hmrlke / prompterator

Iterate efficiently towards more effective prompts

Prompterator

Prompterator is a Streamlit-based prompt-iterating IDE. It runs locally but connects to external APIs exposed by various LLMs.

A screenshot of the prompterator interface, with highligted features / areas of interest: 1. Data Upload, 2. Compose Prompt, 3. Run Prompt, 4. Evaluate and 5. Prompt History.

Requirements

Create a virtual environment that uses Python 3.10. Then, install project requirements:

1. pip install poetry==1.4.2

2. poetry install --no-root

How to run

1. Set environment variables (optional)

If you use PyCharm, consider storing these in your run configuration.

• OPENAI_API_KEY: Optional. Only if you want to use OpenAI models (ChatGPT, GPT-4, etc.).
• PROMPTERATOR_DATA_DIR: Optional. Where to store the files with your prompts and generated texts. Defaults to ~/prompterator-data. If you plan to work on prompts for different tasks or datasets, it's a good idea to use a separate directory for each one.

If you do not happen to have access to OPENAI_API_KEY, feel free to use the mock-gpt-3.5-turbo model, which is a mocked version of the OpenAI's GPT-3.5 model. This is also very helpful when developing Prompterator itself.

2. Run the Streamlit app

From the root of the repository, run:

make run

If you want to run the app directly from PyCharm, create a run configuration:

1. Right-click prompterator/main.py -> More Run/Debug -> Modify Run Configuration
2. Under "Interpreter options", enter -m poetry run streamlit run
3. Optionally, configure environment variables as described above
4. Save and use 🚀

Using model-specific configuration

To use the models Prompterator supports out of the box, you generally need to at least specify an API key and/or the endpoint Prompterator ought to use when contacting them.

The sections below specify how to do that for each supported model family.

OpenAI

• Set the OPENAI_API_KEY environment variable as per the docs.

Google Vertex

• Set the GOOGLE_VERTEX_AUTH_TOKEN environment variable to the output of gcloud auth print-access-token.
• Set the TEXT_BISON_URL environment variable to the URL that belongs to your PROJECT_ID, as per the docs

AWS Bedrock

To use the AWS Bedrock-provided models, a version of boto3 that supports AWS Bedrock needs to be installed.

Cohere

• Set the COHERE_API_KEY environment variable to your Cohere api key as per the docs.

Note that to use the Cohere models, the Cohere package needs to be installed as well.