chat-ideyalabs

ChatIdeyalabs PyPI Publishing Guide

🚀 Publishing to PyPI

This guide explains how to securely publish the ChatIdeyalabs package to PyPI while keeping sensitive information hidden.

📋 Prerequisites

• PyPI Account: Create account at pypi.org
• PyPI API Token: Generate API token in PyPI account settings
• Environment Setup: Configure environment variables for security

🔐 Security Setup

Environment Variables (Required)

Create a .env file for local development (DO NOT commit this):

# .env file (for local development only)
CHATIDEYALABS_LLM_BASE_URL=https://your-llm-endpoint.com
CHATIDEYALABS_LLM_API_KEY=your-llm-api-key-here
CHATIDEYALABS_MONGODB_URL=mongodb+srv://username:[e-mail]/database
CHATIDEYALABS_MONGODB_DATABASE=your_database
CHATIDEYALABS_MONGODB_COLLECTION=apiKeys
CHATIDEYALABS_ENABLE_LOGGING=true
CHATIDEYALABS_LOG_SENSITIVE=false

Add .env to .gitignore

echo ".env" >> .gitignore
echo "*.env" >> .gitignore
echo "__pycache__/" >> .gitignore
echo "*.pyc" >> .gitignore
echo "dist/" >> .gitignore
echo "build/" >> .gitignore
echo "*.egg-info/" >> .gitignore

📦 Publishing Workflow

Step 1: Install Publishing Tools

pip install build twine

Step 2: Update Version

Edit setup.py:

setup(
    name="chat-ideyalabs",
    version="0.2.0",  # Increment version
    # ... rest of setup
)

Step 3: Clean Previous Builds

rm -rf dist/ build/ *.egg-info/

Step 4: Build the Package

python -m build

This creates:

• dist/chat_ideyalabs-0.2.0.tar.gz
• dist/chat_ideyalabs-0.2.0-py3-none-any.whl

Step 5: Test Upload (Optional)

# Upload to TestPyPI first
python -m twine upload --repository testpypi dist/*

# Test installation from TestPyPI
pip install --index-url https://test.pypi.org/simple/ chat-ideyalabs

Step 6: Upload to PyPI

python -m twine upload dist/*

Enter your PyPI API token when prompted.

👥 User Installation & Setup

For Package Users

Installation:

pip install chat-ideyalabs

Environment Setup (Required for Users):

Users need to set environment variables:

# Required: LLM configuration (set by admin)
export CHATIDEYALABS_LLM_BASE_URL=https://your-llm-endpoint.com
export CHATIDEYALABS_LLM_API_KEY=your-llm-api-key-here

# Required: MongoDB configuration for API key validation
export CHATIDEYALABS_MONGODB_URL=mongodb+srv://username:[e-mail]/database

# Optional: Logging configuration
export CHATIDEYALABS_ENABLE_LOGGING=false
export CHATIDEYALABS_LOG_SENSITIVE=false

Usage:

from chat_ideyalabs import ChatIdeyalabs

# User provides their API key
chat = ChatIdeyalabs(api_key="user-api-key-from-admin")
response = chat.invoke("Hello, world!")
print(response.content)

🔧 Administrator Setup

Server Environment Variables

Set these on your API server:

# Production environment
export CHATIDEYALABS_LLM_BASE_URL=https://your-llm-endpoint.com
export CHATIDEYALABS_LLM_API_KEY=your-llm-api-key-here
export CHATIDEYALABS_MONGODB_URL=mongodb+srv://username:[e-mail]/database
export CHATIDEYALABS_MONGODB_DATABASE=your_database
export CHATIDEYALABS_MONGODB_COLLECTION=apiKeys
export CHATIDEYALABS_ENABLE_LOGGING=true
export CHATIDEYALABS_LOG_SENSITIVE=false

Docker Deployment Example

# Dockerfile
FROM python:3.10-slim

ENV CHATIDEYALABS_LLM_BASE_URL=https://your-llm-endpoint.com
ENV CHATIDEYALABS_LLM_API_KEY=your-llm-api-key-here
ENV CHATIDEYALABS_MONGODB_URL=mongodb+srv://username:[e-mail]/database

COPY . /app
WORKDIR /app
RUN pip install -e .

CMD ["uvicorn", "chat_ideyalabs.api.main:app", "--host", "0.0.0.0", "--port", "8000"]

📋 User Documentation Template

Create this for your users:

ChatIdeyalabs Package Usage Guide

Installation:

pip install chat-ideyalabs

Environment Setup:

# Set the MongoDB connection (provided by admin) 
export CHATIDEYALABS_MONGODB_URL=mongodb+srv://username:[e-mail]/database
export CHATIDEYALABS_LLM_BASE_URL=https://your-llm-endpoint.com
export CHATIDEYALABS_LLM_API_KEY=your-llm-api-key-here

Get API Key: Contact your administrator to get an API key.

Usage:

from chat_ideyalabs import ChatIdeyalabs

# Initialize with your API key
chat = ChatIdeyalabs(
    api_key="your-api-key-here",
    response_format={"type": "json_object"},
    temperature=0.7
)

# Basic usage
response = chat.invoke("What is AI?")
print(response.content)

# Async usage
response = await chat.ainvoke("Explain machine learning")
print(response.content)

# Streaming
async for chunk in chat.astream("Write a poem"):
    print(chunk, end="", flush=True)

🔒 Security Benefits

✅ Sensitive data hidden - All secrets in environment variables
✅ No hardcoded credentials - Package safely distributed
✅ User authentication - Each user needs valid API key
✅ Configurable logging - Can disable/mask sensitive data
✅ Easy deployment - Environment-based configuration

🚨 Important Notes

• Never commit .env files to version control
• Always test on TestPyPI first before publishing to PyPI
• Users need environment setup - provide clear instructions
• API keys expire - implement key rotation if needed
• Monitor usage - track package downloads and API usage

📈 Version Management

• Patch: Bug fixes (0.1.0 → 0.1.1)
• Minor: New features (0.1.0 → 0.2.0)
• Major: Breaking changes (0.1.0 → 1.0.0)

🆘 Troubleshooting

Publishing Issues

• Check PyPI API token
• Verify package name availability
• Ensure version number is incremented

User Issues

• Verify environment variables are set
• Check API key validity
• Confirm validation endpoint is accessible