fix: improve AI coach chat context, 128k window, and output limits

Co-authored-by: Cursor <cursoragent@cursor.com>

ai_client.py

@@ -104,7 +104,13 @@ def _openai_chat_url() -> str:
     return f"{base}/chat/completions"
 
 
-def _generate_openai(prompt: str, images: List[tuple], temperature: float) -> str:
+def _generate_openai(
+    prompt: str,
+    images: List[tuple],
+    temperature: float,
+    *,
+    max_tokens: int | None = None,
+) -> str:
     api_key = _openai_api_key()
     if not api_key:
         return "AI 调用失败：未配置 OPENAI_API_KEY（请在当前实例目录 .env 中设置，修改后需重启服务）"
@@ -124,12 +130,14 @@ def _generate_openai(prompt: str, images: List[tuple], temperature: float) -> st
         messages = [{"role": "user", "content": content}]
     else:
         messages = [{"role": "user", "content": prompt}]
-    body = {
+    body: dict = {
         "model": _openai_model(),
         "messages": messages,
         "temperature": temperature,
         "stream": False,
     }
+    if max_tokens is not None and max_tokens > 0:
+        body["max_tokens"] = int(max_tokens)
     r = requests.post(
         _openai_chat_url(),
         headers=headers,
@@ -145,12 +153,21 @@ def _generate_openai(prompt: str, images: List[tuple], temperature: float) -> st
     return (msg.get("content") or "").strip() or "AI 生成失败：空内容"
 
 
-def _generate_ollama(prompt: str, images: List[tuple], temperature: float) -> str:
+def _generate_ollama(
+    prompt: str,
+    images: List[tuple],
+    temperature: float,
+    *,
+    max_tokens: int | None = None,
+) -> str:
+    options: dict = {"temperature": temperature}
+    if max_tokens is not None and max_tokens > 0:
+        options["num_predict"] = int(max_tokens)
     payload = {
         "model": _ollama_model(),
         "prompt": prompt,
         "stream": False,
-        "options": {"temperature": temperature},
+        "options": options,
     }
     if images:
         payload["images"] = [b64 for b64, _mime in images]
@@ -165,13 +182,14 @@ def ai_generate(
     image_paths: Optional[Sequence[str]] = None,
     images_b64: Optional[Sequence[str]] = None,
     temperature: float = 0.2,
+    max_tokens: int | None = None,
 ) -> str:
     """统一文本生成；失败时返回以「AI 调用失败」开头的说明。"""
     images = _collect_images(image_paths, images_b64)
     try:
         if _use_openai():
-            return _generate_openai(prompt, images, temperature)
+            return _generate_openai(prompt, images, temperature, max_tokens=max_tokens)
-        return _generate_ollama(prompt, images, temperature)
+        return _generate_ollama(prompt, images, temperature, max_tokens=max_tokens)
     except requests.HTTPError as e:
         detail = ""
         try:

manual_trading_hub/hub_ai/chat.py

@@ -5,7 +5,13 @@ from typing import Any, Optional
 
 from hub_ai.attachments import parse_chat_attachments
 from hub_ai.client import generate_text, model_label
-from hub_ai.config import CHAT_MAX_HISTORY_TURNS, CHAT_TEMPERATURE
+from hub_ai.config import (
+    CHAT_CONTEXT_MAX_CHARS,
+    CHAT_MAX_HISTORY_TURNS,
+    CHAT_MAX_OUTPUT_TOKENS,
+    CHAT_SUMMARY_EXCERPT_MAX_CHARS,
+    CHAT_TEMPERATURE,
+)
 from hub_ai.context import build_daily_context, format_chat_context_for_chat
 from hub_ai.prompts import CHAT_SYSTEM, build_chat_user_prompt
 from hub_ai.store import (
@@ -81,8 +87,8 @@ def send_chat_message(
         attachments=parsed.get("attachment_meta") or [],
     )
 
-    brief_ctx = format_chat_context_for_chat(ctx)
+    brief_ctx = format_chat_context_for_chat(ctx, max_chars=CHAT_CONTEXT_MAX_CHARS)
-    excerpt = summary_excerpt_for_chat(day)
+    excerpt = summary_excerpt_for_chat(day, max_chars=CHAT_SUMMARY_EXCERPT_MAX_CHARS)
 
     user_prompt = build_chat_user_prompt(
         context_text=brief_ctx,
@@ -100,6 +106,7 @@ def send_chat_message(
         user=user_prompt,
         temperature=CHAT_TEMPERATURE,
         images_b64=parsed.get("images_b64") or None,
+        max_tokens=CHAT_MAX_OUTPUT_TOKENS,
     )
     if reply.startswith("AI 调用失败"):
         return {"ok": False, "msg": reply, "session_id": sid}

manual_trading_hub/hub_ai/client.py

@@ -22,6 +22,12 @@ def generate_text(
     user: str,
     temperature: float,
     images_b64: Optional[Sequence[str]] = None,
+    max_tokens: int | None = None,
 ) -> str:
     prompt = f"{system.strip()}\n\n---\n\n{user.strip()}"
-    return ai_generate(prompt, temperature=temperature, images_b64=images_b64)
+    return ai_generate(
+        prompt,
+        temperature=temperature,
+        images_b64=images_b64,
+        max_tokens=max_tokens,
+    )

manual_trading_hub/hub_ai/config.py

@@ -7,7 +7,10 @@ HUB_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 
 SUMMARY_TEMPERATURE = 0.15
 CHAT_TEMPERATURE = 0.5
-CHAT_MAX_HISTORY_TURNS = 20
+CHAT_MAX_HISTORY_TURNS = 40
+CHAT_MAX_OUTPUT_TOKENS = 2048
+CHAT_CONTEXT_MAX_CHARS = 128_000
+CHAT_SUMMARY_EXCERPT_MAX_CHARS = 8000
 SUMMARY_RETENTION_DAYS = 90
 CHAT_SESSION_RETENTION_DAYS = 60
 FUND_HISTORY_DAYS = 180

manual_trading_hub/hub_ai/context.py

@@ -9,7 +9,13 @@ from typing import Any, Optional
 
 import httpx
 
-from hub_ai.config import FUND_HISTORY_DAYS, hub_agent_timeout, hub_flask_timeout, trading_day_reset_hour
+from hub_ai.config import (
+    CHAT_CONTEXT_MAX_CHARS,
+    FUND_HISTORY_DAYS,
+    hub_agent_timeout,
+    hub_flask_timeout,
+    trading_day_reset_hour,
+)
 from hub_ai.fund_history import format_fund_history_text, get_fund_history, record_fund_snapshot
 from hub_trades_lib import current_trading_day, summarize_trades
 
@@ -706,15 +712,71 @@ def format_chat_position_overview(payload: dict) -> str:
     return "\n".join(lines)
 
 
-def format_chat_context_for_chat(payload: dict, max_chars: int = 5200) -> str:
+def format_chat_context_slim(payload: dict) -> str:
+    """聊天专用：不含 180 日资金曲线与昨日平仓明细，避免挤占对话上下文。"""
+    totals = payload.get("totals") or {}
+    day = totals.get("trading_day")
+    lines = [
+        f"【今日合计 {day}】平仓盈亏 {totals.get('total_pnl_u')}U | "
+        f"笔数 {totals.get('closed_count')}（胜{totals.get('win_count')}/负{totals.get('loss_count')}）| "
+        f"实盘持仓 {totals.get('open_position_count', 0)} 仓 | 浮盈亏 {totals.get('float_pnl_u')}U",
+        "【说明】持仓=交易所实盘；趋势/关键位/监控单=本地计划，不等于已开仓。",
+    ]
+    for ac in payload.get("accounts") or []:
+        if ac.get("status") == "未监控":
+            lines.append(f"- {ac.get('name')}：未监控")
+            continue
+        st = ac.get("trade_stats") or {}
+        open_n = int(ac.get("open_position_count") or _account_open_position_count(ac))
+        pos_txt = "空仓" if open_n <= 0 else f"{open_n}仓 浮盈亏{ac.get('float_pnl_u')}U"
+        mc = _monitor_counts(ac)
+        mon = []
+        if mc["trends"]:
+            mon.append(f"趋势{mc['trends']}")
+        if mc["rolls"]:
+            mon.append(f"加仓{mc['rolls']}")
+        if mc["keys"]:
+            mon.append(f"关键位{mc['keys']}")
+        if mc["orders"]:
+            mon.append(f"监控单{mc['orders']}")
+        mon_txt = f"；监控 {'/'.join(mon)}" if mon else ""
+        lines.append(
+            f"- {ac.get('name')}：{pos_txt} | 今日盈亏{st.get('total_pnl_u')}U "
+            f"({st.get('closed_count')}笔) | 资金{_fmt_fund(ac.get('funding_usdt'))} "
+            f"交易{_fmt_fund(ac.get('trading_usdt'))}{mon_txt}"
+        )
+        trades = ac.get("trades") or []
+        if trades:
+            for t in trades[:4]:
+                lines.append(f"    · {_format_trade_line(t)}")
+            if len(trades) > 4:
+                lines.append(f"    · …共{len(trades)}笔今日平仓")
+        positions = ac.get("positions") or []
+        for p in positions[:4]:
+            if not isinstance(p, dict):
+                continue
+            sym = p.get("symbol") or "?"
+            side = p.get("side") or "?"
+            upnl = _position_float_pnl(p)
+            lines.append(f"    · 持仓 {sym} {side} 浮盈亏{upnl:.4f}U")
+    return "\n".join(lines)
+
+
+def format_chat_context_for_chat(
+    payload: dict,
+    max_chars: int = CHAT_CONTEXT_MAX_CHARS,
+) -> str:
     overview = format_chat_position_overview(payload)
-    body = format_context_text(payload)
+    body = str(payload.get("text") or "").strip() or format_context_text(payload)
     text = overview + "\n\n" + body
     if len(text) <= max_chars:
         return text
-    budget = max(800, max_chars - len(overview) - 4)
+    budget = max(2000, max_chars - len(overview) - 4)
-    return overview + "\n\n" + body[:budget].rstrip() + "..."
+    return overview + "\n\n" + body[:budget].rstrip() + "…"
 
 
-def format_chat_context_brief(payload: dict, max_chars: int = 4500) -> str:
+def format_chat_context_brief(
+    payload: dict,
+    max_chars: int = CHAT_CONTEXT_MAX_CHARS,
+) -> str:
     return format_chat_context_for_chat(payload, max_chars=max_chars)

manual_trading_hub/hub_ai/prompts.py

@@ -49,6 +49,9 @@ CHAT_SYSTEM = """
 - 用户口述与快照冲突时，以快照为准并口语说明「我这边看到是空仓/有N仓」。
 - 若附带「今日总结摘要」，那是较早生成的缓存，**实盘持仓以【当前多账户快照】里的「实盘持仓总览」为准**，摘要里若提到持仓可能已过时。
 - 若用户上传图片，可结合图中可见信息讨论，看不清的明确说看不清。
+- **优先接住【用户现在说】和【此前对话】**：用户聊心态、悔单、某笔操作时，先顺着这个话题回应，不要每句都复述账户资金数字。
+- **接续对话**：有【此前对话】时须接着聊，不要重复开场白，回复写完整，不要说到一半戛然而止。
+- 快照里的盈亏/资金仅在需要核对事实时引用；用户口述与快照冲突时，以快照为准并口语说明。
 """.strip()
 
 
@@ -71,19 +74,19 @@ def build_chat_user_prompt(
     user_message: str,
     attachment_note: str = "",
 ) -> str:
-    parts = [
+    parts = [f"【交易日】{trading_day}"]
-        f"【交易日】{trading_day}",
+    if history_lines.strip():
-        "【当前多账户快照（含实盘持仓与本地监控，发送时已刷新）】",
+        parts.extend(["【此前对话（须接续，勿重复开场）】", history_lines.strip()])
+    parts.extend([
+        "【当前多账户快照（事实参考；持仓以「实盘持仓总览」为准）】",
         context_text.strip() or "（无监控数据）",
-    ]
+    ])
     if summary_excerpt.strip():
         parts.extend([
-            "【今日总结摘要（可能滞后，持仓以快照「实盘持仓总览」为准）】",
+            "【今日总结摘要（可能滞后，持仓以快照为准）】",
             summary_excerpt.strip(),
         ])
-    if history_lines.strip():
-        parts.extend(["【此前对话】", history_lines.strip()])
     if attachment_note.strip():
         parts.extend(["【用户附件说明】", attachment_note.strip()])
-    parts.extend(["【用户现在说】", user_message.strip()])
+    parts.extend(["【用户现在说（优先回应这一条）】", user_message.strip()])
     return "\n\n".join(parts)