Connect any GitHub repository. Ask questions in plain English. Get answers with exact file and line references.
Live Demo • Get Started • Pricing
Developers spend ~80% of their time reading and understanding code rather than writing it.
Onboarding to new codebases takes weeks. Finding where specific logic lives means grep-ing through thousands of files. Documentation is always outdated.
RepoDoc indexes your entire codebase into a vector database, then lets you query it conversationally with AI.
- Ask "How does authentication work?" → Get the answer with links to
src/lib/auth.ts:45-89 - Ask "Where are API rate limits configured?" → Instantly see the relevant files
- Generate production-ready READMEs and technical docs in one click
No more digging through files. No more outdated wikis. Just ask.
┌─────────────────────────────────────────────────────────────────────────┐
│ │
│ 1. CONNECT 2. INDEX 3. QUERY │
│ ─────────── ───────── ───────── │
│ Paste your Every file gets Ask anything. │
│ GitHub URL summarized, embedded RAG retrieves relevant │
│ and stored in code + LLM generates │
│ PostgreSQL/pgvector answers with citations │
│ │
└─────────────────────────────────────────────────────────────────────────┘
Under the hood:
- Ingestion → LangChain's
GithubRepoLoaderpulls all files from your repo - Summarization → Each file is summarized by Gemini to capture its purpose
- Embedding → Summaries are converted to 768-dim vectors using
text-embedding-004 - Storage → Vectors stored in PostgreSQL with pgvector extension for similarity search
- Retrieval → When you ask a question, we embed your query and find the top 5 most similar code chunks
- Generation → Retrieved context + your question → Gemini 2.5 Flash generates a detailed answer
Chat with your codebase like you'd chat with a senior engineer who knows every line. Ask follow-up questions. Get code snippets with syntax highlighting. See exactly which files informed each answer.
Generate comprehensive technical documentation from your codebase automatically. The AI analyzes your code structure, patterns, and architecture to produce docs that actually reflect your implementation.
Get professional README files generated from your code. Includes proper sections for installation, usage, API references, and more — all inferred from your actual implementation.
Visualize your codebase at a glance:
- Language distribution with percentages
- File counts and project metrics
- Stars, forks, and activity from GitHub
- Dependency insights
Generate public links to share your documentation with teammates, contributors, or the world. Each link is tokenized and can be revoked anytime.
Don't like something in the generated docs? Ask the AI to modify it. "Add a troubleshooting section" or "Update the API examples" — the docs evolve through conversation.
| Layer | Technology |
|---|---|
| Framework | Next.js 16 (App Router, React 18) |
| Language | TypeScript 5 |
| Styling | Tailwind CSS 4.1, Radix UI |
| State | Redux Toolkit |
| Database | PostgreSQL + pgvector |
| ORM | Prisma 6 |
| AI/LLM | Google Gemini 2.5 Flash, OpenRouter |
| Embeddings | text-embedding-004 (768 dimensions) |
| Auth | Clerk |
| Payments | Stripe |
| Forms | React Hook Form + Zod |
| Animation | Motion (Framer Motion) |
| Testing | Jest, React Testing Library |
| Deployment | Vercel |
┌──────────────────────────────────────────────────────────────────────────────┐
│ NEXT.JS APP ROUTER │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────┐ │
│ │ Chat │ │ Dashboard │ │ Docs │ │ README │ │
│ │ Page │ │ Page │ │ Page │ │ Generation │ │
│ └──────┬──────┘ └──────┬──────┘ └──────┬──────┘ └──────────┬──────────┘ │
│ │ │ │ │ │
│ └────────────────┴────────────────┴─────────────────────┘ │
│ │ │
│ ┌────────▼────────┐ │
│ │ API Routes │ │
│ │ │ │
│ │ /api/query │ ← RAG Pipeline │
│ │ /api/search │ ← Vector Search │
│ │ /api/analytics │ ← Metrics Aggregation │
│ └────────┬────────┘ │
└────────────────────────────────────┼─────────────────────────────────────────┘
│
┌──────────────────────────┼──────────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────────┐
│ PostgreSQL │ │ GitHub API │ │ AI Services │
│ + pgvector │ │ (Octokit) │ │ │
│ │ │ │ │ • Gemini 2.5 Flash │
│ • Users │ │ • Repo metadata │ │ • text-embedding-004 │
│ • Projects │ │ • File contents │ │ • OpenRouter fallback │
│ • Embeddings │ │ • Languages │ │ │
│ • Docs/READMEs │ │ • Stats │ │ │
│ • Share tokens │ │ │ │ │
└─────────────────────┘ └─────────────────────┘ └─────────────────────────┘
model User {
id String @id @default(uuid())
emailAddress String @unique
credits Int @default(150)
plan String @default("starter") // starter | professional | enterprise
projects Project[]
}
model Project {
id String @id @default(uuid())
name String
repoUrl String
userId String
sourceCodeEmbiddings SourceCodeEmbiddings[]
docs Docs?
readme Readme?
}
model SourceCodeEmbiddings {
id String @id @default(uuid())
fileName String
sourceCode String
Summary String
summaryEmbedding Unsupported("vector")? // pgvector
projectId String
}
model Docs {
id String @id @default(uuid())
content String
projectId String @unique
qnaHistory DocsQna[]
publicShare DocsShare?
}
model Readme {
id String @id @default(uuid())
content String
projectId String @unique
qnaHistory ReadmeQna[]
publicShare ReadmeShare?
}The core intelligence lives in src/lib/rag.ts:
// 1. Embed the user's question
const queryEmbedding = await getGenerateEmbeddings(query);
// 2. Vector similarity search with pgvector
const results = await prisma.$queryRaw`
SELECT
"fileName",
"sourceCode",
"Summary",
1 - ("summaryEmbedding" <=> ${queryEmbedding}::vector) as similarity
FROM "SourceCodeEmbiddings"
WHERE "projectId" = ${projectId}
ORDER BY "summaryEmbedding" <=> ${queryEmbedding}::vector
LIMIT 5
`;
// 3. Build context from retrieved chunks
const codeContext = results.map((code, idx) => `
[Source ${idx + 1}: ${code.fileName}] (Relevance: ${(code.similarity * 100).toFixed(1)}%)
Summary: ${code.summary}
Code: ${code.sourceCode.slice(0, 1000)}
`).join('\n\n');
// 4. Generate answer with Gemini
const answer = await openrouterChatCompletion({
model: "google/gemini-2.5-flash",
messages: [
{ role: "system", content: systemPrompt + codeContext },
...conversationHistory,
{ role: "user", content: question }
],
temperature: 0.3
});- Node.js 20+
- PostgreSQL with pgvector extension
- GitHub account
- Clerk account
- Google AI API key (Gemini)
# Clone the repository
git clone https://github.com/parbhatkapila4/repodoc.git
cd repodoc
# Install dependencies
npm install
# Set up environment variables
cp .env.example .env# Database
DATABASE_URL="postgresql://user:password@localhost:5432/repodoc"
# Clerk Authentication
NEXT_PUBLIC_CLERK_PUBLISHABLE_KEY=pk_...
CLERK_SECRET_KEY=sk_...
CLERK_WEBHOOK_SECRET=whsec_...
# AI Services
GOOGLE_API_KEY=your_gemini_api_key
OPENROUTER_API_KEY=your_openrouter_key
# GitHub
GITHUB_TOKEN=ghp_...
# Stripe (Optional)
STRIPE_SECRET_KEY=sk_...
STRIPE_WEBHOOK_SECRET=whsec_...
# App
NEXT_PUBLIC_APP_URL=http://localhost:3000# Generate Prisma client
npm run db:generate
# Run migrations
npm run db:migrate
# (Optional) Open Prisma Studio
npm run db:studio# Start development server
npm run dev
# Run tests
npm test
# Type check
npm run type-check
# Lint
npm run lintrepodoc/
├── src/
│ ├── app/
│ │ ├── (app)/ # Landing page
│ │ ├── (auth)/ # Sign in/up, user sync
│ │ ├── (protected)/ # Authenticated routes
│ │ │ ├── chat/ # AI chat interface
│ │ │ ├── dashboard/ # Project management
│ │ │ ├── docs/ # Documentation viewer
│ │ │ ├── readme/ # README editor
│ │ │ ├── analytics/ # Platform analytics
│ │ │ └── search/ # Semantic search
│ │ └── api/
│ │ ├── query/ # RAG endpoint
│ │ ├── search/ # Vector search
│ │ ├── analytics/ # Metrics API
│ │ ├── create-checkout/# Stripe checkout
│ │ └── webhooks/ # Clerk & Stripe webhooks
│ ├── components/
│ │ ├── ui/ # Radix-based primitives
│ │ └── landing/ # Marketing components
│ ├── lib/
│ │ ├── rag.ts # RAG implementation
│ │ ├── github.ts # GitHub integration
│ │ ├── gemini.ts # AI embeddings & generation
│ │ ├── openrouter.ts # LLM fallback
│ │ ├── prisma.ts # Database client
│ │ ├── actions.ts # Server actions
│ │ └── rate-limiter.ts # API protection
│ └── hooks/ # Custom React hooks
├── prisma/
│ ├── schema.prisma # Database schema
│ └── migrations/ # Migration history
└── __tests__/ # Jest test suites
| Plan | Price | Projects | Features |
|---|---|---|---|
| Starter | $10/mo | 3 | AI chat, README generation, docs generation, basic analytics |
| Professional | $20/mo | 10 | Everything in Starter + public sharing, priority processing, email support |
| Enterprise | $49/mo | Unlimited | Everything in Professional + team features, SLA, custom integrations |
Query your codebase with natural language.
// Request
{
"projectId": "uuid",
"question": "How does authentication work?",
"conversationHistory": [
{ "role": "user", "content": "previous question" },
{ "role": "assistant", "content": "previous answer" }
]
}
// Response
{
"answer": "Authentication in this codebase is handled by...",
"sources": [
{
"fileName": "src/lib/auth.ts",
"similarity": 0.89,
"summary": "Handles user authentication..."
}
]
}Semantic search across your codebase.
// Request
{
"projectId": "uuid",
"query": "rate limiting",
"limit": 10
}
// Response
{
"results": [
{
"fileName": "src/lib/rate-limiter.ts",
"sourceCode": "...",
"summary": "...",
"similarity": 0.92
}
]
}- Authentication: All routes protected by Clerk middleware
- Input validation: Zod schemas on all API inputs
- SQL injection: Prevented by Prisma parameterized queries
- Rate limiting: Token bucket algorithm on API endpoints
- XSS protection: Next.js built-in escaping
- CSRF protection: Same-origin verification
Contributions are welcome. Please open an issue first to discuss what you'd like to change.
# Fork the repo
git checkout -b feature/your-feature
git commit -m "Add your feature"
git push origin feature/your-feature
# Open a PR- Support for GitLab and Bitbucket
- Self-hosted deployment option
- Team workspaces with shared projects
- VS Code extension
- GitHub App for automatic syncing
- Custom embedding models
- Streaming responses
Built by Parbhat Kapila