Setup Development Environment

AG2 uses an environment variable called OAI_CONFIG_LIST in JSON format to store the LLM keys. OAI_CONFIG_LIST is a list of dictionaries where each dictionary contains the following keys: - model(required): The name of the OpenAI/LLM model. - api_key(optional): The API key for the OpenAI/LLM model. - api_type(optional): The type of the API key. It is used for non-OpenAI LLMs. - api_version(optional): The version of the API key. It is used for Azure API. - base_url(optional): The base URL for the OpenAI/LLM model. - tags(optional): A list of tags for the OpenAI/LLM model which can be used for filtering.

Following is an example of the `OAI_CONFIG_LIST` in JSON format which consists of two OpenAI models and a gemini model:
```bash
[
    {
        "model": "gpt-4o",
        "api_key": "<your_api_key>",
        "tags": ["gpt-4o", "tool", "vision"]
    },
    {
        "model": "gpt-4o-mini",
        "api_key": "<your_api_key>",
        "tags": ["gpt-4o-mini", "tool", "vision"]
    },
    {
        "api_type": "google",
        "model": "gemini-pro",
        "api_key": "<your_gemini_api_key>",
    }
]
```

Further, this `OAI_CONFIG_LIST` can be set in two ways:
<Tabs>
    <Tab title="As environment variable">
        Simply set the `OAI_CONFIG_LIST` environment variable in your terminal:
        ```bash
        export OAI_CONFIG_LIST='[{"api_type": "openai", "model": "gpt-4o","api_key": "<your_api_key>","tags": ["gpt-4o", "tool", "vision"]},{"api_type": "openai", "model": "gpt-4o-mini","api_key": "<your_api_key>","tags": ["gpt-4o-mini", "tool", "vision"]},{"api_type": "google", "model": "gemini-pro","api_key": "<your_gemini_api_key>",}]'
        ```

Or you can save the OAI_CONFIG_LIST in a file and set the path of the file as an environment variable. For example, let's say you have saved the OAI_CONFIG_LIST in a file called OAI_CONFIG_LIST.json at the root of the project. You can set the OAI_CONFIG_LIST environment variable as follows:

export OAI_CONFIG_LIST="/path/to/OAI_CONFIG_LIST.json"

1. Setup the necessary LLM keys as mentioned above in your terminal.
   1. Clone the AG2 repository and cd into the repository.
   2. Open the project in Visual Studio Code by running the following command from the root of the repository:

      code .
      

   3. Press Ctrl+Shift+P and select Dev Containers: Reopen in Container.
   4. Select the desired python environment and wait for the container to build.
   5. Once the container is built, you can start developing AG2.

1. Open the AG2 repository on GitHub and fork the repository.
   1. Navigate to Settings -> Secrets and variables -> Codespaces.
   2. Add the necessary LLM keys as mentioned above by clicking on the New repository secret button.
   3. Navigate back to the forked repository.
   4. Click on the Code button and select Open with Codespaces.
   5. Once the container is built, you can start developing AG2.

1. Setup the necessary LLM keys as mentioned above in your terminal.
   1. Fork the AG2 repository and clone the forked repository.
   2. Create a virtual environment by running the following command from the root of the repository:

      python3 -m venv venv
      

   3. Activate the virtual environment by running the following command:

      source venv/bin/activate
      

   4. Install the required dependencies by running the following command:

      pip install -e ".[dev]" && pre-commit install
      

   5. Once the dependencies are installed, you can start developing AG2.