AutoGPT/autogpt/models/base_open_ai_plugin.py

200 lines
7.1 KiB
Python

"""Handles loading of plugins."""
from typing import Any, Dict, List, Optional, Tuple, TypedDict, TypeVar
from auto_gpt_plugin_template import AutoGPTPluginTemplate
PromptGenerator = TypeVar("PromptGenerator")
class Message(TypedDict):
role: str
content: str
class BaseOpenAIPlugin(AutoGPTPluginTemplate):
"""
This is a BaseOpenAIPlugin class for generating Auto-GPT plugins.
"""
def __init__(self, manifests_specs_clients: dict):
# super().__init__()
self._name = manifests_specs_clients["manifest"]["name_for_model"]
self._version = manifests_specs_clients["manifest"]["schema_version"]
self._description = manifests_specs_clients["manifest"]["description_for_model"]
self._client = manifests_specs_clients["client"]
self._manifest = manifests_specs_clients["manifest"]
self._openapi_spec = manifests_specs_clients["openapi_spec"]
def can_handle_on_response(self) -> bool:
"""This method is called to check that the plugin can
handle the on_response method.
Returns:
bool: True if the plugin can handle the on_response method."""
return False
def on_response(self, response: str, *args, **kwargs) -> str:
"""This method is called when a response is received from the model."""
return response
def can_handle_post_prompt(self) -> bool:
"""This method is called to check that the plugin can
handle the post_prompt method.
Returns:
bool: True if the plugin can handle the post_prompt method."""
return False
def post_prompt(self, prompt: PromptGenerator) -> PromptGenerator:
"""This method is called just after the generate_prompt is called,
but actually before the prompt is generated.
Args:
prompt (PromptGenerator): The prompt generator.
Returns:
PromptGenerator: The prompt generator.
"""
return prompt
def can_handle_on_planning(self) -> bool:
"""This method is called to check that the plugin can
handle the on_planning method.
Returns:
bool: True if the plugin can handle the on_planning method."""
return False
def on_planning(
self, prompt: PromptGenerator, messages: List[Message]
) -> Optional[str]:
"""This method is called before the planning chat completion is done.
Args:
prompt (PromptGenerator): The prompt generator.
messages (List[str]): The list of messages.
"""
pass
def can_handle_post_planning(self) -> bool:
"""This method is called to check that the plugin can
handle the post_planning method.
Returns:
bool: True if the plugin can handle the post_planning method."""
return False
def post_planning(self, response: str) -> str:
"""This method is called after the planning chat completion is done.
Args:
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_pre_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the pre_instruction method.
Returns:
bool: True if the plugin can handle the pre_instruction method."""
return False
def pre_instruction(self, messages: List[Message]) -> List[Message]:
"""This method is called before the instruction chat is done.
Args:
messages (List[Message]): The list of context messages.
Returns:
List[Message]: The resulting list of messages.
"""
return messages
def can_handle_on_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the on_instruction method.
Returns:
bool: True if the plugin can handle the on_instruction method."""
return False
def on_instruction(self, messages: List[Message]) -> Optional[str]:
"""This method is called when the instruction chat is done.
Args:
messages (List[Message]): The list of context messages.
Returns:
Optional[str]: The resulting message.
"""
pass
def can_handle_post_instruction(self) -> bool:
"""This method is called to check that the plugin can
handle the post_instruction method.
Returns:
bool: True if the plugin can handle the post_instruction method."""
return False
def post_instruction(self, response: str) -> str:
"""This method is called after the instruction chat is done.
Args:
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_pre_command(self) -> bool:
"""This method is called to check that the plugin can
handle the pre_command method.
Returns:
bool: True if the plugin can handle the pre_command method."""
return False
def pre_command(
self, command_name: str, arguments: Dict[str, Any]
) -> Tuple[str, Dict[str, Any]]:
"""This method is called before the command is executed.
Args:
command_name (str): The command name.
arguments (Dict[str, Any]): The arguments.
Returns:
Tuple[str, Dict[str, Any]]: The command name and the arguments.
"""
return command_name, arguments
def can_handle_post_command(self) -> bool:
"""This method is called to check that the plugin can
handle the post_command method.
Returns:
bool: True if the plugin can handle the post_command method."""
return False
def post_command(self, command_name: str, response: str) -> str:
"""This method is called after the command is executed.
Args:
command_name (str): The command name.
response (str): The response.
Returns:
str: The resulting response.
"""
return response
def can_handle_chat_completion(
self, messages: Dict[Any, Any], model: str, temperature: float, max_tokens: int
) -> bool:
"""This method is called to check that the plugin can
handle the chat_completion method.
Args:
messages (List[Message]): The messages.
model (str): The model name.
temperature (float): The temperature.
max_tokens (int): The max tokens.
Returns:
bool: True if the plugin can handle the chat_completion method."""
return False
def handle_chat_completion(
self, messages: List[Message], model: str, temperature: float, max_tokens: int
) -> str:
"""This method is called when the chat completion is done.
Args:
messages (List[Message]): The messages.
model (str): The model name.
temperature (float): The temperature.
max_tokens (int): The max tokens.
Returns:
str: The resulting response.
"""
pass