dekun
207 lines
7.1 KiB
Python
207 lines
7.1 KiB
Python
"""大模型解读（OpenAI 兼容接口 + 图表图片）。"""
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import asyncio
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import base64
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import logging
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from datetime import datetime
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import httpx
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from .chart_image import render_daily_chart_png_async
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from .config import settings
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from .db import save_llm_interpretation
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from .stats import compute_three_day_stats
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logger = logging.getLogger(__name__)
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_interpret_lock = asyncio.Lock()
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_interpret_state: dict = {
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"running": False,
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"current_symbol": "",
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"done": 0,
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"total": 0,
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"batch_id": "",
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"last_error": "",
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}
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def get_interpret_state() -> dict:
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return dict(_interpret_state)
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def init_interpret_batch() -> dict:
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"""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）。"""
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if _interpret_lock.locked() or _interpret_state.get("running"):
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return {"ok": False, "message": "解读任务进行中", **get_interpret_state()}
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stats = compute_three_day_stats()
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if not stats.get("ok"):
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return {"ok": False, "message": stats.get("message", "统计数据未就绪")}
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sym_list = stats.get("symbols") or [x["symbol"] for x in stats.get("items", [])]
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if not sym_list:
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return {"ok": False, "message": "三日交集为空"}
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bid = datetime.now().strftime("%Y-%m-%d-%H%M")
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_interpret_state.update(
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{
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"running": True,
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"current_symbol": "",
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"done": 0,
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"total": len(sym_list),
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"batch_id": bid,
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"last_error": "",
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}
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)
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return {"ok": True, "batch_id": bid, "total": len(sym_list), **get_interpret_state()}
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def _api_url() -> str:
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base = settings.llm_base_url.rstrip("/")
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if base.endswith("/v1"):
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return f"{base}/chat/completions"
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return f"{base}/v1/chat/completions"
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def _build_prompt(symbol: str, stats_row: dict | None) -> str:
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lines = [
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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。",
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"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。",
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"控制在 200-350 字，条理清晰，不要废话。",
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]
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if stats_row:
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t, y, b = stats_row.get("today", {}), stats_row.get("yesterday", {}), stats_row.get("daybefore", {})
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lines.append(
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f"\n三日均为成交额Top30交集："
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f"\n今日 排名{t.get('rank')} 涨跌{t.get('price_change_pct_fmt')} 额{t.get('quote_volume_fmt')}"
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f"\n昨日 排名{y.get('rank')} 涨跌{y.get('price_change_pct_fmt')} 额{y.get('quote_volume_fmt')}"
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f"\n前日 排名{b.get('rank')} 涨跌{b.get('price_change_pct_fmt')} 额{b.get('quote_volume_fmt')}"
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f"\n资金费率(当前)：{t.get('funding_rate_fmt', '—')}"
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)
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return "\n".join(lines)
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async def interpret_symbol(
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symbol: str,
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stats_row: dict | None = None,
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batch_id: str | None = None,
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) -> str:
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if not settings.llm_api_key.strip():
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raise RuntimeError("LLM_API_KEY 未配置")
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png = await render_daily_chart_png_async(symbol, settings.chart_kline_limit)
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b64 = base64.standard_b64encode(png).decode("ascii")
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prompt = _build_prompt(symbol, stats_row)
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payload = {
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"model": settings.llm_model,
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"messages": [
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{
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"role": "user",
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"content": [
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{"type": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/png;base64,{b64}"},
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},
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],
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}
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],
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"max_tokens": 800,
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"temperature": 0.4,
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}
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headers = {
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"Authorization": f"Bearer {settings.llm_api_key}",
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"Content-Type": "application/json",
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}
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async with httpx.AsyncClient(timeout=120.0) as client:
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resp = await client.post(_api_url(), json=payload, headers=headers)
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if resp.status_code >= 400:
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# 部分模型不支持 vision，降级纯文本
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logger.warning("LLM vision failed %s, fallback text", resp.status_code)
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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)"}]
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resp = await client.post(_api_url(), json=payload, headers=headers)
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resp.raise_for_status()
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data = resp.json()
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content = data["choices"][0]["message"]["content"]
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bid = batch_id or datetime.now().strftime("%Y-%m-%d-%H%M")
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save_llm_interpretation(symbol, bid, content)
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return content
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async def run_interpretation_batch(
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symbols: list[str] | None = None,
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*,
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batch_id: str | None = None,
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) -> dict:
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if _interpret_lock.locked():
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return {"ok": False, "message": "解读任务进行中"}
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stats = compute_three_day_stats()
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if not stats.get("ok"):
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_interpret_state["running"] = False
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return {"ok": False, "message": stats.get("message", "统计数据未就绪")}
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sym_list = symbols or stats.get("symbols") or [x["symbol"] for x in stats.get("items", [])]
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if not sym_list:
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_interpret_state["running"] = False
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return {"ok": False, "message": "三日交集为空"}
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stats_map = {x["symbol"]: x for x in stats.get("items", [])}
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bid = batch_id or _interpret_state.get("batch_id") or datetime.now().strftime("%Y-%m-%d-%H%M")
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interval = settings.llm_symbol_interval_sec
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async with _interpret_lock:
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_interpret_state.update(
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{
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"running": True,
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"current_symbol": "",
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"done": _interpret_state.get("done", 0),
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"total": len(sym_list),
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"batch_id": bid,
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}
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)
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for i, sym in enumerate(sym_list):
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_interpret_state["current_symbol"] = sym
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try:
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await interpret_symbol(sym, stats_map.get(sym), bid)
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logger.info("LLM interpreted %s (%d/%d)", sym, i + 1, len(sym_list))
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except Exception as e:
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_interpret_state["last_error"] = str(e)
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logger.error("LLM %s failed: %s", sym, e)
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save_llm_interpretation(sym, bid, f"[解读失败] {e}")
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_interpret_state["done"] = i + 1
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if i < len(sym_list) - 1:
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await asyncio.sleep(interval)
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_interpret_state["running"] = False
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_interpret_state["current_symbol"] = ""
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return {
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"ok": True,
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"batch_id": bid,
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"count": len(sym_list),
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"interval_sec": interval,
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}
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def schedule_interpret_background(symbols: list[str] | None = None) -> None:
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"""后台启动解读，不阻塞请求。"""
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info = init_interpret_batch()
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if not info.get("ok"):
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logger.info("Startup LLM skip: %s", info.get("message"))
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return
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bid = info.get("batch_id")
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async def _run():
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try:
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await run_interpretation_batch(symbols, batch_id=bid)
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except Exception as e:
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logger.error("Background LLM batch failed: %s", e)
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_interpret_state["running"] = False
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asyncio.create_task(_run())
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