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feat: implement AI-driven METAR summary service and dashboard UI components

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This commit is contained in:
2569718930@qq.com
2026-04-26 14:03:13 +08:00
parent f9ad34d7c0
commit ef8ef833b9
7 changed files with 614 additions and 36 deletions
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.env.example
+3
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@@ -142,13 +142,16 @@ POLYWEATHER_DEEPSEEK_API_KEY=
POLYWEATHER_DEEPSEEK_BASE_URL=https://api.deepseek.com
POLYWEATHER_SCAN_AI_MODEL=deepseek-v4-pro
POLYWEATHER_SCAN_CITY_AI_MODEL=deepseek-v4-flash
POLYWEATHER_METAR_SUMMARY_AI_MODEL=deepseek-v4-flash
POLYWEATHER_SCAN_AI_TIMEOUT_SEC=40
POLYWEATHER_SCAN_CITY_AI_TIMEOUT_SEC=18
POLYWEATHER_METAR_SUMMARY_AI_TIMEOUT_SEC=8
POLYWEATHER_SCAN_CITY_AI_RETRY_ON_STREAM_PARSE_ERROR=false
POLYWEATHER_SCAN_AI_CACHE_TTL_SEC=1800
POLYWEATHER_SCAN_AI_MAX_ROWS=40
POLYWEATHER_SCAN_AI_MAX_TOKENS=3200
POLYWEATHER_SCAN_CITY_AI_MAX_TOKENS=900
POLYWEATHER_METAR_SUMMARY_AI_MAX_TOKENS=160
POLYWEATHER_SCAN_AI_PROXY_TIMEOUT_MS=55000
POLYWEATHER_PREWARM_CITIES=ankara,istanbul,shanghai,beijing,shenzhen,guangzhou,wuhan,chengdu,chongqing,hong kong,taipei,singapore,tokyo,seoul,busan,london,paris,madrid
POLYWEATHER_CITY_SUMMARY_CACHE_TTL_SEC=1800
frontend/app/api/ai/metar-summary/route.ts
+69
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@@ -0,0 +1,69 @@
import { NextRequest, NextResponse } from "next/server";
import {
applyAuthResponseCookies,
buildBackendRequestHeaders,
} from "@/lib/backend-auth";
const API_BASE = process.env.POLYWEATHER_API_BASE_URL;
export const dynamic = "force-dynamic";
export const maxDuration = 30;
export async function POST(req: NextRequest) {
if (!API_BASE) {
return NextResponse.json(
{ error: "POLYWEATHER_API_BASE_URL is not configured" },
{ status: 500 },
);
}
let body: unknown = {};
try {
body = await req.json();
} catch {
body = {};
}
const requestBody =
body && typeof body === "object" ? (body as Record<string, unknown>) : {};
const auth = await buildBackendRequestHeaders(req);
const headers = new Headers(auth.headers);
headers.set("Content-Type", "application/json");
headers.set("Accept", "text/event-stream");
try {
const res = await fetch(`${API_BASE}/api/ai/metar-summary`, {
method: "POST",
headers,
cache: "no-store",
body: JSON.stringify(requestBody),
});
if (!res.ok || !res.body) {
const raw = await res.text();
const response = NextResponse.json(
{ error: `Backend returned ${res.status}`, detail: raw.slice(0, 300) },
{ status: res.status === 402 || res.status === 403 ? res.status : 502 },
);
return applyAuthResponseCookies(response, auth.response);
}
const response = new NextResponse(res.body, {
status: 200,
headers: {
"Content-Type": "text/event-stream; charset=utf-8",
"Cache-Control": "no-store, no-transform",
"X-Accel-Buffering": "no",
},
});
return applyAuthResponseCookies(response, auth.response);
} catch (error) {
const response = NextResponse.json(
{
error: "Failed to stream METAR summary",
detail: String(error),
},
{ status: 500 },
);
return applyAuthResponseCookies(response, auth.response);
}
}
frontend/components/dashboard/scan-terminal/AiPinnedForecastView.tsx
+72 -34
View File
@@ -16,6 +16,7 @@ import { LoadingSignal } from "@/components/dashboard/scan-terminal/LoadingSigna
import type { AiPinnedCity } from "@/components/dashboard/scan-terminal/types";
import {
useAiCityForecast,
useAiMetarSummary,
useCityMarketScan,
} from "@/components/dashboard/scan-terminal/use-ai-city-card-data";
import type { CityDetail, ScanOpportunityRow } from "@/lib/dashboard-types";
@@ -152,6 +153,20 @@ function AiPinnedCityCard({
detail?.airport_primary?.station_code ||
"";
const detailCityName = detail?.name || item.cityName;
const debText =
debNumber != null
? formatTemperatureValue(debNumber, tempSymbol, { digits: 1 })
: "";
const { metarSummary } = useAiMetarSummary({
airport: airportStation,
city: displayName,
deb: debText,
enabled: Boolean(detail && report),
isEn,
locale,
metar: report,
modelRange,
});
const { aiForecast, refreshAiForecast } = useAiCityForecast({
detail,
detailCityName,
@@ -249,7 +264,6 @@ function AiPinnedCityCard({
? [String(localizedRisksRaw)]
: [];
const aiBullets = [
localizedMetarRead,
localizedReasoning !== localizedFinalJudgment ? localizedReasoning : "",
localizedModelNote || localModelSupportNote,
...localizedRisks,
@@ -420,21 +434,66 @@ function AiPinnedCityCard({
<div className="scan-ai-city-section-title">
{isEn ? "Evidence · AI airport read" : "证据 · AI 机场报文解读"}
</div>
{metarSummary.status === "loading" ? (
<>
<p className="scan-ai-weather-summary">
{metarSummary.streamText ||
(isEn
? "Streaming the lightweight METAR read..."
: "轻量 METAR 解读正在流式输出…")}
</p>
<p className="scan-ai-city-muted">
{isEn
? "This read is independent from the full city review below."
: "这一步已和下方完整城市分析解耦，不等待完整 JSON。"}
</p>
</>
) : metarSummary.status === "ready" && metarSummary.streamText ? (
<p className="scan-ai-weather-summary">{metarSummary.streamText}</p>
) : metarSummary.status === "failed" ? (
<p>
{localizedMetarRead ||
(isEn
? "Lightweight AI METAR read is unavailable; raw METAR remains below."
: "轻量 AI METAR 解读暂不可用；下方保留原始 METAR。")}
</p>
) : (
<p>
{report
? isEn
? "Waiting for lightweight AI to read the latest METAR."
: "等待轻量 AI 解读最新 METAR。"
: isEn
? "Raw METAR is unavailable."
: "暂无原始 METAR。"}
</p>
)}
<p className="scan-ai-raw-metar">
{report
? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
: isEn
? "Raw METAR: unavailable."
: "原始 METAR：暂无。"}
</p>
</section>
<section className="scan-ai-city-section">
<div className="scan-ai-city-section-title">
{isEn ? "Complete city AI review" : "完整城市 AI 判断"}
</div>
{aiForecast.status === "loading" ? (
<>
<p className={aiForecast.streamText ? "scan-ai-weather-summary" : undefined}>
{aiForecast.streamText ||
{localizedFinalJudgment ||
aiForecast.streamText ||
(isEn
? "DeepSeek is reading the latest airport bulletin..."
: "DeepSeek 正在解读最新机场报文...")}
? "Generating the full city review in the background..."
: "后台正在生成完整城市分析…")}
</p>
<p className="scan-ai-city-muted">
{isEn
? "This heavier JSON review can finish after the airport read."
: "这部分是较重的 JSON 分析，可以晚于机场报文解读完成。"}
</p>
{!aiForecast.streamText ? (
<p className="scan-ai-city-muted">
{isEn
? "The final airport read will appear here shortly."
: "机场报文解读稍后将在这里显示。"}
</p>
) : null}
</>
) : aiForecast.status === "ready" && aiCityForecast ? (
<>
@@ -447,13 +506,6 @@ function AiPinnedCityCard({
<li key={`${line}-${index}`}>{line}</li>
))}
</ul>
<p className="scan-ai-raw-metar">
{report
? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
: isEn
? "Raw METAR: unavailable."
: "原始 METAR：暂无。"}
</p>
</>
) : aiForecast.status === "ready" ? (
<>
@@ -469,13 +521,6 @@ function AiPinnedCityCard({
</p>
<ul className="scan-ai-weather-bullets">
<li>{localModelSupportNote}</li>
<li>
{report
? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
: isEn
? "Raw METAR is unavailable."
: "暂无原始 METAR。"}
</li>
</ul>
</>
) : aiForecast.status === "failed" ? (
@@ -488,20 +533,13 @@ function AiPinnedCityCard({
</p>
<ul className="scan-ai-weather-bullets">
<li>{localModelSupportNote}</li>
<li>
{report
? `${isEn ? "Raw METAR" : "原始 METAR"}：${`${airportStation} ${report}`.trim()}`
: isEn
? "Raw METAR is unavailable."
: "暂无原始 METAR。"}
</li>
</ul>
</>
) : (
<p>
{isEn
? "Waiting for AI to read the latest airport bulletin."
: "等待 AI 解读最新机场报文。"}
? "The complete city review is queued in the background."
: "完整城市分析正在后台排队。"}
</p>
)}
</section>
frontend/components/dashboard/scan-terminal/types.ts
+17
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@@ -39,3 +39,20 @@ export type AiCityForecastState = {
streamRaw?: string | null;
};
export type AiMetarSummaryPayload = {
status?: string | null;
summary?: string | null;
reason?: string | null;
degraded?: boolean | null;
duration_ms?: number | null;
model?: string | null;
provider?: string | null;
};
export type AiMetarSummaryState = {
status: "idle" | "loading" | "ready" | "failed";
payload?: AiMetarSummaryPayload | null;
error?: string | null;
streamText?: string | null;
};
frontend/components/dashboard/scan-terminal/use-ai-city-card-data.ts
+232
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@@ -4,6 +4,8 @@ import { useCallback, useEffect, useMemo, useState } from "react";
import type {
AiCityForecastPayload,
AiCityForecastState,
AiMetarSummaryPayload,
AiMetarSummaryState,
} from "@/components/dashboard/scan-terminal/types";
import { useDashboardStore } from "@/hooks/useDashboardStore";
import {
@@ -22,6 +24,10 @@ const pendingAiCityForecastRequests = new Map<
string,
Promise<AiCityForecastPayload>
>();
const pendingMetarSummaryRequests = new Map<
string,
Promise<AiMetarSummaryPayload>
>();
type AiCityStreamProgress = {
stage?: string | null;
@@ -39,6 +45,13 @@ type AiCityStreamEvent = {
event: string;
};
type AiMetarSummaryProgress = {
message_en?: string | null;
message_zh?: string | null;
content?: string | null;
raw_length?: number | null;
};
function getStorage() {
if (typeof window === "undefined") return null;
try {
@@ -270,6 +283,118 @@ function getAiCityStreamProgressText(progress: AiCityStreamProgress, isEn: boole
return "";
}
async function readMetarSummaryStream(
response: Response,
onProgress?: (progress: AiMetarSummaryProgress) => void,
) {
if (!response.body) {
return response.json() as Promise<AiMetarSummaryPayload>;
}
const reader = response.body.getReader();
const decoder = new TextDecoder();
let buffer = "";
let accumulated = "";
let finalPayload: AiMetarSummaryPayload | null = null;
const consumeBlock = (block: string) => {
const parsed = parseAiCityStreamBlock(block);
if (!parsed) return;
const { data, event } = parsed;
if (event === "final") {
finalPayload = data as AiMetarSummaryPayload;
return;
}
if (event === "progress") {
onProgress?.(data as AiMetarSummaryProgress);
return;
}
if (event === "delta") {
const content = String(data.content || "");
if (content) {
accumulated += content;
}
onProgress?.({
content: accumulated,
raw_length: Number(data.raw_length),
});
}
};
for (;;) {
const { done, value } = await reader.read();
buffer += decoder.decode(value || new Uint8Array(), { stream: !done });
const blocks = buffer.split(/\r?\n\r?\n/);
buffer = blocks.pop() || "";
blocks.forEach(consumeBlock);
if (done) break;
}
if (buffer.trim()) {
consumeBlock(buffer);
}
return (
finalPayload || {
status: accumulated ? "ready" : "failed",
summary: accumulated,
}
);
}
function requestMetarSummary({
airport,
city,
deb,
locale,
metar,
modelRange,
onProgress,
requestKey,
}: {
airport: string;
city: string;
deb: string;
locale: string;
metar: string;
modelRange: string;
onProgress?: (progress: AiMetarSummaryProgress) => void;
requestKey: string;
}) {
const pending = pendingMetarSummaryRequests.get(requestKey);
if (pending) return pending;
const request = buildBrowserBackendHeaders({
Accept: "text/event-stream",
"Content-Type": "application/json",
})
.then((headers) =>
fetchBackendApi("/api/ai/metar-summary", {
method: "POST",
headers,
cache: "no-store",
body: JSON.stringify({
airport,
city,
deb,
locale,
metar,
model_range: modelRange,
}),
}),
)
.then(async (response) => {
if (!response.ok) {
throw new Error(`HTTP ${response.status}`);
}
return readMetarSummaryStream(response, onProgress);
})
.finally(() => {
pendingMetarSummaryRequests.delete(requestKey);
});
pendingMetarSummaryRequests.set(requestKey, request);
return request;
}
function buildAiCityFallbackPayload({
detail,
error,
@@ -337,6 +462,113 @@ function buildAiCityFallbackPayload({
};
}
export function useAiMetarSummary({
airport,
city,
deb,
enabled = true,
isEn,
locale,
metar,
modelRange,
}: {
airport: string;
city: string;
deb: string;
enabled?: boolean;
isEn: boolean;
locale: string;
metar: string;
modelRange: string;
}) {
const [metarSummary, setMetarSummary] = useState<AiMetarSummaryState>({
status: "idle",
});
const requestKey = useMemo(
() =>
metar
? [
"metar-summary",
normalizeCityKey(city) || city,
airport,
locale,
deb,
modelRange,
metar,
].join(":")
: "",
[airport, city, deb, locale, metar, modelRange],
);
useEffect(() => {
if (!enabled || !requestKey || !metar) {
setMetarSummary({ status: "idle" });
return;
}
let cancelled = false;
setMetarSummary({
status: "loading",
streamText: isEn
? "Reading current METAR with a lightweight AI pass..."
: "正在用轻量 AI 快速解读当前 METAR…",
});
void requestMetarSummary({
airport,
city,
deb,
locale,
metar,
modelRange,
onProgress: (progress) => {
if (cancelled) return;
const text = String(
progress.content ||
(isEn ? progress.message_en : progress.message_zh) ||
"",
).trim();
if (!text) return;
setMetarSummary((current) => ({
...current,
status: "loading",
streamText: text,
}));
},
requestKey,
})
.then((payload) => {
if (cancelled) return;
const summary = String(payload?.summary || "").trim();
if (summary) {
setMetarSummary({
payload,
status: "ready",
streamText: summary,
});
} else {
setMetarSummary({
payload,
status: "failed",
error: String(payload?.reason || payload?.status || "empty response"),
});
}
})
.catch((error) => {
if (!cancelled) {
setMetarSummary({
status: "failed",
error: String(error),
});
}
});
return () => {
cancelled = true;
};
}, [airport, city, deb, enabled, isEn, locale, metar, modelRange, requestKey]);
return { metarSummary };
}
export function useAiCityForecast({
detail,
detailCityName,
web/routes.py
+19
View File
@@ -36,6 +36,7 @@ from web.scan_terminal_service import (
build_scan_city_ai_forecast_payload,
build_scan_terminal_ai_payload,
build_scan_terminal_payload,
stream_metar_summary_payload,
stream_scan_city_ai_forecast_payload,
)
from web.core import (
@@ -1811,3 +1812,21 @@ async def scan_terminal_ai_city_stream(request: Request):
},
)
@router.post("/api/ai/metar-summary")
async def ai_metar_summary_stream(request: Request):
_assert_entitlement(request)
try:
body = await request.json()
except Exception:
body = {}
if not isinstance(body, dict):
raise HTTPException(status_code=400, detail="Invalid JSON body")
return StreamingResponse(
stream_metar_summary_payload(body),
media_type="text/event-stream",
headers={
"Cache-Control": "no-store",
"X-Accel-Buffering": "no",
},
)
web/scan_terminal_service.py
+202 -2
View File
@@ -98,6 +98,24 @@ SCAN_CITY_AI_MAX_TOKENS = _env_int(
min_value=800,
max_value=64000,
)
METAR_SUMMARY_AI_MODEL = str(
os.getenv("POLYWEATHER_METAR_SUMMARY_AI_MODEL")
or os.getenv("POLYWEATHER_SCAN_CITY_AI_MODEL")
or os.getenv("POLYWEATHER_SCAN_AI_MODEL")
or "deepseek-v4-flash"
).strip()
METAR_SUMMARY_AI_TIMEOUT_SEC = _env_int(
"POLYWEATHER_METAR_SUMMARY_AI_TIMEOUT_SEC",
8,
min_value=3,
max_value=30,
)
METAR_SUMMARY_AI_MAX_TOKENS = _env_int(
"POLYWEATHER_METAR_SUMMARY_AI_MAX_TOKENS",
160,
min_value=80,
max_value=1000,
)
SCAN_CITY_AI_PROMPT_VERSION = "city-airport-read-v3"
CITY_AI_REQUIRED_FIELDS = [
@@ -627,14 +645,25 @@ def _build_city_ai_fallback(
if partial_ai.get("final_judgment_zh") or partial_ai.get("final_judgment_en"):
final_zh = str(partial_ai.get("final_judgment_zh") or partial_ai.get("final_judgment_en") or "").strip()
final_en = str(partial_ai.get("final_judgment_en") or partial_ai.get("final_judgment_zh") or "").strip()
elif partial_ai:
final_zh = f"{city} 预计最高温暂以 {predicted_text} 附近为中枢；AI 已先完成机场报文解读，最高温结论结合 DEB、多模型与最新 METAR 校准。"
final_en = f"{city} daily high is centered near {predicted_text}; AI has already read the airport bulletin, with the high calibrated against DEB, the model cluster and latest METAR."
elif timed_out:
final_zh = f"{city} 预计最高温暂以 {predicted_text} 附近为中枢；当前已先用 DEB、多模型和 METAR 快速证据模式判断。"
final_en = f"{city} daily high is centered near {predicted_text}; the current read uses the fast DEB/model/METAR evidence mode."
else:
final_zh = f"{city} 预计最高温暂以 {predicted_text} 附近为中枢；当前已先用 DEB、多模型和 METAR 快速证据模式判断。"
final_en = f"{city} daily high is centered near {predicted_text}; the current read uses the fast DEB/model/METAR evidence mode."
reasoning_zh = str(partial_ai.get("reasoning_zh") or "").strip() or "DEB、多模型集合和最新 METAR 已足够给出当前方向判断；AI 增强可作为后续补充，不阻塞本轮读数。"
reasoning_en = str(partial_ai.get("reasoning_en") or "").strip() or "DEB, the model cluster and latest METAR are enough for the current directional read; AI enhancement can be added later without blocking this card."
reasoning_zh = str(partial_ai.get("reasoning_zh") or "").strip() or (
"AI 机场报文解读已用于校准日内节奏；DEB 与多模型集合继续约束最高温中枢，后续 METAR 用于确认是否需要上调或下修。"
if partial_ai
else "DEB、多模型集合和最新 METAR 已足够给出当前方向判断；AI 增强可作为后续补充，不阻塞本轮读数。"
)
reasoning_en = str(partial_ai.get("reasoning_en") or "").strip() or (
"The AI airport-bulletin read is already used to calibrate the intraday pace; DEB and the model cluster still constrain the high-temperature center, while later METAR reports confirm whether to revise it."
if partial_ai
else "DEB, the model cluster and latest METAR are enough for the current directional read; AI enhancement can be added later without blocking this card."
)
risks_zh = ["后续 METAR 若明显偏离模型路径，需及时修正最高温中枢。"]
risks_en = ["If later METAR reports diverge from the model path, revise the daily-high center promptly."]
return {
@@ -1750,6 +1779,177 @@ def _build_city_ai_stream_request(
}
def stream_metar_summary_payload(body: Dict[str, Any]) -> Iterator[str]:
"""Stream a tiny METAR-only AI read.
This intentionally does not share the full city-review prompt. The goal is
first-token speed for the "AI airport read" section, while the heavier city
JSON review continues separately.
"""
started_at = time.time()
normalized_locale = _normalize_locale(str(body.get("locale") or "zh-CN"))
city = str(body.get("city") or "").strip()
airport = str(body.get("airport") or body.get("station") or "").strip()
metar = str(body.get("metar") or "").strip()
model_range = str(body.get("model_range") or "").strip()
deb = str(body.get("deb") or "").strip()
if not metar:
yield _sse_event(
"final",
{
"status": "failed",
"model": METAR_SUMMARY_AI_MODEL,
"provider": "deepseek",
"summary": "",
"reason": "metar is required",
"duration_ms": int((time.time() - started_at) * 1000),
},
)
return
yield _sse_event(
"progress",
{
"stage": "calling_ai",
"message_zh": "DeepSeek 正在快速解读当前 METAR…",
"message_en": "DeepSeek is quickly reading the current METAR…",
},
)
if not SCAN_AI_ENABLED:
yield _sse_event(
"final",
{
"status": "disabled",
"model": METAR_SUMMARY_AI_MODEL,
"provider": "deepseek",
"summary": "",
"reason": "POLYWEATHER_SCAN_AI_ENABLED is not enabled",
"duration_ms": int((time.time() - started_at) * 1000),
},
)
return
if not str(os.getenv("POLYWEATHER_DEEPSEEK_API_KEY") or "").strip():
yield _sse_event(
"final",
{
"status": "missing_key",
"model": METAR_SUMMARY_AI_MODEL,
"provider": "deepseek",
"summary": "",
"reason": "POLYWEATHER_DEEPSEEK_API_KEY is not configured",
"duration_ms": int((time.time() - started_at) * 1000),
},
)
return
is_en = normalized_locale == "en-US"
system_prompt = (
"You are PolyWeather's fast airport-bulletin module. "
"Use only the current METAR, DEB and model range. "
"Do not output JSON. Do not repeat the full METAR. Do not predict market prices. "
"Keep the answer within 80 Chinese characters or 45 English words."
if is_en
else "你是 PolyWeather 的机场报文快速解读模块。"
"请用中文用1到2句话解读当前 METAR 对今日最高温判断的影响。"
"要求：只基于当前 METAR、DEB 和模型区间；不输出 JSON；不要复述完整报文；"
"不要预测市场价格；不超过80个中文字。"
)
user_prompt = (
f"City: {city or 'unknown'}\n"
f"Airport: {airport or 'unknown'}\n"
f"METAR: {metar}\n"
f"DEB: {deb or 'unknown'}\n"
f"Model range: {model_range or 'unknown'}"
)
request_json = {
"model": METAR_SUMMARY_AI_MODEL,
"temperature": 0.15,
"max_tokens": METAR_SUMMARY_AI_MAX_TOKENS,
"stream": True,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
}
timeout = httpx.Timeout(
timeout=float(METAR_SUMMARY_AI_TIMEOUT_SEC),
connect=min(5.0, float(METAR_SUMMARY_AI_TIMEOUT_SEC)),
read=float(METAR_SUMMARY_AI_TIMEOUT_SEC),
write=5.0,
pool=3.0,
)
headers = {
"Authorization": f"Bearer {os.getenv('POLYWEATHER_DEEPSEEK_API_KEY')}",
"Content-Type": "application/json",
}
accumulated = ""
try:
logger.info(
"metar summary stream request city={} airport={} model={} timeout_sec={}",
city,
airport,
METAR_SUMMARY_AI_MODEL,
METAR_SUMMARY_AI_TIMEOUT_SEC,
)
with httpx.Client(timeout=timeout) as client:
with client.stream(
"POST",
f"{SCAN_AI_BASE_URL}/chat/completions",
headers=headers,
json=request_json,
) as response:
response.raise_for_status()
for line in response.iter_lines():
text = str(line or "").strip()
if not text or not text.startswith("data:"):
continue
payload_text = text[5:].strip()
if payload_text == "[DONE]":
break
try:
chunk = json.loads(payload_text)
except Exception:
continue
delta = _extract_provider_stream_delta(chunk)
if delta:
accumulated += delta
yield _sse_event(
"delta",
{
"content": delta,
"raw_length": len(accumulated),
},
)
summary = _truncate_ai_text(accumulated, 260)
yield _sse_event(
"final",
{
"status": "ready" if summary else "empty",
"model": METAR_SUMMARY_AI_MODEL,
"provider": "deepseek",
"summary": summary,
"duration_ms": int((time.time() - started_at) * 1000),
},
)
except Exception as exc:
summary = _truncate_ai_text(accumulated, 260)
yield _sse_event(
"final",
{
"status": "ready" if summary else "failed",
"degraded": bool(summary),
"model": METAR_SUMMARY_AI_MODEL,
"provider": "deepseek",
"summary": summary,
"reason": str(exc),
"duration_ms": int((time.time() - started_at) * 1000),
},
)
def _cache_city_ai_payload(
cache_key: str,
*,