Arcee Python Client

The Arcee Python client allows you to manage CPT, SFT, DPO, and Merge models on the Arcee Platform.

This client may be used as a CLI by invoking arcee from the terminal, or as an SDK for programmatic use by import arcee in Python.

Learn more at https://docs.arcee.ai

Installation

pip install --upgrade arcee-py

Authenticating

Your Arcee API key is obtained at https://app.arcee.ai

In bash:

export ARCEE_API_KEY=********

In notebook:

import os
os.environ["ARCEE_API_KEY"] = "********"

(Optional) To customize the URL of the Arcee platform:

export ARCEE_API_URL="https://your-url.arcee.ai"

(Optional) To specify an organization to issue requests for:

export ARCEE_ORG="my-organization"

If you do not specify an organization, your default organization will be used. You can change the default in your Arcee account settings.

Upload Context

Upload context for retriever training:

import arcee
arcee.upload_docs("pubmed", docs=[{"doc_name": "doc1", "doc_text": "foo"}, {"doc_name": "doc2", "doc_text": "bar"}])

Upload Finetuning Dataset

Method 1: Via CSV

arcee.upload_instructions_from_csv(
  "finetuning-dataset-name",
  csv_path="./your_data.csv",
  prompt_column="prompt",
  completion_column="completion"
)

Method 2: Via HF Dataset

NOTE: you will need to set HUGGINGFACE_TOKEN in your environment to use this function.

arcee.api.upload_hugging_face_dataset_qa_pairs(
    "my_qa_pairs",
    hf_dataset_id="org/dataset",
    dataset_split="train",
    data_format="chatml"
)

Using the Arcee CLI

You can easily train and use your Domain-Adapted Language Model (DALM) with Arcee using the CLI. Follow these steps post installation to train and utilize your DALM:

Upload Context

Upload a context file for your DALM like,

arcee upload context pubmed --file doc1

Upload all files in a directory like,

arcee upload context pubmed --directory docs

Upload any combination of files and directories with,

arcee upload context pubmed --directory some_docs --file doc1 --directory more_docs --file doc2

Note: The upload command ensures only valid and unique files are uploaded.

Train your DALM:

Train your DALM with any uploaded context like,

arcee train medical_dalm --context pubmed
# wait for training to complete...

DALM Generation:

Generate text completions from a model like,

arcee generate medical_dalm --query "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"

DALM Retrieval:

Retrieve documents for a given query and to view them or plug into a different LLM like,

arcee retrieve medical_dalm --query "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"

Contributing

We use invoke to manage this repo. You don't need to use it, but it simplifies the workflow.

Set up the repo

git clone https://github.com/arcee-ai/arcee-python && cd arcee-python
# optionally setup your virtual environment (recommended)
python -m venv .venv && source .venv/bin/activate
# install repo
pip install invoke
inv install

Format, lint, test

inv format  # run black and ruff
inv lint    # black check, ruff check, mypy
inv test    # pytest

Publishing

We publish in this repo by creating a new release/tag in github. On release, a github action will publish the __version__ of arcee-py that is in arcee/__init__.py

So you need to increase that version before releasing, otherwise it will fail

To create a new release

1. Open a PR increasing the __version__ of arcee-py. You can manually edit it or run inv version
2. Create a new release, with the name being the __version__ of arcee-py

Manual release [not recommended]

We do not recommend this. If you need to, please make the version number an alpha or beta release.
If you need to create a manual release, you can run inv build && inv publish