prompt-caller 0.2.0

This package is responsible for calling prompts in a specific format. It uses LangChain and OpenAI API

PromptCaller

PromptCaller is a Python package for calling prompts in a specific format, using LangChain and the OpenAI API. It enables users to load prompts from a template, render them with contextual data, and make structured requests to the OpenAI API.

Features

• Load prompts from a .prompt file containing a YAML configuration and a message template.
• Invoke prompts using LangChain and OpenAI API, with support for structured output.

Installation

To install the package, simply run:

pip install prompt-caller

You will also need an .env file that contains your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here

Usage

1. Define a prompt file:

Create a .prompt file in the prompts directory, e.g., prompts/sample.prompt:

---
model: gpt-4o-mini
temperature: 0.7
max_tokens: 512
output:
  result: "Final result of the expression"
  explanation: "Explanation of the calculation"
---
<system>
You are a helpful assistant.
</system>

<user>
How much is {{expression}}?
</user>

This .prompt file contains:

• A YAML header for configuring the model and parameters.
• A template body using Jinja2 to inject the context (like {{ expression }}).
• Messages structured in a JSX-like format (<system>, <user>).

2. Load and call a prompt:

from prompt_caller import PromptCaller

ai = PromptCaller()

response = ai.call("sample", {"expression": "3+8/9"})

print(response)

In this example:

• The expression value 3+8/9 is injected into the user message.
• The model will respond with both the result of the expression and an explanation, as specified in the output section of the prompt.

3. Using the agent feature:

The agent method allows you to enhance the prompt's functionality by integrating external tools. Here’s an example where we evaluate a mathematical expression using Python’s eval in a safe execution environment:

from prompt_caller import PromptCaller

ai = PromptCaller()

def evaluate_expression(expression: str):
      """
      Evaluate a math expression using eval.
      """
      safe_globals = {"__builtins__": None}
      return eval(expression, safe_globals, {})

response = ai.agent(
      "sample-agent", {"expression": "3+8/9"}, tools=[evaluate_expression]
)

print(response)

In this example:

• The agent method is used to process the prompt while integrating external tools.
• The evaluate_expression function evaluates the mathematical expression securely.
• The response includes the processed result based on the prompt and tool execution.

How It Works

1. _loadPrompt: Loads the prompt file, splits the YAML header from the body, and parses them.
2. _renderTemplate: Uses the Jinja2 template engine to render the body with the provided context.
3. _parseJSXBody: Parses the message body written in JSX-like tags to extract system and user messages.
4. call: Invokes the OpenAI API with the parsed configuration and messages, and handles structured output via dynamic Pydantic models.

Build and Upload

To build the distribution and upload it to a package repository like PyPI, follow these steps:

1. Build the distribution:

   Run the following command to create both source (sdist) and wheel (bdist_wheel) distributions:

   python setup.py sdist bdist_wheel
   

   This will generate the distribution files in the dist/ directory.

2. Upload to PyPI using Twine:

   Use twine to securely upload the distribution to PyPI:

   twine upload dist/*
   

   Ensure you have configured your PyPI credentials before running this command. You can find more information on configuring credentials in the Twine documentation.

License

This project is licensed under the Apache License 2.0. You may use, modify, and distribute this software as long as you provide proper attribution and include the full text of the license in any distributed copies or derivative works.