90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
import datetime
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from abc import ABC, abstractmethod
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from typing import Callable, Dict, List, Tuple, Union
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from letta.constants import MESSAGE_SUMMARY_REQUEST_ACK, MESSAGE_SUMMARY_WARNING_FRAC
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from letta.embeddings import embedding_model, parse_and_chunk_text, query_embedding
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from letta.llm_api.llm_api_tools import create
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from letta.prompts.gpt_summarize import SYSTEM as SUMMARY_PROMPT_SYSTEM
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from letta.schemas.agent import AgentState
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from letta.schemas.enums import MessageRole
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from letta.schemas.memory import Memory
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from letta.schemas.message import Message
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from letta.schemas.passage import Passage
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from letta.utils import (
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count_tokens,
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extract_date_from_timestamp,
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get_local_time,
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printd,
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validate_date_format,
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)
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def get_memory_functions(cls: Memory) -> Dict[str, Callable]:
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"""Get memory functions for a memory class"""
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functions = {}
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# collect base memory functions (should not be included)
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base_functions = []
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for func_name in dir(Memory):
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funct = getattr(Memory, func_name)
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if callable(funct):
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base_functions.append(func_name)
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for func_name in dir(cls):
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if func_name.startswith("_") or func_name in ["load", "to_dict"]: # skip base functions
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continue
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if func_name in base_functions: # dont use BaseMemory functions
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continue
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func = getattr(cls, func_name)
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if not callable(func): # not a function
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continue
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functions[func_name] = func
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return functions
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def _format_summary_history(message_history: List[Message]):
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# TODO use existing prompt formatters for this (eg ChatML)
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return "\n".join([f"{m.role}: {m.text}" for m in message_history])
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def summarize_messages(
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agent_state: AgentState,
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message_sequence_to_summarize: List[Message],
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):
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"""Summarize a message sequence using GPT"""
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# we need the context_window
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context_window = agent_state.llm_config.context_window
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summary_prompt = SUMMARY_PROMPT_SYSTEM
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summary_input = _format_summary_history(message_sequence_to_summarize)
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summary_input_tkns = count_tokens(summary_input)
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if summary_input_tkns > MESSAGE_SUMMARY_WARNING_FRAC * context_window:
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trunc_ratio = (MESSAGE_SUMMARY_WARNING_FRAC * context_window / summary_input_tkns) * 0.8 # For good measure...
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cutoff = int(len(message_sequence_to_summarize) * trunc_ratio)
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summary_input = str(
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[summarize_messages(agent_state, message_sequence_to_summarize=message_sequence_to_summarize[:cutoff])]
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+ message_sequence_to_summarize[cutoff:]
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)
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agent_state.user_id
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dummy_agent_id = agent_state.id
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message_sequence = []
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message_sequence.append(Message(agent_id=dummy_agent_id, role=MessageRole.system, text=summary_prompt))
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message_sequence.append(Message(agent_id=dummy_agent_id, role=MessageRole.assistant, text=MESSAGE_SUMMARY_REQUEST_ACK))
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message_sequence.append(Message(agent_id=dummy_agent_id, role=MessageRole.user, text=summary_input))
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# TODO: We need to eventually have a separate LLM config for the summarizer LLM
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llm_config_no_inner_thoughts = agent_state.llm_config.model_copy(deep=True)
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llm_config_no_inner_thoughts.put_inner_thoughts_in_kwargs = False
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response = create(
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llm_config=llm_config_no_inner_thoughts,
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user_id=agent_state.user_id,
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messages=message_sequence,
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stream=False,
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)
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printd(f"summarize_messages gpt reply: {response.choices[0]}")
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reply = response.choices[0].message.content
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return reply
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