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
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
|
from autogpt.core.configuration import SystemConfiguration, UserConfigurable
from autogpt.core.planning.base import PromptStrategy
from autogpt.core.planning.schema import (
LanguageModelClassification,
LanguageModelPrompt,
Task,
TaskType,
)
from autogpt.core.planning.strategies.utils import json_loads, to_numbered_list
from autogpt.core.resource.model_providers import (
LanguageModelFunction,
LanguageModelMessage,
MessageRole,
)
class InitialPlanConfiguration(SystemConfiguration):
model_classification: LanguageModelClassification = UserConfigurable()
system_prompt_template: str = UserConfigurable()
system_info: list[str] = UserConfigurable()
user_prompt_template: str = UserConfigurable()
create_plan_function: dict = UserConfigurable()
class InitialPlan(PromptStrategy):
DEFAULT_SYSTEM_PROMPT_TEMPLATE = (
"You are an expert project planner. You're responsibility is to create work plans for autonomous agents. "
"You will be given a name, a role, set of goals for the agent to accomplish. Your job is to "
"break down those goals into a set of tasks that the agent can accomplish to achieve those goals. "
"Agents are resourceful, but require clear instructions. Each task you create should have clearly defined "
"`ready_criteria` that the agent can check to see if the task is ready to be started. Each task should "
"also have clearly defined `acceptance_criteria` that the agent can check to evaluate if the task is complete. "
"You should create as many tasks as you think is necessary to accomplish the goals.\n\n"
"System Info:\n{system_info}"
)
DEFAULT_SYSTEM_INFO = [
"The OS you are running on is: {os_info}",
"It takes money to let you run. Your API budget is ${api_budget:.3f}",
"The current time and date is {current_time}",
]
DEFAULT_USER_PROMPT_TEMPLATE = (
"You are {agent_name}, {agent_role}\n" "Your goals are:\n" "{agent_goals}"
)
DEFAULT_CREATE_PLAN_FUNCTION = {
"name": "create_initial_agent_plan",
"description": "Creates a set of tasks that forms the initial plan for an autonomous agent.",
"parameters": {
"type": "object",
"properties": {
"task_list": {
"type": "array",
"items": {
"type": "object",
"properties": {
"objective": {
"type": "string",
"description": "An imperative verb phrase that succinctly describes the task.",
},
"type": {
"type": "string",
"description": "A categorization for the task. ",
"enum": [t.value for t in TaskType],
},
"acceptance_criteria": {
"type": "array",
"items": {
"type": "string",
"description": "A list of measurable and testable criteria that must be met for the task to be considered complete.",
},
},
"priority": {
"type": "integer",
"description": "A number between 1 and 10 indicating the priority of the task relative to other generated tasks.",
"minimum": 1,
"maximum": 10,
},
"ready_criteria": {
"type": "array",
"items": {
"type": "string",
"description": "A list of measurable and testable criteria that must be met before the task can be started.",
},
},
},
"required": [
"objective",
"type",
"acceptance_criteria",
"priority",
"ready_criteria",
],
},
},
},
},
}
default_configuration = InitialPlanConfiguration(
model_classification=LanguageModelClassification.SMART_MODEL,
system_prompt_template=DEFAULT_SYSTEM_PROMPT_TEMPLATE,
system_info=DEFAULT_SYSTEM_INFO,
user_prompt_template=DEFAULT_USER_PROMPT_TEMPLATE,
create_plan_function=DEFAULT_CREATE_PLAN_FUNCTION,
)
def __init__(
self,
model_classification: LanguageModelClassification,
system_prompt_template: str,
system_info: list[str],
user_prompt_template: str,
create_plan_function: dict,
):
self._model_classification = model_classification
self._system_prompt_template = system_prompt_template
self._system_info = system_info
self._user_prompt_template = user_prompt_template
self._create_plan_function = create_plan_function
@property
def model_classification(self) -> LanguageModelClassification:
return self._model_classification
def build_prompt(
self,
agent_name: str,
agent_role: str,
agent_goals: list[str],
abilities: list[str],
os_info: str,
api_budget: float,
current_time: str,
**kwargs,
) -> LanguageModelPrompt:
template_kwargs = {
"agent_name": agent_name,
"agent_role": agent_role,
"os_info": os_info,
"api_budget": api_budget,
"current_time": current_time,
**kwargs,
}
template_kwargs["agent_goals"] = to_numbered_list(
agent_goals, **template_kwargs
)
template_kwargs["abilities"] = to_numbered_list(abilities, **template_kwargs)
template_kwargs["system_info"] = to_numbered_list(
self._system_info, **template_kwargs
)
system_prompt = LanguageModelMessage(
role=MessageRole.SYSTEM,
content=self._system_prompt_template.format(**template_kwargs),
)
user_prompt = LanguageModelMessage(
role=MessageRole.USER,
content=self._user_prompt_template.format(**template_kwargs),
)
create_plan_function = LanguageModelFunction(
json_schema=self._create_plan_function,
)
return LanguageModelPrompt(
messages=[system_prompt, user_prompt],
functions=[create_plan_function],
# TODO:
tokens_used=0,
)
def parse_response_content(
self,
response_content: dict,
) -> dict:
"""Parse the actual text response from the objective model.
Args:
response_content: The raw response content from the objective model.
Returns:
The parsed response.
"""
parsed_response = json_loads(response_content["function_call"]["arguments"])
parsed_response["task_list"] = [
Task.parse_obj(task) for task in parsed_response["task_list"]
]
return parsed_response
|
