Makes working with OpenAI's GPT API and other LLM's super easy
Project description
JustAI
Package to make working with Large Language models in Python super easy.
Author: Hans-Peter Harmsen (hp@harmsen.nl)
Current version: 3.4.1
Installation
- Install the package:
python -m pip install justai
- Create an OpenAI acccount (for GPT3.5 / 4) here or an Anthropic account here
- Create an OpenAI api key (for Claude) here or an Anthropic api key here
- 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
Usage
from justai import Agent
if __name__ == "__main__":
agent = Agent('gpt-3.5-turbo')
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-preview
Anthropic models like claude-3-opus-20240229 and claude-3-sonnet-20240229
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
Starts an interactive session. In the session you dan chat with GPT-4 or another model.
Returning json
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 define a return type, just pass return_json=True to agent.chat().
See the example code for more details.
Interactive
python examples/interactive.py
Starts an interactive session. In the session you dan chat with GPT-4 or another model.
Special commands
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 |
| :stop ["\n", " Human:", " AI:"] | Up to 4 sequences where the API will stop generating further tokens |
| :bye | quits but saves the conversation first |
| :exit or :quit | quits the program |
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