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from dataclasses import asdict, dataclass
from logging import getLogger
from typing import TYPE_CHECKING, Any, Dict, List, TypedDict, Union
from smolagents.models import ChatMessage, MessageRole
from smolagents.monitoring import AgentLogger, LogLevel
from smolagents.utils import AgentError, make_json_serializable
if TYPE_CHECKING:
from smolagents.models import ChatMessage
from smolagents.monitoring import AgentLogger
logger = getLogger(__name__)
class Message(TypedDict):
role: MessageRole
content: str | list[dict]
@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, **kwargs) -> List[Dict[str, Any]]:
raise NotImplementedError
@dataclass
class ActionStep(MemoryStep):
model_input_messages: List[Message] | None = None
tool_calls: List[ToolCall] | None = None
start_time: float | None = None
end_time: float | None = None
step_number: int | None = None
error: AgentError | None = None
duration: float | None = None
model_output_message: ChatMessage = None
model_output: str | None = None
observations: str | None = None
observations_images: List[str] | None = None
action_output: Any = None
def dict(self):
# We overwrite the method to parse the tool_calls and action_output manually
return {
"model_input_messages": self.model_input_messages,
"tool_calls": [tc.dict() for tc in self.tool_calls] if self.tool_calls else [],
"start_time": self.start_time,
"end_time": self.end_time,
"step": self.step_number,
"error": self.error.dict() if self.error else None,
"duration": self.duration,
"model_output_message": self.model_output_message,
"model_output": self.model_output,
"observations": self.observations,
"action_output": make_json_serializable(self.action_output),
}
def to_messages(self, summary_mode: bool = False, show_model_input_messages: bool = False) -> List[Message]:
messages = []
if self.model_input_messages is not None and show_model_input_messages:
messages.append(Message(role=MessageRole.SYSTEM, content=self.model_input_messages))
if self.model_output is not None and not summary_mode:
messages.append(
Message(role=MessageRole.ASSISTANT, content=[{"type": "text", "text": self.model_output.strip()}])
)
if self.tool_calls is not None:
messages.append(
Message(
role=MessageRole.ASSISTANT,
content=[
{
"type": "text",
"text": "Calling tools:\n" + str([tc.dict() for tc in self.tool_calls]),
}
],
)
)
if self.observations is not None:
messages.append(
Message(
role=MessageRole.TOOL_RESPONSE,
content=[
{
"type": "text",
"text": f"Call id: {self.tool_calls[0].id}\nObservation:\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(
Message(role=MessageRole.TOOL_RESPONSE, content=[{"type": "text", "text": message_content}])
)
if self.observations_images:
messages.append(
Message(
role=MessageRole.USER,
content=[{"type": "text", "text": "Here are the observed images:"}]
+ [
{
"type": "image",
"image": image,
}
for image in self.observations_images
],
)
)
return messages
@dataclass
class PlanningStep(MemoryStep):
model_input_messages: List[Message]
model_output_message_facts: ChatMessage
facts: str
model_output_message_plan: ChatMessage
plan: str
def to_messages(self, summary_mode: bool, **kwargs) -> List[Message]:
messages = []
messages.append(
Message(
role=MessageRole.ASSISTANT, content=[{"type": "text", "text": f"[FACTS LIST]:\n{self.facts.strip()}"}]
)
)
if not summary_mode: # This step is not shown to a model writing a plan to avoid influencing the new plan
messages.append(
Message(
role=MessageRole.ASSISTANT, content=[{"type": "text", "text": f"[PLAN]:\n{self.plan.strip()}"}]
)
)
return messages
@dataclass
class TaskStep(MemoryStep):
task: str
task_images: List[str] | None = None
def to_messages(self, summary_mode: bool = False, **kwargs) -> List[Message]:
content = [{"type": "text", "text": f"New task:\n{self.task}"}]
if self.task_images:
for image in self.task_images:
content.append({"type": "image", "image": image})
return [Message(role=MessageRole.USER, content=content)]
@dataclass
class SystemPromptStep(MemoryStep):
system_prompt: str
def to_messages(self, summary_mode: bool = False, **kwargs) -> List[Message]:
if summary_mode:
return []
return [Message(role=MessageRole.SYSTEM, content=[{"type": "text", "text": self.system_prompt}])]
class AgentMemory:
def __init__(self, system_prompt: str):
self.system_prompt = SystemPromptStep(system_prompt=system_prompt)
self.steps: List[Union[TaskStep, ActionStep, PlanningStep]] = []
def reset(self):
self.steps = []
def get_succinct_steps(self) -> list[dict]:
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 [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, optional): 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:")
for step in self.steps:
if isinstance(step, SystemPromptStep) and detailed:
logger.log_markdown(title="System prompt", content=step.system_prompt, level=LogLevel.ERROR)
elif 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:
logger.log_messages(step.model_input_messages)
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:
logger.log_messages(step.model_input_messages, level=LogLevel.ERROR)
logger.log_markdown(title="Agent output:", content=step.facts + "\n" + step.plan, level=LogLevel.ERROR)
__all__ = ["AgentMemory"]
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