f5c4ab50f4
* auto fixes * auto fix pt2 and transitive deps and undefined var checking locals() * manual fixes (ignored or letta-code fixed) * fix circular import * remove all ignores, add FastAPI rules and Ruff rules * add ty and precommit * ruff stuff * ty check fixes * ty check fixes pt 2 * error on invalid
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
from typing import List, Optional
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from letta.log import get_logger
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from letta.otel.tracing import trace_method
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from letta.schemas.llm_config import LLMConfig
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from letta.schemas.message import Message, MessageRole
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from letta.schemas.user import User
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from letta.services.summarizer.summarizer import simple_summary
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from letta.services.summarizer.summarizer_config import CompactionSettings
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logger = get_logger(__name__)
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@trace_method
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async def summarize_all(
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# Required to tag LLM calls
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actor: User,
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# LLM config for the summarizer model
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llm_config: LLMConfig,
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# Actual summarization configuration
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summarizer_config: CompactionSettings,
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in_context_messages: List[Message],
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# Telemetry context
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agent_id: Optional[str] = None,
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agent_tags: Optional[List[str]] = None,
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run_id: Optional[str] = None,
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step_id: Optional[str] = None,
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) -> str:
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"""
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Summarize the entire conversation history into a single summary.
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Returns:
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- The summary string
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"""
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logger.info(
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f"Summarizing all messages (index 1 to {len(in_context_messages) - 2}), keeping last message: {in_context_messages[-1].role}"
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)
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if in_context_messages[-1].role == MessageRole.approval:
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# cannot evict a pending approval request (will cause client-side errors)
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# Also protect the assistant message before it if they share the same step_id
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# (both are part of the same LLM response - assistant has thinking/tool_calls, approval has approval-required subset)
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protected_messages = [in_context_messages[-1]]
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# Check if the message before approval is also from the same step (has reasoning/tool_calls)
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if len(in_context_messages) >= 2:
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potential_assistant = in_context_messages[-2]
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approval_request = in_context_messages[-1]
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if potential_assistant.role == MessageRole.assistant and potential_assistant.step_id == approval_request.step_id:
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# They're part of the same LLM response - protect both
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protected_messages = [potential_assistant, approval_request]
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messages_to_summarize = in_context_messages[1:-2]
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else:
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messages_to_summarize = in_context_messages[1:-1]
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else:
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messages_to_summarize = in_context_messages[1:-1]
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else:
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messages_to_summarize = in_context_messages[1:]
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protected_messages = []
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# TODO: add fallback in case this has a context window error
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summary_message_str = await simple_summary(
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messages=messages_to_summarize,
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llm_config=llm_config,
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actor=actor,
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include_ack=bool(summarizer_config.prompt_acknowledgement),
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prompt=summarizer_config.prompt,
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agent_id=agent_id,
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agent_tags=agent_tags,
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run_id=run_id,
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step_id=step_id,
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compaction_settings={
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"mode": "summarize_all",
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"clip_chars": summarizer_config.clip_chars,
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},
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)
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logger.info(f"Summarized {len(messages_to_summarize)} messages")
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if summarizer_config.clip_chars is not None and len(summary_message_str) > summarizer_config.clip_chars:
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logger.warning(f"Summary length {len(summary_message_str)} exceeds clip length {summarizer_config.clip_chars}. Truncating.")
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summary_message_str = summary_message_str[: summarizer_config.clip_chars] + "... [summary truncated to fit]"
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return summary_message_str, [in_context_messages[0], *protected_messages]
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