1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
|
import abc
from autogpt.core.configuration import SystemConfiguration
from autogpt.core.planning.schema import (
LanguageModelClassification,
LanguageModelPrompt,
)
# class Planner(abc.ABC):
# """Manages the agent's planning and goal-setting by constructing language model prompts."""
#
# @staticmethod
# @abc.abstractmethod
# async def decide_name_and_goals(
# user_objective: str,
# ) -> LanguageModelResponse:
# """Decide the name and goals of an Agent from a user-defined objective.
#
# Args:
# user_objective: The user-defined objective for the agent.
#
# Returns:
# The agent name and goals as a response from the language model.
#
# """
# ...
#
# @abc.abstractmethod
# async def plan(self, context: PlanningContext) -> LanguageModelResponse:
# """Plan the next ability for the Agent.
#
# Args:
# context: A context object containing information about the agent's
# progress, result, memories, and feedback.
#
#
# Returns:
# The next ability the agent should take along with thoughts and reasoning.
#
# """
# ...
#
# @abc.abstractmethod
# def reflect(
# self,
# context: ReflectionContext,
# ) -> LanguageModelResponse:
# """Reflect on a planned ability and provide self-criticism.
#
#
# Args:
# context: A context object containing information about the agent's
# reasoning, plan, thoughts, and criticism.
#
# Returns:
# Self-criticism about the agent's plan.
#
# """
# ...
class PromptStrategy(abc.ABC):
default_configuration: SystemConfiguration
@property
@abc.abstractmethod
def model_classification(self) -> LanguageModelClassification:
...
@abc.abstractmethod
def build_prompt(self, *_, **kwargs) -> LanguageModelPrompt:
...
@abc.abstractmethod
def parse_response_content(self, response_content: dict) -> dict:
...
|
