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Zhongwei Li
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skills/denario/references/llm_configuration.md
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skills/denario/references/llm_configuration.md
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# LLM API Configuration
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## Overview
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Denario requires API credentials from supported LLM providers to power its multiagent research system. The system is built on AG2 and LangGraph, which support multiple LLM backends.
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## Supported LLM Providers
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### Google Vertex AI
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- Full integration with Google's Vertex AI platform
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- Supports Gemini and PaLM models
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- Requires Google Cloud project setup
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### OpenAI
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- GPT-4, GPT-3.5, and other OpenAI models
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- Direct API integration
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### Other Providers
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- Any LLM compatible with AG2/LangGraph frameworks
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- Anthropic Claude (via compatible interfaces)
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- Azure OpenAI
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- Custom model endpoints
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## Obtaining API Keys
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### Google Vertex AI
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1. **Create Google Cloud Project**
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- Navigate to [Google Cloud Console](https://console.cloud.google.com/)
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- Create a new project or select existing
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2. **Enable Vertex AI API**
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- Go to "APIs & Services" → "Library"
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- Search for "Vertex AI API"
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- Click "Enable"
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3. **Create Service Account**
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- Navigate to "IAM & Admin" → "Service Accounts"
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- Create service account with Vertex AI permissions
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- Download JSON key file
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4. **Set up authentication**
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```bash
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export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account-key.json"
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```
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### OpenAI
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1. **Create OpenAI Account**
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- Visit [platform.openai.com](https://platform.openai.com/)
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- Sign up or log in
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2. **Generate API Key**
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- Navigate to API Keys section
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- Click "Create new secret key"
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- Copy and store securely
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3. **Set environment variable**
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```bash
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export OPENAI_API_KEY="sk-..."
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```
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## Storing API Keys
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### Method 1: Environment Variables (Recommended)
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**Linux/macOS:**
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```bash
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export OPENAI_API_KEY="your-key-here"
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export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials.json"
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```
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Add to `~/.bashrc`, `~/.zshrc`, or `~/.bash_profile` for persistence.
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**Windows:**
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```bash
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set OPENAI_API_KEY=your-key-here
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```
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Or use System Properties → Environment Variables for persistence.
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### Method 2: .env Files
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Create a `.env` file in your project directory:
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```env
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# OpenAI Configuration
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OPENAI_API_KEY=sk-your-openai-key-here
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OPENAI_MODEL=gpt-4
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# Google Vertex AI Configuration
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GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
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GOOGLE_CLOUD_PROJECT=your-project-id
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# Optional: Model preferences
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DEFAULT_MODEL=gpt-4
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TEMPERATURE=0.7
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```
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Load the environment file in Python:
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```python
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from dotenv import load_dotenv
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load_dotenv()
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from denario import Denario
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den = Denario(project_dir="./project")
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```
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### Method 3: Docker Environment Files
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For Docker deployments, pass environment variables:
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```bash
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# Using --env-file flag
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docker run -p 8501:8501 --env-file .env --rm pablovd/denario:latest
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# Using -e flag for individual variables
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docker run -p 8501:8501 \
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-e OPENAI_API_KEY=sk-... \
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-e GOOGLE_APPLICATION_CREDENTIALS=/credentials.json \
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-v /local/path/to/creds.json:/credentials.json \
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--rm pablovd/denario:latest
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```
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## Vertex AI Detailed Setup
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### Prerequisites
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- Google Cloud account with billing enabled
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- gcloud CLI installed (optional but recommended)
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### Step-by-Step Configuration
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1. **Install Google Cloud SDK (if not using Docker)**
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```bash
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# Linux/macOS
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curl https://sdk.cloud.google.com | bash
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exec -l $SHELL
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gcloud init
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```
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2. **Authenticate gcloud**
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```bash
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gcloud auth application-default login
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```
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3. **Set project**
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```bash
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gcloud config set project YOUR_PROJECT_ID
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```
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4. **Enable required APIs**
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```bash
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gcloud services enable aiplatform.googleapis.com
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gcloud services enable compute.googleapis.com
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```
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5. **Create service account (alternative to gcloud auth)**
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```bash
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gcloud iam service-accounts create denario-service-account \
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--display-name="Denario AI Service Account"
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gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
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--member="serviceAccount:denario-service-account@YOUR_PROJECT_ID.iam.gserviceaccount.com" \
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--role="roles/aiplatform.user"
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gcloud iam service-accounts keys create credentials.json \
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--iam-account=denario-service-account@YOUR_PROJECT_ID.iam.gserviceaccount.com
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```
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6. **Configure denario to use Vertex AI**
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```python
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import os
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os.environ['GOOGLE_CLOUD_PROJECT'] = 'YOUR_PROJECT_ID'
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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = '/path/to/credentials.json'
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from denario import Denario
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den = Denario(project_dir="./research")
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```
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## Model Selection
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Configure which models denario uses for different tasks:
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```python
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# In your code
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from denario import Denario
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# Example configuration (if supported by denario API)
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den = Denario(
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project_dir="./project",
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# Model configuration may vary based on denario version
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)
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```
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Check denario's documentation for specific model selection APIs.
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## Cost Management
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### Monitoring Costs
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- **OpenAI**: Track usage at [platform.openai.com/usage](https://platform.openai.com/usage)
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- **Google Cloud**: Monitor in Cloud Console → Billing
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- Set up billing alerts to avoid unexpected charges
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### Cost Optimization Tips
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1. **Use appropriate model tiers**
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- GPT-3.5 for simpler tasks
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- GPT-4 for complex reasoning
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2. **Batch operations**
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- Process multiple research tasks in single sessions
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3. **Cache results**
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- Reuse generated ideas, methods, and results when possible
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4. **Set token limits**
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- Configure maximum token usage for cost control
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## Security Best Practices
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### Do NOT commit API keys to version control
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Add to `.gitignore`:
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```gitignore
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.env
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*.json # If storing credentials
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credentials.json
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service-account-key.json
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```
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### Rotate keys regularly
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- Generate new API keys periodically
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- Revoke old keys after rotation
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### Use least privilege access
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- Grant only necessary permissions to service accounts
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- Use separate keys for development and production
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### Encrypt sensitive files
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- Store credential files in encrypted volumes
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- Use cloud secret management services for production
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## Troubleshooting
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### "API key not found" errors
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- Verify environment variables are set: `echo $OPENAI_API_KEY`
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- Check `.env` file is in correct directory
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- Ensure `load_dotenv()` is called before importing denario
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### Vertex AI authentication failures
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- Verify `GOOGLE_APPLICATION_CREDENTIALS` points to valid JSON file
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- Check service account has required permissions
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- Ensure APIs are enabled in Google Cloud project
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### Rate limiting issues
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- Implement exponential backoff
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- Reduce concurrent requests
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- Upgrade API plan if needed
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### Docker environment variable issues
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- Use `docker run --env-file .env` to pass environment
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- Mount credential files with `-v` flag
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- Check environment inside container: `docker exec <container> env`
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