prompttools 0.0.15

Tools for LLM prompt testing and experimentation

PromptTools

:wrench: Test and experiment with prompts, LLMs, and vector databases. :hammer:

Welcome to prompttools created by Hegel AI! This repo offers a set of free, open-source tools for testing and experimenting with prompts. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks.

In just a few lines of codes, you can test your prompts and parameters across different models (whether you are using OpenAI, Anthropic, or LLaMA models). You can even evaluate the retrieval accuracy of vector databases.

prompts = ["Tell me a joke.", "Is 17077 a prime number?"]
models = ["gpt-3.5-turbo", "gpt-4"]
temperatures = [0.0]
openai_experiment = OpenAIChatExperiment(models, prompts, temperature=temperatures)
openai_experiment.run()
openai_experiment.visualize()

To stay in touch with us about issues and future updates, join the Discord.

Quickstart

To install prompttools, you can use pip:

pip install prompttools

You can run a simple example of a prompttools locally with the following

git clone https://github.com/hegelai/prompttools.git
cd prompttools && jupyter notebook examples/notebooks/OpenAIChatExperiment.ipynb

You can also run the notebook in Google Colab

Using prompttools

There are primarily two ways you can use prompttools in your LLM workflow:

1. Run experiments on top of LLMs or vector databases in notebooks and evaluate the outputs.
2. Turn evaluations into unit tests and integrate them into your CI/CD workflow via Github Actions.

Notebooks

There are a few different ways to run an experiment in a notebook.

The simplest way is to define an experiment and an evaluation function:

from typing import Dict, List
from prompttools.experiment import OpenAIChatExperiment
from prompttools.utils import similarity

models = ["gpt-3.5-turbo", "gpt-3.5-turbo-0613"]
messages = [
    [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Who was the first president?"},
    ]
]
temperatures = [0.0, 1.0]
# You can add more parameters that you'd like to test here.

experiment = OpenAIChatExperiment(models, messages, temperature=temperatures)
experiment.run()
experiment.evaluate("similar_to_expected", similarity.evaluate, expected=["George Washington"])
experiment.visualize()

You should get a table that looks like this.

You can also manually enter feedback to evaluate prompts, see HumanFeedback.ipynb.

The rest of our notebook examples, including evaluation of vector databases, can be found here.

Using prompttools for Continuous Testing

Unit tests in prompttools are called prompttests. They use the @prompttest annotation to transform an evaluation function into an efficient unit test. The prompttest framework executes and evaluates experiments so you can test prompts over time. You can see an example test here and an example of that test being used as a Github Action here.

Persisting Results

To persist the results of your tests and experiments, you can export your Experiment with the methods to_csv, to_json, or to_lora_json. We are happy to further discuss your use cases, pain points, and what export options may be useful for you.

Setting API keys

If you would like to use a remote API (e.g. OpenAI, Anthropic), you will need to bring your own OpenAI API key. This is because prompttools makes a call to those APIs directly from your machine.

In Python, you can set:

import os
os.environ['OPENAI_API_KEY'] = ""

In command line:

OPENAI_API_KEY=sk-... python examples/prompttests/test_openai_chat.py

You will find more examples of these in our notebooks.

Documentation

Our documentation website contains the full API reference and more description of individual components. Check it out!

Supported Integrations

Here is a list of APIs that we support with our experiments:

LLMs

• OpenAI (Completion, ChatCompletion) - Supported
• LLaMA.Cpp (LLaMA 1, LLaMA 2) - Supported
• HuggingFace (Hub API, Inference Endpoints) - Supported
• Anthropic - Supported
• Google PaLM API - Supported

Vector Databases

• Chroma - Supported
• Weaviate - Supported
• Milvus - Exploratory
• Pinecone - Exploratory
• LanceDB - Exploratory

If you have any API that you'd like to see being supported soon, please open an issue or a PR to add it. Feel free to discuss in our Discord channel as well.

Installation

To install prompttools using pip:

pip install prompttools

To install from source, first clone this GitHub repo to your local machine, then, from the repo, run:

pip install .

You can then proceed to run our examples.

Frequently Asked Questions (FAQs)

1. Will this library forward my LLM calls to a server before sending it to OpenAI, Anthropic, and etc.?

   • No, the source code will be executed on your machine. Any call to LLM APIs will be directly executed from your machine without any forwarding.

2. Does prompttools store my API keys or LLM inputs and outputs to a server?

   • No, all data stay on your local machine.

Contributing

We welcome PRs and suggestions! Don't hesitate to open a PR/issue or to reach out to us via email. Please have a look at our contribution guide and "Help Wanted" issues to get started!

Usage and Feedback

We will be delighted to work with early adopters to shape our designs. Please reach out to us via email if you're interested in using this tooling for your project or have any feedback.

License

We will be gradually releasing more components to the open-source community. The current license can be found in the LICENSE file. If there is any concern, please contact us and we will be happy to work with you.