ell-llm-prompting/README.md

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ell is a lightweight, functional prompt engineering framework built on a few core principles:

1. Prompts are programs, not strings.

Prompts aren't just strings; they are all the code that leads to strings being sent to a language model. In ell we think of one particular way of using a language model as a discrete subroutine called a language model program.

import ell

@ell.lm(model="gpt-4o")
def hello(world : str):
    """You are a helpful assistant that writes in lower case.""" # System Message
    return f"Say hello to {world[::-1]} with a poem."    # User Message

hello("sama")

2. Prompts are actually parameters of a machine learning model.

• Add notes on serialization and lexical closures ...

3. Every call to a language model is worth its weight in credits.

...

Installation

To install ell, follow these steps:

1. Clone the repository:

   git clone https://github.com/MadcowD/ell.git
   

2. Navigate to the cloned directory:

   cd ell
   

3. Install the package using pip:

   poetry install
   poetry run build-hook
   

This will install ell and its dependencies, including building the necessary frontend components for ell-studio.

Note: Ensure you have Node.js and npm installed on your system, as they are required for building the frontend components during the installation process.

Using ell-studio

To visaulize and track your promtps over the course of prompt engineering you need to install a store into your project.

# The entry point to your program
if __name__ == "__main__":

    store = './logdir' # <---- automatically stores all LMP's invocations, and version history in a sqlite database in ./logdir
    store.install(autocommit=True) # <---- installs the store into your project so that tracking happens and change log commit messages are automatically generated using gpt-4o-mini

    your_language_model_program()
    print(greeting[::-1])

Now you can visualize your prompt engineering process just like tensorboard by using ell.studio:

python3 -m ell.studio --storage-dir ./logdir

Then visit http://localhost:8000 in your browser to see your prompt engineering process visualized.

Contributing

To contribute, install ell in development mode using:

poetry install
poetry shell

To run ell-studio you must seperately run the backend and the front-end. To start the front-end:

cd ell-studio
npm run dev
npm run start:dev

To start the backend:

python -m ell.studio --storage-dir ./logdir --dev # the dev flag is important!

You can then visualize your promtps by visiting the frontend on http://localhost:3000

v1 release todos

• LMP UX Finished entirely
• Postgres backend
• Clean all of the pydantic schema serialziation santiziation etc that we do throughout the codebase
• Full version history UX
• Package on pypy
• Docs
• Jupyter compatability
• Tests
• Convert all of our todos into issues and milestones
• Multimodality
• Output parsing.

Architecture v0

graph TD
    A[Python Code<br>with LMP calls] -->|Invokes| B[Language Model<br>Programs LMPs]
    B -->|Writes| C[Storage Backend<br>SQLite or PostgreSQL]
    D[ell Studio<br>Python Server] -->|Reads| C
    D -->|HTTP API| E[ell Studio<br>JavaScript Client]
    D -->|WebSocket Updates| E