13bdd9d40a
Normalize OpenAI-style and LangChain standard image blocks into LangChain standard content blocks so provider block_translators auto-convert for either OpenAI or Anthropic. Flatten multimodal content to plain text when persisting history and computing term embeddings. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
"""Utility functions for handling LangGraph checkpoint data."""
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import logging
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from typing import List, Dict, Any, Optional
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from langgraph.checkpoint.memory import MemorySaver
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logger = logging.getLogger('app')
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async def get_checkpoint_history(checkpointer: MemorySaver, thread_id: str) -> List:
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"""
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Get the chat history for the specified thread_id from the checkpointer.
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Args:
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checkpointer: MemorySaver instance
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thread_id: Thread ID, usually the session_id
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Returns:
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List[Dict]: Historical messages, or an empty list if no history exists or an error occurs
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"""
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if not checkpointer or not thread_id:
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logger.debug(f"No checkpointer or thread_id: checkpointer={bool(checkpointer)}, thread_id={thread_id}")
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return []
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try:
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config = {"configurable": {"thread_id": thread_id}}
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checkpoint_tuple = await checkpointer.aget_tuple(config)
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if checkpoint_tuple is None or checkpoint_tuple.checkpoint is None:
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logger.debug(f"No checkpoint found for thread_id: {thread_id}")
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return []
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# Extract message history from the checkpoint.
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checkpoint_data = checkpoint_tuple.checkpoint
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# In LangGraph checkpoints, messages are usually stored in channel_values['messages'].
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if "channel_values" not in checkpoint_data:
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logger.debug(f"No channel_values in checkpoint for thread_id: {thread_id}")
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return []
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channel_values = checkpoint_data["channel_values"]
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if isinstance(channel_values, dict) and "messages" in channel_values:
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history_messages = channel_values["messages"]
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converted = history_messages
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logger.info(f"Loaded {len(converted)} messages from checkpoint for thread_id: {thread_id}")
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return converted
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elif isinstance(channel_values, list):
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# In some cases, channel_values is directly the message list.
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converted = channel_values
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logger.info(f"Loaded {len(converted)} messages from checkpoint for thread_id: {thread_id}")
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return converted
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else:
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logger.debug(f"Unexpected channel_values format: {type(channel_values)}")
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return []
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except Exception as e:
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import traceback
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logger.error(f"Error getting checkpoint history for thread_id {thread_id}: {e}")
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logger.error(f"Full traceback: {traceback.format_exc()}")
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return []
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async def prepare_checkpoint_message(config, checkpointer):
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"""
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Prepare checkpoint-related messages:
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1. Fetch and filter history, excluding messages that contain think tags.
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2. Decide which messages to send based on whether history exists.
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"""
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if not config.session_id or not checkpointer or len(config.messages) == 0:
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logger.debug("No session_id/checkpointer or empty messages, skipping checkpoint")
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return
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# Load history.
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history = await get_checkpoint_history(checkpointer, config.session_id)
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has_history = len(history) > 0
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# Filter history and keep the most recent 20 messages.
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if has_history:
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filtered_history = [
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h for h in history
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if getattr(h, "type", None) in ("human", "ai")
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and "<think>" not in str(getattr(h, "content", "")).lower()
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]
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logger.info(f"Filtered {len(filtered_history)} human/ai messages from history")
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config._session_history = filtered_history[-20:]
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# If history exists, only send the latest user message; otherwise send all messages.
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if has_history:
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last_user_msg = next((m for m in reversed(config.messages) if m.get('role') == 'user'), None)
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if last_user_msg:
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config.messages = [last_user_msg]
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from utils.fastapi_utils import extract_text_from_content
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preview = extract_text_from_content(last_user_msg.get('content', ''))
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logger.info(f"Has history, sending last user message: {preview[:50]}...")
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else:
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logger.info(f"No history, sending all {len(config.messages)} messages")
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