Promptix

A Python library for managing and using prompts with Promptix Studio integration. Promptix makes it easy to manage, version, and use prompts in your applications with a built-in web interface.

Features

• 🎯 Built-in Promptix Studio - Visual prompt management interface (access via promptix studio)
• 🔄 Version Control - Track changes with live/draft states for each prompt
• 🔌 Simple Integration - Easy-to-use Python interface
• 📝 Variable Substitution - Dynamic prompts using {{variable_name}} syntax
• 🤖 LLM Integration - Direct integration with OpenAI and other LLM providers
• 🏃 Local First - No external API dependencies
• 🎨 Web Interface - Edit and manage prompts through a modern UI
• 🔍 Schema Validation - Automatic validation of prompt variables and structure

Installation

# Install from PyPI
pip install promptix

Quick Start

1. Launch Promptix Studio to manage your prompts:

promptix studio

This opens Promptix Studio in your default browser at localhost:8501.

2. Use prompts in your code:

from promptix import Promptix

# Simple prompt with variables
prompt = Promptix.get_prompt(
    prompt_template="Greeting",
    user_name="John Doe"
)
print(prompt)  # Output: Hello John Doe! How can I help you today?

# Advanced prompt with multiple variables
support_prompt = Promptix.get_prompt(
    prompt_template="CustomerSupport",
    user_name="Jane Smith",
    issue_type="password reset",
    technical_level="intermediate",
    interaction_history="2 previous tickets about 2FA setup"
)

OpenAI Integration

Promptix provides seamless integration with OpenAI's chat models:

from promptix import Promptix
import openai

client = openai.OpenAI()

# Prepare model configuration with conversation memory
memory = [
    {"role": "user", "content": "I'm having trouble resetting my password"},
    {"role": "assistant", "content": "I understand you're having password reset issues. Could you tell me what happens when you try?"}
]

model_config = Promptix.prepare_model_config(
    prompt_template="CustomerSupport",
    user_name="John Doe",
    issue_type="password reset",
    technical_level="intermediate",
    interaction_history="2 previous tickets about 2FA setup",
    issue_description="User is unable to reset their password after multiple attempts",
    custom_data={"product_version": "2.1.0", "subscription_tier": "standard"},
    memory=memory,
)

# Use the configuration with OpenAI
response = client.chat.completions.create(**model_config)

Builder Pattern

Promptix provides a fluent builder pattern interface for creating model configurations:

from promptix import Promptix
import openai

client = openai.OpenAI()

# Using builder pattern for CustomerSupport
model_config = (
    Promptix.builder("CustomerSupport")
    .with_user_name("John Doe")
    .with_issue_type("account_settings")
    .with_issue_description("User cannot access account settings page")
    .with_technical_level("intermediate")
    .with_priority("medium")
    .with_memory([
        {"role": "user", "content": "I'm having trouble with my account settings"}
    ])
    .build()
)

response = client.chat.completions.create(**model_config)

# Using builder pattern for Code Review
code_config = (
    Promptix.builder("CodeReview")
    .with_code_snippet(code_snippet)
    .with_programming_language("Python")
    .with_review_focus("Security and SQL Injection")
    .with_severity("high")
    .build()
)

# Anthropic Integration
anthropic_config = (
    Promptix.builder("CustomerSupport")
    .with_version("v5")
    .with_user_name("John Doe")
    .with_issue_type("account_settings")
    .with_memory(memory)
    .for_client("anthropic")
    .build()
)

The builder pattern provides:

• Type-safe configuration building
• Fluent interface for better code readability
• Automatic validation of required fields
• Support for multiple LLM providers
• Clear separation of configuration concerns

Advanced Usage

Version Control

# Get specific version of a prompt
prompt_v1 = Promptix.get_prompt(
    prompt_template="CustomerSupport",
    version="v1",
    user_name="John"
)

# Get latest live version (default behavior)
prompt_latest = Promptix.get_prompt(
    prompt_template="CustomerSupport",
    user_name="John"
)

Schema Validation

Promptix automatically validates your prompt variables against defined schemas:

# Schema validation ensures correct variable types and values
try:
    prompt = Promptix.get_prompt(
        prompt_template="CustomerSupport",
        user_name="John",
        technical_level="expert"  # Will raise error if not in ["beginner", "intermediate", "advanced"]
    )
except ValueError as e:
    print(f"Validation Error: {str(e)}")

Error Handling

try:
    prompt = Promptix.get_prompt(
        prompt_template="NonExistentTemplate",
        user_name="John"
    )
except ValueError as e:
    print(f"Error: {str(e)}")

Development

Setup

1. Clone the repository:

git clone https://github.com/promptix/promptix-python.git
cd promptix-python

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install development dependencies:

pip install -e ".[dev]"

Running Tests

pytest

Code Style

We use black for code formatting and isort for import sorting:

black .
isort .

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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