about the frontend’s dependencies and scripts . The CHANGELOG.md file details the version history and highlights new features and improvements over time .

Wiki pages you might want to explore:

• Overview (gwdg/chat-ai)

View this search on DeepWiki: https://deepwiki.com/search/explain-what-this-github-repo_30b68fb2-deb7-40a2-8046-3d3e26e702e8

This GitHub repository, gwdg/chat-ai, contains the standalone web interface for a privacy-focused web application called Chat AI . This application allows users to interact with Large Language Models (LLMs) through OpenAI-compatible APIs . It is designed to be deployable independently, but also functions as a component of the larger SAIA (Slurm-Native AI Architecture) ecosystem .

Purpose and Architecture #

The primary purpose of Chat AI is to provide a web-based interface for LLMs, emphasizing user privacy by storing all user data locally in the browser using IndexedDB .

The system is composed of two main services:

• Frontend (front): A React-based single-page application built with Vite .
• Backend (back): A Node.js/Express proxy service that handles API requests and document processing .

The architecture follows a three-tier design:

1. Browser Layer: The React application (App.jsx) manages global state via Redux and persists conversation data to IndexedDB .
2. Backend Proxy Layer: An Express server (back/src/index.js) handles CORS, authentication, and routes requests to the OpenAI-compatible API, also processing document uploads .
3. External Services Layer: This layer interacts with LLM inference services and optional tool-enabled gateways for advanced capabilities like web search and RAG .

graph TB
    subgraph "Browser Environment"
        User["User"]
        FrontApp["front/src/App.jsx<br/>React Application"]
        Redux["Redux Store<br/>interface_settings<br/>user_settings"]
        IndexedDB["IndexedDB (Dexie)<br/>conversations<br/>messages<br/>files_data"]
        i18n["i18next<br/>en.js / de.js"]
    end
    
    subgraph "Backend Service"
        BackServer["back/src/index.js<br/>Express Server<br/>Port 8081"]
        ChatProxy["/chat/completions<br/>POST endpoint"]
        ModelsProxy["/models<br/>GET endpoint"]
        DocProcessor["docling integration<br/>PDF processing"]
    end
    
    subgraph "External Services"
        OpenAIAPI["OpenAI-compatible API<br/>apiEndpoint from back.json"]
        DoclingService["Docling Service<br/>Document conversion"]
        GWDGTools["GWDG Tools<br/>saia-openai-gateway"]
    end
    
    subgraph "Configuration"
        FrontConfig["secrets/front.json"]
        BackConfig["secrets/back.json"]
    end
    
    User -->|"Interacts"| FrontApp
    FrontApp -->|"Reads/Writes"| Redux
    FrontApp -->|"useSyncConversation"| IndexedDB
    FrontApp -->|"useTranslation"| i18n
    FrontApp -->|"API Calls :8080"| BackServer
    
    BackServer --> ChatProxy
    BackServer --> ModelsProxy
    BackServer --> DocProcessor
    
    ChatProxy -->|"Streaming"| OpenAIAPI
    ModelsProxy -->|"List models"| OpenAIAPI
    DocProcessor -->|"Convert PDFs"| DoclingService
    ChatProxy -->|"enable_tools=true"| GWDGTools
    
    FrontConfig -.->|"Configure"| FrontApp
    BackConfig -.->|"Configure"| BackServer
    
    Redux -.->|"redux-persist"| LocalStorage["localStorage"]

Key Features #

Chat AI offers various features, including:

• Multiple Conversations: Management of unlimited conversations with local storage .
• Streaming Responses: Real-time message streaming from LLMs .
• User Memory System: Contextual memory across conversations with recall modes .
• File Attachments: Support for various file types including images, audio, video, PDFs, and CSV files .
• GWDG Tools Integration: Features like web search, image/audio generation, RAG, and MCP servers .
• Markdown & LaTeX Rendering: Full support for formatted text and mathematical notation .
• Personas: Ability to import pre-configured system prompts from GitHub .
• Internationalization: English and German interface support .
• Export Functionality: Conversations can be exported in JSON, PDF, and plain text formats .

Technology Stack #

The project utilizes a modern web development stack :

• Frontend: React 19.1.1 with React Router, Vite 7.1.2, Redux Toolkit 2.8.2, Dexie 4.0.11 (for IndexedDB), Tailwind CSS, Headless UI, Framer Motion, react-markdown, rehype-katex, and i18next .
• Backend: Node.js with Express framework, and docling integration for document processing .
• Development Tools: npm for package management, Docker with Docker Compose for orchestration, and Git for version control .

Data Persistence #

Chat AI employs a dual-layer persistence strategy :

1. Redux Store → localStorage: Global UI settings, user memories, and the last conversation ID are persisted to localStorage using redux-persist .
2. Component State → IndexedDB: Conversation data, including metadata, messages, content items, and file attachments, is stored in IndexedDB via Dexie . The useSyncConversation hook manages synchronization and conflict resolution .

This approach ensures data privacy, performance, reliability, and portability .

Configuration #

The system is configured using two JSON files :

• secrets/front.json: Contains frontend settings such as API paths, default models, and UI configuration .
• secrets/back.json: Holds backend settings including the OpenAI-compatible API URL, API key, and service name .

Notes #

The CITATION.cff file indicates that this software is part of a larger project named SAIA, which focuses on providing a Slurm-native solution for HPC-based services . The README.md further clarifies that this repository specifically contains the web interface, while other components of the SAIA architecture (server and HPC components) are located in separate repositories . The front/src/i18n/en.js file provides English translations for various UI elements and help texts, including explanations for features like Arcana, MCP, models, memory, system prompt, temperature, tools, web search, and top_p . The front/src/db/dbTypes.ts and front/src/db/index.ts files define the data structures and database schema for storing conversations, messages, content items, and file data using Dexie (IndexedDB) .

Wiki pages you might want to explore:

• Overview (gwdg/chat-ai)

View this search on DeepWiki: https://deepwiki.com/search/explain-what-this-github-repo_ac5e4b34-af75-4e8c-8901-fc5222c40bf6