Refactor and implement queueing messages
This commit is contained in:
@@ -4,10 +4,9 @@ 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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import asyncio
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from django.utils import timezone
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import random
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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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@@ -16,96 +15,13 @@ MAX_SUMMARIES = 3 # Keep last 5 summaries
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log = logs.get_logger("prompts")
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def gen_prompt(msg: dict, person: Person, manip: Manipulation, chat_history: str):
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"""
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Generate a structured prompt using the attributes of the provided Person and Manipulation models.
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"""
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now = timezone.now()
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persona = manip.persona
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system_message = (
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"You are my digital persona, responding on my behalf while embodying my personality, preferences, and unique style.\n\n"
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"### Persona Profile ###\n"
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f"- **MBTI:** {persona.mbti} ({persona.mbti_identity} balance)\n"
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f"- **Tone:** {persona.tone} | **Humor:** {persona.humor_style}\n"
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f"- **Core Values:** {persona.core_values}\n"
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f"- **Communication Style:** {persona.communication_style}\n"
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f"- **Flirting Style:** {persona.flirting_style}\n"
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f"- **Likes:** {persona.likes} | **Dislikes:** {persona.dislikes}\n"
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f"- **Response Tactics:** {persona.response_tactics}\n"
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f"- **Persuasion Techniques:** {persona.persuasion_tactics}\n"
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f"- **Boundaries:** {persona.boundaries} | **Adaptability:** {persona.adaptability}%\n\n"
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"### Contact Information ###\n"
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f"- **Summary:** {person.summary or 'N/A'}\n"
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f"- **Profile:** {person.profile or 'N/A'}\n"
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f"- **Revealed Details:** {person.revealed or 'N/A'}\n"
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f"- **Sentiment Score:** {person.sentiment}\n"
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f"- **Timezone:** {person.timezone or 'N/A'}\n"
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f"- **Last Interaction:** {person.last_interaction or 'Never'}\n"
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f"- **Current Date/Time:** {now}\n\n"
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"### Conversation Context ###\n"
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f"{chat_history if chat_history else 'No prior chat history.'}\n\n"
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"### Response Guidelines ###\n"
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"- **Engagement**: Keep responses engaging, with a balance of wit, depth, and confidence.\n"
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"- **Flirting**: Be direct, playful, and, when appropriate, subtly provocative—without hesitation.\n"
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"- **Pauses**: Use double newlines (`\\n\\n`) to pause where it enhances realism.\n"
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"- **Flow Awareness**: Maintain continuity, avoid redundancy, and adjust response length based on interaction.\n"
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)
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user_message = f"[{msg['timestamp']}] <{person.name}> {msg['text']}"
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return [
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{"role": "system", "content": system_message},
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{"role": "user", "content": user_message},
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]
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async def run_context_prompt(
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c,
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prompt: list[str],
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ai: AI,
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):
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cast = {"api_key": ai.api_key}
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if ai.base_url is not None:
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cast["api_key"] = ai.base_url
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client = AsyncOpenAI(**cast)
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await c.start_typing()
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response = await client.chat.completions.create(
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model=ai.model,
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messages=prompt,
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)
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await c.stop_typing()
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content = response.choices[0].message.content
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return content
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async def run_prompt(
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prompt: list[str],
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ai: AI,
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):
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cast = {"api_key": ai.api_key}
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if ai.base_url is not None:
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cast["api_key"] = ai.base_url
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client = AsyncOpenAI(**cast)
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response = await client.chat.completions.create(
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model=ai.model,
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messages=prompt,
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)
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content = response.choices[0].message.content
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return content
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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: AI,
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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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@@ -123,7 +39,6 @@ async def truncate_and_summarize(
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)
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num_messages = len(messages)
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log.info(f"num_messages for {chat_session.id}: {num_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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@@ -144,13 +59,12 @@ async def truncate_and_summarize(
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)
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# Delete old summaries if there are too many
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log.info(f"Summaries: {len(summary_messages)}")
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if len(summary_messages) >= MAX_SUMMARIES:
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summary_text = await summarize_conversation(chat_session, summary_messages, ai, is_summary=True)
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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)} summarized summaries.")
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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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@@ -167,14 +81,13 @@ async def truncate_and_summarize(
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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)
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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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log.info("About to delete messages1")
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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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@@ -191,23 +104,13 @@ async def truncate_and_summarize(
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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("✅ Summarization cycle complete.")
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def messages_to_string(messages: list):
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"""
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Converts message objects to a formatted string, showing custom_author if set.
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"""
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message_texts = [
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f"[{msg.ts}] <{msg.custom_author if msg.custom_author else msg.session.identifier.person.name}> {msg.text}"
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for msg in messages
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]
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return "\n".join(message_texts)
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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,
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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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@@ -236,62 +139,9 @@ async def summarize_conversation(
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]
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# Generate AI-based summary
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summary_text = await run_prompt(summary_prompt, ai)
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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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async def natural_send_message(chat_session, ts, c, text):
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"""
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Parses and sends messages with natural delays based on message length.
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Args:
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chat_session: The active chat session.
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ts: Timestamp of the message.
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c: The context or object with `.send()`, `.start_typing()`, and `.stop_typing()` methods.
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text: A string containing multiple messages separated by double newlines (`\n\n`).
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Behavior:
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- Short messages are sent quickly with minimal delay.
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- Longer messages include a "thinking" pause before typing.
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- Typing indicator (`c.start_typing() / c.stop_typing()`) is used dynamically.
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"""
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await sync_to_async(Message.objects.create)(
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user=chat_session.user,
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session=chat_session,
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custom_author="BOT",
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text=text,
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ts=ts + 1,
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)
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parts = text.split("\n\n") # Split into separate messages
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log.info(f"Processing messages: {parts}")
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for message in parts:
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message = message.strip()
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if not message:
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continue
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# Compute natural "thinking" delay based on message length
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base_delay = 0.8 # Minimum delay
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length_factor = len(message) / 25
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# ~50 chars ≈ +1s processing
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# ~25 chars ≈ +1s processing
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natural_delay = min(base_delay + length_factor, 10) # Cap at 5s max
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# Decide when to start thinking *before* typing
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if natural_delay > 3.5: # Only delay if response is long
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await asyncio.sleep(natural_delay - 3.5) # "Thinking" pause before typing
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# Start typing
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await c.start_typing()
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await asyncio.sleep(natural_delay) # Finish remaining delay
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await c.stop_typing()
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# Send the message
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await c.send(message)
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# Optional: Small buffer between messages to prevent rapid-fire responses
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await asyncio.sleep(0.5)
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