Every developer has wasted hours explaining their own codebase. ContextDump already knows it.
ContextDump is an AI-powered codebase intelligence tool. Connect any public GitHub repo, and ask natural language questions about your code. The agent finds the exact files, cites line numbers, and explains everything — instantly. Show Image
Semantic code search — finds relevant code across your entire repo
Autonomous agent — decides which tool to use based on your question
Source citations — every answer links back to exact file + line numbers
Chat history — conversations saved per repo
Smart chunking — splits by functions and classes, not character count
Fast inference — powered by Groq (300-1000+ tokens/sec)
Free embeddings — runs HuggingFace model locally, no OpenAI key needed
Voice input — ask questions by speaking, answers are spoken back via TTS
Local STT/TTS — Whisper runs on-device, edge-tts uses free Microsoft neural voices
| Feature | Description |
|---|---|
Dashboard |
Chat |
Add any public GitHub repository |
Ask questions and get cited answers |
Real-time Ingestion |
Agent picks the right tool automatically |
View files, chunks, and languages |
Sources show exact file + line number |
| Layer | Technology |
|---|---|
Frontend |
React 18 + Tailwind CSS + Vite |
Backend |
Node.js + Express |
Database |
MongoDB + Mongoose |
Vector Store |
FAISS |
Embeddings |
HuggingFace all-MiniLM-L6-v2 |
LLM + Agent |
Groq API — llama-3.3-70b-versatile |
Auth |
JWT |
Speech-to-Text |
faster-whisper (OpenAI Whisper, runs locally) |
Text-to-Speech |
edge-tts (Microsoft Neural voices, free) |
Voice Microservice |
Python FastAPI |
Prerequisites:
Node.js 18+
MongoDB running locally
Groq API key
1. Clone the repo:
git clone https://github.com/LaibaBatoool/contextdump.git
cd contextdump
2. Setup Backend:
cd backend
npm install
cp .env.example .env
Fill in .env:
envPORT=5000
MONGO_URI=mongodb://localhost:27017/contextdump
JWT_SECRET=your_secret_key_here
JWT_EXPIRES_IN=7d
GROQ_API_KEY=your_groq_api_key_here
3. Create data directories:
mkdir -p data/vectorstores data/repos
4. Start the backend:
npm run dev
5. Setup Frontend:
cd ../frontend
npm install
npm run dev
6. Open the app
The voice feature requires a separate Python microservice for Whisper STT.
Prerequisites: Python 3.10+
- cd whisper-service
- python -m venv venv && venv\Scripts\activate
- pip install fastapi uvicorn faster-whisper python-multipart edge-tts
- uvicorn main:app --port 8001
Once running, the mic button in the chat UI will be active.
Auth:
POST /api/auth/register { name, email, password }
POST /api/auth/login { email, password }
GET /api/auth/me
Repos:
POST /api/repos { githubUrl }
GET /api/repos
GET /api/repos/:id
DELETE /api/repos/:id
Chat:
POST /api/chat/:repoId { content }
GET /api/chat/:repoId
DELETE /api/chat/:repoId
POST /api/chat/:repoId/voice multipart/form-data: audio file
Most RAG tutorials chunk by character count (every 500 chars). That's bad for code — it splits functions in half and loses context. ContextDump chunks by code structure:
// Bad — naive chunking
"...const handleLogin = async (req, res" // cut off mid-function
// Good — structure-aware chunking
"// File: controllers/auth.js (lines 12-45) const handleLogin = async (req, res) => { const { email, password } = req.body; ..." // complete function
This means:
Semantically complete chunks
Accurate line number citations
Much better retrieval quality
The agent has 4 tools it chooses from automatically:
| Tool | When it's used |
|---|---|
search_codebase |
General questions about how something works |
get_file |
Questions about a specific file |
explain_function |
Questions about a named function or class |
generate_docs |
Requests to document a module |
Laiba Batool — CS student at PIEAS