A powerful, LLM-driven platform that allows developers to understand, document, visualize, and interact with their codebase intelligently.
Supports Python, JavaScript, and Java projects with features like:
- 🔍 Semantic search and Q&A
- 🧾 Automatic docstring generation
- 📄 Code summary tutorial PDF creation
- 🎨 Code structure visualization (Mermaid diagrams)
- ⚙️ Function execution with input/output
- 📈 Business logic impact analysis on code changes
Each module (Embedder, Summarizer, Executor, etc.) is designed independently with FastAPI micro-routes and React-driven UI.
Module | Description |
---|---|
Embedder |
Embeds codebase files into vector DB using OpenAI embeddings |
QA |
Semantic search + LLM answers from code |
Docstring |
Generates missing docstrings in Python/JS/Java |
PDF Generator |
Summarizes code into a navigable tutorial PDF |
Executor |
Executes functions with parameters and returns results |
Visualizer |
Class, flow, and dependency diagrams via Mermaid |
Impact Analyzer |
Identifies business logic changes between original and updated code |
cd backend
python -m venv venv
source venv/bin/activate # Or venv\Scripts\activate on Windows
pip install -r requirements.txt
uvicorn main:app --reload
- OpenAPI Docs: http://localhost:8000/docs
- Environment: Set
OPENAI_API_KEY
in.env
or system env
cd frontend
npm install
npm run dev
- Open in browser: http://localhost:5173
backend/
├── api/routes/
│ ├── embedder_api.py
│ ├── qa_api.py
│ ├── pdf_api.py
│ ├── executor_api.py
│ ├── visualizer_api.py
│ ├── docstring_api.py
│ └── summarizer_api.py
├── modules/
│ ├── embedder.py
│ ├── retriever.py
│ ├── executor.py
│ ├── docstring_generator.py
│ ├── diagram_generator.py
│ ├── summary_generator.py
│ └── pdf_generator.py
└── main.py
frontend/
├── src/
│ ├── components/
│ ├── pages/
│ ├── styles/
│ ├── App.jsx
│ └── main.jsx
└── public/
Name |
---|
Harini Ganeshan |
Mitali Laroia |
MIT License.
This project uses OpenAI APIs and must comply with their terms of use.