structgenie 0.1.17

LLM Generation Framework for structured outputs with type validation

StructGenie Documentation

Overview

StructGenie is a sophisticated yet easy-to-use engine designed for the generation and validation of structured outputs from large language models (LLMs). The uniqueness of StructGenie lies in its ability to streamline the configuration process through a single, intuitive input template. This template, supporting a myriad of syntaxes, acts as the central hub for defining the generation task, its constraints, and the expected structured outputs.

Key Features:

1. Unified Template Configuration: Simplify your generation tasks using a single input template. This holistic approach eliminates the need to juggle multiple configuration files or settings.
2. Multifaceted Syntax Support: Catering to a wide range of use-cases, StructGenie supports multiple syntaxes within its template, ensuring flexibility and ease of use.
3. Dynamic Output Parsing: Leveraging the power of the LLMs, StructGenie parses generated outputs into a structured dictionary format, enabling straightforward data extraction and further processing.
4. Integrated Validation: With embedded validation rules within the template, StructGenie ensures the outputs conform to the desired structure and type, guaranteeing both accuracy and consistency.
5. Framework-agnostic Execution: Thanks to a versatile executor driver, StructGenie seamlessly integrates with various LLM API frameworks.
6. Example Selector: Pass a path to an example file or list your examples directly in the prompt. The ExampleSelector will take care of selecting the best examples according to the token limitations.
7. YAML Syntax: Using YAML as the default syntax for examples, input and outputs instructions, StructGenie provides a clean and intuitive template structure, making it easy to learn and use, while minimizing the token usage.
8. System Instructions: Call system instructions/functions directly from the template, allowing for a more streamlined generation process e.g.: {{!system:load_file("examples.yaml")}}

Installation Guide

To embark on your journey with StructGenie:

Note: Python 3.9 or higher is recommended for a smooth experience with StructGenie. Make sure to have langchain installed as well.

Option 1: Installation from GitHub

# Clone the repository (assuming it's hosted on GitHub)
git clone https://github.com/elokus/StructGenie.git

# Transition into the project directory
cd StructGenie

# Install dependencies
pip install -r requirements.txt

Option 2: Installation from PyPI

# Install the project (ensure you have a Python environment set up)
pip install structgenie

Usage Examples

Generation from a simple Template

from structgenie.engine import StructEngine

# Define your input template
template = """
Generate a Person for each of the following family roles:
{family_roles}

Begin!
Family Members: {family_roles}
---
Family: <list (for each $role in {family_roles})>
Family.$role: <dict>
Family.$role.name: <str>
Family.$role.age: <int>
"""

# Initialize StructGenie with the template
genie = StructEngine.from_template(template)

# Supply your inputs in dictionary format
inputs = {
    "family_roles": ["Father", "Mother", "Son", "Daughter"]
}

# Run the generation task
result, metrics = genie.run(inputs)

# Enjoy your structured result!
print(result)

Which returns:

{
  'family': [
    {
      'father': {
        'name': 'John',
        'age': 40
      }
    },
    {
      'mother': {
        'name': 'Emily',
        'age': 38
      }
    },
    {
      'son': {
        'name': 'Ethan',
        'age': 12
      }
    },
    {
      'daughter': {
        'name': 'Olivia',
        'age': 8
      }
    }
  ]
}

Template Structure

The input configuration of StructGenie is done by templates. The template is divided into four sections:Instruction, Examples, Input and Output. You can additionally pass also an System Config for setting template internal variables and setting attributes for the underlying engine. You can select different styles for your template to wrap each sections:

SEPERATOR_STYLE: === {section} ===, === {section} end ===
HTML_TAG_STYLE: <{section}>, </{section}>
GUIDANCE_STYLE: {{#{section}~}}, {{~/{section}}}
MARKDOWN_STYLE: # {section}, # ---
MARKDOWN_STYLE2: # {section}, # {section} end

You do not need to use the end tags for the sections. The engine will automatically detect the end of the section. The default style is the MARKDOWN_STYLE. As shown in the above example. When no instruction tag is present the first paragraph is parsed as Instruction section. The Input Section can also be introduced by simply adding 'Begin!' beforehand. The Output Section can be introduced by adding '---' after the Input Section at the end of your template. The recommanded template should have the following structure:

# Instruction
...
# Examples
...
# Input
...
# Output
...

RoadMap

Our development plans for StructGenie include:

1. Version 0.0.1: Initial release with core functionalities.
2. Validation Rules: Adding a set of validation rules for more robust structured outputs.
3. OptimizationEngine Release: Launching the OptimizationEngine to enhance template effectiveness.

OptimizationEngine (Moved to separate Project -> LLMP)

OptimizationEngine is an addition to StructGenie that aims to test and improve templates for LLM generation tasks.

How it Works:

1. Testing: The engine uses random examples from the provided template. These examples contain both the input and the expected output.
2. Evaluation: The generation tasks are run to check if the LLM can produce the expected output.
3. Optimization: If the output is not as expected, OptimizationEngine adjusts the prompt's instruction and can create more examples. The goal is to improve the LLM's output accuracy.

The OptimizationEngine provides a methodical approach to refining templates for better results from language models.

Contributing

We welcome contributions! Please see our contributing guide for more details.

License

StructGenie is open source software licensed under the MIT license.