Prompete 0.0.2

Prompting and templating

Prompete

Prompete is a simple Python SDK for Large Language Models (LLMs) that combines LiteLLM, LLMEasyTools, and Jinja2 templates to create a flexible system for managing prompts and chat interactions. At its core, the Chat class maintains chat history and handles prompt rendering, llm replies and processing of tool calls.

The key concept behind templating in Prompete is to bridge the gap between traditional software and LLMs. While conventional software primarily deals with structured data, LLMs require both data and instructions on how to interpret and use that data. Prompete addresses this by separating prompts into two distinct components:

1. Data: The raw information generated by traditional software.
2. Template: Instructions for the LLM on how to interpret and reason about the data.

This separation allows for a more natural and flexible interface between traditional software systems and LLMs, enabling developers to easily manage the semantic interpretation step required for effective LLM integration.

You can start working in Prompete with simple string prompts without using templates, then progressively adopt more advanced features as your needs evolve.

Features

• Template-based prompt generation using Jinja2
• Integration with various LLM APIs through LiteLLM
• Easy function calling with LLMEasyTools
• Conversation management with the Chat interface
• System prompts and custom prompt roles

Installation

Install Prompete using pip:

pip install prompete

Quick Start

First you need to set up your API credentials in your environment variables. Depending on the model you choose, you'll need to set:

• OPENAI_API_KEY for OpenAI models
• ANTHROPIC_API_KEY for Anthropic models

For example:

export OPENAI_API_KEY='your-openai-api-key-here'

Basic example

from prompete import Chat

model = "gpt-4o-mini"  # OpenAI model

# Create a Chat instance with a system prompt
chat = Chat(
    model=model,
    system_prompt="You are a helpful assistant specializing in Python programming."
)

# Start the conversation
user_message = "What's the difference between a list and a tuple in Python?"
response = chat(user_message)

print("User:", user_message)
print("AI:", response)

Templating example

A full example of templating with Prompete can be found in the examples/templating.py file.

To use templating with Prompete, you need to create a Jinja2 Environment. You can use all features of Jinja2 like multiple directories to search for template files, add custom filters, etc.

# Create a Jinja2 Environment instance with multiple template directories
renderer = Environment(
    loader=ChoiceLoader([
        FileSystemLoader(os.path.join(current_dir, "templates")),
    ])
)

The data is passed to the template as a subclass of Prompt.

@dataclass(frozen=True)
class TaskPrompt(Prompt):
    user_name: str
    language: str
    task: str

task_prompt = TaskPrompt(
    user_name="Alice",
    language="Python",
    task="write a function to calculate the factorial of a number"
)

print(chat(task_prompt))

The prompt tempalte is found by looking up the class name in the templates defined in the renderer. The template can use the prompt fields as variables.

Function Calling

Prompete integrates LLMEasyTools for easy function calling. Here is the common weather example:

from prompete import Chat

def get_current_weather(location: str, unit: str = "celsius") -> str:
    """Get the current weather in a given location"""
    # In a real scenario, you would call an actual weather API here
    return {
        "location": location,
        "temperature": 22,
        "unit": unit,
        "forecast": ["sunny", "windy"]
    }

# Create a Chat instance
chat = Chat(model="gpt-4o-mini")

# Define the user's question
user_question = "What's the weather like in London?"
content = chat(user_question, tools=[get_current_weather])

# Process the response
outputs = chat.process()

# Print the results
print("User:", user_question)
print("Content of the response:", content)
# There might be more than one function call in the response - this is why output is a list
print("Weather data:", outputs[0] if outputs else "No weather data retrieved")

Key Concepts

• Chat: The main class for managing conversations and interacting with LLMs.
• Prompt: Base class for creating custom prompt types.
• renderer: Jinja2 Environment for rendering prompts with dynamic content.

Contributing

We welcome contributions to Prompete! Please follow these steps:

1. Fork the repository
2. Create a new branch for your feature or bug fix
3. Make your changes and write tests if applicable
4. Submit a pull request with a clear description of your changes

For major changes, please open an issue first to discuss the proposed changes.

Writing Test Cases

We strongly encourage writing test cases for both bug reports and feature requests:

• For bugs: Include a test case that reproduces the issue, showing expected vs. actual behavior.
• For features: Provide test cases describing the desired functionality, including inputs and expected outputs.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Support

If you encounter any issues or have questions, please open an issue on our GitHub repository.

Acknowledgements

Prompete is built upon several excellent libraries:

• LiteLLM for universal LLM API support
• Jinja2 for powerful templating capabilities

We're grateful to the maintainers and contributors of these projects.