Update got-researcher #1541
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Update got-researcher #1541
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I'll help you deploy GPT-Researcher on Zeabur! Let me first check the current deployment configuration and then guide you through the Zeabur setup. Now let me check if there are any Zeabur-specific configuration files: Perfect! Now I'll help you deploy GPT-Researcher on Zeabur. Here's a comprehensive guide: 🚀 Zeabur Deployment Guide for GPT-Researcher Step 1: Prepare Your Repository First, let's create a Zeabur-specific configuration file: Now let's create a production-ready Dockerfile optimized for Zeabur: Let's also create a .zeaburignore file to exclude unnecessary files: Now let's create a startup script to handle environment setup: 🚀 Zeabur Deployment Steps Step 2: Deploy on Zeabur Go to Zeabur Dashboard Create a New Project Click "New Project" Name it "gpt-researcher" Connect Your Repository Click "Add Service" Choose "GitHub" and select your repository Select the branch (usually main or master) Configure the Service Build Method: Dockerfile Dockerfile Path: Dockerfile.zeabur (or use the main Dockerfile) Root Directory: / (root of repository) Set Environment Variables Add these required environment variables in Zeabur: # Required API Keys OPENAI_API_KEY=your_openai_api_key_here TAVILY_API_KEY=your_tavily_api_key_here # Optional but recommended LANGCHAIN_API_KEY=your_langchain_api_key_here LANGCHAIN_TRACING_V2=true # Server Configuration HOST=0.0.0.0 PORT=8000 WORKERS=1 # Optional: Custom OpenAI endpoint # OPENAI_BASE_URL=https://your-custom-endpoint.com/v1 # Optional: Additional retrievers # RETRIEVER=tavily,google,bing # Optional: Document path # DOC_PATH=/app/my-docs Step 3: Configure Domain (Optional) In your Zeabur project, go to "Domains" Add a custom domain or use the provided Zeabur domain The app will be available at https://your-app-name.zeabur.app Step 4: Monitor Deployment Check the "Logs" tab in Zeabur dashboard Look for successful startup messages Test the health check endpoint: https://your-app.zeabur.app/ 🔧 Troubleshooting Common Issues If Build Fails: # Check if all dependencies are in requirements.txt # Ensure Dockerfile.zeabur is in the root directory # Verify Python version compatibility If Runtime Fails: # Check environment variables are set correctly # Verify API keys are valid # Check logs for specific error messages If Web Scraping Doesn't Work: # Ensure Chromium is properly installed # Check if the service has enough memory (at least 1GB) # Verify network access is enabled 📊 Resource Requirements Minimum Recommended: Memory: 1GB RAM CPU: 1 vCPU Storage: 2GB For Production: Memory: 2GB+ RAM CPU: 2+ vCPUs Storage: 5GB+ 🎯 Post-Deployment Setup Test the API: curl https://your-app.zeabur.app/ Test Research Generation: curl -X POST https://your-app.zeabur.app/report/ \ -H "Content-Type: application/json" \ -d '{ "task": "What is artificial intelligence?", "report_type": "research_report", "report_source": "web", "tone": "objective" }' Monitor Performance: Check Zeabur dashboard for resource usage Monitor logs for any errors Test WebSocket functionality if needed 🔐 Security Considerations Environment Variables: Never commit API keys to the repository Rate Limiting: Consider implementing rate limiting for production CORS: Configure CORS settings for your domain HTTPS: Zeabur provides HTTPS by default Your GPT-Researcher should now be successfully deployed on Zeabur! The app will be accessible via the provided Zeabur domain and ready to handle research requests. :-)I'll help you deploy GPT-Researcher on Zeabur! Let me first check the current deployment configuration and then guide you through the Zeabur setup. Now let me check if there are any Zeabur-specific configuration files: Perfect! Now I'll help you deploy GPT-Researcher on Zeabur. Here's a comprehensive guide: 🚀 Zeabur Deployment Guide for GPT-Researcher Step 1: Prepare Your Repository First, let's create a Zeabur-specific configuration file: Now let's create a production-ready Dockerfile optimized for Zeabur: Let's also create a .zeaburignore file to exclude unnecessary files: Now let's create a startup script to handle environment setup: 🚀 Zeabur Deployment Steps Step 2: Deploy on Zeabur Go to Zeabur Dashboard Create a New Project Click "New Project" Name it "gpt-researcher" Connect Your Repository Click "Add Service" Choose "GitHub" and select your repository Select the branch (usually main or master) Configure the Service Build Method: Dockerfile Dockerfile Path: Dockerfile.zeabur (or use the main Dockerfile) Root Directory: / (root of repository) Set Environment Variables Add these required environment variables in Zeabur: # Required API Keys OPENAI_API_KEY=your_openai_api_key_here TAVILY_API_KEY=your_tavily_api_key_here # Optional but recommended LANGCHAIN_API_KEY=your_langchain_api_key_here LANGCHAIN_TRACING_V2=true # Server Configuration HOST=0.0.0.0 PORT=8000 WORKERS=1 # Optional: Custom OpenAI endpoint # OPENAI_BASE_URL=https://your-custom-endpoint.com/v1 # Optional: Additional retrievers # RETRIEVER=tavily,google,bing # Optional: Document path # DOC_PATH=/app/my-docs Step 3: Configure Domain (Optional) In your Zeabur project, go to "Domains" Add a custom domain or use the provided Zeabur domain The app will be available at https://your-app-name.zeabur.app Step 4: Monitor Deployment Check the "Logs" tab in Zeabur dashboard Look for successful startup messages Test the health check endpoint: https://your-app.zeabur.app/ 🔧 Troubleshooting Common Issues If Build Fails: # Check if all dependencies are in requirements.txt # Ensure Dockerfile.zeabur is in the root directory # Verify Python version compatibility If Runtime Fails: # Check environment variables are set correctly # Verify API keys are valid # Check logs for specific error messages If Web Scraping Doesn't Work: # Ensure Chromium is properly installed # Check if the service has enough memory (at least 1GB) # Verify network access is enabled 📊 Resource Requirements Minimum Recommended: Memory: 1GB RAM CPU: 1 vCPU Storage: 2GB For Production: Memory: 2GB+ RAM CPU: 2+ vCPUs Storage: 5GB+ 🎯 Post-Deployment Setup Test the API: curl https://your-app.zeabur.app/ Test Research Generation: curl -X POST https://your-app.zeabur.app/report/ \ -H "Content-Type: application/json" \ -d '{ "task": "What is artificial intelligence?", "report_type": "research_report", "report_source": "web", "tone": "objective" }' Monitor Performance: Check Zeabur dashboard for resource usage Monitor logs for any errors Test WebSocket functionality if needed 🔐 Security Considerations Environment Variables: Never commit API keys to the repository Rate Limiting: Consider implementing rate limiting for production CORS: Configure CORS settings for your domain HTTPS: Zeabur provides HTTPS by default Your GPT-Researcher should now be successfully deployed on Zeabur! The app will be accessible via the provided Zeabur domain and ready to handle research requests. :-)
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