231 lines
8.1 KiB
Python
231 lines
8.1 KiB
Python
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"""Context manager: builds the LLM prompt context with guaranteed-recent + dynamic summarization."""
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from __future__ import annotations
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import json
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import uuid
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from typing import Any, Dict, List, Optional
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.core.llm import LlmClient
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from app.core.settings_service import cast_setting, get_all_settings
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from app.logging_setup import get_logger
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from app.models import Message, World
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from app.prompts.templates import get_prompt
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log = get_logger("context")
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async def build_orchestrator_messages(
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db: AsyncSession,
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world: World,
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session_id: uuid.UUID,
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action_text: str,
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) -> tuple[List[Dict[str, Any]], Dict[str, Any]]:
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"""Build the messages list for the orchestrator LLM call.
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Returns (messages, settings_used).
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"""
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settings_map = await get_all_settings(db)
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recent_n = int(cast_setting("context.recent_messages", settings_map.get("context.recent_messages", 10)))
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threshold = int(cast_setting("context.compress_threshold", settings_map.get("context.compress_threshold", 20)))
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summary_n = int(cast_setting("context.summary_messages", settings_map.get("context.summary_messages", 10)))
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# Load all messages ordered by seq
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result = await db.execute(
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select(Message).where(Message.session_id == session_id).order_by(Message.seq)
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)
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all_msgs: List[Message] = list(result.scalars().all())
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# Check if we need to compress
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if len(all_msgs) >= threshold:
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await _maybe_compress(db, session_id, all_msgs, summary_n, recent_n, world, settings_map)
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# Reload after compression
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result = await db.execute(
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select(Message).where(Message.session_id == session_id).order_by(Message.seq)
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)
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all_msgs = list(result.scalars().all())
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# Get summary message (the latest summary before the recent window)
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summary_text = ""
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visible_msgs = [m for m in all_msgs if not m.hidden]
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if len(visible_msgs) > recent_n:
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# Look for the latest summary
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summaries = [m for m in all_msgs if m.kind == "summary"]
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if summaries:
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summary_text = summaries[-1].content
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recent = visible_msgs[-recent_n:] if visible_msgs else []
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# Build orchestrator system prompt with current state
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defn = world.definition or {}
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system_prompt_template = get_prompt("orchestrator", world.language)
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player_state = world.state.get("player", {}) if world.state else {}
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system_prompt = system_prompt_template.format(
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world_name=world.name,
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setting_description=defn.get("setting_description", "")[:800],
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rules=json.dumps(defn.get("rules", {}), ensure_ascii=False)[:600],
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current_time=world.current_time or "",
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player_state=json.dumps(player_state, ensure_ascii=False)[:600],
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plot_rails=json.dumps(defn.get("plot_rails", {}), ensure_ascii=False)[:400],
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summary=summary_text or "(нет сводки)",
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)
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messages: List[Dict[str, Any]] = [{"role": "system", "content": system_prompt}]
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# Add summary as a system note if present
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if summary_text:
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messages.append({"role": "system", "content": f"Сводка прошлого:\n{summary_text}"})
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# Add recent visible messages
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for m in recent:
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if m.kind == "player_action":
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messages.append({"role": "user", "content": m.content})
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elif m.kind == "narrative_step":
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messages.append({"role": "assistant", "content": m.content})
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# The current action
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messages.append({"role": "user", "content": f'Действие игрока: "{action_text}"'})
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return messages, settings_map
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async def _maybe_compress(
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db: AsyncSession,
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session_id: uuid.UUID,
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all_msgs: List[Message],
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summary_n: int,
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recent_n: int,
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world: World,
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settings_map: Dict[str, Any],
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) -> None:
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"""If history exceeds threshold, summarize older messages into a single summary message."""
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visible = [m for m in all_msgs if not m.hidden]
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if len(visible) <= recent_n + summary_n:
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return
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# Take the messages that will be summarized (everything before the recent window)
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to_summarize = visible[:-recent_n]
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if not to_summarize:
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return
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# Build summarization input
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summary_input_lines = []
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for m in to_summarize:
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prefix = {
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"player_action": "Игрок",
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"narrative_step": "Сцена",
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"summary": "Сводка",
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"orchestrator_plan": "GM",
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"technical_offscreen": "За кадром",
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}.get(m.kind, m.kind)
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summary_input_lines.append(f"{prefix}: {m.content[:300]}")
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summary_input = "\n\n".join(summary_input_lines)
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llm = LlmClient(settings_map)
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system_prompt = get_prompt("summarizer", world.language)
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response = await llm.chat(
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messages=[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": summary_input[:4000]},
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],
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temperature=float(cast_setting("llm.summary_temperature", settings_map.get("llm.summary_temperature", 0.3))),
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max_tokens=300,
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purpose="summary",
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session_id=session_id,
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db=db,
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)
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# Parse summary response
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summary_text = response.text
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facts: List[Dict[str, Any]] = []
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import re as _re
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json_match = _re.search(r"\{[\s\S]*\}", response.text)
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if json_match:
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try:
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data = json.loads(json_match.group(0))
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summary_text = data.get("summary", response.text)
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facts = data.get("facts", [])
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except json.JSONDecodeError:
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pass
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# Create summary message
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next_seq = (max((m.seq for m in all_msgs), default=0)) + 1
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summary_msg = Message(
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session_id=session_id,
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seq=next_seq,
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role="system",
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kind="summary",
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content=summary_text,
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payload={"summarized_count": len(to_summarize), "facts": facts},
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is_pinned=True,
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hidden=False,
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)
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db.add(summary_msg)
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# Hide the summarized messages (but keep them in DB)
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for m in to_summarize:
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m.hidden = True
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# Index facts into RAG glossary
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if facts:
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from app.core.rag import get_rag
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from app.models import GlossaryEntry
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rag = await get_rag(settings_map)
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for f in facts:
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if not isinstance(f, dict):
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continue
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entry = GlossaryEntry(
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world_id=world.id,
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session_id=session_id,
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kind=f.get("kind", "lore"),
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name=f.get("name", "unknown"),
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description=f.get("description", ""),
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payload={},
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)
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db.add(entry)
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await db.flush()
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await rag.upsert_glossary(
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world_id=world.id,
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entry_id=entry.id,
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kind=entry.kind,
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name=entry.name,
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description=entry.description,
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payload={},
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settings_map=settings_map,
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)
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await db.commit()
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log.info("context_compressed", session_id=str(session_id), summarized=len(to_summarize))
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async def build_step_writer_messages(
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db: AsyncSession,
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world: World,
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session_id: uuid.UUID,
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outcome: str,
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narrative_prompt: str,
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) -> List[Dict[str, Any]]:
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"""Build messages for the step writer LLM call."""
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defn = world.definition or {}
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player_state = world.state.get("player", {}) if world.state else {}
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system_prompt = get_prompt("step_writer", world.language).format(
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setting_description=defn.get("setting_description", "")[:600],
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current_time=world.current_time or "",
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player_state=json.dumps(player_state, ensure_ascii=False)[:400],
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outcome=outcome,
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narrative_prompt=narrative_prompt[:600],
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)
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return [{"role": "system", "content": system_prompt}]
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async def build_subagent_messages(
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world: World,
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task: str,
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context: str,
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) -> List[Dict[str, Any]]:
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"""Build messages for a clean-context sub-agent call."""
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system_prompt = get_prompt("subagent", world.language).format(task=task, context=context[:600])
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return [{"role": "system", "content": system_prompt}]
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