25d54dd896
* auto fixes * auto fix pt2 and transitive deps and undefined var checking locals() * manual fixes (ignored or letta-code fixed) * fix circular import
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
## Voice chat + sleeptime tools
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from typing import List, Optional
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from pydantic import BaseModel, Field
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def rethink_user_memory(agent_state: "AgentState", new_memory: str) -> None: # noqa: F821
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"""
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Rewrite memory block for the main agent, new_memory should contain all current information from the block that is not outdated or inconsistent, integrating any new information, resulting in a new memory block that is organized, readable, and comprehensive.
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Args:
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new_memory (str): The new memory with information integrated from the memory block. If there is no new information, then this should be the same as the content in the source block.
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Returns:
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None: None is always returned as this function does not produce a response.
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"""
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# This is implemented directly in the agent loop
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return None
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def finish_rethinking_memory(agent_state: "AgentState") -> None: # type: ignore # noqa: F821
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"""
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This function is called when the agent is done rethinking the memory.
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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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return None
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class MemoryChunk(BaseModel):
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start_index: int = Field(
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...,
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description="Zero-based index of the first evicted line in this chunk.",
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)
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end_index: int = Field(
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...,
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description="Zero-based index of the last evicted line (inclusive).",
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)
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context: str = Field(
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...,
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description="1-3 sentence paraphrase capturing key facts/details, user preferences, or goals that this chunk reveals—written for future retrieval.",
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)
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def store_memories(agent_state: "AgentState", chunks: List[MemoryChunk]) -> None: # noqa: F821
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"""
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Persist dialogue that is about to fall out of the agent’s context window.
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Args:
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chunks (List[MemoryChunk]):
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Each chunk pinpoints a contiguous block of **evicted** lines and provides a short, forward-looking synopsis (`context`) that will be embedded for future semantic lookup.
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Returns:
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None
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"""
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# This is implemented directly in the agent loop
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return None
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def search_memory(
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agent_state: "AgentState", # noqa: F821
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convo_keyword_queries: Optional[List[str]],
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start_minutes_ago: Optional[int],
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end_minutes_ago: Optional[int],
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) -> Optional[str]:
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"""
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Look in long-term or earlier-conversation memory only when the user asks about something missing from the visible context. The user’s latest utterance is sent automatically as the main query.
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Args:
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convo_keyword_queries (Optional[List[str]]): Extra keywords (e.g., order ID, place name). Use *null* if not appropriate for the latest user message.
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start_minutes_ago (Optional[int]): Newer bound of the time window for results, specified in minutes ago. Use *null* if no lower time bound is needed.
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end_minutes_ago (Optional[int]): Older bound of the time window, in minutes ago. Use *null* if no upper bound is needed.
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Returns:
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Optional[str]: A formatted string of matching memory entries, or None if no
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relevant memories are found.
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"""
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# This is implemented directly in the agent loop
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return None
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