qchen34--tradingagents
wehub-resource-sync
604 行
21 KiB
Python
604 行
21 KiB
Python
from __future__ import annotations
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import json
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import re
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import time
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from collections import Counter
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from pathlib import Path
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from typing import Any
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from langchain_core.messages import HumanMessage
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from tradingagents.llm_clients.base_client import normalize_content
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from tradingagents.llm_clients.factory import create_llm_client
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from web.services.x_brief_data import brief_config_path, x_cache_dir
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def _write_debug_file(filename: str, content: str) -> None:
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debug_dir = x_cache_dir() / "debug"
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debug_dir.mkdir(parents=True, exist_ok=True)
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(debug_dir / filename).write_text(content, encoding="utf-8")
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def _llm_for_role(cfg: dict[str, Any], role: str):
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if role == "orchestrator":
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provider = cfg.get("xbrief_orchestrator_provider") or cfg.get("llm_provider")
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model = cfg.get("xbrief_orchestrator_model") or cfg.get("deep_think_llm") or cfg.get("quick_think_llm")
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base_url = cfg.get("xbrief_orchestrator_base_url") or cfg.get("backend_url")
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else:
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provider = cfg.get("xbrief_translate_provider") or cfg.get("llm_provider")
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model = cfg.get("xbrief_translate_model") or cfg.get("quick_think_llm") or cfg.get("deep_think_llm")
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base_url = cfg.get("xbrief_translate_base_url") or cfg.get("backend_url")
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client = create_llm_client(
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provider=provider,
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model=model,
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base_url=base_url,
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google_thinking_level=cfg.get("google_thinking_level"),
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openai_reasoning_effort=cfg.get("openai_reasoning_effort"),
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anthropic_effort=cfg.get("anthropic_effort"),
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)
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print(f"[xbrief][llm] role={role} provider={provider} model={model} base_url={base_url}")
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return client.get_llm()
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def _to_text(resp: Any) -> str:
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if hasattr(resp, "content"):
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normalize_content(resp)
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c = resp.content
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return c if isinstance(c, str) else str(c)
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return str(resp)
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def _parse_json_obj(text: str) -> dict[str, Any]:
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raw = text.strip()
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try:
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obj = json.loads(raw)
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if isinstance(obj, dict):
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return obj
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except json.JSONDecodeError:
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pass
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m = re.search(r"\{[\s\S]*\}", raw)
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if m:
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obj = json.loads(m.group(0))
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if isinstance(obj, dict):
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return obj
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raise ValueError("LLM output does not contain a JSON object")
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def _parse_json_array(text: str) -> list[Any]:
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raw = text.strip()
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try:
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arr = json.loads(raw)
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if isinstance(arr, list):
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return arr
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except json.JSONDecodeError:
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pass
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m = re.search(r"\[[\s\S]*\]", raw)
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if m:
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arr = json.loads(m.group(0))
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if isinstance(arr, list):
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return arr
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raise ValueError("LLM output does not contain a JSON array")
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def _translate_one(llm: Any, text: str) -> str:
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prompt = f"""请将这条推文总结为“中文新闻播报语气”的一句话快讯，只输出结果本身：
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- 不要直译，不要逐字对应；
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- 提炼核心信息与影响，长度 28~70 字；
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- 保持客观，不要口语化和夸张词。
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{text[:800]}
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"""
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resp = llm.invoke([HumanMessage(content=prompt)])
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return _to_text(resp).strip()
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def _translate_texts(llm: Any, texts: list[str], chunk_size: int = 20) -> list[str]:
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out = [""] * len(texts)
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for start in range(0, len(texts), chunk_size):
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chunk = texts[start : start + chunk_size]
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# first try: batch translation
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zh_list: list[str] | None = None
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for _ in range(2):
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try:
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lines = [f"{i}. {t[:500]}" for i, t in enumerate(chunk)]
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prompt = f"""你是财经频道快讯编辑。请把下面每条推文改写成“新闻播报语气”的中文摘要。
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要求：
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1) 仅输出 JSON 数组，长度与输入一致。
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2) 每个元素格式：{{"title_zh":"..."}}
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3) 不要附加解释、不要省略任何一条。
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4) 不要直译原句；要总结“这条在说什么、对市场/行业意味着什么”，每条 28~70 字。
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5) 语言风格客观、凝练、可播报。
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输入：
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{chr(10).join(lines)}
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"""
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resp = llm.invoke([HumanMessage(content=prompt)])
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arr = _parse_json_array(_to_text(resp))
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trial: list[str] = []
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for i in range(len(chunk)):
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if i < len(arr) and isinstance(arr[i], dict):
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trial.append(str(arr[i].get("title_zh", "")).strip())
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else:
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trial.append("")
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if len(trial) == len(chunk) and all(x.strip() for x in trial):
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zh_list = trial
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break
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except Exception:
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continue
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if zh_list is None:
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# second try: per-item translation
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zh_list = []
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for t in chunk:
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src = t.strip()
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if not src:
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zh_list.append("")
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continue
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try:
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one = _translate_one(llm, src)
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zh_list.append(one or src)
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except Exception:
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zh_list.append(src)
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for i, val in enumerate(zh_list):
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out[start + i] = val
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return out
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def _safe_modules(modules: dict[str, Any]) -> dict[str, Any]:
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out = {
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"headline": str(modules.get("headline") or ""),
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"trending_keywords": modules.get("trending_keywords") or [],
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"themes": modules.get("themes") or [],
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"p0_events": modules.get("p0_events") or [],
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"top_quotes": modules.get("top_quotes") or [],
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"category_updates": modules.get("category_updates") or [],
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"risk_signals": modules.get("risk_signals") or [],
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}
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# hard caps
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out["trending_keywords"] = [str(k) for k in out["trending_keywords"] if k][:6]
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out["themes"] = out["themes"][:3]
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out["p0_events"] = out["p0_events"][:6]
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out["top_quotes"] = out["top_quotes"][:3]
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out["category_updates"] = out["category_updates"][:6]
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out["risk_signals"] = out["risk_signals"][:6]
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return out
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def _dedupe_modules_by_url(modules: dict[str, Any]) -> dict[str, Any]:
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"""
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去重规则：同一条推文（同一 url）只允许在一个模块出现。
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模块优先级：themes > p0_events > top_quotes > category_updates
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"""
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seen_urls: set[str] = set()
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def _take(url: str) -> bool:
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u = (url or "").strip()
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if not u:
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return True
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if u in seen_urls:
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return False
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seen_urls.add(u)
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return True
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out = dict(modules)
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themes = []
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for t in out.get("themes", []) or []:
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t2 = dict(t)
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samples = []
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for s in t.get("samples", []) or []:
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if _take(str(s.get("url", ""))):
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samples.append(s)
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t2["samples"] = samples
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themes.append(t2)
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out["themes"] = themes
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p0 = []
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for e in out.get("p0_events", []) or []:
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if _take(str(e.get("url", ""))):
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p0.append(e)
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out["p0_events"] = p0
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quotes = []
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for q in out.get("top_quotes", []) or []:
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if _take(str(q.get("url", ""))):
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quotes.append(q)
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out["top_quotes"] = quotes
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cats = []
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for c in out.get("category_updates", []) or []:
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c2 = dict(c)
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items = []
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for it in c.get("items", []) or []:
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if _take(str(it.get("url", ""))):
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items.append(it)
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c2["items"] = items
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cats.append(c2)
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out["category_updates"] = cats
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return out
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def _load_brief_config_context(max_chars: int = 12000) -> str:
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"""
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第一层 LLM 的编辑偏好上下文：
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- 读取 Claude_input/x资讯/简报配置.md
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- 传入“关心主题、账号权重、内容偏好、简报格式偏好”等约束
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"""
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p: Path = brief_config_path()
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if not p.exists():
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return ""
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try:
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text = p.read_text(encoding="utf-8")
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except Exception:
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return ""
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clean = text.strip()
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if len(clean) <= max_chars:
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return clean
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return clean[:max_chars]
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def build_x_brief_modules(payload: dict[str, Any], tweet_summary_md: str, cfg: dict[str, Any]) -> dict[str, Any]:
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t0 = time.perf_counter()
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run_ts = time.strftime("%Y%m%d_%H%M%S")
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tweets = payload.get("tweets", [])[:120]
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compact = []
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for t in tweets:
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compact.append(
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{
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"handle": t.get("handle", ""),
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"name": t.get("name", ""),
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"published_at": t.get("published_at", ""),
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"title": (t.get("title_zh") or t.get("title") or "")[:280],
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"url": t.get("url", ""),
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}
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)
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editorial_context = _load_brief_config_context()
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llm = _llm_for_role(cfg, role="orchestrator")
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print(f"[xbrief][llm][l1] stage=start tweets={len(tweets)} summary_chars={len(tweet_summary_md)} mode=two_stage")
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prompt_stage1 = f"""你是“X资讯月报总编”。请先产出“结构草案 JSON”（阶段1），供后续阶段2定稿使用。
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你必须遵循“用户配置文档”的偏好：
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- 优先覆盖“我现在最关心的事”；
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- 按“账号权重与偏好”做信息优先级排序（高权重账号更容易进入 themes/p0）；
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- 按“内容类型偏好（✅/❌）”进行取舍；
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- 尽量贴合“简报格式偏好”的表达风格（中文总结为主，保留关键英文原句）；
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- 页面是固定模块位，内容应服务于前端模块整合，不要输出散乱信息。
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阶段1输出要求（仅草案）：
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1) 只输出 JSON 对象，不要 markdown，不要解释文字。
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2) 使用如下字段（严格保持键名）：
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{{
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"headline":"今日一句话总结，<40字，点出主旋律",
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"trending_keywords":["关键词1","关键词2","关键词3"],
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"themes":[{{"id":"t1","title":"...","subtitle":"...","priority":"P0|P1|P2","bullets":["..."],"sample_urls":["..."]}}],
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"p0_events":[{{"id":"p01","title":"...","meta":"...","summary":"...","url":"..."}}],
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"top_quotes":[{{"id":"q1","quote":"...","speaker":"...","date":"...","note":"...","url":"..."}}],
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"category_updates":[{{"id":"c1","title":"...","subtitle":"...","level":"P0|P1|P2|WARN","item_urls":["..."],"item_texts":["..."]}}],
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"risk_signals":[{{"id":"r1","level":"HIGH|MID|OPP","title":"...","detail":"..."}}]
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}}
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3) 数量限制：themes=3, p0_events<=6, top_quotes<=3, category_updates<=6, risk_signals<=6。
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4) trending_keywords：3-6 个当期最热词条（中英均可），直接从推文内容提炼。
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5) themes 的 priority 字段：首个主题通常为 P0，其余为 P1/P2；根据重要性自判。
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6) 每个模块优先包含可验证 URL；无法确定时宁缺毋滥。
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7) 内容必须中文，简洁可执行。
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8) JSON 必须合法：双引号、无注释、无尾逗号。
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用户配置文档（用于本次简报编排）：
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{editorial_context[:12000] if editorial_context else "（未提供，按默认财经简报策略）"}
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上下文（摘要）：
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{tweet_summary_md[:12000]}
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推文样本（JSON）：
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{json.dumps(compact, ensure_ascii=False)}
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"""
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_write_debug_file(
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f"{run_ts}_l1_stage1_input.txt",
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"\n\n".join(
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[
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"[meta]",
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f"tweets={len(tweets)}",
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f"tweet_summary_chars={len(tweet_summary_md)}",
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"phase=stage1_draft",
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"",
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"[prompt]",
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prompt_stage1,
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]
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),
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)
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resp1 = llm.invoke([HumanMessage(content=prompt_stage1)])
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text1 = _to_text(resp1)
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try:
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draft_obj = _parse_json_obj(text1)
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except Exception as exc:
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_write_debug_file(
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f"{run_ts}_l1_stage1_output_error.json",
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json.dumps(
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{
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"parse_error": str(exc),
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"raw_text": text1,
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},
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ensure_ascii=False,
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indent=2,
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),
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)
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raise
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_write_debug_file(
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f"{run_ts}_l1_stage1_output.json",
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json.dumps(
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{
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"raw_text": text1,
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"parsed_draft": draft_obj,
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},
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ensure_ascii=False,
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indent=2,
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),
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)
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prompt_stage2 = f"""你是“X资讯月报总编”。请基于阶段1草案，输出最终可渲染 JSON（阶段2定稿）。
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要求：
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1) 只输出 JSON 对象，不要 markdown，不要解释文字。
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2) 必须严格使用最终字段：
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{{
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"headline":"今日一句话总结，<40字，点出主旋律",
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"trending_keywords":["关键词1","关键词2","关键词3"],
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"themes":[{{"id":"t1","title":"...","subtitle":"...","priority":"P0|P1|P2","bullets":["..."],"samples":[{{"text":"...","url":"...","handle":"...","date":"..."}}]}}],
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"p0_events":[{{"id":"p01","title":"...","meta":"...","summary":"...","url":"..."}}],
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"top_quotes":[{{"id":"q1","quote":"...","speaker":"...","date":"...","note":"...","url":"..."}}],
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"category_updates":[{{"id":"c1","title":"...","subtitle":"...","level":"P0|P1|P2|WARN","items":[{{"text":"...","url":"..."}}]}}],
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"risk_signals":[{{"id":"r1","level":"HIGH|MID|OPP","title":"...","detail":"..."}}]
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}}
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3) 保证 JSON 合法（双引号、无尾逗号）。
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4) 引用 URL 时，优先从“推文索引”中取值；samples/items 尽量带 url。
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5) 数量限制：themes=3, p0_events<=6, top_quotes<=3, category_updates<=6, risk_signals<=6。
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6) 严格保留 stage1 草案中每个 theme 的 priority 字段（P0/P1/P2）。
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7) 严格保留 stage1 草案中的 trending_keywords 数组。
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阶段1草案（JSON）：
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{json.dumps(draft_obj, ensure_ascii=False)}
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推文索引（按 url 可回填 handle/date/text）：
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{json.dumps(compact, ensure_ascii=False)}
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"""
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_write_debug_file(
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f"{run_ts}_l1_stage2_input.txt",
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"\n\n".join(
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[
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"[meta]",
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"phase=stage2_finalize",
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f"tweets={len(tweets)}",
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"",
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"[prompt]",
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prompt_stage2,
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]
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),
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)
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resp2 = llm.invoke([HumanMessage(content=prompt_stage2)])
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text2 = _to_text(resp2)
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try:
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obj = _parse_json_obj(text2)
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except Exception as exc:
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_write_debug_file(
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f"{run_ts}_l1_stage2_output_error.json",
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json.dumps(
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{
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"parse_error": str(exc),
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"raw_text": text2,
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"stage1_draft": draft_obj,
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},
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ensure_ascii=False,
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indent=2,
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),
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)
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raise
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modules = _dedupe_modules_by_url(_safe_modules(obj))
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_write_debug_file(
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f"{run_ts}_l1_stage2_output.json",
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json.dumps(
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{
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"raw_text": text2,
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"parsed_modules": modules,
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},
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ensure_ascii=False,
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indent=2,
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),
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)
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elapsed_ms = int((time.perf_counter() - t0) * 1000)
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print(
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"[xbrief][llm][l1] stage=done "
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f"themes={len(modules.get('themes', []))} "
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f"p0={len(modules.get('p0_events', []))} "
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f"quotes={len(modules.get('top_quotes', []))} "
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f"cats={len(modules.get('category_updates', []))} "
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f"risk={len(modules.get('risk_signals', []))} "
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f"elapsed_ms={elapsed_ms}"
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)
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return modules
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def translate_display_content(payload: dict[str, Any], modules: dict[str, Any], cfg: dict[str, Any]) -> dict[str, Any]:
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"""
|
||
第二层翻译：只翻译会展示到前端模块的推文内容，不做全量翻译。
|
||
"""
|
||
t0 = time.perf_counter()
|
||
run_ts = time.strftime("%Y%m%d_%H%M%S")
|
||
tweets: list[dict[str, Any]] = list(payload.get("tweets", []))
|
||
if not tweets:
|
||
print("[xbrief][llm][l2] stage=skip reason=no_tweets")
|
||
return payload
|
||
llm = _llm_for_role(cfg, role="translator")
|
||
url_to_idx: dict[str, int] = {}
|
||
for i, t in enumerate(tweets):
|
||
u = str(t.get("url", "")).strip()
|
||
if u:
|
||
url_to_idx[u] = i
|
||
|
||
target_indexes: list[int] = []
|
||
seen: set[int] = set()
|
||
|
||
def _push_url(url: str) -> None:
|
||
idx = url_to_idx.get((url or "").strip())
|
||
if idx is None:
|
||
return
|
||
if idx in seen:
|
||
return
|
||
seen.add(idx)
|
||
target_indexes.append(idx)
|
||
|
||
for theme in modules.get("themes", []) or []:
|
||
for sample in theme.get("samples", []) or []:
|
||
_push_url(str(sample.get("url", "")))
|
||
for e in modules.get("p0_events", []) or []:
|
||
_push_url(str(e.get("url", "")))
|
||
for c in modules.get("category_updates", []) or []:
|
||
for item in c.get("items", []) or []:
|
||
_push_url(str(item.get("url", "")))
|
||
for q in modules.get("top_quotes", []) or []:
|
||
_push_url(str(q.get("url", "")))
|
||
|
||
if not target_indexes:
|
||
print("[xbrief][llm][l2] stage=skip reason=no_display_targets")
|
||
return payload
|
||
|
||
src_texts = [str(tweets[i].get("title", "")).strip() for i in target_indexes]
|
||
_write_debug_file(
|
||
f"{run_ts}_l2_input.json",
|
||
json.dumps(
|
||
{
|
||
"target_indexes": target_indexes,
|
||
"target_count": len(target_indexes),
|
||
"targets": [
|
||
{
|
||
"idx": i,
|
||
"handle": tweets[i].get("handle", ""),
|
||
"url": tweets[i].get("url", ""),
|
||
"title": tweets[i].get("title", ""),
|
||
}
|
||
for i in target_indexes
|
||
],
|
||
},
|
||
ensure_ascii=False,
|
||
indent=2,
|
||
),
|
||
)
|
||
print(
|
||
f"[xbrief][llm][l2] stage=start target_count={len(target_indexes)} "
|
||
f"chunk_size=20 total_tweets={len(tweets)}"
|
||
)
|
||
zh_texts = _translate_texts(llm, src_texts, chunk_size=20)
|
||
|
||
out = [dict(t) for t in tweets]
|
||
for i, zh in zip(target_indexes, zh_texts):
|
||
out[i]["title_zh"] = zh or out[i].get("title", "")
|
||
_write_debug_file(
|
||
f"{run_ts}_l2_output.json",
|
||
json.dumps(
|
||
{
|
||
"target_indexes": target_indexes,
|
||
"broadcast_zh": zh_texts,
|
||
},
|
||
ensure_ascii=False,
|
||
indent=2,
|
||
),
|
||
)
|
||
|
||
cloned = dict(payload)
|
||
cloned["tweets"] = out
|
||
elapsed_ms = int((time.perf_counter() - t0) * 1000)
|
||
print(f"[xbrief][llm][l2] stage=done translated={len(target_indexes)} elapsed_ms={elapsed_ms}")
|
||
return cloned
|
||
|
||
|
||
def build_fallback_modules(payload: dict[str, Any]) -> dict[str, Any]:
|
||
tweets = payload.get("tweets", [])
|
||
if not tweets:
|
||
return {
|
||
"themes": [],
|
||
"p0_events": [],
|
||
"top_quotes": [],
|
||
"category_updates": [],
|
||
"risk_signals": [],
|
||
}
|
||
|
||
theme_counter: Counter[str] = Counter()
|
||
for t in tweets:
|
||
text = (t.get("title") or "").lower()
|
||
if any(k in text for k in ("gpt", "claude", "llm", "model", "openai")):
|
||
theme_counter["模型能力"] += 1
|
||
elif any(k in text for k in ("gpu", "chip", "nvidia", "compute", "inference")):
|
||
theme_counter["算力与芯片"] += 1
|
||
elif any(k in text for k in ("fund", "valuation", "ipo", "acquire", "merger")):
|
||
theme_counter["投融资与并购"] += 1
|
||
elif any(k in text for k in ("stock", "market", "trading", "etf", "crypto", "price")):
|
||
theme_counter["交易与市场"] += 1
|
||
else:
|
||
theme_counter["其他"] += 1
|
||
|
||
themes = []
|
||
for idx, (k, v) in enumerate(theme_counter.most_common(3), start=1):
|
||
themes.append(
|
||
{
|
||
"id": f"t{idx}",
|
||
"title": k,
|
||
"subtitle": f"近30天样本 {v} 条",
|
||
"bullets": [f"{k}相关讨论热度位居前列。"],
|
||
}
|
||
)
|
||
|
||
latest_tweets = sorted(tweets, key=lambda x: x.get("published_at", ""), reverse=True)
|
||
p0_events = []
|
||
top_quotes = []
|
||
for i, t in enumerate(latest_tweets[:6], start=1):
|
||
p0_events.append(
|
||
{
|
||
"id": f"p{i}",
|
||
"title": (t.get("title") or "")[:60] or "关键动态",
|
||
"meta": f"@{t.get('handle','')} | {t.get('published_at','')}",
|
||
"summary": (t.get("title") or "")[:180],
|
||
"url": t.get("url", ""),
|
||
}
|
||
)
|
||
if i <= 3:
|
||
top_quotes.append(
|
||
{
|
||
"id": f"q{i}",
|
||
"quote": (t.get("title") or "")[:160],
|
||
"speaker": f"@{t.get('handle','')}",
|
||
"date": t.get("published_at", ""),
|
||
"note": "自动抽取",
|
||
"url": t.get("url", ""),
|
||
}
|
||
)
|
||
|
||
acc_counter: Counter[str] = Counter([t.get("handle", "") for t in tweets])
|
||
category_updates = []
|
||
for i, (handle, cnt) in enumerate(acc_counter.most_common(4), start=1):
|
||
category_updates.append(
|
||
{
|
||
"id": f"c{i}",
|
||
"title": f"@{handle}",
|
||
"subtitle": f"活跃推文 {cnt} 条",
|
||
"level": "P1",
|
||
"items": [{"text": f"该账号近30天持续活跃，建议重点跟踪。", "url": ""}],
|
||
}
|
||
)
|
||
|
||
risk_signals = [
|
||
{
|
||
"id": "r1",
|
||
"level": "MID",
|
||
"title": "已启用规则兜底",
|
||
"detail": "本次 LLM 结构化输出为空，已切换到规则归纳结果。",
|
||
}
|
||
]
|
||
return {
|
||
"themes": themes,
|
||
"p0_events": p0_events,
|
||
"top_quotes": top_quotes,
|
||
"category_updates": category_updates,
|
||
"risk_signals": risk_signals,
|
||
}
|
||
|