File size: 9,899 Bytes
d7949de |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 |
from dataclasses import asdict, dataclass
from logging import getLogger
from typing import TYPE_CHECKING, Any
from smolagents.models import ChatMessage, MessageRole
from smolagents.monitoring import AgentLogger, LogLevel, Timing, TokenUsage
from smolagents.utils import AgentError, make_json_serializable
if TYPE_CHECKING:
import PIL.Image
from smolagents.models import ChatMessage
from smolagents.monitoring import AgentLogger
logger = getLogger(__name__)
@dataclass
class ToolCall:
name: str
arguments: Any
id: str
def dict(self):
return {
"id": self.id,
"type": "function",
"function": {
"name": self.name,
"arguments": make_json_serializable(self.arguments),
},
}
@dataclass
class MemoryStep:
def dict(self):
return asdict(self)
def to_messages(self, summary_mode: bool = False) -> list[ChatMessage]:
raise NotImplementedError
@dataclass
class ActionStep(MemoryStep):
step_number: int
timing: Timing
model_input_messages: list[ChatMessage] | None = None
tool_calls: list[ToolCall] | None = None
error: AgentError | None = None
model_output_message: ChatMessage | None = None
model_output: str | list[dict[str, Any]] | None = None
code_action: str | None = None
observations: str | None = None
observations_images: list["PIL.Image.Image"] | None = None
action_output: Any = None
token_usage: TokenUsage | None = None
is_final_answer: bool = False
def dict(self):
# We overwrite the method to parse the tool_calls and action_output manually
return {
"step_number": self.step_number,
"timing": self.timing.dict(),
"model_input_messages": self.model_input_messages,
"tool_calls": [tc.dict() for tc in self.tool_calls] if self.tool_calls else [],
"error": self.error.dict() if self.error else None,
"model_output_message": self.model_output_message.dict() if self.model_output_message else None,
"model_output": self.model_output,
"code_action": self.code_action,
"observations": self.observations,
"observations_images": [image.tobytes() for image in self.observations_images]
if self.observations_images
else None,
"action_output": make_json_serializable(self.action_output),
"token_usage": asdict(self.token_usage) if self.token_usage else None,
"is_final_answer": self.is_final_answer,
}
def to_messages(self, summary_mode: bool = False) -> list[ChatMessage]:
messages = []
if self.model_output is not None and not summary_mode:
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=[{"type": "text", "text": self.model_output.strip()}])
)
if self.tool_calls is not None:
messages.append(
ChatMessage(
role=MessageRole.TOOL_CALL,
content=[
{
"type": "text",
"text": "Calling tools:\n" + str([tc.dict() for tc in self.tool_calls]),
}
],
)
)
if self.observations_images:
messages.append(
ChatMessage(
role=MessageRole.USER,
content=[
{
"type": "image",
"image": image,
}
for image in self.observations_images
],
)
)
if self.observations is not None:
messages.append(
ChatMessage(
role=MessageRole.TOOL_RESPONSE,
content=[
{
"type": "text",
"text": f"Observation:\n{self.observations}",
}
],
)
)
if self.error is not None:
error_message = (
"Error:\n"
+ str(self.error)
+ "\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\n"
)
message_content = f"Call id: {self.tool_calls[0].id}\n" if self.tool_calls else ""
message_content += error_message
messages.append(
ChatMessage(role=MessageRole.TOOL_RESPONSE, content=[{"type": "text", "text": message_content}])
)
return messages
@dataclass
class PlanningStep(MemoryStep):
model_input_messages: list[ChatMessage]
model_output_message: ChatMessage
plan: str
timing: Timing
token_usage: TokenUsage | None = None
def to_messages(self, summary_mode: bool = False) -> list[ChatMessage]:
if summary_mode:
return []
return [
ChatMessage(role=MessageRole.ASSISTANT, content=[{"type": "text", "text": self.plan.strip()}]),
ChatMessage(
role=MessageRole.USER, content=[{"type": "text", "text": "Now proceed and carry out this plan."}]
),
# This second message creates a role change to prevent models models from simply continuing the plan message
]
@dataclass
class TaskStep(MemoryStep):
task: str
task_images: list["PIL.Image.Image"] | None = None
def to_messages(self, summary_mode: bool = False) -> list[ChatMessage]:
content = [{"type": "text", "text": f"New task:\n{self.task}"}]
if self.task_images:
content.extend([{"type": "image", "image": image} for image in self.task_images])
return [ChatMessage(role=MessageRole.USER, content=content)]
@dataclass
class SystemPromptStep(MemoryStep):
system_prompt: str
def to_messages(self, summary_mode: bool = False) -> list[ChatMessage]:
if summary_mode:
return []
return [ChatMessage(role=MessageRole.SYSTEM, content=[{"type": "text", "text": self.system_prompt}])]
@dataclass
class FinalAnswerStep(MemoryStep):
output: Any
class AgentMemory:
"""Memory for the agent, containing the system prompt and all steps taken by the agent.
This class is used to store the agent's steps, including tasks, actions, and planning steps.
It allows for resetting the memory, retrieving succinct or full step information, and replaying the agent's steps.
Args:
system_prompt (`str`): System prompt for the agent, which sets the context and instructions for the agent's behavior.
**Attributes**:
- **system_prompt** (`SystemPromptStep`) -- System prompt step for the agent.
- **steps** (`list[TaskStep | ActionStep | PlanningStep]`) -- List of steps taken by the agent, which can include tasks, actions, and planning steps.
"""
def __init__(self, system_prompt: str):
self.system_prompt: SystemPromptStep = SystemPromptStep(system_prompt=system_prompt)
self.steps: list[TaskStep | ActionStep | PlanningStep] = []
def reset(self):
"""Reset the agent's memory, clearing all steps and keeping the system prompt."""
self.steps = []
def get_succinct_steps(self) -> list[dict]:
"""Return a succinct representation of the agent's steps, excluding model input messages."""
return [
{key: value for key, value in step.dict().items() if key != "model_input_messages"} for step in self.steps
]
def get_full_steps(self) -> list[dict]:
"""Return a full representation of the agent's steps, including model input messages."""
if len(self.steps) == 0:
return []
return [step.dict() for step in self.steps]
def replay(self, logger: AgentLogger, detailed: bool = False):
"""Prints a pretty replay of the agent's steps.
Args:
logger (`AgentLogger`): The logger to print replay logs to.
detailed (`bool`, default `False`): If True, also displays the memory at each step. Defaults to False.
Careful: will increase log length exponentially. Use only for debugging.
"""
logger.console.log("Replaying the agent's steps:")
logger.log_markdown(title="System prompt", content=self.system_prompt.system_prompt, level=LogLevel.ERROR)
for step in self.steps:
if isinstance(step, TaskStep):
logger.log_task(step.task, "", level=LogLevel.ERROR)
elif isinstance(step, ActionStep):
logger.log_rule(f"Step {step.step_number}", level=LogLevel.ERROR)
if detailed and step.model_input_messages is not None:
logger.log_messages(step.model_input_messages, level=LogLevel.ERROR)
if step.model_output is not None:
logger.log_markdown(title="Agent output:", content=step.model_output, level=LogLevel.ERROR)
elif isinstance(step, PlanningStep):
logger.log_rule("Planning step", level=LogLevel.ERROR)
if detailed and step.model_input_messages is not None:
logger.log_messages(step.model_input_messages, level=LogLevel.ERROR)
logger.log_markdown(title="Agent output:", content=step.plan, level=LogLevel.ERROR)
def return_full_code(self) -> str:
"""Returns all code actions from the agent's steps, concatenated as a single script."""
return "\n\n".join(
[step.code_action for step in self.steps if isinstance(step, ActionStep) and step.code_action is not None]
)
__all__ = ["AgentMemory"]
|