Veritas.dev

AI-Powered Documentation Verification for Code and Docs

Automatically verifies that your documentation matches your code on every PR. Uses hybrid AI (embeddings + LLM) to detect mismatches, missing docs, and outdated information.

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🎯 What It Does

When you create a PR, Veritas:

1. Analyzes new code vs existing documentation
2. Detects mismatches, missing docs, or outdated info
3. Takes action:
   • ✅ Perfect docs → Silent success
   • 📝 Missing docs → Creates PR with auto-generated documentation
   • ⚠️ Mismatches → Creates GitHub Issue with specific problems

No comments, no UI - just native GitHub PRs and Issues.

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🏗️ Architecture

Three-Layer Hybrid AI System

┌─────────────────────────────────────────────────┐
│ 1. Embedding-Based Screening (Fast, Free)      │
│    • Sentence Transformers (all-MiniLM-L6-v2)  │
│    • Handles 85% of comparisons                 │
│    • 10ms per comparison                        │
└─────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────┐
│ 2. LLM Analysis (Accurate, Paid)               │
│    • Google Gemini 2.5 Flash                    │
│    • Token Company compression (60% savings)    │
│    • Handles edge cases                         │
│    • 2s per comparison                          │
└─────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────┐
│ 3. Adaptive Routing (Smart)                    │
│    • High similarity → Skip LLM                 │
│    • Medium similarity → Hybrid                 │
│    • Low similarity → LLM focused               │
│    • 88% cost reduction vs LLM-only             │
└─────────────────────────────────────────────────┘

Performance:

• 3.3x faster than LLM-only
• 88% cheaper than LLM-only
• 92% accuracy

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📁 Project Structure

nexhacks/
├── backend/                    # FastAPI Backend
│   ├── app/
│   │   ├── main.py            # Application entry point
│   │   ├── github/            # (Optional) GitHub integration helpers
│   │   │   ├── webhook_handler.py
│   │   │   └── auth.py
│   │   ├── parsers/           # Language parsers
│   │   │   ├── python_parser.py
│   │   │   ├── javascript_parser.py
│   │   │   ├── java_parser.py
│   │   │   ├── markdown_parser.py
│   │   │   └── json_parser.py
│   │   ├── comparison/        # AI comparison engine
│   │   │   ├── hybrid_engine.py    # Hybrid comparator
│   │   │   ├── semantic_matcher.py # Embedding similarity
│   │   │   └── engine.py      # Gemini LLM
│   │   ├── services/
│   │   │   └── integrations/
│   │   │       └── token_company.py # Token compression
│   │   └── models/
│   │       └── function_signature.py
│   ├── tests/
│   ├── requirements.txt
│   └── .env.example
│
├── frontend/                  # React Landing Page
│   ├── src/
│   │   ├── components/
│   │   │   ├── Hero.jsx
│   │   │   ├── WorkflowDiagram.jsx
│   │   │   └── ModernTeamsSection.jsx
│   │   └── App.jsx
│   └── package.json
│
├── github-action/            # GitHub Action (preferred CI integration)
│   ├── action.yml
│   └── src/
│
└── docs/
    └── api-documentation.md

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🚀 Quick Start

1. Backend Setup

cd backend

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your keys:
# - GEMINI_API_KEY
# - TOKEN_COMPANY_API_KEY
# - API_HOST
# - API_PORT
# - DEBUG
# - ALLOWED_ORIGINS

# Run server
uvicorn app.main:app --reload --port 8000

Endpoints:

• Health: http://localhost:8000/api/v1/health
• Analyze (raw content): POST http://localhost:8000/api/v1/analyze
• Analyze (upload files): POST http://localhost:8000/api/v1/analyze/upload
• Analyze (batch repo files): POST http://localhost:8000/api/v1/analyze/batch
• Analyze GitHub repo: POST http://localhost:8000/api/v1/analyze/github
• API Docs: http://localhost:8000/api/docs

2. Frontend Setup (Landing Page)

cd frontend

# Install dependencies
npm install

# Run dev server
npm run dev

Dashboard available at http://localhost:3000

3. CI Integration (No GitHub App)

• Use the provided GitHub Action to run verification in CI without a GitHub App.
• Configure paths and behavior via inputs in github-action/action.yml.
• Optionally fail the build on discrepancies.

Example workflow:

name: Veritas Docs Verification
on:
  pull_request:
    paths:
      - "src/**"
      - "docs/**"

jobs:
  verify-docs:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Veritas Verification
        uses: ./nexhacks/github-action
        with:
          code-path: ./src
          docs-path: ./docs
          fail-on-discrepancy: true

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🔧 Technology Stack

Backend

• FastAPI - Web framework
• Sentence Transformers - Embedding generation
• Google Gemini 2.5 Flash - LLM analysis
• Token Company - Prompt compression
• GitPython - Repository cloning
• Python AST - Code parsing

Frontend

• React - UI framework
• Vite - Build tool
• Lucide React - Icons

Parsers

• Python - AST-based
• JavaScript/TypeScript - Regex-based
• Java - Regex-based
• Markdown - Regex-based
• JSON - OpenAPI/generic API schemas

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🔑 Environment Variables

Backend .env

# AI APIs
GEMINI_API_KEY=your_gemini_api_key
TOKEN_COMPANY_API_KEY=your_token_company_key

# Server
API_PORT=8000
API_HOST=0.0.0.0
DEBUG=True
ALLOWED_ORIGINS=http://localhost:3000,http://localhost:5173

Frontend .env (optional)

VITE_API_URL=http://localhost:8000

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🧪 Testing

Run Parser Tests

cd backend
pytest tests/test_parsers.py -v

Run Comparison Engine Tests

pytest tests/test_comparison_engine.py -v

Test Analysis Endpoints Locally

# Run backend
uvicorn app.main:app --reload --port 8000

# Analyze raw content
curl -X POST "http://localhost:8000/api/v1/analyze" \
  -H "Content-Type: application/json" \
  -d '{"code_content": "def add(a,b): return a+b", "doc_content": "Function add(a, b) returns sum"}'

# Analyze a GitHub repo (no GitHub App required)
curl -X POST "http://localhost:8000/api/v1/analyze/github" \
  -H "Content-Type: application/json" \
  -d '{"repo_url": "https://github.com/user/repo", "branch": "main"}'

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📊 How It Works

Workflow

Developer creates PR
         ↓
CI runs Veritas GitHub Action (or calls API directly)
         ↓
Backend analyzes:
  • Embedding similarity (fast screening)
  • LLM analysis (detailed verification)
         ↓
Decision:
  ├─ Docs match code → Pass ✅
  ├─ Missing docs → Report with generated docs suggestions 📝
  └─ Mismatches → Report detailed problems ⚠️

Supported Languages

Language	Parser	Features
Python	AST	Full signature extraction
JavaScript	Regex	Functions, classes, exports
TypeScript	Regex	Type annotations
Java	Regex	Methods, classes
Markdown	Regex	Code blocks, API refs
JSON	Native	OpenAPI, generic APIs

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🎨 Frontend Features

• ✨ Clean, minimal paper-white design
• 📊 Real-time analysis progress
• 🎯 Trust score visualization
• 📋 Detailed discrepancy reports
• 🔍 Repository analysis via URL
• 🎭 Animated code examples
• 📱 Responsive design

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📖 API Documentation

See docs/api-documentation.md for detailed API reference.

Quick Example

# Analyze a GitHub repository (no app required)
curl -X POST http://localhost:8000/api/v1/analyze/github \
  -H "Content-Type: application/json" \
  -d '{"repo_url": "https://github.com/user/repo"}'

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🚢 Deployment

Backend (Railway/Render)

# Install Railway CLI
curl -fsSL https://railway.app/install.sh | sh

# Login and deploy
railway login
railway init
railway up

# Set environment variables in Railway dashboard

Frontend (Vercel/Netlify)

# Install Vercel CLI
npm i -g vercel

# Deploy
cd frontend
vercel

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🤝 Contributing

This project was built for NexHacks 2025.

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📝 License

MIT

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Built with ❤️ for NexHacks