infosci-spark-client

A simple Python client for the Information Science Department Spark API.

Installation

pip install git+https://github.com/mrpeterss/infosci-spark-client.git

Quick Start

from infosci_spark_client import LLMClient

# Initialize with your API key
client = LLMClient(api_key="your-api-key")

# Non-streaming chat
response = client.chat([
    {"role": "user", "content": "What is the best seafood?"}
])
print(response["content"])

# Streaming chat
for chunk in client.chat([
    {"role": "user", "content": "What is the best seafood?"}
], stream=True):
    print(chunk["content"], end="", flush=True)

Features

• Simple and intuitive API
• Support for streaming and non-streaming responses
• Optional reasoning/thinking display
• Configurable reasoning levels
• Full type hints for better IDE support

API Reference

LLMClient

__init__(api_key: str, base_url: str = "https://4300spark.infosci.cornell.edu")

Initialize the LLM client.

Parameters:

• api_key (str): Your API key for authentication
• base_url (str): Base URL for the API (default: https://4300spark.infosci.cornell.edu)

chat(messages: List[Dict[str, str]], stream: bool = False, show_thinking: bool = False, reasoning_level: Optional[str] = None)

Send a chat request to the API.

Parameters:

• messages (List[Dict[str, str]]): List of message dictionaries with 'role' and 'content' keys
• stream (bool): Whether to stream the response (default: False)
• show_thinking (bool): Whether to include the model's reasoning process (default: False)
• reasoning_level (Optional[str]): Reasoning level - "low", "medium", or "high" (default: None)

Returns:

• If stream=False: Dict with 'content' and 'reasoning' keys
• If stream=True: Generator that yields dicts with 'content' and 'reasoning' keys

Example:

# Non-streaming with reasoning
response = client.chat(
    messages=[{"role": "user", "content": "Explain quantum computing"}],
    show_thinking=True,
    reasoning_level="high"
)
print("Content:", response["content"])
print("Reasoning:", response["reasoning"])

# Streaming
for chunk in client.chat(
    messages=[{"role": "user", "content": "Tell me a story"}],
    stream=True
):
    print(chunk["content"], end="", flush=True)

Requirements

• Python 3.7+
• requests >= 2.25.0

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Issues

If you encounter any issues, please report them on the GitHub Issues page.