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@@ -19,6 +19,32 @@ load_dotenv()
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@dataclass
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class LLMConfig(metaclass=Singleton):
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+ """
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+ Configuration for the LLM model.
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+
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+ Attributes:
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+ model: The model to use.
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+ api_key: The API key to use.
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+ base_url: The base URL for the API. This is necessary for local LLMs. It is also used for Azure embeddings.
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+ api_version: The version of the API.
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+ embedding_model: The embedding model to use.
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+ embedding_base_url: The base URL for the embedding API.
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+ embedding_deployment_name: The name of the deployment for the embedding API. This is used for Azure OpenAI.
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+ aws_access_key_id: The AWS access key ID.
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+ aws_secret_access_key: The AWS secret access key.
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+ aws_region_name: The AWS region name.
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+ num_retries: The number of retries to attempt.
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+ retry_min_wait: The minimum time to wait between retries, in seconds. This is exponential backoff minimum. For models with very low limits, this can be set to 15-20.
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+ retry_max_wait: The maximum time to wait between retries, in seconds. This is exponential backoff maximum.
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+ timeout: The timeout for the API.
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+ max_chars: The maximum number of characters to send to and receive from the API. This is a fallback for token counting, which doesn't work in all cases.
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+ temperature: The temperature for the API.
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+ top_p: The top p for the API.
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+ custom_llm_provider: The custom LLM provider to use. This is undocumented in opendevin, and normally not used. It is documented on the litellm side.
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+ max_input_tokens: The maximum number of input tokens. Note that this is currently unused, and the value at runtime is actually the total tokens in OpenAI (e.g. 128,000 tokens for GPT-4).
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+ max_output_tokens: The maximum number of output tokens. This is sent to the LLM.
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+ """
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+
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model: str = 'gpt-3.5-turbo'
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api_key: str | None = None
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base_url: str | None = None
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@@ -52,6 +78,15 @@ class LLMConfig(metaclass=Singleton):
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@dataclass
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class AgentConfig(metaclass=Singleton):
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+ """
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+ Configuration for the agent.
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+
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+ Attributes:
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+ name: The name of the agent.
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+ memory_enabled: Whether long-term memory (embeddings) is enabled.
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+ memory_max_threads: The maximum number of threads indexing at the same time for embeddings.
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+ """
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+
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name: str = 'CodeActAgent'
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memory_enabled: bool = False
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memory_max_threads: int = 2
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@@ -68,6 +103,35 @@ class AgentConfig(metaclass=Singleton):
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@dataclass
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class AppConfig(metaclass=Singleton):
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+ """
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+ Configuration for the app.
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+
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+ Attributes:
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+ llm: The LLM configuration.
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+ agent: The agent configuration.
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+ runtime: The runtime environment.
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+ file_store: The file store to use.
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+ file_store_path: The path to the file store.
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+ workspace_base: The base path for the workspace. Defaults to ./workspace as an absolute path.
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+ workspace_mount_path: The path to mount the workspace. This is set to the workspace base by default.
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+ workspace_mount_path_in_sandbox: The path to mount the workspace in the sandbox. Defaults to /workspace.
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+ workspace_mount_rewrite: The path to rewrite the workspace mount path to.
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+ cache_dir: The path to the cache directory. Defaults to /tmp/cache.
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+ sandbox_container_image: The container image to use for the sandbox.
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+ run_as_devin: Whether to run as devin.
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+ max_iterations: The maximum number of iterations.
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+ e2b_api_key: The E2B API key.
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+ sandbox_type: The type of sandbox to use. Options are: ssh, exec, e2b, local.
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+ use_host_network: Whether to use the host network.
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+ ssh_hostname: The SSH hostname.
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+ disable_color: Whether to disable color. For terminals that don't support color.
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+ sandbox_user_id: The user ID for the sandbox.
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+ sandbox_timeout: The timeout for the sandbox.
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+ github_token: The GitHub token.
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+ debug: Whether to enable debugging.
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+ enable_auto_lint: Whether to enable auto linting. This is False by default, for regular runs of the app. For evaluation, please set this to True.
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+ """
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+
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llm: LLMConfig = field(default_factory=LLMConfig)
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agent: AgentConfig = field(default_factory=AgentConfig)
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runtime: str = 'server'
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@@ -306,6 +370,26 @@ finalize_config(config)
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def get_llm_config_arg(llm_config_arg: str):
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"""
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Get a group of llm settings from the config file.
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+
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+ A group in config.toml can look like this:
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+
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+ ```
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+ [gpt-3.5-for-eval]
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+ model = 'gpt-3.5-turbo'
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+ api_key = '...'
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+ temperature = 0.5
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+ num_retries = 10
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+ ...
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+ ```
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+
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+ The user-defined group name, like "gpt-3.5-for-eval", is the argument to this function. The function will load the LLMConfig object
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+ with the settings of this group, from the config file, and set it as the LLMConfig object for the app.
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+
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+ Args:
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+ llm_config_arg: The group of llm settings to get from the config.toml file.
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+
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+ Returns:
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+ LLMConfig: The LLMConfig object with the settings from the config file.
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"""
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# keep only the name, just in case
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Engel Nyst