ppromptor 0.1.3

An autonomous agent framework for prompt engineering

Prompt-Promptor: An Autonomous Agent Framework for Prompt Engineering

Prompt-Promptor(or shorten for ppromptor) is a Python library designed to automatically generate and improve prompts for LLMs. It draws inspiration from autonomous agents like AutoGPT and consists of three agents: Proposer, Evaluator, and Analyzer. These agents work together with human experts to continuously improve the generated prompts.

🚀 Features:

• 🤖 The use of LLMs to prompt themself by giving few samples.

• 💪 Guidance for OSS LLMs(eg, LLaMA) by more powerful LLMs(eg, GPT4)

• 📈 Continuously improvement.

• 👨‍👨‍👧‍👦 Collaboration with human experts.

• 💼 Experiment management for prompt engineering.

• 🖼 Web GUI interface.

• 🏳️‍🌈 Open Source.

Warning

• This project is currently in its earily stage, and it is anticipated that there will be major design changes in the future.

• The main function utilizes an infinite loop to enhance the generation of prompts. If you opt for OpenAI's ChatGPT as Target/Analysis LLMs, kindly ensure that you set a usage limit.

Concept

A more detailed class diagram could be found in doc

Installations

From Github

1. Clone Repository from Github

git clone https://github.com/pikho/ppromptor.git
cd ppromptor

2. Install Required Packages

pip install -r requirements.txt

3. Add Package Path to PythonPath

export PYTHONPATH=PYTHONPATH:<path_to_repo>/ppromptor

4. Run Tests

pip install -r requirements_test.txt

pytest

Running Local Model(WizardLM)

1. Install Required Packages

pip install requirements_local_model.txt

2. Test if WizardLM can run correctly

cd <path_to_ppromptor>/ppromptor/llms
python wizardlm.py

Usage

1. Start the Web App

cd <path_to_ppromptor>
streamlit run ui/app.py

2. Load the Demo Project Load examples/antonyms.db(default) for demo purposes. This demonstrates how to use ChatGPT to guide WizardLM to generate antonyms for given inputs.

3. Configuration In the Configuration tab, set Target LLM as wizardlm if you can infer this model locally. Or choose both Target LLM and Analysis LLM as chatgpt. If chatgpt is used, please provide the OpenAI API Key.

4. Load the dataset The demo project has already loaded 5 records. You can add your own dataset.(Optional)

5. Start the Workload Press the Start button to activate the workflow.

6. Prompt Candidates Generated prompts can be found in the Prompt Candidates tab. Users can modify generated prompts by selecting only 1 Candidate, then modifying the prompt, then Create Prompt. This new prompt will be evaluated by Evaluator agent and then keep improving by Analyzer agent. By selecting 2 prompts, we can compare these prompts side by side.

Contribution

We welcome all kinds of contributions, including new feature requests, bug fixes, new feature implementation, examples, and documentation updates. If you have a specific request, please use the "Issues" section. For other contributions, simply create a pull request (PR). Your participation is highly valued in improving our project. Thank you!