justai 3.10.2

Makes working with LLMs like OpenAI GPT, Anthropic Claude, Google Gemini and Open source models super easy

JustAI

Package to make working with Large Language models in Python super easy.

Author: Hans-Peter Harmsen ([e-mail])
Current version: 3.10.2

Installation

1. Install the package:

python -m pip install justai

2. Create an OpenAI acccount (for GPT3.5 / 4) here or an Anthropic account here or a Google account
3. Create an OpenAI api key (for Claude) here or an Anthropic api key here or a Google api key here
4. Create a .env file with the following content:

OPENAI_API_KEY=your-openai-api-key
OPENAI_ORGANIZATION=your-openai-organization-id
ANTHROPIC_API_KEY=your-anthropic-api-key
GOOGLE_API_KEY=your-google-api-key

Usage

from justai import Agent

if __name__ == "__main__":
    agent = Agent('gpt-4o')
    agent.system = "You are a movie critic. I feed you with movie titles and you give me a review in 50 words."

    message = agent.chat("Forrest Gump")
    print(message)

output

Forrest Gump is an American classic that tells the story of
a man with a kind heart and simple mind who experiences major
events in history. Tom Hanks gives an unforgettable performance, 
making us both laugh and cry. A heartwarming and nostalgic 
movie that still resonates with audiences today.

Other models

Justai can use different types of models:

OpenAI models like GPT-3.5, GPT-4-turbo and GPT-4o
Anthropic models like the Claude-3 models
Google models like the Gemini models
Open source models like Llama2-7b or Mixtral-8x7b-instruct as long as they are in the GGUF format.

The provider is chosen depending on the model name. E.g. if a model name starts with gpt, OpenAI is chosen as the provider. To use an open source model, just pass the full path to the .gguf file as the model name.

Using the examples

Install dependencies:

python -m pip install -r requirements.txt

Basic

python examples/basic.py

Shows basic use of Justai

Returning json or other types

python examples/return_types.py

You can specify a specific return type (like a list of dicts) for the completion. This is useful when you want to extract structured data from the completion.

To return json, just pass return_json=True to agent.chat() and tell the model in the prompt how you want your json to be structured.

To define a specific return type you can use the return_type parameter. Currently this works with the Google models (pass a Python type definition, returns Json) and with OpenAI (pass a Pydatic type definition, returns a Pydantic model).
See the example code for more details.

Images

python examples/vision.py

Pass images to the model. An image can either be:

• An url to an image
• The raw image data
• A PIL image

Asynchronous use

python examples/async.py

Shows how to use Justai asynchronously.

Prompt caching

python examples/prompt_caching.py

Shows how to use Prompt caching in Anthropic models.

Interactive

python examples/interactive.py

Starts an interactive session. In the session you dan chat with GPT-4 or another model.

Special commands in interactive mode

In the interactive mode you can use these special commands which each start with a colon:

Syntax	Description
:reset	resets the conversation
:load name	loads the saved conversation with the specified name
:save name	saves the conversation under the specified name
:input filename	loads an input from the specified file
:model gpt-4	Sets the AI model
:max_tokens 800	The maximum number of tokens to generate in the completion
:temperature 0.9	What sampling temperature to use, between 0 and 2
:n 1	Specifies the number answers given
:bye	quits but saves the conversation first
:exit or :quit	quits the program