5a743d1dc4
git-subtree-dir: apps/core git-subtree-mainline: a8963e11e7a5a0059acbc849ce768e1eee80df61 git-subtree-split: ea2a7395f4023f5b9fab03e6273db3b64a1181d5
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
from typing import List, Optional, Union
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from letta.agent import Agent, save_agent
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from letta.interface import AgentInterface
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from letta.schemas.agent import AgentState
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from letta.schemas.message import Message
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from letta.schemas.openai.chat_completion_response import UsageStatistics
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from letta.schemas.usage import LettaUsageStatistics
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from letta.schemas.user import User
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def send_thinking_message(self: "Agent", message: str) -> Optional[str]:
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"""
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Sends a thinking message so that the model can reason out loud before responding.
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Args:
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message (str): Message contents. All unicode (including emojis) are supported.
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Returns:
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Optional[str]: None is always returned as this function does not produce a response.
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"""
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self.interface.internal_monologue(message)
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return None
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def send_final_message(self: "Agent", message: str) -> Optional[str]:
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"""
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Sends a final message to the human user after thinking for a while.
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Args:
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message (str): Message contents. All unicode (including emojis) are supported.
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Returns:
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Optional[str]: None is always returned as this function does not produce a response.
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"""
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self.interface.internal_monologue(message)
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return None
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class O1Agent(Agent):
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def __init__(
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self,
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interface: AgentInterface,
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agent_state: AgentState,
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user: User,
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max_thinking_steps: int = 10,
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first_message_verify_mono: bool = False,
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):
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super().__init__(interface, agent_state, user)
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self.max_thinking_steps = max_thinking_steps
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self.first_message_verify_mono = first_message_verify_mono
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def step(
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self,
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messages: Union[Message, List[Message]],
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chaining: bool = True,
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max_chaining_steps: Optional[int] = None,
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**kwargs,
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) -> LettaUsageStatistics:
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"""Run Agent.inner_step in a loop, terminate when final thinking message is sent or max_thinking_steps is reached"""
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# assert ms is not None, "MetadataStore is required"
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next_input_message = messages if isinstance(messages, list) else [messages]
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counter = 0
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total_usage = UsageStatistics()
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step_count = 0
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while step_count < self.max_thinking_steps:
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if counter > 0:
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next_input_message = []
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kwargs["first_message"] = False
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step_response = self.inner_step(
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messages=next_input_message,
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**kwargs,
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)
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usage = step_response.usage
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step_count += 1
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total_usage += usage
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counter += 1
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self.interface.step_complete()
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# check if it is final thinking message
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if step_response.messages[-1].name == "send_final_message":
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break
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save_agent(self)
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return LettaUsageStatistics(**total_usage.model_dump(), step_count=step_count)
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