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DeepWiki-Open uses environment variables to configure AI providers, server settings, authentication, and advanced features. This guide covers all available environment variables and their usage.

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Required Environment Variables

At minimum, you need API keys for at least one AI provider: 
# Choose ONE of these AI providers
GOOGLE_API_KEY=your_google_api_key_here
# OR
OPENAI_API_KEY=your_openai_api_key_here

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AI Provider Configuration

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Google Gemini

Google AI Studio API key for Gemini models. How to get:
  1. Visit Google AI Studio
  2. Click “Create API Key”
  3. Copy the generated key
Supported models:
  • gemini-2.0-flash (default, recommended)
  • gemini-1.5-flash
  • gemini-1.0-pro
Google Gemini offers generous free tier limits and excellent performance for documentation generation.

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OpenAI

OpenAI API key for GPT models. Custom OpenAI API endpoint (for OpenAI-compatible services). How to get:
  1. Visit OpenAI Platform
  2. Create new secret key
  3. Copy the key (starts with sk-)
Supported models:
  • gpt-4o (default)
  • gpt-4.1
  • o1
  • o3
  • o4-mini
OpenAI requires a paid account. Free tier users cannot access the API.

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OpenRouter

OpenRouter API key for access to multiple model providers. How to get:
  1. Sign up at OpenRouter
  2. Go to Keys section
  3. Create new API key
Available models:
  • openai/gpt-4o
  • anthropic/claude-3.5-sonnet
  • deepseek/deepseek-r1
  • google/gemini-pro
  • And 100+ more models
OpenRouter provides access to multiple AI providers through a single API, perfect for comparing models.

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Azure OpenAI

Azure OpenAI service API key. Your Azure OpenAI resource endpoint URL. API version (e.g., 2024-02-15-preview). How to get:
  1. Create Azure OpenAI resource in Azure Portal
  2. Deploy a model (GPT-4, GPT-3.5-turbo, etc.)
  3. Get endpoint and API key from resource overview
  4. Note the API version from the deployment
Example configuration: 
AZURE_OPENAI_API_KEY=abc123def456ghi789
AZURE_OPENAI_ENDPOINT=https://my-resource.openai.azure.com
AZURE_OPENAI_VERSION=2024-02-15-preview

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AWS Bedrock

AWS access key for Bedrock access. AWS secret access key. AWS region where Bedrock models are available. Supported models:
  • anthropic.claude-3-sonnet-20240229-v1:0
  • anthropic.claude-3-haiku-20240307-v1:0
  • anthropic.claude-3-opus-20240229-v1:0
  • amazon.titan-text-express-v1

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Ollama (Local Models)

Ollama server URL for local AI models. Setup Ollama:
  • macOS
  • Linux
  • Docker
# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Start service
ollama serve

# Pull a model
ollama pull llama3:8b
Supported models:
  • qwen3:1.7b (lightweight)
  • llama3:8b (balanced)
  • qwen3:8b (high context)

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DashScope (Alibaba)

Alibaba DashScope API key for Qwen models. How to get:
  1. Sign up at DashScope
  2. Create API key in console
  3. Add key to environment
Supported models:
  • qwen-plus
  • qwen-turbo
  • deepseek-r1

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Server Configuration

Port for the FastAPI backend server. Base URL for API server (used by frontend). Environment mode (development, production, test). Example server configuration: 
PORT=8002
SERVER_BASE_URL=https://api.deepwiki.example.com
NODE_ENV=production

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Security & Authentication

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Authorization Mode

Enable authorization requirement for wiki generation. Secret code required when authorization mode is enabled. Usage: 
DEEPWIKI_AUTH_MODE=true
DEEPWIKI_AUTH_CODE=my-secret-code-123
When enabled, users must enter the auth code to generate wikis.
Authorization mode provides basic frontend protection but doesn’t secure direct API access.

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Logging & Debugging

Logging verbosity level. Options: DEBUG, INFO, WARNING, ERROR, CRITICAL Path for log file output. Example logging configuration: 
LOG_LEVEL=DEBUG
LOG_FILE_PATH=./logs/deepwiki-debug.log
In production, use INFO or WARNING level to reduce log volume.

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Advanced Configuration

Directory containing configuration JSON files. Redis connection URL for caching (optional). Example: 
DEEPWIKI_CONFIG_DIR=/custom/config/path
REDIS_URL=redis://localhost:6379/0

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Environment File Templates

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Development

# Development Environment
NODE_ENV=development
LOG_LEVEL=DEBUG
LOG_FILE_PATH=./api/logs/development.log

# Server Config
PORT=8001
SERVER_BASE_URL=http://localhost:8001

# API Keys
GOOGLE_API_KEY=your_development_google_key
OPENAI_API_KEY=your_development_openai_key

# Local Ollama
OLLAMA_HOST=http://localhost:11434

# No authentication for dev
DEEPWIKI_AUTH_MODE=false

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Production

# Production Environment  
NODE_ENV=production
LOG_LEVEL=INFO
LOG_FILE_PATH=/var/log/deepwiki/application.log

# Server Config
PORT=8001
SERVER_BASE_URL=https://api.yourdomain.com

# Production API Keys
GOOGLE_API_KEY=your_production_google_key
OPENAI_API_KEY=your_production_openai_key
AZURE_OPENAI_API_KEY=your_azure_key
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_VERSION=2024-02-15-preview

# Enable authentication
DEEPWIKI_AUTH_MODE=true
DEEPWIKI_AUTH_CODE=your_secure_production_code

# Redis caching
REDIS_URL=redis://redis-server:6379/0

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Docker

# Docker Environment
NODE_ENV=production
LOG_LEVEL=INFO

# Container networking
PORT=8001
SERVER_BASE_URL=http://deepwiki-api:8001

# API Keys
GOOGLE_API_KEY=your_google_key
OPENAI_API_KEY=your_openai_key

# External Ollama
OLLAMA_HOST=http://ollama-server:11434

# Persistent data
LOG_FILE_PATH=/app/logs/application.log
DEEPWIKI_CONFIG_DIR=/app/config

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Validation & Testing

1

Validate Environment

# Check environment variables are loaded
python -c "
import os
from dotenv import load_dotenv
load_dotenv()

# Check API keys
providers = {
    'Google': os.getenv('GOOGLE_API_KEY'),
    'OpenAI': os.getenv('OPENAI_API_KEY'),
    'OpenRouter': os.getenv('OPENROUTER_API_KEY'),
    'Azure': os.getenv('AZURE_OPENAI_API_KEY'),
}

for name, key in providers.items():
    status = '✓ Configured' if key else '✗ Missing'
    print(f'{name}: {status}')
"
2

Test API Connections

# Test backend startup
python -m api.main

# Check for successful startup messages
# Look for: "Starting Streaming API on port 8001"
# No API key warnings for your configured providers
3

Verify Frontend Connection

# Start frontend
npm run dev

# Test API connection at http://localhost:3000
# Model selection should show your configured providers

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Security Best Practices

  • Never commit .env files to version control
  • Use different API keys for development and production
  • Regularly rotate API keys
  • Monitor API usage for unexpected activity
  • Use environment-specific keys when possible
# Secure file permissions
chmod 600 .env

# Use secrets management in production
# - AWS Secrets Manager
# - Azure Key Vault  
# - Kubernetes secrets
# - Docker secrets
  • Use HTTPS in production
  • Configure proper CORS settings
  • Use private networks for internal components
  • Enable authorization mode for public deployments

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Troubleshooting

Symptoms: API key errors, default values usedSolutions:
  1. Verify .env file is in project root
  2. Check file permissions (readable by application)
  3. Ensure no syntax errors in .env file
  4. Restart application after changes
Symptoms: “Invalid API key” errorsSolutions:
  1. Test API keys with provider’s documentation
  2. Check for extra spaces or characters
  3. Verify key has correct permissions/scopes
  4. Confirm key hasn’t expired or been revoked
Symptoms: “Port already in use” errorsSolutions:
  1. Change PORT environment variable
  2. Kill existing processes on the port
  3. Use Docker with port mapping
  4. Configure reverse proxy

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Next Steps

Model Provider Setup

Configure specific AI model providers and their settings

Production Deployment

Deploy DeepWiki with production-ready configuration

Configuration Files

Learn about JSON configuration files for advanced customization

Security Guide

Implement security best practices for production deployments