"""大模型解读（OpenAI 兼容接口 + 图表图片）。""" import asyncio import base64 import logging from datetime import datetime import httpx from .chart_image import render_daily_chart_png_async from .config import settings from .db import save_llm_interpretation from .stats import compute_three_day_stats logger = logging.getLogger(__name__) _interpret_lock = asyncio.Lock() _interpret_state: dict = { "running": False, "current_symbol": "", "done": 0, "total": 0, "batch_id": "", "last_error": "", } def get_interpret_state() -> dict: return dict(_interpret_state) def init_interpret_batch() -> dict: """同步初始化批次（API 立即返回 batch_id，避免前端刷新拉错旧批次）。""" if _interpret_lock.locked() or _interpret_state.get("running"): return {"ok": False, "message": "解读任务进行中", **get_interpret_state()} stats = compute_three_day_stats() if not stats.get("ok"): return {"ok": False, "message": stats.get("message", "统计数据未就绪")} sym_list = stats.get("symbols") or [x["symbol"] for x in stats.get("items", [])] if not sym_list: return {"ok": False, "message": "三日交集为空"} bid = datetime.now().strftime("%Y-%m-%d-%H%M") _interpret_state.update( { "running": True, "current_symbol": "", "done": 0, "total": len(sym_list), "batch_id": bid, "last_error": "", } ) return {"ok": True, "batch_id": bid, "total": len(sym_list), **get_interpret_state()} def _api_url() -> str: base = settings.llm_base_url.rstrip("/") if base.endswith("/v1"): return f"{base}/chat/completions" return f"{base}/v1/chat/completions" def _build_prompt(symbol: str, stats_row: dict | None) -> str: lines = [ f"你是加密货币合约分析师。请根据附图（{symbol} 近300日K+成交量）及数据给出中文简析。", "关注：趋势、关键支撑阻力、成交量变化、资金费率含义、未来1-3日可能节奏。", "控制在 200-350 字，条理清晰，不要废话。", ] if stats_row: t, y, b = stats_row.get("today", {}), stats_row.get("yesterday", {}), stats_row.get("daybefore", {}) lines.append( f"\n三日均为成交额Top30交集：" f"\n今日 排名{t.get('rank')} 涨跌{t.get('price_change_pct_fmt')} 额{t.get('quote_volume_fmt')}" f"\n昨日 排名{y.get('rank')} 涨跌{y.get('price_change_pct_fmt')} 额{y.get('quote_volume_fmt')}" f"\n前日 排名{b.get('rank')} 涨跌{b.get('price_change_pct_fmt')} 额{b.get('quote_volume_fmt')}" f"\n资金费率(当前)：{t.get('funding_rate_fmt', '—')}" ) return "\n".join(lines) async def interpret_symbol( symbol: str, stats_row: dict | None = None, batch_id: str | None = None, ) -> str: if not settings.llm_api_key.strip(): raise RuntimeError("LLM_API_KEY 未配置") png = await render_daily_chart_png_async(symbol, settings.chart_kline_limit) b64 = base64.standard_b64encode(png).decode("ascii") prompt = _build_prompt(symbol, stats_row) payload = { "model": settings.llm_model, "messages": [ { "role": "user", "content": [ {"type": "text", "text": prompt}, { "type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}, }, ], } ], "max_tokens": 800, "temperature": 0.4, } headers = { "Authorization": f"Bearer {settings.llm_api_key}", "Content-Type": "application/json", } async with httpx.AsyncClient(timeout=120.0) as client: resp = await client.post(_api_url(), json=payload, headers=headers) if resp.status_code >= 400: # 部分模型不支持 vision，降级纯文本 logger.warning("LLM vision failed %s, fallback text", resp.status_code) payload["messages"] = [{"role": "user", "content": prompt + "\n(附图日K+成交量未能传入，请基于数据简析)"}] resp = await client.post(_api_url(), json=payload, headers=headers) resp.raise_for_status() data = resp.json() content = data["choices"][0]["message"]["content"] bid = batch_id or datetime.now().strftime("%Y-%m-%d-%H%M") save_llm_interpretation(symbol, bid, content) return content async def run_interpretation_batch( symbols: list[str] | None = None, *, batch_id: str | None = None, ) -> dict: if _interpret_lock.locked(): return {"ok": False, "message": "解读任务进行中"} stats = compute_three_day_stats() if not stats.get("ok"): _interpret_state["running"] = False return {"ok": False, "message": stats.get("message", "统计数据未就绪")} sym_list = symbols or stats.get("symbols") or [x["symbol"] for x in stats.get("items", [])] if not sym_list: _interpret_state["running"] = False return {"ok": False, "message": "三日交集为空"} stats_map = {x["symbol"]: x for x in stats.get("items", [])} bid = batch_id or _interpret_state.get("batch_id") or datetime.now().strftime("%Y-%m-%d-%H%M") interval = settings.llm_symbol_interval_sec async with _interpret_lock: _interpret_state.update( { "running": True, "current_symbol": "", "done": _interpret_state.get("done", 0), "total": len(sym_list), "batch_id": bid, } ) for i, sym in enumerate(sym_list): _interpret_state["current_symbol"] = sym try: await interpret_symbol(sym, stats_map.get(sym), bid) logger.info("LLM interpreted %s (%d/%d)", sym, i + 1, len(sym_list)) except Exception as e: _interpret_state["last_error"] = str(e) logger.error("LLM %s failed: %s", sym, e) save_llm_interpretation(sym, bid, f"[解读失败] {e}") _interpret_state["done"] = i + 1 if i < len(sym_list) - 1: await asyncio.sleep(interval) _interpret_state["running"] = False _interpret_state["current_symbol"] = "" return { "ok": True, "batch_id": bid, "count": len(sym_list), "interval_sec": interval, } def schedule_interpret_background(symbols: list[str] | None = None) -> None: """后台启动解读，不阻塞请求。""" info = init_interpret_batch() if not info.get("ok"): logger.info("Startup LLM skip: %s", info.get("message")) return bid = info.get("batch_id") async def _run(): try: await run_interpretation_batch(symbols, batch_id=bid) except Exception as e: logger.error("Background LLM batch failed: %s", e) _interpret_state["running"] = False asyncio.create_task(_run())