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Environment Setup for Intelligent Content Analysis

Quick Start

  1. Copy the example environment file:

    cp .env.example .env

  2. Edit .env and add your OpenAI API key:

    nano .env
# or
open .env

  3. Add your API key:

    OPENAI_API_KEY=sk-proj-your-actual-key-here

  4. Save and close the file

Getting Your OpenAI API Key

  1. Go to https://platform.openai.com/api-keys
  2. Sign in or create an account
  3. Click "Create new secret key"
  4. Copy the key (starts with sk-proj-...)
  5. Paste it into .env

Security

  • ✅ .env is in .gitignore - your key will NOT be committed to git
  • ✅ Never share your .env file or commit it to version control
  • ✅ Use .env.example to show the structure without exposing secrets

Testing the Setup

Run the test script to verify everything works:

python3 test_intelligent_cleaning.py

If you see an error about the API key, double-check:

  1. The file is named exactly .env (not .env.txt or anything else)
  2. The key is on a line like: OPENAI_API_KEY=sk-proj-...
  3. There are no extra spaces around the = sign
  4. The key is valid and not expired

Cost Estimates

The intelligent content analysis uses GPT-4o-mini, which is very cost-effective:

  • Per document: ~$0.0003 (0.03 cents)
  • 1,000 documents: ~$0.30
  • 10,000 documents: ~$3.00

Optional Configuration

You can override default settings in .env:

# Change the LLM model (default: gpt-4o-mini)
LLM_MODEL=gpt-4o-mini

# Adjust temperature for more/less randomness (default: 0.1)
LLM_TEMPERATURE=0.1

# Maximum tokens for LLM response (default: 1500)
LLM_MAX_TOKENS=1500

# Rate limit requests per minute (default: 500)
LLM_RATE_LIMIT_RPM=500

Troubleshooting

"API key not set" error

  • Make sure .env exists in the same directory as the scripts
  • Check that OPENAI_API_KEY= has your actual key after the =
  • Try printing the env var: python3 -c "import os; print(os.getenv('OPENAI_API_KEY'))"

"Invalid API key" error

"Rate limit exceeded" error

  • You're making requests too quickly
  • Wait a minute and try again
  • Or reduce LLM_RATE_LIMIT_RPM in .env