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https://github.com/open-webui/open-webui.git
synced 2025-12-29 00:24:39 +01:00
refac
This commit is contained in:
Timothy Jaeryang Baek
@@ -1,7 +1,7 @@
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import json
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import logging
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from typing import Optional
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import asyncio
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from open_webui.utils.misc import get_message_list
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from open_webui.socket.main import get_event_emitter
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@@ -209,8 +209,161 @@ class ChatStatsExportList(BaseModel):
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page: int
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def calculate_chat_stats(user_id, skip=0, limit=10, filter=None):
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if filter is None:
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filter = {}
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result = Chats.get_chats_by_user_id(
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user_id,
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skip=skip,
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limit=limit,
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filter=filter,
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)
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chat_stats_export_list = []
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for chat in result.items:
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try:
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messages_map = chat.chat.get("history", {}).get("messages", {})
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message_id = chat.chat.get("history", {}).get("currentId")
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history_models = {}
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history_message_count = len(messages_map)
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history_user_messages = []
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history_assistant_messages = []
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export_messages = {}
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for key, message in messages_map.items():
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try:
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content = message.get("content", "")
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if isinstance(content, str):
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content_length = len(content)
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else:
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content_length = (
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0 # Handle cases where content might be None or not string
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)
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# Extract rating safely
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rating = message.get("annotation", {}).get("rating")
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tags = message.get("annotation", {}).get("tags")
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message_stat = MessageStats(
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id=message.get("id"),
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role=message.get("role"),
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model=message.get("model"),
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timestamp=message.get("timestamp"),
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content_length=content_length,
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token_count=None, # Populate if available, e.g. message.get("info", {}).get("token_count")
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rating=rating,
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tags=tags,
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)
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export_messages[key] = message_stat
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# --- Aggregation Logic (copied/adapted from usage stats) ---
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role = message.get("role", "")
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if role == "user":
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history_user_messages.append(message)
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elif role == "assistant":
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history_assistant_messages.append(message)
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model = message.get("model")
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if model:
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if model not in history_models:
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history_models[model] = 0
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history_models[model] += 1
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except Exception as e:
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log.debug(f"Error processing message {key}: {e}")
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continue
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# Calculate Averages
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average_user_message_content_length = (
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sum(
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len(m.get("content", ""))
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for m in history_user_messages
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if isinstance(m.get("content"), str)
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)
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/ len(history_user_messages)
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if history_user_messages
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else 0
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)
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average_assistant_message_content_length = (
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sum(
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len(m.get("content", ""))
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for m in history_assistant_messages
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if isinstance(m.get("content"), str)
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)
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/ len(history_assistant_messages)
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if history_assistant_messages
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else 0
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)
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# Response Times
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response_times = []
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for message in history_assistant_messages:
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user_message_id = message.get("parentId", None)
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if user_message_id and user_message_id in messages_map:
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user_message = messages_map[user_message_id]
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# Ensure timestamps exist
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t1 = message.get("timestamp")
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t0 = user_message.get("timestamp")
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if t1 and t0:
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response_times.append(t1 - t0)
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average_response_time = (
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sum(response_times) / len(response_times) if response_times else 0
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)
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# Current Message List Logic (Main path)
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message_list = get_message_list(messages_map, message_id)
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message_count = len(message_list)
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models = {}
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for message in reversed(message_list):
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if message.get("role") == "assistant":
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model = message.get("model")
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if model:
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if model not in models:
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models[model] = 0
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models[model] += 1
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# Construct Aggregate Stats
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stats = AggregateChatStats(
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average_response_time=average_response_time,
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average_user_message_content_length=average_user_message_content_length,
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average_assistant_message_content_length=average_assistant_message_content_length,
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models=models,
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message_count=message_count,
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history_models=history_models,
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history_message_count=history_message_count,
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history_user_message_count=len(history_user_messages),
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history_assistant_message_count=len(history_assistant_messages),
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)
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# Construct Chat Body
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chat_body = ChatBody(
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history=ChatHistoryStats(messages=export_messages, currentId=message_id)
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)
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chat_stat = ChatStatsExport(
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id=chat.id,
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user_id=chat.user_id,
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created_at=chat.created_at,
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updated_at=chat.updated_at,
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tags=chat.meta.get("tags", []),
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stats=stats,
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chat=chat_body,
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)
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chat_stats_export_list.append(chat_stat)
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except Exception as e:
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log.debug(f"Error exporting stats for chat {chat.id}: {e}")
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continue
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return chat_stats_export_list, result.total
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@router.get("/stats/export", response_model=ChatStatsExportList)
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def export_chat_stats(
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async def export_chat_stats(
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request: Request,
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chat_id: Optional[str] = None,
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start_time: Optional[int] = None,
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@@ -244,155 +397,11 @@ def export_chat_stats(
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if end_time:
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filter["end_time"] = end_time
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result = Chats.get_chats_by_user_id(
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user.id,
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skip=skip,
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limit=limit,
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filter=filter,
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chat_stats_export_list, total = await asyncio.to_thread(
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calculate_chat_stats, user.id, skip, limit, filter
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)
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chat_stats_export_list = []
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for chat in result.items:
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try:
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messages_map = chat.chat.get("history", {}).get("messages", {})
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message_id = chat.chat.get("history", {}).get("currentId")
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history_models = {}
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history_message_count = len(messages_map)
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history_user_messages = []
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history_assistant_messages = []
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export_messages = {}
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for key, message in messages_map.items():
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try:
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content = message.get("content", "")
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if isinstance(content, str):
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content_length = len(content)
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else:
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content_length = 0 # Handle cases where content might be None or not string
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# Extract rating safely
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rating = message.get("annotation", {}).get("rating")
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tags = message.get("annotation", {}).get("tags")
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message_stat = MessageStats(
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id=message.get("id"),
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role=message.get("role"),
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model=message.get("model"),
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timestamp=message.get("timestamp"),
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content_length=content_length,
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token_count=None, # Populate if available, e.g. message.get("info", {}).get("token_count")
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rating=rating,
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tags=tags,
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)
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export_messages[key] = message_stat
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# --- Aggregation Logic (copied/adapted from usage stats) ---
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role = message.get("role", "")
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if role == "user":
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history_user_messages.append(message)
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elif role == "assistant":
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history_assistant_messages.append(message)
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model = message.get("model")
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if model:
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if model not in history_models:
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history_models[model] = 0
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history_models[model] += 1
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except Exception as e:
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log.debug(f"Error processing message {key}: {e}")
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continue
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# Calculate Averages
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average_user_message_content_length = (
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sum(
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len(m.get("content", ""))
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for m in history_user_messages
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if isinstance(m.get("content"), str)
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)
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/ len(history_user_messages)
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if history_user_messages
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else 0
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)
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average_assistant_message_content_length = (
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sum(
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len(m.get("content", ""))
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for m in history_assistant_messages
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if isinstance(m.get("content"), str)
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)
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/ len(history_assistant_messages)
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if history_assistant_messages
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else 0
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)
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# Response Times
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response_times = []
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for message in history_assistant_messages:
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user_message_id = message.get("parentId", None)
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if user_message_id and user_message_id in messages_map:
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user_message = messages_map[user_message_id]
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# Ensure timestamps exist
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t1 = message.get("timestamp")
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t0 = user_message.get("timestamp")
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if t1 and t0:
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response_times.append(t1 - t0)
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average_response_time = (
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sum(response_times) / len(response_times) if response_times else 0
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)
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# Current Message List Logic (Main path)
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message_list = get_message_list(messages_map, message_id)
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message_count = len(message_list)
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models = {}
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for message in reversed(message_list):
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if message.get("role") == "assistant":
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model = message.get("model")
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if model:
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if model not in models:
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models[model] = 0
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models[model] += 1
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# Construct Aggregate Stats
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stats = AggregateChatStats(
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average_response_time=average_response_time,
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average_user_message_content_length=average_user_message_content_length,
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average_assistant_message_content_length=average_assistant_message_content_length,
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models=models,
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message_count=message_count,
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history_models=history_models,
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history_message_count=history_message_count,
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history_user_message_count=len(history_user_messages),
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history_assistant_message_count=len(history_assistant_messages),
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)
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# Construct Chat Body
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chat_body = ChatBody(
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history=ChatHistoryStats(
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messages=export_messages, currentId=message_id
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)
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)
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chat_stat = ChatStatsExport(
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id=chat.id,
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user_id=chat.user_id,
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created_at=chat.created_at,
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updated_at=chat.updated_at,
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tags=chat.meta.get("tags", []),
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stats=stats,
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chat=chat_body,
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)
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chat_stats_export_list.append(chat_stat)
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except Exception as e:
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log.debug(f"Error exporting stats for chat {chat.id}: {e}")
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continue
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return ChatStatsExportList(
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items=chat_stats_export_list, total=result.total, page=page
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)
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return ChatStatsExportList(items=chat_stats_export_list, total=total, page=page)
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except Exception as e:
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log.debug(f"Error exporting chat stats: {e}")
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