crypto_monitor/manual_trading_hub/hub_ai/supervisor.py

"""交易监管:AI 评语与用户回聊."""
from __future__ import annotations

import sys
from pathlib import Path
from typing import Any, Optional

_REPO_ROOT = Path(__file__).resolve().parents[2]
if str(_REPO_ROOT) not in sys.path:
    sys.path.insert(0, str(_REPO_ROOT))

from lib.ai.ai_client import ai_generate  # noqa: E402

from hub_ai.client import generate_text, model_label
from hub_ai.config import (
    CHAT_MAX_OUTPUT_TOKENS,
    CHAT_TEMPERATURE,
    trading_day_reset_hour,
)
from hub_ai.context import build_chat_context, format_chat_context_for_chat, format_chat_position_overview
from hub_ai.prompts import SUPERVISOR_SYSTEM, build_supervisor_ai_prompt, build_supervisor_chat_prompt
from hub_ai.supervisor_store import (
    append_supervisor_ai_message,
    ensure_supervisor_session,
    get_supervisor_session_state,
)
from hub_ai.store import append_chat_message
from hub_ai.text_util import is_ai_error_reply
from hub_supervisor_lib import build_supervisor_fallback_reply
from lib.hub.hub_trades_lib import current_trading_day

SUPERVISOR_AI_MAX_TOKENS = 320


def generate_supervisor_ai_reply(
    *,
    event: dict,
    warnings: list[dict],
    trading_day: str,
    session_id: str,
    exchanges: list[dict],
) -> str:
    ctx = build_chat_context(exchanges, trading_day=trading_day)
    brief = format_chat_position_overview(ctx) + "\n" + format_chat_context_for_chat(
        ctx, max_chars=2400
    )
    user_prompt = build_supervisor_ai_prompt(
        context_text=brief,
        trading_day=trading_day,
        event=event,
        warnings=warnings,
    )
    prompt = f"{SUPERVISOR_SYSTEM.strip()}\n\n---\n\n{user_prompt.strip()}"
    text = ai_generate(prompt, temperature=0.35, max_tokens=SUPERVISOR_AI_MAX_TOKENS)
    text = str(text or "").strip()
    if not text or is_ai_error_reply(text):
        return build_supervisor_fallback_reply(event, warnings)
    return text


def make_supervisor_ai_reply_fn(exchanges: list[dict]):
    def _fn(*, event: dict, warnings: list[dict], trading_day: str, session_id: str) -> str:
        return generate_supervisor_ai_reply(
            event=event,
            warnings=warnings or [],
            trading_day=trading_day,
            session_id=session_id,
            exchanges=exchanges,
        )

    return _fn


def send_supervisor_chat(
    exchanges: list[dict],
    message: str,
    *,
    trading_day: str | None = None,
) -> dict[str, Any]:
    text = (message or "").strip()
    if not text:
        return {"ok": False, "msg": "消息不能为空"}
    day = (trading_day or "").strip()[:10] or current_trading_day(
        reset_hour=trading_day_reset_hour()
    )
    session = ensure_supervisor_session(day)
    sid = str(session.get("id") or "")
    prior = session.get("messages") or []
    ctx = build_chat_context(exchanges, trading_day=day)
    brief = format_chat_context_for_chat(ctx, max_chars=6000)
    recent = []
    for m in prior[-8:]:
        role = m.get("role")
        if role not in ("user", "assistant", "system"):
            continue
        label = {"user": "用户", "assistant": "监管", "system": "系统"}.get(role, role)
        recent.append(f"{label}:{str(m.get('content') or '').strip()}")
    user_prompt = build_supervisor_chat_prompt(
        context_text=brief,
        trading_day=day,
        history_lines="\n".join(recent),
        user_message=text,
    )
    reply = generate_text(
        system=SUPERVISOR_SYSTEM,
        user=user_prompt,
        temperature=min(0.4, CHAT_TEMPERATURE),
        max_tokens=min(768, CHAT_MAX_OUTPUT_TOKENS),
        max_continuations=1,
    )
    reply = str(reply or "").strip()
    if not reply or is_ai_error_reply(reply):
        return {"ok": False, "msg": "AI 暂时不可用,请稍后再试", "session_id": sid}
    append_chat_message(sid, "user", text)
    session = append_supervisor_ai_message(sid, reply)
    state = get_supervisor_session_state(day)
    return {
        "ok": True,
        "trading_day": day,
        "session": session,
        "reply": reply,
        "model": model_label(),
        "message_count": state.get("message_count"),
        "unread_system": state.get("unread_system"),
    }