neuroprompt 0.1.1

A smart prompt compression and optimization tool for LLMs

NeuroPrompt

A smart prompt compression and optimization tool for Large Language Models that automatically adapts to different types of content and provides comprehensive quality evaluation.

Installation

To install NeuroPrompt, follow these steps:

1. Set up your OpenAI API key. NeuroPrompt relies on OpenAI services, so make sure you have access to the OpenAI API.

export OPENAI_API_KEY=<your_openai_key>

2. Install NeuroPrompt via pip:

pip install neuroprompt

You can find more information about OpenAI API keys here.

Features

• Smart Prompt Compression: Compresses prompts while retaining the core information, optimizing token usage.
• Content-Aware Parameter Optimization: Automatically adapts to different types of content like code, technical lists, and text.
• Comprehensive Response Quality Evaluation: Evaluates the quality of compressed responses using standard metrics.
• Cost Optimization for OpenAI API Calls: Helps reduce the cost of API calls by minimizing token counts.
• Automatic Token Counting and Cost Estimation: Estimates the number of tokens and calculates associated costs.

Quick Start

To start using NeuroPrompt, follow the example below to implement a basic prompt compression decorator.

from neuroprompt import NeuroPromptCompress
from openai import OpenAI

@NeuroPromptCompress()
def chat_completion(messages, model="gpt-4o", temperature=0.7):
    client = OpenAI()
    return client.chat.completions.create(
        messages=messages,
        model=model,
        temperature=temperature
    )

# Example usage
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Your prompt here..."}
]

response = chat_completion(messages=messages)
print(response)

Documentation

Basic Usage

NeuroPrompt provides two main decorators that help you utilize prompt compression and quality evaluation features:

1. NeuroPromptCompress: Use this for basic prompt compression without any extra evaluation. It helps to optimize token usage and cost without checking response quality.
2. NeuroPromptCompressWithEval: This decorator compresses prompts while also evaluating the compressed response's quality. It provides detailed insights into metrics such as relevance, coherence, and accuracy.

Advanced Features

Quality Metrics

The quality of compressed responses is measured using various standard metrics:

• ROUGE Scores: Measures the overlap of key elements between original and compressed responses.
• BLEU Score: Evaluates the similarity of the compressed response to the original text based on word overlap.
• Semantic Similarity: Uses embeddings to calculate the semantic consistency of the compressed response.
• Information Coverage: Assesses whether critical information is retained in the compressed output.
• Expert Evaluation (using GPT-4o): Applies GPT-4o to evaluate the quality of the response in terms of accuracy, completeness, and coherence.

Examples

Compressing a Chat with Evaluation

To use NeuroPrompt with evaluation metrics:

from neuroprompt import NeuroPromptCompressWithEval
from openai import OpenAI

@NeuroPromptCompressWithEval()
def chat_completion(messages, model="gpt-4o", temperature=0.7):
    client = OpenAI()
    return client.chat.completions.create(
        messages=messages,
        model=model,
        temperature=temperature
    )

# Example usage with evaluation
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain quantum computing in simple terms."}
]

response = chat_completion(messages=messages)
print(response)

The above code will compress the prompt and provide comprehensive quality metrics, ensuring that the response remains relevant and complete.

Cost Optimization

One of NeuroPrompt's key features is reducing token usage, which directly impacts the cost of using models like GPT-4. By reducing prompt size, NeuroPrompt helps make OpenAI API usage more affordable.

License

Copyright © 2024 Tejas Chopra.

All rights reserved.

This is proprietary software. Unauthorized copying, modification, distribution, or use of this software, in whole or in part, is strictly prohibited.

Third-Party Components

This software uses LLMLingua under the MIT license. See the LICENSE file for full terms.

For more details about MIT licensing, visit the Open Source Initiative.

Support and Contributions

We welcome contributions to NeuroPrompt! If you have suggestions or find bugs, please feel free to email [e-mail]