tamu-chat 0.1.0

A Python library for interacting with the TAMU Chat AI API

TAMU Chat API Python Library

A simple, clean Python library for interacting with the TAMU Chat API. This library provides an easy-to-use interface for generating chat completions using various LLM models.

Features

• 🚀 Simple and intuitive API
• 🔒 Secure API key management via environment variables
• 📦 Type hints throughout for better IDE support
• 🎯 Support for multiple models (o3, GPT-4, Claude, Gemini, etc.)
• 🔄 Handles both JSON and SSE streaming responses
• ⚡ Built-in error handling with custom exceptions
• 📝 Full response objects with easy access to text content

Installation

pip install tamu-chat

Or install from source:

git clone https://github.com/yourusername/tamu-chat.git
cd tamu-chat
pip install -e .

Quick Start

Basic Usage

from tamu_chat import TAMUChatClient

# Initialize client with API key
client = TAMUChatClient(api_key="sk-your-api-key-here")

# Simple string input
result = client.chat_completion("What is the capital of India?")
print(result.text)
# Output: "The capital of India is New Delhi."

# Access full response
print(result.model)  # Model used
print(result.id)     # Response ID
print(result.full_response)  # Complete API response

Using Environment Variables

You can set the API key via environment variable or .env file:

Option 1: Environment Variable

import os
os.environ['TAMU_CHAT_API_KEY'] = 'sk-your-api-key-here'

from tamu_chat import TAMUChatClient

# API key will be automatically loaded from environment
client = TAMUChatClient()

Option 2: .env File (Recommended)

Create a .env file in your project root:

TAMU_CHAT_API_KEY=sk-your-api-key-here
TAMU_CHAT_BASE_URL=https://chat-api.tamu.ai/openai

Then use the client:

from tamu_chat import TAMUChatClient

# API key will be automatically loaded from .env file
client = TAMUChatClient()

Note: The library uses python-dotenv to automatically load .env files. Make sure to add .env to your .gitignore to keep your API key secure!

Advanced Usage

# Different model
result = client.chat_completion(
    "Explain quantum computing",
    model="protected.gpt-4o"
)

# With bypass filter
result = client.chat_completion(
    "What is Python?",
    bypass_filter=True
)

# Multi-turn conversation
messages = [
    {"role": "user", "content": "My name is Alice."},
    {"role": "assistant", "content": "Hello Alice! How can I help you?"},
    {"role": "user", "content": "What's my name?"}
]
result = client.chat_completion(messages)
print(result.text)  # "Your name is Alice."

# List available models
models = client.list_models()
for model in models:
    print(model['id'])

API Reference

TAMUChatClient

Main client class for interacting with the TAMU Chat API.

__init__(api_key=None, base_url=None)

Initialize the client.

Parameters:

• api_key (str, optional): API key for authentication. If not provided, will try to get from TAMU_CHAT_API_KEY environment variable.
• base_url (str, optional): Base URL for the API. Defaults to https://chat-api.tamu.ai/openai.

Raises:

• AuthenticationError: If API key is not provided and not found in environment.

chat_completion(messages, model="protected.o3", bypass_filter=False, stream=False)

Generate chat completion.

Parameters:

• messages (str | List[Dict[str, str]]): String or list of message dictionaries with 'role' and 'content' keys.
• model (str): Model ID to use. Default: "protected.o3".
• bypass_filter (bool): Whether to bypass content filtering. Default: False.
• stream (bool): Whether to stream response. Default: False.

Returns:

• If stream=False: ChatCompletionResponse object
• If stream=True: Iterator of chunk dictionaries

Raises:

• APIError: If API returns an error
• AuthenticationError: If authentication fails
• InvalidModelError: If model is invalid
• NoResponseError: If no valid response received
• NetworkError: If network error occurs

list_models()

List available models from the API.

Returns:

• List of model dictionaries

Raises:

• APIError: If API returns an error
• AuthenticationError: If authentication fails

ChatCompletionResponse

Response object returned by chat_completion().

Attributes:

• text (str): The actual response text content
• full_response (dict): Complete API response object
• model (str): Model name used
• id (str): Response ID

Error Handling

The library provides custom exceptions for better error handling:

from tamu_chat import (
    TAMUChatClient,
    APIError,
    AuthenticationError,
    InvalidModelError,
    NetworkError
)

client = TAMUChatClient(api_key="sk-...")

try:
    result = client.chat_completion("Hello")
except AuthenticationError:
    print("Invalid API key")
except InvalidModelError:
    print("Model not found")
except NetworkError:
    print("Network error occurred")
except APIError as e:
    print(f"API error: {e}")

Available Models

Common model IDs include:

• protected.o3
• protected.gpt-4o
• protected.gpt-4.1
• protected.gpt-5
• protected.Claude 3.5 Sonnet
• protected.Claude Opus 4.1
• protected.gemini-2.0-flash

To get a complete list of available models:

models = client.list_models()
for model in models:
    print(model['id'])

Configuration

Environment Variables

• TAMU_CHAT_API_KEY: Your API key (required if not passed to constructor)
• TAMU_CHAT_BASE_URL: Base URL for the API (optional, defaults to https://chat-api.tamu.ai/openai)

Requirements

• Python 3.11+
• requests
• python-dotenv

Development

To contribute to this project:

1. Clone the repository
2. Install development dependencies:

   pip install -e ".[dev]"
   

3. Create a new branch in your forked repository for your changes.

   git checkout -b <branch_name>
   

4. Add code to the library and test it.

   git add .
   git commit -m "Add <feature_name>" (Describe the changes you made)
   

5. Run tests:

   pytest tests/<test_file>.py
   

6. Test the library and raise a pull request.

   git push forked-repository-name <branch_name>
   

7. Wait for the pull request to be reviewed and merged.

License

MIT License

Support

For issues and questions, please open an issue on GitHub.

Creator

• Dheeraj Mudireddy ([e-mail])

Changelog

0.1.0 (2025-11-20)

• Initial release
• Basic chat completion support
• Model listing support
• Custom exception handling
• Environment variable configuration