110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
from typing import TYPE_CHECKING, Callable, Dict, List
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from letta.constants import MESSAGE_SUMMARY_REQUEST_ACK
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from letta.llm_api.llm_api_tools import create
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from letta.llm_api.llm_client import LLMClient
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from letta.otel.tracing import trace_method
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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.letta_message_content import TextContent
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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.settings import summarizer_settings
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from letta.utils import count_tokens, printd
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if TYPE_CHECKING:
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from letta.orm import User
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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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def get_message_text(content):
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if content and len(content) == 1 and isinstance(content[0], TextContent):
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return content[0].text
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return ""
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return "\n".join([f"{m.role}: {get_message_text(m.content)}" for m in message_history])
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# @trace_method
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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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# actor: "User",
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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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#
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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 > summarizer_settings.memory_warning_threshold * context_window:
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# trunc_ratio = (summarizer_settings.memory_warning_threshold * 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], actor=actor)]
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# + message_sequence_to_summarize[cutoff:]
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# )
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#
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# dummy_agent_id = agent_state.id
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# message_sequence = [
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# Message(agent_id=dummy_agent_id, role=MessageRole.system, content=[TextContent(text=summary_prompt)]),
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# Message(agent_id=dummy_agent_id, role=MessageRole.assistant, content=[TextContent(text=MESSAGE_SUMMARY_REQUEST_ACK)]),
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# Message(agent_id=dummy_agent_id, role=MessageRole.user, content=[TextContent(text=summary_input)]),
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# ]
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#
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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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#
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# llm_client = LLMClient.create(
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# provider_type=agent_state.llm_config.model_endpoint_type,
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# put_inner_thoughts_first=False,
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# actor=actor,
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# )
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# # try to use new client, otherwise fallback to old flow
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# # TODO: we can just directly call the LLM here?
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# if llm_client:
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# response = llm_client.send_llm_request(
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# agent_type=agent_state.agent_type,
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# messages=message_sequence,
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# llm_config=llm_config_no_inner_thoughts,
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# )
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# else:
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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.created_by_id,
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# messages=message_sequence,
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# stream=False,
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# )
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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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#
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