andrewyng · hubertusgbecker · Mar 3, 2025 · Mar 3, 2025 · Mar 3, 2025 · Mar 3, 2025
diff --git a/.gitignore b/.gitignore
@@ -4,6 +4,7 @@ __pycache__/
 env/
 .env
 .google-adc
+repomix-output.*
 
 # Testing
 .coverage

diff --git a/LICENSE b/LICENSE
@@ -1,6 +1,7 @@
 MIT License
 
 Copyright (c) 2024 Andrew Ng
+Copyright (c) 2025 Dr. Hubertus Becker
 
 Permission is hereby granted, free of charge, to any person obtaining a copy of this software and
 associated documentation files (the "Software"), to deal in the Software without restriction, including

diff --git a/README.md b/README.md
@@ -1,211 +1,32 @@
-# aisuite
+# AISuite
 
-[![PyPI](https://img.shields.io/pypi/v/aisuite)](https://pypi.org/project/aisuite/)
-[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
-
-Simple, unified interface to multiple Generative AI providers.
-
-`aisuite` makes it easy for developers to use multiple LLM through a standardized interface. Using an interface similar to OpenAI's, `aisuite` makes it easy to interact with the most popular LLMs and compare the results. It is a thin wrapper around python client libraries, and allows creators to seamlessly swap out and test responses from different LLM providers without changing their code. Today, the library is primarily focussed on chat completions. We will expand it cover more use cases in near future.
-
-Currently supported providers are -
-OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace Ollama, Sambanova and Watsonx.
-To maximize stability, `aisuite` uses either the HTTP endpoint or the SDK for making calls to the provider.
+This is my fork of the original [aisuite](https://github.com/andrewyng/aisuite) repository. Everything remains the same except that a new **GitHub Provider** has been added to enable free GPT-4 access via a GitHub Copilot subscription. This addition is inspired by [freegpt](https://github.com/B00TK1D/freegpt).
 
 ## Installation
 
-You can install just the base `aisuite` package, or install a provider's package along with `aisuite`.
-
-This installs just the base package without installing any provider's SDK.
-
-```shell
-pip install aisuite
-```
-
-This installs aisuite along with anthropic's library.
+Clone the repository and install the dependencies:
 
-```shell
-pip install 'aisuite[anthropic]'
+```bash
+git clone https://github.com/hubertusgbecker/aisuite.git
+cd aisuite
+pip install -e .
 ```
 
-This installs all the provider-specific libraries
+## Usage
 
-```shell
-pip install 'aisuite[all]'
-```
-
-## Set up
-
-To get started, you will need API Keys for the providers you intend to use. You'll need to
-install the provider-specific library either separately or when installing aisuite.
-
-The API Keys can be set as environment variables, or can be passed as config to the aisuite Client constructor.
-You can use tools like [`python-dotenv`](https://pypi.org/project/python-dotenv/) or [`direnv`](https://direnv.net/) to set the environment variables manually. Please take a look at the `examples` folder to see usage.
-
-Here is a short example of using `aisuite` to generate chat completion responses from gpt-4o and claude-3-5-sonnet.
-
-Set the API keys.
-
-```shell
-export OPENAI_API_KEY="your-openai-api-key"
-export ANTHROPIC_API_KEY="your-anthropic-api-key"
-```
-
-Use the python client.
+Use the new GitHub Provider as follows:
 
 ```python
-import aisuite as ai
-client = ai.Client()
-
-models = ["openai:gpt-4o", "anthropic:claude-3-5-sonnet-20240620"]
-
-messages = [
-    {"role": "system", "content": "Respond in Pirate English."},
-    {"role": "user", "content": "Tell me a joke."},
-]
-
-for model in models:
-    response = client.chat.completions.create(
-        model=model,
-        messages=messages,
-        temperature=0.75
-    )
-    print(response.choices[0].message.content)
-
-```
-
-Note that the model name in the create() call uses the format - `<provider>:<model-name>`.
-`aisuite` will call the appropriate provider with the right parameters based on the provider value.
-For a list of provider values, you can look at the directory - `aisuite/providers/`. The list of supported providers are of the format - `<provider>_provider.py` in that directory. We welcome  providers adding support to this library by adding an implementation file in this directory. Please see section below for how to contribute.
-
-For more examples, check out the `examples` directory where you will find several notebooks that you can run to experiment with the interface.
-
-## Adding support for a provider
-
-We have made easy for a provider or volunteer to add support for a new platform.
-
-### Naming Convention for Provider Modules
-
-We follow a convention-based approach for loading providers, which relies on strict naming conventions for both the module name and the class name. The format is based on the model identifier in the form `provider:model`.
+from aisuite.providers.github_provider import GitHubProvider
+from aisuite.framework.message import Message
 
-- The provider's module file must be named in the format `<provider>_provider.py`.
-- The class inside this module must follow the format: the provider name with the first letter capitalized, followed by the suffix `Provider`.
-
-#### Examples
-
-- **Hugging Face**:
-  The provider class should be defined as:
-
-  ```python
-  class HuggingfaceProvider(BaseProvider)
-  ```
-
-  in providers/huggingface_provider.py.
-
-- **OpenAI**:
-  The provider class should be defined as:
-
-  ```python
-  class OpenaiProvider(BaseProvider)
-  ```
-
-  in providers/openai_provider.py
-
-This convention simplifies the addition of new providers and ensures consistency across provider implementations.
-
-## Tool Calling
-
-`aisuite` provides a simple abstraction for tool/function calling that works across supported providers. This is in addition to the regular abstraction of passing JSON spec of the tool to the `tools` parameter. The tool calling abstraction makes it easy to use tools with different LLMs without changing your code.
-
-There are two ways to use tools with `aisuite`:
-
-### 1. Manual Tool Handling
-
-This is the default behavior when `max_turns` is not specified.
-You can pass tools in the OpenAI tool format:
-
-```python
-def will_it_rain(location: str, time_of_day: str):
-    """Check if it will rain in a location at a given time today.
-
-    Args:
-        location (str): Name of the city
-        time_of_day (str): Time of the day in HH:MM format.
-    """
-    return "YES"
-
-tools = [{
-    "type": "function",
-    "function": {
-        "name": "will_it_rain",
-        "description": "Check if it will rain in a location at a given time today",
-        "parameters": {
-            "type": "object",
-            "properties": {
-                "location": {
-                    "type": "string",
-                    "description": "Name of the city"
-                },
-                "time_of_day": {
-                    "type": "string",
-                    "description": "Time of the day in HH:MM format."
-                }
-            },
-            "required": ["location", "time_of_day"]
-        }
-    }
-}]
-
-response = client.chat.completions.create(
-    model="openai:gpt-4o",
-    messages=messages,
-    tools=tools
-)
-```
-
-### 2. Automatic Tool Execution
-
-When `max_turns` is specified, you can pass a list of callable Python functions as the `tools` parameter. `aisuite` will automatically handle the tool calling flow:
-
-```python
-def will_it_rain(location: str, time_of_day: str):
-    """Check if it will rain in a location at a given time today.
-
-    Args:
-        location (str): Name of the city
-        time_of_day (str): Time of the day in HH:MM format.
-    """
-    return "YES"
-
-client = ai.Client()
-messages = [{
-    "role": "user",
-    "content": "I live in San Francisco. Can you check for weather "
-               "and plan an outdoor picnic for me at 2pm?"
-}]
-
-# Automatic tool execution with max_turns
-response = client.chat.completions.create(
-    model="openai:gpt-4o",
-    messages=messages,
-    tools=[will_it_rain],
-    max_turns=2  # Maximum number of back-and-forth tool calls
-)
+provider = GitHubProvider()
+prompt = {"content": "Hello, what is the capital of Germany?", "role": "user"}
+response = provider.chat_completions_create("gpt-4", [prompt])
 print(response.choices[0].message.content)
 ```
 
-When `max_turns` is specified, `aisuite` will:
-1. Send your message to the LLM
-2. Execute any tool calls the LLM requests
-3. Send the tool results back to the LLM
-4. Repeat until the conversation is complete or max_turns is reached
-
-In addition to `response.choices[0].message`, there is an additional field `response.choices[0].intermediate_messages`: which contains the list of all messages including tool interactions used. This can be used to continue the conversation with the model.
-For more detailed examples of tool calling, check out the `examples/tool_calling_abstraction.ipynb` notebook.
-
 ## License
 
 aisuite is released under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.
 
-## Contributing
-
-If you would like to contribute, please read our [Contributing Guide](https://github.com/andrewyng/aisuite/blob/main/CONTRIBUTING.md) and join our [Discord](https://discord.gg/T6Nvn8ExSb) server!