promptscript-python 0.4.0

A library for writing LLM prompts as Python functions

PromptScript

PromptScript bridges natural language and programming by integrating English with Python. It introduces a single @prompt decorator along with Python conventions to seamlessly integrate LLM interactions into your code.

Key Principles

1. Lowering Friction for Experimentation: PromptScript aims to reduce the overhead of working with LLMs in Python. By keeping syntax minimal, you can move quickly from idea to functioning prototype.

2. Modularity and Ecosystem Integration: We build on Python’s extensive libraries and tooling. You can incorporate data handling, Pydantic models, and other Python-based systems while relying on standard Python packaging approaches.

3. User-Focused Experience: PromptScript is designed to integrate with your existing workflow—whether that’s a code editor (like VS Code), a notebook environment, or a custom application. You write Python code; PromptScript handles the LLM aspects in a straightforward way.

Key Features

• @prompt Decorator: Wrap Python functions that contain prompt logic.
• Type-Safe Prompting: Optionally use type hints or Pydantic models for safer, more reliable structured outputs.
• Multi-Modal Inputs: Use images, text, or binary attachments alongside your prompts.
• Built-in Observability: Collect execution traces and debug logs (through our tracer and persistence modules).
• Persistent Sessions: Save execution traces in JSON format for later analysis.
• Minimal Dependencies: The library is self-contained and fits well in existing Python development environments.

Setup

Below are instructions for a typical setup using Python 3.10 or higher.

Using pyenv, venv, and pip (Ubuntu example)

1. Install System Dependencies

   sudo apt update
   sudo apt install -y make build-essential libssl-dev zlib1g-dev \
       libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
       libncurses5-dev libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev \
       python3-openssl git
   

2. Install pyenv Follow the official pyenv installation instructions.

3. Set up Python environment:

   cd promptscript
   pyenv install
   pyenv local
   python -m venv venv
   source venv/bin/activate
   pip install --upgrade pip
   pip install -r requirements.txt
   

Getting Started on Windows

1. Clone the repository:

   git clone https://github.com/jflam/promptscript.git
   cd promptscript
   

2. Create and activate a virtual environment:

   python -m venv .venv
   .venv\Scripts\activate.bat
   

3. Install the dependencies:

   pip install -r requirements.txt
   

4. Optionally install PromptScript in editable mode:

   pip install -e .
   

   This installs symlinks so that editing the source immediately reflects in your environment.

5. Set up your OpenAI API key (if you plan to use OpenAI as a provider):

   setx OPENAI_API_KEY <your-api-key-here>
   

   (Restart the terminal if needed to pick up environment changes.)

Basic Example

With PromptScript, you define a function that uses the @prompt decorator and write your prompt inside. When you call that Python function, the library manages the LLM interaction. For example:

from pscript import gen, prompt

@prompt
def tell_joke() -> str:
    """
    Write a short dad joke about Java.
    """
    return gen("Tell me a dad joke about Java.")

if __name__ == "__main__":
    joke = tell_joke()
    print(joke)

• Use the return gen(...) syntax within your decorated function to supply the prompt text.
• When the function is called, PromptScript invokes the LLM provider (e.g. OpenAI, Anthropic, etc.).
• The result is captured and returned as a normal Python string.

Structured Output with Pydantic

You can optionally specify a Pydantic model return type for more robust type-safety and structured data handling:

from pydantic import BaseModel
from pscript import gen, prompt

class JokeData(BaseModel):
    setup: str
    punchline: str

@prompt
def typed_joke() -> JokeData:
    """
    Prompt the LLM to return a structured joke with fields setup and punchline.
    """
    return gen("Tell me a dad joke about Java in JSON format with 'setup' and 'punchline'.")

PromptScript will then parse the LLM’s JSON response directly into the JokeData model. If parsing fails, you get a Python exception right away.

Observability & Tracing

PromptScript has a built-in tracer that captures:

• The function name and timestamp
• Input data, output data, and errors
• Execution duration

This data is stored in JSON under a .promptscript/traces directory. You can customize or disable the tracer/persistence if needed. It’s useful for logging, debugging, or replaying your experiments.

Multi-Modal Support

PromptScript supports sending additional binary attachments (e.g., images) to the LLM. For example:

from pscript import gen, prompt, Attachment

@prompt
def analyze_image(image: Attachment) -> str:
    return gen("Describe what you see in this image.", attachments=[image])

if __name__ == "__main__":
    with open("example.jpg", "rb") as f:
        img_data = f.read()
    image_attachment = Attachment(data=img_data, mime_type="image/jpeg")
    result = analyze_image(image_attachment)
    print(result)

Advanced Usage

• Provider-Specific Settings: You can configure providers (OpenAI, Anthropic, Azure OpenAI, etc.) in the promptscript.yml config file.
• Environment Variables: For authentication keys, you typically set environment variables (e.g. OPENAI_API_KEY, ANTHROPIC_API_KEY).
• Concurrency: For more complex usage, you can manage concurrency or parallel calls using standard Python concurrency patterns.
• Error Handling: If a response cannot be parsed into your Pydantic model, PromptScript raises an exception right away, letting you handle the error in pure Python.

Contributing

1. Fork the repo and create a feature branch.
2. Make your changes, add tests if applicable.
3. Submit a pull request for review.

We welcome fixes, new features, and documentation improvements!