1

.env.example

@@ -38,6 +38,7 @@ ALPHAX_LLM_MAX_TOKENS=900
 ALPHAX_LLM_RECOMMENDATIONS_ENABLED=1
 ALPHAX_LLM_SENTIMENT_ENABLED=1
 ALPHAX_LLM_REVIEW_ENABLED=1
+ALPHAX_LLM_SENTIMENT_BATCH_MAX_EVENTS=12
 
 # 策略交易挂单门控。wait_pullback 只是候选，必须通过这些条件才会创建挂单。
 ALPHAX_PAPER_TRADING_MODE=intraday_trading

app/config/system_config.py

@@ -63,6 +63,8 @@ def default_llm_config():
         "model": _env_str("ALPHAX_LLM_MODEL", "gpt-4o-mini"),
         "timeout": _env_int("ALPHAX_LLM_TIMEOUT", 20),
         "max_tokens": _env_int("ALPHAX_LLM_MAX_TOKENS", 900),
+        "reasoning_effort": _env_str("ALPHAX_LLM_REASONING_EFFORT", ""),
+        "thinking_enabled": _env_bool("ALPHAX_LLM_THINKING_ENABLED", False),
         "modules": {
             "recommendations": _env_bool("ALPHAX_LLM_RECOMMENDATIONS_ENABLED", True),
             "sentiment": _env_bool("ALPHAX_LLM_SENTIMENT_ENABLED", True),
@@ -82,6 +84,8 @@ def _llm_env_overrides():
         "ALPHAX_LLM_MODEL": ("model", lambda: _env_str("ALPHAX_LLM_MODEL", "gpt-4o-mini")),
         "ALPHAX_LLM_TIMEOUT": ("timeout", lambda: _env_int("ALPHAX_LLM_TIMEOUT", 20)),
         "ALPHAX_LLM_MAX_TOKENS": ("max_tokens", lambda: _env_int("ALPHAX_LLM_MAX_TOKENS", 900)),
+        "ALPHAX_LLM_REASONING_EFFORT": ("reasoning_effort", lambda: _env_str("ALPHAX_LLM_REASONING_EFFORT", "")),
+        "ALPHAX_LLM_THINKING_ENABLED": ("thinking_enabled", lambda: _env_bool("ALPHAX_LLM_THINKING_ENABLED", False)),
     }
     for env_name, (key, loader) in checks.items():
         if _env_present(env_name):

app/services/chat_assistant.py

@@ -21,7 +21,7 @@ from app.db import chat_assistant_db
 from app.db.analytics import get_pipeline_runs
 from app.db.llm_insights import compute_input_hash, repair_mojibake_json, repair_mojibake_text
 from app.db.schema import get_conn
-from app.services.llm_insights import _message_content_text, _parse_json_object_text, get_llm_params
+from app.services.llm_insights import _chat_completion_body, _message_content_text, _parse_json_object_text, get_llm_params
 from app.services.market_overview import get_crypto_market_overview
 
 
@@ -598,19 +598,20 @@ def _call_chat_llm(message: str, context: dict, history=None) -> dict:
     }
     system_prompt = "你是 AlphaX Agent 的 Crypto 研究助手。你只能基于提供的结构化数据回答，不能编造数据。"
     try:
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": _compact_json(payload)},
+        ]
         resp = requests.post(
             f"{base_url}/chat/completions",
             headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
-            json={
-                "model": model,
-                "messages": [
-                    {"role": "system", "content": system_prompt},
-                    {"role": "user", "content": _compact_json(payload)},
-                ],
-                "temperature": 0.15,
-                "max_tokens": int(params.get("max_tokens") or 900),
-                "response_format": {"type": "json_object"},
-            },
+            json=_chat_completion_body(
+                params,
+                messages,
+                int(params.get("max_tokens") or 900),
+                response_format=True,
+                temperature=0.15,
+            ),
             timeout=int(params.get("timeout") or 20),
         )
         if resp.status_code >= 400:

app/services/llm_insights.py

@@ -26,6 +26,13 @@ def _env_bool(name, default=False):
     return str(value).strip().lower() in ("1", "true", "yes", "on")
 
 
+def _env_int(name, default=0):
+    try:
+        return int(os.getenv(name, default))
+    except Exception:
+        return int(default or 0)
+
+
 def get_llm_params():
     """Runtime LLM config. This is system config, not strategy config."""
     cfg = llm_config()
@@ -36,6 +43,8 @@ def get_llm_params():
         "model": str(cfg.get("model") or "gpt-4o-mini").strip(),
         "timeout": int(cfg.get("timeout") or 20),
         "max_tokens": int(cfg.get("max_tokens") or 900),
+        "reasoning_effort": str(cfg.get("reasoning_effort") or "").strip(),
+        "thinking_enabled": bool(cfg.get("thinking_enabled", False)),
         "modules": cfg.get("modules") or {},
     }
 
@@ -91,10 +100,11 @@ def _message_content_text(message: dict) -> str:
     return ""
 
 
-def _parse_json_object_text(content: str) -> dict:
+def _parse_json_object_text(content: str, *, detail=None) -> dict:
     text = str(content or "").strip()
     if not text:
-        raise ValueError("llm_empty_content")
+        suffix = f":{_dump_json(detail)}" if detail else ""
+        raise ValueError(f"llm_empty_content{suffix}")
     if text.startswith("```"):
         text = text.strip("`").strip()
         if text.lower().startswith("json"):
@@ -114,15 +124,28 @@ def _parse_json_object_text(content: str) -> dict:
     return parsed
 
 
-def _chat_completion_request(base_url, api_key, model, messages, max_tokens, timeout, *, response_format=True):
+def _chat_completion_body(params, messages, max_tokens, *, response_format=True, temperature=0.2):
+    model = str(params.get("model") or "").strip()
     body = {
         "model": model,
         "messages": messages,
-        "temperature": 0.2,
+        "temperature": temperature,
         "max_tokens": max_tokens,
     }
     if response_format:
         body["response_format"] = {"type": "json_object"}
+    reasoning_effort = str(params.get("reasoning_effort") or "").strip()
+    if reasoning_effort:
+        body["reasoning_effort"] = reasoning_effort
+    if bool(params.get("thinking_enabled", False)):
+        body["thinking"] = {"type": "enabled"}
+    return body
+
+
+def _chat_completion_request(base_url, api_key, model, messages, max_tokens, timeout, *, response_format=True, params=None, temperature=0.2):
+    request_params = dict(params or {})
+    request_params.setdefault("model", model)
+    body = _chat_completion_body(request_params, messages, max_tokens, response_format=response_format, temperature=temperature)
     return requests.post(
         f"{base_url}/chat/completions",
         headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
@@ -135,9 +158,15 @@ def _parse_llm_response(resp, model):
     if resp.status_code >= 400:
         return {"status": "failed", "error": f"http_{resp.status_code}", "raw": resp.text[:1000], "model": model}
     data = resp.json()
-    message = (((data.get("choices") or [{}])[0]).get("message") or {})
+    choice = (data.get("choices") or [{}])[0]
+    message = (choice.get("message") or {})
     content = _message_content_text(message)
-    parsed = _parse_json_object_text(content)
+    detail = {
+        "finish_reason": choice.get("finish_reason"),
+        "message_keys": sorted(message.keys()) if isinstance(message, dict) else [],
+        "usage": data.get("usage") or {},
+    }
+    parsed = _parse_json_object_text(content, detail=detail)
     return {"status": "success", "content": parsed, "model": model}
 
 
@@ -164,7 +193,7 @@ def _call_llm_json(prompt_version, payload):
         {"role": "user", "content": user_prompt},
     ]
     try:
-        resp = _chat_completion_request(base_url, api_key, model, messages, max_tokens, timeout, response_format=True)
+        resp = _chat_completion_request(base_url, api_key, model, messages, max_tokens, timeout, response_format=True, params=params)
         first = _parse_llm_response(resp, model)
         if first.get("status") == "success":
             return first
@@ -173,9 +202,10 @@ def _call_llm_json(prompt_version, payload):
         # Some OpenAI-compatible providers occasionally return an empty message
         # when strict JSON response_format is enabled. Retry once with prompt-only
         # JSON enforcement so the job does not fail silently on provider quirks.
-        retryable = str(exc) in ("llm_empty_content", "llm_empty_json_object")
+        error_text = str(exc)
+        retryable = error_text.startswith("llm_empty_content") or error_text.startswith("llm_empty_json_object")
         if not retryable:
-            return {"status": "failed", "error": str(exc)[:1000], "model": model}
+            return {"status": "failed", "error": error_text[:1000], "model": model}
         try:
             retry_messages = [
                 {
@@ -184,7 +214,7 @@ def _call_llm_json(prompt_version, payload):
                 },
                 {"role": "user", "content": user_prompt},
             ]
-            resp = _chat_completion_request(base_url, api_key, model, retry_messages, max_tokens, timeout, response_format=False)
+            resp = _chat_completion_request(base_url, api_key, model, retry_messages, max_tokens, timeout, response_format=False, params=params)
             result = _parse_llm_response(resp, model)
             if result.get("status") == "success":
                 result["retry"] = "without_response_format"
@@ -423,6 +453,83 @@ def _build_sentiment_batch_payload(hours=24, limit=40):
     }
 
 
+def _sentiment_event_rank(item):
+    importance_weight = {"S": 100, "A": 80, "RISK": 75, "B": 50, "C": 20}
+    score = importance_weight.get(str(item.get("importance") or "").upper(), 30)
+    if item.get("rec_id"):
+        score += 18
+    if item.get("pushed"):
+        score += 10
+    if item.get("related_symbol") or item.get("related_base"):
+        score += 8
+    if item.get("tech_score") not in (None, ""):
+        try:
+            score += min(max(float(item.get("tech_score") or 0), 0), 100) / 10
+        except Exception:
+            pass
+    event_type = str(item.get("event_type") or "").lower()
+    title = str(item.get("title") or "").lower()
+    if any(k in event_type or k in title for k in ("listing", "launch", "hack", "exploit", "upgrade", "partnership", "上币", "主网", "黑客", "漏洞", "升级", "合作")):
+        score += 12
+    if item.get("trend_rank"):
+        try:
+            score += max(0, 20 - float(item.get("trend_rank") or 0))
+        except Exception:
+            pass
+    return score
+
+
+def _compact_sentiment_batch_payload(payload, max_events=None):
+    """Shrink noisy news/event rows before sending to the LLM."""
+    max_events = int(max_events or _env_int("ALPHAX_LLM_SENTIMENT_BATCH_MAX_EVENTS", 12) or 12)
+    events = list((payload or {}).get("events") or [])
+    ranked = sorted(events, key=_sentiment_event_rank, reverse=True)
+    compact_events = []
+    for idx, item in enumerate(ranked[:max(max_events, 1)], start=1):
+        compact_events.append({
+            "rank": idx,
+            "event_id": item.get("event_id"),
+            "source": item.get("source"),
+            "symbol": item.get("related_symbol") or item.get("symbol"),
+            "base": item.get("related_base"),
+            "title": str(item.get("title") or "")[:160],
+            "published_at": item.get("published_at"),
+            "importance": item.get("importance"),
+            "event_type": item.get("event_type"),
+            "decision": item.get("decision"),
+            "tech_score": item.get("tech_score"),
+            "trend_rank": item.get("trend_rank"),
+            "trend_score": item.get("trend_score"),
+            "change_24h_pct": item.get("change_24h_pct"),
+        })
+    return {
+        "target_type": payload.get("target_type"),
+        "target_id": payload.get("target_id"),
+        "hours": payload.get("hours"),
+        "generated_at": payload.get("generated_at"),
+        "source_event_count": len(events),
+        "event_count": len(compact_events),
+        "events": compact_events,
+        "instructions": {
+            "role": "作为加密市场舆情分析师，基于精简事件判断短线市场影响。",
+            "focus": [
+                "只提炼真正可能影响 Binance 山寨币交易决策的主题",
+                "给出受影响币种、方向、理由和置信度",
+                "标记是否需要进入技术检查",
+                "如果没有有效事件，也必须返回空数组和 neutral 结论",
+            ],
+            "expected_schema": {
+                "market_mood": "risk_on|neutral|risk_off",
+                "summary": "中文摘要",
+                "hot_themes": [{"theme": "", "impact": "", "symbols": [], "confidence": 0}],
+                "coin_impacts": [{"symbol": "", "direction": "positive|negative|risk|neutral", "reason": "", "confidence": 0, "need_technical_check": False}],
+                "risk_events": [{"title": "", "symbols": [], "risk_type": "", "severity": "low|medium|high"}],
+                "watchlist": [{"symbol": "", "why": "", "trigger": ""}],
+            },
+        },
+    }
+
+
 def _build_review_payload(item):
     return {
         "target_type": "review",
@@ -565,9 +672,10 @@ def generate_sentiment_insights(limit=30):
 def generate_sentiment_batch_analysis(limit=40, hours=24):
     if not get_llm_module_enabled("sentiment"):
         return {"status": "skipped", "reason": "module_disabled", "processed": 0}
-    payload = _build_sentiment_batch_payload(hours=hours, limit=limit)
-    if not payload.get("events"):
+    source_payload = _build_sentiment_batch_payload(hours=hours, limit=limit)
+    if not source_payload.get("events"):
         return {"status": "skipped", "reason": "no_sentiment_events", "processed": 0}
+    payload = _compact_sentiment_batch_payload(source_payload)
     input_hash = compute_input_hash(payload)
     cached = get_any_insight("sentiment_batch", payload["target_id"], PROMPTS["sentiment_batch_analyze_v1"], input_hash)
     if cached:

tests/test_llm_insights.py

@@ -221,6 +221,33 @@ def test_llm_json_code_fence_is_parsed(monkeypatch):
     assert result["content"]["summary"] == "ok"
 
 
+def test_deepseek_thinking_is_not_sent_unless_enabled():
+    body = llm_insights._chat_completion_body(
+        {
+            "model": "deepseek-v4-pro",
+            "reasoning_effort": "",
+            "thinking_enabled": False,
+        },
+        [{"role": "user", "content": "Hello"}],
+        100,
+    )
+    assert body["model"] == "deepseek-v4-pro"
+    assert "thinking" not in body
+    assert "reasoning_effort" not in body
+
+    enabled = llm_insights._chat_completion_body(
+        {
+            "model": "deepseek-v4-pro",
+            "reasoning_effort": "high",
+            "thinking_enabled": True,
+        },
+        [{"role": "user", "content": "Hello"}],
+        100,
+    )
+    assert enabled["reasoning_effort"] == "high"
+    assert enabled["thinking"] == {"type": "enabled"}
+
+
 def test_only_key_samples_generate_insights(monkeypatch, temp_db):
     _insert_recommendation(temp_db, symbol="CCC/USDT", action_status="观察", execution_status="observe", display_bucket="watch_pool", state_reason="普通观察")
     _insert_recommendation(temp_db, symbol="DDD/USDT", action_status="等回踩", execution_status="wait_pullback", display_bucket="realtime", rec_time="2026-05-01T12:00:00")
@@ -312,6 +339,28 @@ def test_sentiment_batch_analysis_api_returns_cached_result(temp_db):
     assert data["source_events"][0]["related_symbol"] == "ABC/USDT"
 
 
+def test_sentiment_batch_payload_is_compacted_before_llm(monkeypatch):
+    payload = {
+        "target_type": "sentiment_batch",
+        "target_id": "sentiment_batch:24h",
+        "hours": 24,
+        "generated_at": "2026-06-08T00:00:00",
+        "events": [
+            {"event_id": "low", "title": "small update", "importance": "C", "url": "https://example.com/low"},
+            {"event_id": "listing", "title": "Binance Will List AAAUSDT", "importance": "A", "related_symbol": "AAA/USDT", "event_type": "listing", "url": "https://example.com/high"},
+            {"event_id": "risk", "title": "BBB exploit risk", "importance": "RISK", "related_symbol": "BBB/USDT", "event_type": "exploit", "url": "https://example.com/risk"},
+        ],
+    }
+    monkeypatch.setenv("ALPHAX_LLM_SENTIMENT_BATCH_MAX_EVENTS", "2")
+
+    compact = llm_insights._compact_sentiment_batch_payload(payload)
+
+    assert compact["source_event_count"] == 3
+    assert compact["event_count"] == 2
+    assert [item["event_id"] for item in compact["events"]] == ["listing", "risk"]
+    assert "url" not in compact["events"][0]
+
+
 def test_sentiment_batch_enqueues_technical_check_candidates(monkeypatch, temp_db):
     event_driven_screener.init_event_tables()
     conn = sqlite3.connect(temp_db)