llm-prompting-gen 0.1.10

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LLM Prompt Engineering Generator

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Installation

pip install llm-prompting-gen

What is LLM Prompt Engineering Generator?

Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. However, fine-tune them is not always possible or too expansive. Prompt Engineering techniques like In-Context Learning or Few-Shot Learning tries to solve this problem. Based on langchain, this library enables prompt engineering in a straightforward way.

How can I use it?

This library consists of two main building blocks LLMs and Prompt Engineering. LLMs are provided by langchain, whereas the Prompt Engineering is part of this library. Both building blocks are combined in the generators module. The class PromptEngineeringGenerator defines all requirements for a LLM to generate text based on prompt engineering techniques. If you want your output to be parsed into an pydantic dataclass checkout the class ParsablePromptEngineeringGenerator.

Simple Example

# Task: extract keywords
# Wiki article about gen AI
text_with_keywords = """
Generative artificial intelligence (also generative AI or GenAI[1]) is artificial intelligence capable of generating text, images, or other media, using generative models.[2][3][4] Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.
In the early 2020s, advances in transformer-based deep neural networks enabled a number of generative AI systems notable for accepting natural language prompts as input. These include large language model chatbots such as ChatGPT, Bing Chat, Bard, and LLaMA, and text-to-image artificial intelligence art systems such as Stable Diffusion, Midjourney, and DALL-E.
"""
from llm_prompting_gen.generators import PromptEngineeringGenerator
from llm_prompting_gen.models.prompt_engineering import PromptElements
from langchain.chat_models import ChatOpenAI

## Option 1: Simply load a JSON or YAML file following the format of llm_prompting_gen.models.prompt_engineering.PromptElements
# Make sure env variable OPENAI_API_KEY is set
llm = ChatOpenAI(temperature=0.0)
# JSON 
keyword_extractor = PromptEngineeringGenerator.from_json("templates/keyword_extractor.json", llm=llm)
# YAML
keyword_extractor = PromptEngineeringGenerator.from_yaml("templates/keyword_extractor.yaml", llm=llm)
llm_output = keyword_extractor.generate(text=text_with_keywords)

## Option 2: Simply create a Prompt Engineering class yourself
prompt_elements = PromptElements(role="You are a keyword extractor", instruction="Extract the keyword from the text delimited by '''", input="'''{text}'''")
llm = ChatOpenAI(temperature=0.0)
keyword_extractor = PromptEngineeringGenerator(llm=llm, prompt_elements=prompt_elements)
llm_output = keyword_extractor.generate(text=text_with_keywords)

How to customise the class for my own use case?

The class PromptEngineeringGenerator contains two core parts 1. LLM 2. prompt engineering dataclass. If you want to initialise the generator class for your custom use case, you need to define a prompt engineering JSON file matching the format of llm_prompting_gen.models.prompt_engineering.PromptElements.

The JSON file can contain the following prompt elements in any combination tailored to your use case:

Role: The role in which the LLM should respond

Instruction: The task of the LLM

Context: Context with relevant information to solve the task

Output Format: Description how the LLM output format should look like

Few Shot Examples: Few shot examples with optional introduction

Input: Target which the LLM should execute the task on. Could be for example a user question, or a text block to summarize.

Showcases

• Notebook Showcase: Midjourney Prompt
• Notebook Showcase: Keyword Extractor
• App: Image Gen AI Prompt Generator

License

llm-prompting-gen is distributed under the terms of the MIT license.