neuroprompt 0.1.3
A smart prompt compression and optimization tool for LLMs
Requires Python: >=3.8

# 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.

```bash
export OPENAI_API_KEY=<your_openai_key>
```

2. Install NeuroPrompt via `pip`:

```bash
pip install neuroprompt
```

You can find more information about OpenAI API keys [here](https://beta.openai.com/signup/).

## 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.

```python
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:

```python
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](https://opensource.org/licenses/MIT).

## Support and Contributions

We welcome contributions to NeuroPrompt! If you have suggestions or find bugs, please feel free to email `chopratejas@gmail.com`