148 lines
5.3 KiB
Python
148 lines
5.3 KiB
Python
from core.lib.prompts import bases
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from openai import AsyncOpenAI
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from asgiref.sync import sync_to_async
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from core.models import Message, ChatSession, AI, Person, Manipulation
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from core.util import logs
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import json
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from django.utils import timezone
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from core.messaging import ai
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from core.messaging.utils import messages_to_string
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SUMMARIZE_WHEN_EXCEEDING = 10
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SUMMARIZE_BY = 5
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MAX_SUMMARIES = 3 # Keep last 5 summaries
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log = logs.get_logger("prompts")
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async def delete_messages(queryset):
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await sync_to_async(queryset.delete, thread_sensitive=True)()
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async def truncate_and_summarize(
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chat_session: ChatSession,
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ai_obj: AI,
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):
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"""
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Summarizes messages in chunks to prevent unchecked growth.
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- Summarizes only non-summary messages.
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- Deletes older summaries if too many exist.
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- Ensures only messages belonging to `chat_session.user` are modified.
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"""
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user = chat_session.user # Store the user for ownership checks
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# 🔹 Get non-summary messages owned by the session's user
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messages = await sync_to_async(list)(
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Message.objects.filter(session=chat_session, user=user)
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.exclude(custom_author="SUM")
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.order_by("ts")
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)
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num_messages = len(messages)
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if num_messages >= SUMMARIZE_WHEN_EXCEEDING:
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log.info(f"Summarizing {SUMMARIZE_BY} messages for session {chat_session.id}")
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# Get the first `SUMMARIZE_BY` non-summary messages
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chunk_to_summarize = messages[:SUMMARIZE_BY]
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if not chunk_to_summarize:
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log.warning("No messages available to summarize (only summaries exist). Skipping summarization.")
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return
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last_ts = chunk_to_summarize[-1].ts # Preserve timestamp
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# 🔹 Get past summaries, keeping only the last few (owned by the session user)
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summary_messages = await sync_to_async(list)(
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Message.objects.filter(session=chat_session, user=user, custom_author="SUM")
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.order_by("ts")
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)
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# Delete old summaries if there are too many
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if len(summary_messages) >= MAX_SUMMARIES:
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summary_text = await summarize_conversation(chat_session, summary_messages, ai_obj, is_summary=True)
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chat_session.summary = summary_text
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await sync_to_async(chat_session.save)()
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log.info(f"Updated ChatSession summary with {len(summary_messages)} consolidated summaries.")
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num_to_delete = len(summary_messages) - MAX_SUMMARIES
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# await sync_to_async(
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# Message.objects.filter(session=chat_session, user=user, id__in=[msg.id for msg in summary_messages[:num_to_delete]])
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# .delete()
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# )()
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await delete_messages(
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Message.objects.filter(
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session=chat_session,
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user=user,
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id__in=[msg.id for msg in summary_messages[:num_to_delete]]
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)
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)
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log.info(f"Deleted {num_to_delete} old summaries.")
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# 🔹 Summarize conversation chunk
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summary_text = await summarize_conversation(chat_session, chunk_to_summarize, ai_obj)
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# 🔹 Replace old messages with the summary
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# await sync_to_async(
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# Message.objects.filter(session=chat_session, user=user, id__in=[msg.id for msg in chunk_to_summarize])
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# .delete()
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# )()
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await delete_messages(Message.objects.filter(session=chat_session, user=user, id__in=[msg.id for msg in chunk_to_summarize]))
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log.info(f"Deleted {len(chunk_to_summarize)} messages, replacing with summary.")
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# 🔹 Store new summary message (ensuring session=user consistency)
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await sync_to_async(Message.objects.create)(
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user=user,
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session=chat_session,
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custom_author="SUM",
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text=summary_text,
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ts=last_ts, # Preserve timestamp
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)
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# 🔹 Update ChatSession summary with latest merged summary
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# chat_session.summary = summary_text
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# await sync_to_async(chat_session.save)()
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async def summarize_conversation(
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chat_session: ChatSession,
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messages: list[Message],
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ai_obj,
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is_summary=False,
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):
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"""
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Summarizes all stored messages into a single summary.
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- If `is_summary=True`, treats input as previous summaries and merges them while keeping detail.
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- If `is_summary=False`, summarizes raw chat messages concisely.
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"""
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log.info(f"Summarizing messages for session {chat_session.id}")
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# Convert messages to structured text format
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message_texts = messages_to_string(messages)
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#log.info(f"Raw messages to summarize:\n{message_texts}")
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# Select appropriate summarization instruction
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instruction = (
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"Merge and refine these past summaries, keeping critical details and structure intact."
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if is_summary
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else "Summarize this conversation concisely, maintaining important details and tone."
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)
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summary_prompt = [
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{"role": "system", "content": instruction},
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{"role": "user", "content": f"Conversation:\n{message_texts}\n\nProvide a clear and structured summary:"},
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]
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# Generate AI-based summary
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summary_text = await ai.run_prompt(summary_prompt, ai_obj)
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#log.info(f"Generated Summary: {summary_text}")
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return f"Summary: {summary_text}"
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