gangdan 1.0.3

Offline Development Assistant powered by Ollama and ChromaDB

GangDan - Offline Dev Assistant

A local-first, offline programming assistant powered by Ollama and ChromaDB. Chat with LLMs, build a vector knowledge base from documentation, run terminal commands, and get AI-generated shell suggestions -- all from a single browser tab.

GangDan (纲担) -- Principled and Accountable.

Features

• RAG Chat -- Ask questions with optional retrieval from a local ChromaDB knowledge base and/or web search (DuckDuckGo, SearXNG, Brave). Responses stream in real-time via SSE. A Knowledge Base Scope Selector lets you pick exactly which KBs to query.
• Strict KB Mode -- When enabled, the system refuses to answer if no relevant content is found in the knowledge base, ensuring responses are grounded in reliable sources.
• Citation References -- Each response automatically includes a reference list showing the source documents, making it easy to verify and trace information.
• Cross-Lingual Search -- Automatically detects query and document languages, using Ollama translation for cross-lingual RAG retrieval (e.g., query English documents in Chinese).
• AI Command Assistant -- Describe what you want to do in natural language; the assistant generates a shell command you can drag-and-drop into the terminal, execute, and auto-summarize.
• Built-in Terminal -- Run commands directly in the browser with stdout/stderr display.
• Documentation Manager -- One-click download and indexing of 30+ popular library docs (Python, Rust, Go, JS, C/C++, CUDA, Docker, SciPy, Scikit-learn, SymPy, Jupyter, etc.). Batch operations and GitHub repo search included.
• Custom Knowledge Base Upload -- Upload your own Markdown (.md) and plain text (.txt) documents to create named knowledge bases. Files are automatically indexed for RAG retrieval. Duplicate file detection with skip or overwrite options.
• Conversation Save & Load -- Save chat conversations as JSON files and load them later to continue where you left off. The existing markdown export is also available for human-readable sharing.
• 10-Language UI -- Switch between Chinese, English, Japanese, French, Russian, German, Italian, Spanish, Portuguese, and Korean without page reload.
• Proxy Support -- None / system / manual proxy modes for both the chat backend and documentation downloads.
• Offline by Design -- Runs entirely on your machine. No cloud APIs required.

Screenshots

Chat	Terminal

Documentation	Settings

Upload Documents	KB Scope Selection

Strict KB Chat with Citations

The above screenshot demonstrates Strict KB Mode in action: after selecting a specific knowledge base, the system retrieves content only from that KB and automatically appends a reference list at the end of each response, citing the source documents.

Load Conversation	Conversation Loaded

Save your chat as a JSON file and load it anytime to continue the conversation. The file picker accepts .json files exported by GangDan, restoring full message history and context.

Requirements

• Python 3.10+
• Ollama running locally (default http://localhost:11434)
• A chat model pulled in Ollama (e.g. ollama pull qwen2.5)
• An embedding model for RAG (e.g. ollama pull nomic-embed-text)

Installation

Method 1: Install from PyPI (Recommended)

pip install gangdan

After installation, launch directly:

# Start GangDan
gangdan

# Or use python -m
python -m gangdan

# Custom host and port
gangdan --host 127.0.0.1 --port 8080

# Specify a custom data directory
gangdan --data-dir /path/to/my/data

Method 2: Install from Source (Development)

# 1. Clone the repository
git clone https://github.com/cycleuser/GangDan.git
cd GangDan

# 2. (Optional) Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate      # Linux/macOS
# .venv\Scripts\activate       # Windows

# 3. Install in editable mode with all dependencies
pip install -e .

# 4. Launch GangDan
gangdan

Ollama Setup

Make sure Ollama is installed and running before starting GangDan:

# Start Ollama service
ollama serve

# Pull a chat model
ollama pull qwen2.5

# Pull an embedding model for RAG
ollama pull nomic-embed-text

Open http://127.0.0.1:5000 in your browser.

CLI Options

gangdan [OPTIONS]

Options:
  --host TEXT       Host to bind to (default: 0.0.0.0)
  --port INT        Port to listen on (default: 5000)
  --debug           Enable Flask debug mode
  --data-dir PATH   Custom data directory
  --version         Show version and exit

Project Structure

GangDan/
├── pyproject.toml              # Package metadata & build config
├── MANIFEST.in                 # Source distribution manifest
├── LICENSE                     # GPL-3.0-or-later
├── README.md                   # English documentation
├── README_CN.md                # Chinese documentation
├── gangdan/
│   ├── __init__.py             # Package version
│   ├── __main__.py             # python -m gangdan entry
│   ├── cli.py                  # CLI argument parsing & startup
│   ├── app.py                  # Flask backend (routes, Ollama, ChromaDB, i18n)
│   ├── templates/
│   │   └── index.html          # Jinja2 HTML template
│   └── static/
│       ├── css/
│       │   └── style.css       # Application styles (dark theme)
│       └── js/
│           ├── i18n.js         # Internationalization & state management
│           ├── utils.js        # Panel switching & toast notifications
│           ├── markdown.js     # Markdown / LaTeX (KaTeX) rendering
│           ├── chat.js         # Chat panel & SSE streaming
│           ├── terminal.js     # Terminal & AI command assistant
│           ├── docs.js         # Documentation download & indexing
│           └── settings.js     # Settings panel & initialization
├── images/                     # Screenshots
├── publish.py                  # PyPI publish helper script
└── test_package.py             # Comprehensive package test suite

Runtime data (created automatically):

~/.gangdan/                     # Default when installed via pip
  ├── gangdan_config.json       # Persisted settings
  ├── docs/                     # Downloaded documentation
  └── chroma/                   # ChromaDB vector store

Architecture

The frontend and backend are fully decoupled:

• Backend (app.py) -- A single Python file containing Flask routes, the Ollama client, ChromaDB manager, documentation downloader, web searcher, and conversation manager. All server-side configuration is injected into the template via a window.SERVER_CONFIG block.
• Frontend (templates/ + static/) -- Pure HTML/CSS/JS with no build step. JavaScript files are loaded in dependency order and share state through global functions. KaTeX is loaded from CDN for LaTeX rendering.

ChromaDB is initialized with automatic corruption recovery: if the database is damaged, it is backed up and recreated transparently.

Configuration

All settings are managed through the Settings tab in the UI:

Setting	Description
Ollama URL	Ollama server address (default http://localhost:11434)
Chat Model	Model for conversation (e.g. qwen2.5:7b-instruct)
Embedding Model	Model for RAG embeddings (e.g. nomic-embed-text)
Reranker Model	Optional reranker for better search results
Proxy Mode	none / system / manual for network requests

Settings are persisted to gangdan_config.json in the data directory.

License

GPL-3.0-or-later. See LICENSE for details.