This project aims to enhance the readability, searchability, and overall user experience of product documentation using a VSCode extension. By integrating intelligent search, contextual recommendations, and navigation improvements, we help developers find relevant information faster and boost productivity.
Developers often face difficulties navigating and understanding product documentation due to:
- Outdated or incomplete documentation.
- Inefficient search mechanisms.
- Lack of contextual help and relevant examples.
- Time-consuming knowledge retrieval processes.
This VSCode extension leverages AI-driven search and contextual recommendations to improve documentation accessibility. Key features include:
- Intelligent Search: Uses all-MiniLM-L6-v2 for semantic search, ensuring relevant results.
- Context-Aware Linking: Uses bert-base-uncased to provide interlinked references within documentation.
- Chatbot Support: AI chatbot powered by Gemini Flash 2.0 Lite to assist developers with queries.
- Forums Support: Quick access to relevant forums across the internet.
- VSCode Extension UI
- Document Processing Phase
- Core Services
- External Services
- Node.js & npm
- VSCode
- Python (for AI models, FAISS DB and Flask setup)
- Clone the repository:
git clone https://github.com/ParasY1724/DocHelp.git cd DocHelp/code-extension - Install dependencies:
npm install
- Compile the extension:
npm run compile
- Open
extension.tsfile in VSCode, pressF5to run in debug mode. - Press
F1, select "Load Documentation", and paste the documentation URL.
- Navigate to the server directory:
cd ../server - Create a virtual environment:
python3 -m venv venv source venv/bin/activate # On Windows use `venv\Scripts\activate`
- Install dependencies:
pip install -r requirements.txt
- Run the Flask server:
python3 app.py
Before running the application, update the required API keys in the configuration files
- Paras Yerunkar
- Piyush Dahake
- Pratik Patil
- Tanay Duddalwar
🔍 Enhancing product documentation, one query at a time! 🚀
