kani (カニ)
kani (カニ) is a lightweight and highly hackable framework for chat-based language models with tool usage/function calling.
Compared to other LM frameworks, kani is less opinionated and offers more fine-grained customizability over the parts of the control flow that matter, making it the perfect choice for NLP researchers, hobbyists, and developers alike.
kani comes with support for the following models out of the box, with a model-agnostic framework to add support for many more:
- OpenAI Models (GPT-3.5-turbo, GPT-4, GPT-4-turbo)
- Anthropic Models (Claude, Claude Instant)
- LLaMA v2 (via Hugging Face or ctransformers) & fine-tunes
- Vicuna v1.3 (via Hugging Face) & fine-tunes
Interested in contributing? Check out our guide.
Features
- Lightweight and high-level - kani implements common boilerplate to interface with language models without forcing you to use opinionated prompt frameworks or complex library-specific tooling.
- Model agnostic - kani provides a simple interface to implement: token counting and completion generation. Implement these two, and kani can run with any language model.
- Automatic chat memory management - Allow chat sessions to flow without worrying about managing the number of tokens in the history - kani takes care of it.
- Function calling with model feedback and retry - Give models access to functions in just one line of code. kani elegantly provides feedback about hallucinated parameters and errors and allows the model to retry calls.
- You control the prompts - There are no hidden prompt hacks. We will never decide for you how to format your own data, unlike other popular language model libraries.
- Fast to iterate and intuitive to learn - With kani, you only write Python - we handle the rest.
- Asynchronous design from the start - kani can scale to run multiple chat sessions in parallel easily, without having to manage multiple processes or programs.
Quickstart
kani requires Python 3.10 or above.
First, install the library. In this quickstart, we'll use the OpenAI engine, though kani is model-agnostic.
$ pip install "kani[openai]"Then, let's use kani to create a simple chatbot using ChatGPT as a backend.
# import the library
from kani import Kani, chat_in_terminal
from kani.engines.openai import OpenAIEngine
# Replace this with your OpenAI API key: https://platform.openai.com/account/api-keys
api_key = "sk-..."
# kani uses an Engine to interact with the language model. You can specify other model
# parameters here, like temperature=0.7.
engine = OpenAIEngine(api_key, model="gpt-3.5-turbo")
# The kani manages the chat state, prompting, and function calling. Here, we only give
# it the engine to call ChatGPT, but you can specify other parameters like
# system_prompt="You are..." here.
ai = Kani(engine)
# kani comes with a utility to interact with a kani through your terminal! Check out
# the docs for how to use kani programmatically.
chat_in_terminal(ai)kani makes the time to set up a working chat model short, while offering the programmer deep customizability over every prompt, function call, and even the underlying language model.
Function Calling
Function calling gives language models the ability to choose when to call a function you provide based off its documentation.
With kani, you can write functions in Python and expose them to the model with just one line of code: the @ai_function
decorator.
# import the library
from typing import Annotated
from kani import AIParam, Kani, ai_function, chat_in_terminal
from kani.engines.openai import OpenAIEngine
# set up the engine as above
api_key = "sk-..."
engine = OpenAIEngine(api_key, model="gpt-3.5-turbo")
# subclass Kani to add AI functions
class MyKani(Kani):
# Adding the annotation to a method exposes it to the AI
@ai_function()
def get_weather(
self,
# and you can provide extra documentation about specific parameters
location: Annotated[str, AIParam(desc="The city and state, e.g. San Francisco, CA")],
):
"""Get the current weather in a given location."""
# In this example, we mock the return, but you could call a real weather API
return f"Weather in {location}: Sunny, 72 degrees fahrenheit."
ai = MyKani(engine)
chat_in_terminal(ai)kani guarantees that function calls are valid by the time they reach your methods while allowing you to focus on writing code. For more information, check out the function calling docs.
Why kani?
Existing frameworks for language models like LangChain and simpleaichat are opinionated and/or heavyweight - they edit developers' prompts under the hood, are challenging to learn, and are difficult to customize without adding a lot of high-maintenance bloat to your codebase.
We built kani as a more flexible, simple, and robust alternative. A good analogy between frameworks would be to say that kani is to LangChain as Flask (or FastAPI) is to Django.
kani is appropriate for everyone from academic researchers to industry professionals to hobbyists to use without worrying about under-the-hood hacks.
Docs
To learn more about how to customize kani with your own prompt wrappers, function calling, and more, read the docs!
Or take a look at the hands-on examples in this repo.
Demo
Want to see kani in action? Using 4-bit quantization to shrink the model, we run LLaMA v2 as part of our test suite right on GitHub Actions:
https://github.com/zhudotexe/kani/actions/workflows/pytest.yml?query=branch%3Amain+is%3Asuccess
Simply click on the latest build to see LLaMA's output!
Kani in the News
Kani will appear at the NLP Open Source Software workshop at EMNLP 2023!
We are really excited and grateful to see people talking about Kani online. We are also trending on Papers With Code, GitHub, and OSS Insight. Check out some recent articles and videos below!
- Researchers from the University of Pennsylvania Introduce Kani: A Lightweight, Flexible, and Model-Agnostic Open-Source AI Framework for Building Language Model Applications
- Unlocking AI Potential: Unveiling Kani, the Groundbreaking Open-Source Framework Revolutionizing Large Language Model Applications
- Kani: A Lightweight and Customizable Framework for Language Model Applications
- Introducing Kani (Sanskrit Word): A Game-Changing Open-Source AI Framework for Language Models
- Kani was originally named after the Japanese word for crab and coincidentally means "knowledge" in Sanskrit.
- kani: lightweight LLM framework (Japanese)
- Top Trending LLM Projects of the Week: Dive into the Future of Tech! 🚀
Who we are
The core development team is made of three PhD students in the Department of Computer and Information Science at the University of Pennsylvania. We're all members of Prof. Chris Callison-Burch's lab, working towards advancing the future of NLP.
- Andrew Zhu started in Fall 2022. His research interests include natural language processing, programming languages, distributed systems, and more. He's also a full-stack software engineer, proficient in all manner of backend, devops, database, and frontend engineering. Andrew strives to make idiomatic, clean, performant, and low-maintenance code — philosophies that are often rare in academia.
- Liam Dugan started in Fall 2021. His research focuses primarily on large language models and how humans interact with them. In particular, he is interested in human detection of generated text and whether we can apply those insights to automatic detection systems. He is also interested in the practical application of large language models to education.
- Alyssa Hwang started in Fall 2020 and is advised by Chris Callison-Burch and Andrew Head. Her research focuses on AI assistants that effectively communicate complex information, like voice assistants guiding users through instructions or audiobooks allowing users to seamlessly navigate through spoken text. Beyond research, Alyssa chairs the Penn CIS Doctoral Association, founded the CIS PhD Mentorship Program, and was supported by the NSF Graduate Research Fellowship Program.
Citation
If you use Kani, please cite us as:
@misc{zhu2023kani,
title={Kani: A Lightweight and Highly Hackable Framework for Building Language Model Applications},
author={Andrew Zhu and Liam Dugan and Alyssa Hwang and Chris Callison-Burch},
year={2023},
eprint={2309.05542},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
Acknowledgements
We would like to thank the members of the lab of Chris Callison-Burch for their testing and detailed feedback on the contents of both our paper and the Kani repository. In addition, we’d like to thank Henry Zhu (no relation to the first author) for his early and enthusiastic support of the project.
This research is based upon work supported in part by the Air Force Research Laboratory (contract FA8750-23-C-0507), the IARPA HIATUS Program (contract 2022-22072200005), and the NSF (Award 1928631). Approved for Public Release, Distribution Unlimited. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of IARPA, NSF, or the U.S. Government.