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110 lines
4.5 KiB
Markdown
110 lines
4.5 KiB
Markdown
<picture>
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<source media="(prefers-color-scheme: dark)" srcset="https://docs.ell.so/_static/ell-wide-dark.png">
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<source media="(prefers-color-scheme: light)" srcset="https://docs.ell.so/_static/ell-wide-light.png">
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<img alt="ell logo that inverts based on color scheme" src="https://docs.ell.so/_static/ell-wide.png">
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</picture>
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[](https://docs.ell.so/) [](https://docs.ell.so/installation) [](https://discord.gg/vWntgU52Xb) [](https://x.com/wgussml)
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```bash
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pip install -U ell-ai
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```
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`ell` is a lightweight, functional prompt engineering framework built on a few core principles:
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### 1. Prompts are programs, not strings.
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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**.
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```python
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import ell
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@ell.simple(model="gpt-4o")
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def hello(world : str):
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"""You are a helpful assistant that writes in lower case.""" # System Message
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return f"Say hello to {world[::-1]} with a poem." # User Message
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hello("sama")
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```
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### 2. Prompts are actually parameters of a machine learning model.
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The process of prompt engineering involves many iterations, similar to the optimization processes in machine learning. Because LMPs are just functions, `ell` provides rich tooling for this process.
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`ell` provides **automatic versioning and serialization of prompts** through static and dynamic analysis and `gpt-4o-mini` **autogenerated commit messages** directly to a *local store*. This process is similar to `checkpointing` in a machine learning training loop, but it doesn't require any special IDE or editor - it's all done with regular Python code.
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### 3. Tools for monitoring, versioning, and visualization
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Prompt engineering goes from a dark art to a science with the right tools. **Ell Studio is a local, open source tool for prompt version control, monitoring, visualization**. With Ell Studio you can empiricize your prompt optimization process over time and catch regressions before it's too late.
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<picture>
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<source srcset="https://docs.ell.so/_static/ell_studio_better.webp" type="image/webp">
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<img src="docs/src/_static/ell_studio_better.webp" alt="ell studio demonstration">
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</picture>
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```bash
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ell-studio --storage ./logdir
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```
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### 4. Multimodality should be first class
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LLMs can process and generate various types of content, including text, images, audio, and video. Prompt engineering with these data types should be as easy as it is with text.
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```python
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from PIL import Image
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import ell
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@ell.simple(model="gpt-4o", temperature=0.1)
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def describe_activity(image: Image.Image):
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return [
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ell.system("You are VisionGPT. Answer <5 words all lower case."),
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ell.user(["Describe what the person in the image is doing:", image])
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]
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# Capture an image from the webcam
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describe_activity(capture_webcam_image()) # "they are holding a book"
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```
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`ell` supports rich type coercion for multimodal inputs and outputs. You can use PIL images, audio, and other multimodal inputs inline in `Message` objects returned by LMPs.
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### ...and much more!
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Read more in the [docs](https://docs.ell.so/)!
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## Installation
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To install `ell` and `ell studio`, you can use pip. Follow these steps:
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1. Open your terminal or command prompt.
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2. Run the following command to install the `ell-ai` package from PyPI:
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```bash
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pip install ell-ai
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```
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3. Verify the installation by checking the version of `ell`:
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```bash
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python -c "import ell; print(ell.__version__)"
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```
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This will install both `ell` and `ell studio` on your system, allowing you to start using the tools for prompt engineering and visualization.
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## Next Steps
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Explore the [documentation](https://docs.ell.so/) to learn more about `ell` and its features. Follow the [Getting Started guide](https://docs.ell.so/getting_started.html) to create your first Language Model Program. Join our [Discord community](https://discord.gg/vWntgU52Xb) to connect with other users and get support.
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