Introduction
The LLMP (Large Language Model Programming) framework offers developers an integrated system to employ large language models effectively. Simplifying various aspects, from prompt crafting and validation to job management, LLMP aims to streamline generative NLP tasks for developers.
Documentation Overview
Before diving deep, here's a brief guide on how this documentation is structured to help you navigate:
Modules: Detailed explanations of the core components and services of LLMP. If you're keen on understanding the backbone of LLMP, this section is for you.
How Tos: Practical guides showcasing the implementation of common tasks using LLMP. If you're looking for step-by-step guides, start here.
API reference: Dive into the specifics. Here, you'll find comprehensive information about classes, functions, and methods in the LLMP framework.
Get started: A beginner's guide to setting up and taking the first steps with LLMP. If you're new, this section will help you hit the ground running.
Understood. Let's restructure the introduction to include a separate "Navigation Guide" or "Documentation Overview" section that briefly explains the purpose of each major section.
Features
High-Level Abstractions: Effortlessly create, store, and optimize prompts, enabling smoother integrations into software applications.
Structured Outputs: By leveraging the YAML format, LLMP achieves reduced token usage and structures that resonate with large language models.
Efficient Initialization: The Program class in LLMP transforms the way tasks are initiated. By naming a job and outlining input/output models, tasks are set up swiftly.
StructGenie Integration: A synergy with StructGenie ensures robust type validation and the ability to incorporate intricate rules into the output model.
Management & Optimization System: Beyond prompt creation, LLMP excels with features like performance logging, structured storage, and prompt/job optimization.
Type Validation: Assures dependable structured outputs while minimizing the need for additional LLM calls.
Incorporation of Advanced Rules: Adaptability is key. From multiple-choice queries to loops, LLMP's tasks can accommodate diverse needs.
Ongoing Development: The LLMP framework is under continuous improvement, aligning with the ever-evolving domain of generative NLP.
Quick Install
Prerequisites
Ensure you have:
- Python 3.8 or higher.
- An environment suitable for Python projects.
Installation Process
Install LLMP with:
pip install pyllmp
Verification
After installation, verify the setup:
import llmp
print(llmp.__version__)
This will display the LLMP version you've installed.