262 lines
10 KiB
Python
262 lines
10 KiB
Python
import json
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from collections.abc import AsyncIterator
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from typing import Any
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from sqlalchemy import select
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from sqlalchemy.orm import Session
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from app.config import get_settings
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from app.character.service import CharacterService
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from app.chat.notices import (
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POMODORO_TOOL_NAMES,
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format_pomodoro_context,
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format_tool_notice,
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)
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from app.fitness.context import format_fitness_context, get_fitness_snapshot
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from app.homelab.context import format_datetime_context
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from app.homelab.openmeteo import format_weather_snapshot
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from app.memory.context import (
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format_identity_hint,
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format_memory_context,
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get_memory_snapshot,
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)
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from app.memory.extract import extract_after_turn
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from app.projects.context import format_projects_context, get_projects_snapshot
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from app.shopping.context import format_shopping_context, get_shopping_snapshot
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from app.db.models import ChatSession, Message
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from app.llm.client import LLMClient
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from app.pomodoro.service import PomodoroService
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from app.tools.registry import TOOL_DEFINITIONS, execute_tool
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MAX_TOOL_ROUNDS = 5
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MAX_HISTORY_MESSAGES = 40
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class ChatService:
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def __init__(self, db: Session):
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self.db = db
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self.llm = LLMClient()
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self.character = CharacterService()
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def list_sessions(self) -> list[ChatSession]:
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stmt = select(ChatSession).order_by(ChatSession.updated_at.desc())
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return list(self.db.scalars(stmt).all())
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def get_session(self, session_id: int) -> ChatSession | None:
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return self.db.get(ChatSession, session_id)
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def create_session(self, title: str = "Новый чат") -> ChatSession:
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session = ChatSession(title=title)
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self.db.add(session)
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self.db.commit()
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self.db.refresh(session)
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return session
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def delete_session(self, session_id: int) -> bool:
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session = self.get_session(session_id)
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if not session:
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return False
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self.db.delete(session)
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self.db.commit()
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return True
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def _build_system_prompt(self, session_id: int | None = None) -> str:
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status = PomodoroService(self.db).get_status()
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memory_snapshot = get_memory_snapshot(self.db, session_id)
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fitness_snapshot = get_fitness_snapshot(self.db)
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shopping_snapshot = get_shopping_snapshot(self.db)
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projects_snapshot = get_projects_snapshot(self.db)
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return (
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f"{self.character.get_system_prompt()}\n\n"
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f"{format_datetime_context(self.db)}\n\n"
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f"{format_memory_context(memory_snapshot)}\n\n"
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f"{format_fitness_context(fitness_snapshot)}\n\n"
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f"{format_shopping_context(shopping_snapshot)}\n\n"
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f"{format_weather_snapshot()}\n\n"
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f"{format_pomodoro_context(status)}\n\n"
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f"{format_projects_context(projects_snapshot)}"
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)
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def _build_messages(self, session: ChatSession) -> list[dict[str, Any]]:
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system_prompt = self._build_system_prompt(session.id)
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all_chat = [m for m in session.messages if m.role != "notice"]
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last_user = next((m.content for m in reversed(all_chat) if m.role == "user"), "")
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if last_user:
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memory_snapshot = get_memory_snapshot(self.db, session.id)
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identity_hint = format_identity_hint(memory_snapshot, last_user)
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if identity_hint:
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system_prompt += f"\n\n{identity_hint}"
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if len(all_chat) > MAX_HISTORY_MESSAGES:
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system_prompt += (
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f"\n\n[История чата: в контексте последние {MAX_HISTORY_MESSAGES} "
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f"из {len(all_chat)} сообщений. Раннее — в сводке сессии, если сохранена.]"
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)
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messages: list[dict[str, Any]] = [
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{"role": "system", "content": system_prompt}
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]
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chat_messages = all_chat[-MAX_HISTORY_MESSAGES:] if len(all_chat) > MAX_HISTORY_MESSAGES else all_chat
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for msg in chat_messages:
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content = msg.content or None
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entry: dict[str, Any] = {"role": msg.role, "content": content}
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if msg.tool_calls_json:
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entry["tool_calls"] = json.loads(msg.tool_calls_json)
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if not content:
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entry["content"] = None
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reasoning_data = LLMClient.deserialize_reasoning(msg.reasoning_json)
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if reasoning_data:
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LLMClient.attach_reasoning_to_message(
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entry,
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reasoning=reasoning_data.get("reasoning", ""),
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reasoning_details=reasoning_data.get("reasoning_details"),
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)
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if msg.role == "tool" and msg.tool_call_id:
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entry["tool_call_id"] = msg.tool_call_id
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messages.append(entry)
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return messages
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def _save_message(
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self,
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session_id: int,
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role: str,
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content: str = "",
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tool_calls: list[dict[str, Any]] | None = None,
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tool_call_id: str | None = None,
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reasoning_json: str | None = None,
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) -> Message:
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message = Message(
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session_id=session_id,
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role=role,
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content=content,
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tool_calls_json=json.dumps(tool_calls, ensure_ascii=False) if tool_calls else None,
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reasoning_json=reasoning_json,
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tool_call_id=tool_call_id,
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)
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self.db.add(message)
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session = self.get_session(session_id)
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if session and role == "user" and session.title == "Новый чат" and content:
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session.title = content[:60] + ("..." if len(content) > 60 else "")
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self.db.commit()
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self.db.refresh(message)
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return message
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async def stream_response(self, session_id: int, user_text: str) -> AsyncIterator[str]:
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session = self.get_session(session_id)
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if not session:
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yield self._sse("error", {"message": "Session not found"})
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return
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self._save_message(session_id, "user", user_text)
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messages = self._build_messages(session)
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for _ in range(MAX_TOOL_ROUNDS):
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content_parts: list[str] = []
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tool_calls: list[dict[str, Any]] = []
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reasoning = ""
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reasoning_details: list[Any] | None = None
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async for event in self.llm.stream_chat(messages, tools=TOOL_DEFINITIONS):
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if event["type"] == "content":
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content_parts.append(event["content"])
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yield self._sse("token", {"content": event["content"]})
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elif event["type"] == "reasoning":
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reasoning = event.get("reasoning", "") or reasoning
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if event.get("reasoning_details"):
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reasoning_details = event["reasoning_details"]
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elif event["type"] == "error":
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yield self._sse("error", {"message": event.get("content", "LLM error")})
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return
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elif event["type"] == "tool_calls":
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tool_calls = event["tool_calls"]
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if tool_calls:
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assistant_msg: dict[str, Any] = {
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"role": "assistant",
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"content": "".join(content_parts) or None,
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"tool_calls": tool_calls,
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}
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LLMClient.attach_reasoning_to_message(
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assistant_msg,
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reasoning=reasoning,
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reasoning_details=reasoning_details,
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)
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reasoning_json = LLMClient.serialize_reasoning(
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reasoning=reasoning,
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reasoning_details=reasoning_details,
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)
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messages.append(assistant_msg)
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self._save_message(
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session_id,
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"assistant",
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"".join(content_parts),
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tool_calls=tool_calls,
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reasoning_json=reasoning_json,
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)
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for tool_call in tool_calls:
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fn = tool_call["function"]
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args = LLMClient.parse_tool_arguments(fn.get("arguments", ""))
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result = await execute_tool(
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self.db, fn["name"], args, session_id=session_id
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)
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tool_message = {
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"role": "tool",
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"tool_call_id": tool_call["id"],
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"content": result,
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}
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messages.append(tool_message)
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self._save_message(session_id, "tool", result, tool_call_id=tool_call["id"])
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notice = format_tool_notice(fn["name"], result)
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if notice:
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self._save_message(session_id, "notice", notice)
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yield self._sse("notice", {"content": notice})
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if fn["name"] in POMODORO_TOOL_NAMES:
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yield self._sse(
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"pomodoro",
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{"name": fn["name"], "result": json.loads(result)},
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)
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continue
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final_content = "".join(content_parts)
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if not final_content.strip() and reasoning:
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final_content = reasoning
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if not final_content.strip():
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yield self._sse(
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"error",
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{
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"message": (
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"Модель не вернула текст. Для deepseek-v4-pro: "
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"OPENROUTER_TOOLS_ENABLED=true и OPENROUTER_REASONING_EFFORT=none. "
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"Для памяти: MEMORY_EXTRACT_MODEL=deepseek/deepseek-chat."
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),
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},
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)
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return
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self._save_message(session_id, "assistant", final_content)
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memory_meta: dict[str, Any] = {}
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if get_settings().memory_auto_extract:
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extraction = await extract_after_turn(
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self.db,
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session_id,
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user_text,
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final_content,
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)
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memory_meta = {
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"memory_extracted": extraction.get("count", 0),
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"memory_saved": extraction.get("saved", []),
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}
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yield self._sse("done", memory_meta)
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return
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yield self._sse("error", {"message": "Too many tool call rounds"})
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@staticmethod
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def _sse(event: str, data: dict[str, Any]) -> str:
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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