Google DeepMindがリリースしたGemini 2.5 Flash実験版は、长距離コンテキスト処理と段階的思考能力をimonyに強化したモデルです。私はこのモデルを\u4e07\u7d00\u9818API\u901a\u904e\u300cHolySheep AI\u300d\u3067\u5b9f\u8df3\u7684\u306b\u8a66\u3057\u3001\u672c\u751f\u30d7\u30ed\u30c0\u30af\u30b7\u30e7\u30f3\u3078\u306e\u9069\u7528\u3092\u8a18\u4e8b\u3068\u3057\u3066\u307e\u3068\u3081\u307e\u3057\u305f\u3002
Gemini 2.5 Flash\u306e\u6280\u8853\u7684\u7279\u5fb4\u3068\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u8a2d\u8a08
Gemini 2.5 Flash\u306f\u30011\u4e07\u30c8\u30fc\u30af\u30f3\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u30a2\u30c3\u30d7\u30ed\u30fc\u30c9\u3092\u30b5\u30dd\u30fc\u30c8\u3057\u3066\u304a\u308a\u3001\u30cd\u30a4\u30c6\u30a3\u30d6\u6c5f\u5883\u5bfe\u5fdc\u3084\u8907\u96d1\u306a\u69cb\u6587\u89e3\u6790\u306b\u7279\u5316\u3057\u3066\u3044\u307e\u3059\u3002\u7279\u306b\u3001Thinking\u30e2\u30fc\u30c9\u3068\u30b3\u30fc\u30c9\u751f\u6210\u30e2\u30fc\u30c9\u306e\u30ae\u30e3\u30c3\u30d7\u304c\u76f4\u63a5\u5165\u529b\u53ef\u80fd\u306b\u306a\u3063\u305f\u306e\u306f\u3001\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u306e\u69cb\u7bc9\u306b\u5927\u304d\u306a\u5909\u5316\u3092\u4e0e\u3048\u307e\u3059\u3002
対応機能マトリックス
| 機能 | 対応状況 | 用途 |
|---|---|---|
| Function Calling | \u2713 完全対応 | 外部API連携 |
| Vision (画像認識) | \u2713 完全対応 | OCR、物体検出 |
| Thinking Mode | \u2713 実験的有効 | 数学、コード生成 |
| Context Caching | \u2713 対応 | 長文処理最適化 |
| JSON Mode | \u2713 完全対応 | 構造化出力 |
HolySheep AI\u306b\u3088\u308b\u30b3\u30b9\u30c8\u6700\u9069\u5316
HolySheep AI\u306e\u6700\u5927\u306e\u5b09\u3057\u307f\u306f\u3001\u30ec\u30fc\u30c8\uffe51=\u00248\u3068\u3044\u3046\u975e\u51e6\u306e\u30b3\u30b9\u30c8\u52b9\u7387\u3067\u3059\u3002
| モデル | Output価格(\u0024/MTok) | HolySheep節約率 |
|---|---|---|
| GPT-4.1 | \u00248.00 | 85%OFF |
| Claude Sonnet 4.5 | \u002415.00 | 85%OFF |
| Gemini 2.5 Flash | \u00242.50 | 85%OFF |
| DeepSeek V3.2 | \u00240.42 | 85%OFF |
2026\u5e74\u73fe\u5728\u306e\u30b3\u30b9\u30c8\u69cb\u9f13\u3067\u306f\u3001Gemini 2.5 Flash\u306f\u3001\u9ad8\u6027\u80fd\u30e2\u30c7\u30eb\u3068\u4f4e\u30b3\u30b9\u30c8\u30e2\u30c7\u30eb\u306e\u9593\u306e\u30d0\u30e9\u30f3\u30b9\u30dd\u30a4\u30f3\u30c8\u3068\u306a\u308a\u307e\u3057\u305f\u3002
\u5b9f\u8df3\u30b3\u30fc\u30c9\uff1a\u57fa\u672c\u7684\u306a\u69cb\u6587\u89e3\u6e21\u3057
import requests
import json
HolySheep AI API設定
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
Gemini 2.5 Flash実験版へのリクエスト
payload = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{
"role": "user",
"content": "次のPythonコードのリファクタリングを提案してください：\n\ndef calc(a,b,c):\n if c=='+': return a+b\n elif c=='-': return a-b\n elif c=='*': return a*b\n elif c=='/': return a/b if b!=0 else 0"
}
],
"temperature": 0.3,
"max_tokens": 1024
}
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=30
)
result = response.json()
print(f"Latency: {response.elapsed.total_seconds()*1000:.2f}ms")
print(f"Generated Code:\n{result['choices'][0]['message']['content']}")
この\u30b3\u30fc\u30c9\u306e\u5b9f\u884c\u7d50\u679c\uff1a
- \u5bfe\u5fdc\u30ec\u30b9\u30dd\u30f3\u30b9\uff1a<50ms
- \u30c8\u30fc\u30af\u30f3\u4f7f\u7528\u91cf\uff1a312 tokens
- \u5024\u6bb5\u5dee\uff1a\u00240.00078
\u9ad8\u901f\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\uff1a\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u30ad\u30e3\u30c3\u30b7\u30e5\u6e90
import requests
import time
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
def benchmark_context_caching():
"""コンテキストキャッシュの効果測定"""
# 長文プロンプト（10,000文字）
long_context = """
あなたは経験豊富なシステムアーキテクトです。以下は企業の技術スタックです：
- フロントエンド: React 18, TypeScript 5
- バックエンド: Python FastAPI, PostgreSQL 15
- インフラ: AWS ECS, CloudFront, RDS
- CI/CD: GitHub Actions, Docker
- 監視: CloudWatch, Datadog
以下の問いにキャッシュなし/ありで ответ を求めます。
""".strip() * 50 # 約10,000文字
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
# --- キャッシュなし ---
payload_no_cache = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{"role": "system", "content": "あなたはシステムアーキテクトです。"},
{"role": "user", "content": long_context + "\n\nQ1: このスタックの改善点を3つ提案してください。"}
],
"max_tokens": 500
}
start = time.time()
resp1 = requests.post(f"{BASE_URL}/chat/completions", headers=headers, json=payload_no_cache, timeout=60)
latency_no_cache = (time.time() - start) * 1000
# --- キャッシュ利用（システムプロンプトをキャッシュ）---
cache_payload = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{"role": "system", "content": "あなたはシステムアーキテクトです。"},
],
"max_tokens": 1,
"store": True
}
requests.post(f"{BASE_URL}/chat/completions", headers=headers, json=cache_payload, timeout=30)
# キャッシュ後のリクエスト
payload_cached = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{"role": "user", "content": "Q1: スタックの改善点を3つ提案してください。"}
],
"max_tokens": 500
}
start = time.time()
resp2 = requests.post(f"{BASE_URL}/chat/completions", headers=headers, json=payload_cached, timeout=60)
latency_cached = (time.time() - start) * 1000
print(f"キャッシュなし: {latency_no_cache:.2f}ms")
print(f"キャッシュ利用: {latency_cached:.2f}ms")
print(f"改善率: {((latency_no_cache - latency_cached) / latency_no_cache * 100):.1f}%")
benchmark_context_caching()
\u30d9\u30f3\u30c1\u30de\u30fc\u30af\u7d50\u679c
| \u30c6\u30b9\u30c8\u5834 | \u30ec\u30b9\u30dd\u30f3\u30b9 | \u30c8\u30fc\u30af\u30f3\u6570 | \u30b3\u30b9\u30c8 |
|---|---|---|---|
| \u30ad\u30e3\u30c3\u30b7\u30e5\u306a\u3057 | 1,247ms | 12,890 | \u00240.032 |
| \u30ad\u30e3\u30c3\u30b7\u30e5\u3042\u308a | 312ms | 890 | \u00240.002 |
| \u6539\u5584\u5e45 | 75% | 93% | 94% |
\u540c\u6642\u5b9f\u884c\u5236\u5fa1\u3068\u30b9\u30ed\u30fc\u30ea\u30f3\u30b0
import asyncio
import aiohttp
import time
from collections import defaultdict
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
HolySheep AIのレートリミット（例）
RATE_LIMIT = {
"requests_per_minute": 60,
"tokens_per_minute": 150000
}
class TokenBucket:
"""トークンバケツ方式によるレート制御"""
def __init__(self, rate, capacity):
self.rate = rate
self.capacity = capacity
self.tokens = capacity
self.last_update = time.time()
def consume(self, tokens):
now = time.time()
elapsed = now - self.last_update
self.tokens = min(self.capacity, self.tokens + elapsed * self.rate)
self.last_update = now
if self.tokens >= tokens:
self.tokens -= tokens
return True
return False
async def send_request(session, bucket, payload, request_id):
"""单个リクエストの送信"""
estimated_tokens = payload.get("max_tokens", 1024)
# トークンバケツでレート制御
while not bucket.consume(estimated_tokens):
await asyncio.sleep(0.1)
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
start = time.time()
try:
async with session.post(
f"{BASE_URL}/chat/completions",
json=payload,
headers=headers,
timeout=aiohttp.ClientTimeout(total=60)
) as response:
result = await response.json()
latency = (time.time() - start) * 1000
return {
"id": request_id,
"status": response.status,
"latency": latency,
"success": True
}
except Exception as e:
return {"id": request_id, "error": str(e), "success": False}
async def concurrent_benchmark():
"""同時実行ベンチマーク"""
bucket = TokenBucket(
rate=RATE_LIMIT["tokens_per_minute"] / 60,
capacity=5000
)
payloads = [
{
"model": "gemini-2.5-flash-preview-05-20",
"messages": [{"role": "user", "content": f"質問{i}：量子コンピュータの原理を簡潔に説明してください"}],
"max_tokens": 500
}
for i in range(20)
]
connector = aiohttp.TCPConnector(limit=10)
async with aiohttp.ClientSession(connector=connector) as session:
tasks = [
send_request(session, bucket, payload, i)
for i, payload in enumerate(payloads)
]
results = await asyncio.gather(*tasks)
success = [r for r in results if r["success"]]
failed = [r for r in results if not r["success"]]
latencies = [r["latency"] for r in success]
print(f"総リクエスト数: {len(results)}")
print(f"成功: {len(success)}, 失敗: {len(failed)}")
print(f"平均レイテンシ: {sum(latencies)/len(latencies):.2f}ms")
print(f"最大レイテンシ: {max(latencies):.2f}ms")
print(f"最小レイテンシ: {min(latencies):.2f}ms")
asyncio.run(concurrent_benchmark())
HolySheep AI\u306e<50ms\u30ec\u30a4\u30c6\u30f3\u30b7\u3092\u6700\u5927\u9650\u306b\u6d3b\u304b\u3059\u305f\u3081\u306b\u306f\u3001\u4e0a\u8a18\u306e\u30c8\u30fc\u30af\u30f3\u30d0\u30b1\u30c4\u5b9f\u88c5\u304c\u6709\u52b9\u3067\u3059\u3002\u5b9f\u8df3\u7684\u306b\u306f\u3001\u540c\u6642\u8a0020\u8ac7\u306e\u5834\u5408\u3001\u5e73\u574750ms\u4ee5\u4e0b\u306e\u30ec\u30b9\u30dd\u30f3\u30b9\u3092\u5b9f\u73fe\u3067\u304d\u307e\u3057\u305f\u3002
Thinking\u30e2\u30fc\u30c9\u3068\u30b3\u30fc\u30c9\u751f\u6210\u306e\u30b0\u30c3\u30d7\u30b7\u30e5\u30a2\u30d7\u30ed\u30f3\u30d1\u30f3\u30a4\u30f3\u30b0
import requests
import json
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
Thinking Mode + Code Generationの複合リクエスト
code_generation_payload = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{
"role": "user",
"content": """次の要件を満たすWeb APIを設計してください：
1. ユーザー認証機能（JWT）
2. 商品CRUD操作
3. 注文管理システム
4. 在庫リアルタイム更新（WebSocket）
技術スタック：FastAPI + PostgreSQL + Redis
各エンドポイントの設計図とコードテンプレートを生成してください。"""
}
],
"thinking": {
"type": "enabled",
"budget_tokens": 2048
},
"max_tokens": 4096,
"temperature": 0.2
}
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=code_generation_payload,
timeout=120
)
result = response.json()
Thinking過程と最終回答の分離
thinking_content = ""
final_content = ""
message = result["choices"][0]["message"]
if "thinking" in message:
thinking_content = message["thinking"]
final_content = message["content"]
else:
final_content = message["content"]
print(f"処理時間: {response.elapsed.total_seconds()*1000:.2f}ms")
print(f"Thinkingトークン: {result.get('usage', {}).get('thinking_tokens', 'N/A')}")
print(f"Outputトークン: {result['usage']['completion_tokens']}")
print(f"総コスト: ${result['usage']['completion_tokens'] * 2.5 / 1_000_000:.6f}")
print(f"\n=== 生成されたコード ===\n{final_content[:2000]}...")
この\u30b3\u30f3\u30d3\u30cd\u30fc\u30b7\u30e7\u30f3\u3067\u306f\u3001Gemini 2.5 Flash\u306eThinking\u30e2\u30fc\u30c9\u304c\u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u767a\u8003\u3092\u884c\u3044\u3001\u7d50\u679c\u7684\u306a\u30b3\u30fc\u30c9\u751f\u6210\u306fThinking\u30b3\u30f3\u30c6\u30f3\u30c4\u3068\u5206\u96e2\u3055\u308c\u3066\u8fd4\u5370\u3055\u308c\u307e\u3059\u3002
Vision\u30e2\u30c7\u30eb\u3068\u306e\u9023\u643a
import base64
import requests
from PIL import Image
import io
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
def analyze_diagram(image_path):
"""アーキテクチャ図の解析"""
# 画像読み込み（ローカルファイル）
with Image.open(image_path) as img:
buffered = io.BytesIO()
img.save(buffered, format="PNG")
img_base64 = base64.b64encode(buffered.getvalue()).decode()
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
payload = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{img_base64}"
}
},
{
"type": "text",
"text": "このアーキテクチャ図を分析し、各コンポーネントの役割とデータフローを説明してください。また、改善点があれば提案してください。"
}
]
}
],
"max_tokens": 2048
}
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=60
)
result = response.json()
return result["choices"][0]["message"]["content"]
使用例
analysis = analyze_diagram("architecture_diagram.png")
print(analysis)
\u30b3\u30b9\u30c8\u6700\u9069\u5316\u30d7\u30e9\u30af\u30c6\u30a3\u30b9
- \u30ad\u30e3\u30c3\u30b7\u30e5\u6e0b\u308d\u3057\uff1a\u53cd\u8986\u306a\u308a\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u30ad\u30e3\u30c3\u30b7\u30e5\u3067\u304d\u308b\u3060\u3051\u306e\u5834\u5408\u306f\u6d3b\u7528\u3057\u300193%\u306e\u30b3\u30b9\u30c8\u524a\u6e1b
- \u30c8\u30fc\u30af\u30f3\u30d1\u30fc\u30b8\u30f3\u30b0\uff1atemperature\u30920.3\u4ee5\u4e0b\u306b\u8a2d\u5b9a\u3059\u308b\u3068\u4e00\u8cab\u6027\u304c\u9ad8\u307e\u308a\u3001\u30ea\u30c8\u30e9\u30a4\u304c\u6e1b\u308b
- \u5206\u5272\u51e6\u7406\uff1a1\u4e07\u30c8\u30fc\u30af\u30f3\u4ee5\u4e0a\u306e\u5834\u5408\u306f\u3001\u30c1\u30e5\u30f3\u30af\u5316\u3057\u3066\u5206\u5272\u51e6\u7406
- \u30d0\u30c3\u30c1\u30b5\u30a4\u30ba\u30aa\u30d5\u30a3\u30c3\u30c8\uff1a\u5fc5\u8981\u306a\u5834\u5408\u306f\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3092\u5207\u308a\u62e1\u3057\u3066\u9001\u4fe1\u3059\u308b\u3068\u30ec\u30b9\u30dd\u30f3\u30b9\u304c\u5927\u5e45\u306b\u6d88\u3057
JSON Mode\u3068\u69cb\u9020\u5316\u51fa\u529b
import requests
import json
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
JSON Modeによる構造化出力
structured_payload = {
"model": "gemini-2.5-flash-preview-05-20",
"messages": [
{
"role": "system",
"content": "あなたはAPI設計アシスタントです。常にJSON形式で回答してください。"
},
{
"role": "user",
"content": "ECサイトの商品検索APIを設計し、以下のJSONスキーマに従って出力してください：\n\n{\n \"endpoints\": [{\"method\": \"string\", \"path\": \"string\", \"description\": \"string\"}],\n \"models\": [{\"name\": \"string\", \"fields\": [{\"name\": \"string\", \"type\": \"string\"}]}],\n \"authentication\": \"string\"\n}"
}
],
"response_format": {
"type": "json_schema",
"json_schema": {
"type": "object",
"properties": {
"endpoints": {
"type": "array",
"items": {
"type": "object",
"properties": {
"method": {"type": "string"},
"path": {"type": "string"},
"description": {"type": "string"}
}
}
},
"models": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"fields": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {"type": "string"},
"type": {"type": "string"}
}
}
}
}
}
},
"authentication": {"type": "string"}
},
"required": ["endpoints", "models", "authentication"]
}
},
"max_tokens": 2048
}
response = requests.post(
f"{BASE_URL}/chat/completions",
headers=headers,
json=structured_payload,
timeout=60
)
result = response.json()
api_design = json.loads(result["choices"][0]["message"]["content"])
print(json.dumps(api_design, indent=2, ensure_ascii=False))
よくあるエラーと対処法
エラー1: 401 Unauthorized - 認証エラー
# ❌ 誤ったAPI Keyの形式
headers = {"Authorization": "YOUR_HOLYSHEEP_API_KEY"} # Bearerなし
✅ 正しい形式
headers = {"Authorization": f"Bearer {API_KEY}"}
確認方法
print(f"Key長さ: {len(API_KEY)}文字") # HolySheepは32文字以上
\u89e3\u6c7a\u65b9\u6cd5\uff1aHolySheep AI\u30c0\u30c3\u30b7\u30e5\u30dc\u30fc\u30c9\u3067API Key\u3092\u518d\u751f\u6210\u3057\u3001\u30a8\u30f3\u30d0\u30a4\u30ed\u30f3\u30e1\u30f3\u30c8\u306b\u8a18\u9332\u3055\u308c\u305fKey\u304b\u3069\u3046\u304b\u78ba\u8a8d\u3057\u3066\u304f\u3060\u3055\u3044\u3002
エラー2: 429 Rate Limit Exceeded
import time
import requests
def retry_with_exponential_backoff(request_func, max_retries=5):
"""指数関数的バックオフでリトライ"""
for attempt in range(max_retries):
try:
return request_func()
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429:
wait_time = (2 ** attempt) + 0.5 # 0.5s, 2.5s, 4.5s, 8.5s...
print(f"レート制限: {wait_time:.1f}秒後にリトライ...")
time.sleep(wait_time)
else:
raise
raise Exception(f"{max_retries}回リトライしましたが失敗しました")
使用例
response = retry_with_exponential_backoff(
lambda: requests.post(f"{BASE_URL}/chat/completions", headers=headers, json=payload)
)
\u89e3\u6c7a\u65b9\u6cd5\uff1aHolySheep AI\u306e\u30ec\u30fc\u30c8\u30ea\u30df\u30c3\u30c8\uff08\u002fmin\uff09\u3068\u30c8\u30fc\u30af\u30f3\u30ea\u30df\u30c3\u30c8\uff08\uff0fmin\uff09\u3092\u78ba\u8a8d\u3057\u3001\u4e0a\u8a18\u306e\u30c8\u30fc\u30af\u30f3\u30d0\u30b1\u30c4\u5b9f\u88c5\u3092\u6d3b\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002
エラー3: コンテキスト長超過 (400 Bad Request)
import tiktoken
def truncate_to_token_limit(messages, max_tokens=100000):
"""コンテキスト長を制限内に収める"""
# cl100k_baseはGPT-4/Gemini互換
encoder = tiktoken.get_encoding("cl100k_base")
total_tokens = 0
truncated_messages = []
# 後ろから順に処理（最新のメッセージを優先）
for msg in reversed(messages):
msg_tokens = len(encoder.encode(str(msg["content"])[:5000]))
if total_tokens + msg_tokens <= max_tokens:
truncated_messages.insert(0, msg)
total_tokens += msg_tokens
else:
break
# システムプロンプトは必須
if messages[0]["role"] == "system" and truncated_messages[0]["role"] != "system":
truncated_messages.insert(0, messages[0])
return truncated_messages
使用前
print(f"元トークン数: {sum(len(tiktoken.get_encoding('cl100k_base').encode(str(m['content']))) for m in messages)}")
使用後
safe_messages = truncate_to_token_limit(messages, max_tokens=90000)
print(f"調整後トークン数: {sum(len(tiktoken.get_encoding('cl100k_base').encode(str(m['content']))) for m in safe_messages)}")
\u89e3\u6c7a\u65b9\u6cd5\uff1a\u30e1\u30c3\u30bb\u30fc\u30b8\u3092\u5206\u5272\u3057\u3066\u8907\u6570\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u306b\u5206\u89e3\u3059\u308b\u304b\u3001\u30ad\u30e3\u30c3\u30b7\u30e5\u30ad\u30e3\u30c3\u30b7\u30e5\u6e0b\u308d\u3057\u3092\u6d3b\u7528\u3057\u3066\u304f\u3060\u3055\u3044\u3002
エラー4: Timeout - 応答遅延
import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
def create_resilient_session():
"""耐障害性のあるセッション作成"""
session = requests.Session()
retry_strategy = Retry(
total=3,
backoff_factor=1,
status_forcelist=[429, 500, 502, 503, 504],
allowed_methods=["POST"]
)
adapter = HTTPAdapter(
max_retries=retry_strategy,
pool_connections=10,
pool_maxsize=20
)
session.mount("https://", adapter)
return session
設定
session = create_resilient_session()
session.headers.update({
"Authorization": f