autogen/website/docs/FAQ.md

Frequently Asked Questions

Set your API endpoints

There are multiple ways to construct a list of configurations for LLM inference.

Option 1: Load a list of endpoints from json

The config_list_from_json function loads a list of configurations from an environment variable or a json file.

For example,

import autogen
config_list = autogen.config_list_from_json(
    "OAI_CONFIG_LIST",
    file_location=".",
    filter_dict={
        "model": {
            "gpt-4",
            "gpt-3.5-turbo",
        }
    }
)

It first looks for environment variable "OAI_CONFIG_LIST" which needs to be a valid json string. If that variable is not found, it then looks for a json file named "OAI_CONFIG_LIST" under the specified file_location. It then filters the configs by models (you can filter by other keys as well).

The OAI_CONFIG_LIST var or file content looks like the following:

[
    {
        "model": "gpt-4",
        "api_key": "<your OpenAI API key here>"
    },
    {
        "model": "gpt-4",
        "api_key": "<your Azure OpenAI API key here>",
        "api_base": "<your Azure OpenAI API base here>",
        "api_type": "azure",
        "api_version": "2023-07-01-preview"
    },
    {
        "model": "gpt-3.5-turbo",
        "api_key": "<your Azure OpenAI API key here>",
        "api_base": "<your Azure OpenAI API base here>",
        "api_type": "azure",
        "api_version": "2023-07-01-preview"
    }
]

Option 2: Construct a list of endpoints for OpenAI or Azure OpenAI

The config_list_from_models function tries to create a list of configurations using Azure OpenAI endpoints and OpenAI endpoints for the provided list of models. It assumes the api keys and api bases are stored in the corresponding environment variables or local txt files:

• OpenAI API key: os.environ["OPENAI_API_KEY"] or openai_api_key_file="key_openai.txt".
• Azure OpenAI API key: os.environ["AZURE_OPENAI_API_KEY"] or aoai_api_key_file="key_aoai.txt". Multiple keys can be stored, one per line.
• Azure OpenAI API base: os.environ["AZURE_OPENAI_API_BASE"] or aoai_api_base_file="base_aoai.txt". Multiple bases can be stored, one per line.

It's OK to have only the OpenAI API key, or only the Azure OpenAI API key + base.

import autogen
config_list = autogen.config_list_from_models(model_list=["gpt-4", "gpt-3.5-turbo", "gpt-3.5-turbo-16k"])

The config list looks like the following, if only OpenAI API key is available:

config_list = [
    {
        'model': 'gpt-4',
        'api_key': '<your OpenAI API key here>',
    },  # OpenAI API endpoint for gpt-4
    {
        'model': 'gpt-3.5-turbo',
        'api_key': '<your OpenAI API key here>',
    },  # OpenAI API endpoint for gpt-3.5-turbo
    {
        'model': 'gpt-3.5-turbo-16k',
        'api_key': '<your OpenAI API key here>',
    },  # OpenAI API endpoint for gpt-3.5-turbo-16k
]

Use the constructed configuration list in agents

Make sure the "config_list" is included in the llm_config in the constructor of the LLM-based agent. For example,

assistant = autogen.AssistantAgent(
    name="assistant",
    llm_config={"config_list": config_list}
)

The llm_config is used in the create function for LLM inference. When llm_config is not provided, the agent will rely on other openai settings such as openai.api_key or the environment variable OPENAI_API_KEY, which can also work when you'd like to use a single endpoint. You can also explicitly specify that by:

assistant = autogen.AssistantAgent(name="assistant", llm_config={"api_key": ...})

Handle Rate Limit Error and Timeout Error

You can set retry_wait_time and max_retry_period to handle rate limit error. And you can set request_timeout to handle timeout error. They can all be specified in llm_config for an agent, which will be used in the create function for LLM inference.

• retry_wait_time (int): the time interval to wait (in seconds) before retrying a failed request.
• max_retry_period (int): the total timeout (in seconds) allowed for retrying failed requests.
• request_timeout (int): the timeout (in seconds) sent with a single request.

Please refer to the documentation for more info.