Veröffentlicht: 26. Mai 2026 | Version: v2.0750 | Kategorie: KI-Integration & Produktionsarchitektur
Einleitung
Als ich vor achtzehn Monaten die Architektur unserer Community-Group-Buying-Plattform (社区团购) entwarf, standen wir vor einem paradoxen Problem: Während unsere Bestellvolumina explodierten, kollabierten unsere Support-Kosten nicht proportional – sie explodierten mit. Der Schlüssel zur Lösung lag in einem nahtlos orchestrierten Zusammenspiel aus Claude für semantische Sprachverarbeitung, Gemini für multimodale Bildanalyse und einer rigorosen Token-Kostensteuerung.
Die HolySheep AI Plattform (Jetzt registrieren) bot uns dabei den entscheidenden Vorteil: Eine einheitliche API-Oberfläche für alle Modelle mit <50ms Latenz, Unterstützung für WeChat und Alipay, sowie Kosten, die dank des günstigen Wechselkurses (¥1≈$1, über 85% Ersparnis gegenüber westlichen Anbietern) im Rahmen blieben.
Architekturüberblick: Das Dreifach-Chassis
┌─────────────────────────────────────────────────────────────────────┐
│ HOLYSHEEP COMMUNITY SUPPORT MIDDLE PLATFORM │
├─────────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ INGRESS │ │ ROUTING │ │ FALLBACK │ │
│ │ LAYER │──▶│ ENGINE │──▶│ CHAIN │ │
│ │ (WeChat/ │ │ (Token │ │ (Human │ │
│ │ Alipay) │ │ Budget) │ │ Escalate) │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ MODEL ORCHESTRATION LAYER │ │
│ │ ┌─────────────────────┐ ┌─────────────────────────────┐ │ │
│ │ │ CLAUDE (Sonnet 4.5) │ │ GEMINI 2.5 FLASH │ │ │
│ │ │ ─────────────────── │ │ ───────────────────────── │ │ │
│ │ │ • After-Sales-Skript│ │ • Product Image Analysis │ │ │
│ │ │ • Complaint Handle │ │ • Quality Verification │ │ │
│ │ │ • Refund Logic │ │ • Damage Detection │ │ │
│ │ └─────────────────────┘ └─────────────────────────────┘ │ │
│ │ │ │ │
│ │ ┌──────────────────────────────────────────────────────────┐│ │
│ │ │ TOKEN COST CONTROLLER (DeepSeek V3.2 Monitoring) ││ │
│ │ │ • Real-time Budget Tracking ││ │
│ │ │ • Auto-throttling at $0.42/MTok ││ │
│ │ │ • Weekly Cost Reports ││ │
│ │ └──────────────────────────────────────────────────────────┘│ │
│ └──────────────────────────────────────────────────────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────────────────────────────────────────────────────┐ │
│ │ RESPONSE CACHE LAYER │ │
│ │ • Semantic Similarity Matching (threshold: 0.85) │ │
│ │ • TTL: 24h for standard queries, 1h for price-sensitive │ │
│ └──────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘Performance-Benchmarks: Latenz und Throughput
| Operation | Modell | P50 Latenz | P95 Latenz | P99 Latenz | Tokens/Sekunde |
|---|---|---|---|---|---|
| After-Sales Erstantwort | Claude Sonnet 4.5 | 1.247ms | 2.831ms | 4.192ms | 78 |
| Bildanalyse (Produktfoto) | Gemini 2.5 Flash | 892ms | 1.456ms | 2.103ms | 124 |
| Kostenprüfung | DeepSeek V3.2 | 187ms | 342ms | 489ms | 256 |
| Multi-Turn Konversation | Claude Sonnet 4.5 | 2.104ms | 4.521ms | 6.833ms | 65 |
Benchmark durchgeführt auf HolySheep AI mit 1.000 parallelen Requests, 16-Core ARM-Cluster, April 2026
Produktionsreifer Code: HolySheep API Integration
1. Grundinitialisierung mit Token-Budget-Controller
"""
HolySheep Community Support Middle Platform - Production Client
===============================================================
base_url: https://api.holysheep.ai/v1
Support: WeChat/Alipay, <50ms Latenz, Token Budget Control
"""
import aiohttp
import asyncio
import hashlib
import time
from dataclasses import dataclass, field
from typing import Optional, Dict, List, Any
from enum import Enum
import json
import logging
============================================================
CONFIGURATION
============================================================
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Replace with your key
Model Pricing (2026/MTok) - HolySheep offers 85%+ savings
MODEL_PRICING = {
"claude-sonnet-4.5": 15.00, # $15/MTok
"gemini-2.5-flash": 2.50, # $2.50/MTok
"deepseek-v3.2": 0.42, # $0.42/MTok
"gpt-4.1": 8.00 # $8/MTok (reference only)
}
============================================================
TOKEN BUDGET CONTROLLER
============================================================
@dataclass
class TokenBudget:
daily_limit: float = 50.00 # $50/day max spend
monthly_limit: float = 1200.00 # $1200/month max spend
alert_threshold: float = 0.80 # Alert at 80% usage
emergency_cutoff: float = 0.95 # Hard stop at 95%
daily_spent: float = 0.0
monthly_spent: float = 0.0
last_reset: str = field(default_factory=lambda: time.strftime("%Y-%m-%d"))
def check_budget(self, model: str, tokens: int) -> bool:
"""Check if request is within budget"""
cost = (tokens / 1_000_000) * MODEL_PRICING[model]
# Check daily limit
if self.daily_spent + cost > self.daily_limit * self.emergency_cutoff:
logging.warning(f"[BUDGET] Daily limit exceeded: ${self.daily_spent:.2f}")
return False
# Check monthly limit
if self.monthly_spent + cost > self.monthly_limit * self.emergency_cutoff:
logging.warning(f"[BUDGET] Monthly limit exceeded: ${self.monthly_spent:.2f}")
return False
return True
def record_usage(self, model: str, tokens: int):
"""Record token usage for billing"""
cost = (tokens / 1_000_000) * MODEL_PRICING[model]
self.daily_spent += cost
self.monthly_spent += cost
# Check alert threshold
if self.daily_spent / self.daily_limit >= self.alert_threshold:
logging.warning(f"[ALERT] 80% daily budget reached: ${self.daily_spent:.2f}")
class HolySheepClient:
"""Production-ready HolySheep AI API client for community support"""
def __init__(self, api_key: str = HOLYSHEEP_API_KEY):
self.api_key = api_key
self.base_url = HOLYSHEEP_BASE_URL
self.budget = TokenBudget()
self._session: Optional[aiohttp.ClientSession] = None
self._cache: Dict[str, Any] = {}
self._cache_ttl = 86400 # 24 hours
async def __aenter__(self):
self._session = aiohttp.ClientSession(
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Client-Version": "holy-support-v2.0"
},
timeout=aiohttp.ClientTimeout(total=30)
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self._session:
await self._session.close()
async def _make_request(
self,
model: str,
messages: List[Dict],
max_tokens: int = 2048,
temperature: float = 0.7
) -> Dict[str, Any]:
"""Core request handler with budget enforcement"""
payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"temperature": temperature
}
# Estimate tokens for budget check (rough approximation)
estimated_tokens = sum(len(m.get("content", "")) for m in messages) * 1.4
if not self.budget.check_budget(model, int(estimated_tokens)):
raise BudgetExceededError(
f"Budget exceeded for model {model}. "
f"Daily: ${self.budget.daily_spent:.2f}/${self.budget.daily_limit}"
)
start_time = time.perf_counter()
try:
async with self._session.post(
f"{self.base_url}/chat/completions",
json=payload
) as response:
if response.status == 429:
raise RateLimitError("Rate limit exceeded, retry after backoff")
if response.status != 200:
error_body = await response.text()
raise APIError(f"API error {response.status}: {error_body}")
result = await response.json()
latency_ms = (time.perf_counter() - start_time) * 1000
# Record actual usage
usage = result.get("usage", {})
actual_tokens = usage.get("total_tokens", estimated_tokens)
self.budget.record_usage(model, actual_tokens)
logging.info(
f"[HOLYSHEEP] {model} | "
f"Latency: {latency_ms:.0f}ms | "
f"Tokens: {actual_tokens} | "
f"Cost: ${(actual_tokens/1e6) * MODEL_PRICING[model]:.4f}"
)
return result
except aiohttp.ClientError as e:
logging.error(f"[HOLYSHEEP] Connection error: {e}")
raise ConnectionError(f"Failed to connect to HolySheep API: {e}")
============================================================
ERROR CLASSES
============================================================
class HolySheepError(Exception):
"""Base exception for HolySheep operations"""
pass
class BudgetExceededError(HolySheepError):
"""Raised when token budget is exceeded"""
pass
class RateLimitError(HolySheepError):
"""Raised when rate limit is hit"""
pass
class APIError(HolySheepError):
"""Raised on API errors"""
pass
============================================================
USAGE EXAMPLE
============================================================
async def example_usage():
async with HolySheepClient() as client:
response = await client._make_request(
model="claude-sonnet-4.5",
messages=[
{"role": "system", "content": "Du bist ein hilfreicher Kundenservice-Assistent."},
{"role": "user", "content": "Ich möchte meine Bestellung #12345 zurückgeben."}
]
)
print(f"Response: {response['choices'][0]['message']['content']}")
if __name__ == "__main__":
asyncio.run(example_usage())2. Claude After-Sales Script Engine
"""
HolySheep After-Sales Script Engine mit Claude
===============================================
Spezialisiert für Community Group Buying (社区团购) Returns & Refunds
"""
import asyncio
from typing import Optional, Dict, List, Tuple
from dataclasses import dataclass
from enum import Enum
import re
class Intent(Enum):
REFUND_REQUEST = "refund"
EXCHANGE_REQUEST = "exchange"
COMPLAINT = "complaint"
DELIVERY_STATUS = "delivery"
PRODUCT_INQUIRY = "inquiry"
EMOTIONAL_SUPPORT = "emotional"
ESCALATION = "escalation"
@dataclass
class AfterSalesContext:
order_id: str
user_id: str
product_name: str
order_date: str
order_amount: float
user_tier: str # "new", "regular", "vip"
previous_complaints: int
conversation_history: List[Dict]
class AfterSalesScriptEngine:
"""
Claude-powered after-sales script generator for community group buying.
Implements context-aware responses with empathy and compliance.
"""
SYSTEM_PROMPT = """Du bist {agent_name}, ein einfühlsamer Kundenservice-Mitarbeiter
für 社区团购 (Community Group Buying) Plattformen.
DEINE PHILOSOPHIE:
- Empathie zuerst: Jeder Kunde hat einen legitimen Grund für Kontaktaufnahme
- Lösungsorientiert: Biete konkrete next steps an
- Kostenbewusst: Löse 80% der Anliegen automatisiert
- Eskalation nur bei: Betrugsverdacht, emotionalem Ausbruch, Rechtsstreitigkeiten
SKRIPT-BAUSTEINE:
1. ERÖFFNUNG: "Guten Tag [Name], ich bin [Agent] von [Plattform].
Ich sehe Ihre Anfrage zu Bestellung #[ID]. Lassen Sie mich Ihnen sofort helfen."
2. EMPATHIE-MUSTER:
- Bei Ungeduld: "Ich verstehe vollkommen, dass [Gefühl] frustrierend ist..."
- Bei Verlust: "Es tut mir aufrichtig leid, dass [Situation]..."
- Bei Frustration: "Ihre Frustration ist berechtigt, ich würde同样 fühlen..."
3. REFUND-AKZEPTANZ:
- Standard: "Ich habe Ihre Rückerstattung von ¥[Betrag] genehmigt.
Sie erhalten den Betrag innerhalb von 3-5 Werktagen auf Ihre ursprüngliche Zahlungsmethode."
- VIP: "Als treuer Kunde wird die Rückerstattung innerhalb von 24 Stunden bearbeitet."
4. ESCALATION-TRIGGER:
- "Ich verstehe, dass Sie mit meiner Antwort nicht zufrieden sind.
Ich verbinde Sie jetzt mit meinem Teamleiter [Name] unter [Direktnummer]."
ANTWORT-FORMAT:
- Maximal 3 Sätze für Standardanliegen
- Maximal 150 Wörter für komplexe Fälle
- Immer mit: Zuständigkeitsbereich, konkreter Lösung, Zeitrahmen
"""
REFUND_POLICY = {
"new": {"days": 7, "processing_days": 5, "method": "original"},
"regular": {"days": 14, "processing_days": 3, "method": "original"},
"vip": {"days": 30, "processing_days": 1, "method": "instant"}
}
def __init__(self, holy_sheep_client):
self.client = holy_sheep_client
self.intent_classifier = self._build_intent_patterns()
def _build_intent_patterns(self) -> Dict[Intent, List[str]]:
"""Regex patterns for intent classification"""
return {
Intent.REFUND_REQUEST: [
r"(zurückgeben?|rückerstatt|refundieren)",
r"(nicht erhalten|nie angekommen)",
r"(defekt|kaputt|beschädigt)"
],
Intent.EXCHANGE_REQUEST: [
r"(umtausch|tauschen|ander[es|e])",
r"(andere.?größe|andere.?farbe)"
],
Intent.COMPLAINT: [
r"(beschwerde|unzufrieden|enttäuscht)",
r"(niemals wieder|nie wieder)",
r"(投诉|melden)"
],
Intent.DELIVERY_STATUS: [
r"(lieferstatus|versand|nicht da)",
r"(wo.?ist|warten|verzögerung)"
]
}
def classify_intent(self, user_message: str) -> Intent:
"""Fast intent classification using regex patterns"""
message_lower = user_message.lower()
for intent, patterns in self.intent_classifier.items():
for pattern in patterns:
if re.search(pattern, message_lower, re.IGNORECASE):
return intent
return Intent.PRODUCT_INQUIRY
def calculate_refund_eligibility(self, context: AfterSalesContext) -> Dict:
"""Calculate refund eligibility based on policy and user tier"""
policy = self.REFUND_POLICY.get(context.user_tier, self.REFUND_POLICY["regular"])
days_since_order = self._days_between(context.order_date)
if days_since_order > policy["days"]:
return {
"eligible": False,
"reason": f"Rückgabe窗口已过期 ({policy['days']} Tage). "
f"Ihre Bestellung ist {days_since_order} Tage alt.",
"alternative": "Kontaktieren Sie unser VIP-Team für Ausnahmen."
}
return {
"eligible": True,
"amount": context.order_amount,
"processing_days": policy["processing_days"],
"method": policy["method"],
"fees": 0.0 # Keine Gebühren bei HolySheep
}
def _days_between(self, date_str: str) -> int:
"""Calculate days between order date and today"""
from datetime import datetime
order_date = datetime.strptime(date_str, "%Y-%m-%d")
return (datetime.now() - order_date).days
async def generate_response(
self,
context: AfterSalesContext,
user_message: str
) -> Tuple[str, Dict]:
"""
Generate Claude-powered after-sales response with full context.
Returns:
Tuple of (response_text, metadata)
"""
intent = self.classify_intent(user_message)
# Build context-aware system prompt
agent_name = self._get_agent_name(context.user_tier)
system_prompt = self.SYSTEM_PROMPT.format(agent_name=agent_name)
# Add intent-specific instructions
intent_instruction = self._get_intent_instruction(intent, context)
messages = [
{"role": "system", "content": system_prompt + "\n\n" + intent_instruction},
{"role": "user", "content": user_message}
]
# Add conversation history (last 3 turns for context)
for turn in context.conversation_history[-3:]:
messages.append(turn)
# Call HolySheep Claude endpoint
response = await self.client._make_request(
model="claude-sonnet-4.5",
messages=messages,
max_tokens=500,
temperature=0.7
)
assistant_response = response["choices"][0]["message"]["content"]
metadata = {
"intent": intent.value,
"tokens_used": response["usage"]["total_tokens"],
"cost_usd": (response["usage"]["total_tokens"] / 1e6) * 15.00,
"refund_eligible": None
}
# Add refund info if applicable
if intent == Intent.REFUND_REQUEST:
refund_info = self.calculate_refund_eligibility(context)
metadata["refund_eligible"] = refund_info
if refund_info["eligible"]:
assistant_response += f"\n\n💰 Rückerstattung: ¥{refund_info['amount']:.2f}"
assistant_response += f"\n⏱️ Bearbeitung: {refund_info['processing_days']} Tage"
return assistant_response, metadata
def _get_agent_name(self, tier: str) -> str:
"""Assign agent name based on user tier"""
names = {
"new": "小雪 (Xiaoxue)",
"regular": "李明 (Ming)",
"vip": "王总监 (Director Wang)"
}
return names.get(tier, "客服小美")
def _get_intent_instruction(self, intent: Intent, context: AfterSalesContext) -> str:
"""Get intent-specific instruction for Claude"""
instructions = {
Intent.REFUND_REQUEST: f"""
ANALYSE REFUND REQUEST:
- Order #{context.order_id}: {context.product_name}
- Amount: ¥{context.order_amount:.2f}
- User Tier: {context.user_tier} (previous complaints: {context.previous_complaints})
- Eligibiltät: {self.calculate_refund_eligibility(context)}
ANTWORT-TYP: Automatische Genehmigung falls eligibel
""",
Intent.COMPLAINT: f"""
ANALYSE COMPLAINT:
- Severity: {"HIGH" if context.previous_complaints > 2 else "MEDIUM"}
- Previous complaints: {context.previous_complaints}
- User tier: {context.user_tier}
ANTWORT-TYP: Empathie + Lösungsangebot + Eskalation vorbereiten
""",
Intent.ESCALATION: """
ESCALATION PROTOCOL:
- Verbale Entschuldigung
- Sofortige Verbindung anbieten
- Direktkontakt bereitstellen
"""
}
return instructions.get(intent, "Standard inquiry handling.")
============================================================
PRODUCTION EXAMPLE: Full After-Sales Flow
============================================================
async def full_after_sales_flow():
"""
Complete after-sales flow demonstrating Claude integration
with HolySheep AI platform.
"""
async with HolySheepClient() as client:
engine = AfterSalesScriptEngine(client)
# Simulate customer context
context = AfterSalesContext(
order_id="ORD-2026-05421",
user_id="USER-88912",
product_name="有机草莓 2kg (Organic Strawberries)",
order_date="2026-05-20",
order_amount=68.00,
user_tier="regular",
previous_complaints=0,
conversation_history=[
{"role": "assistant", "content": "Guten Tag! Wie kann ich Ihnen heute helfen?"}
]
)
# Customer message
customer_message = "Ich habe meine Erdbeeren erhalten, aber die Hälfte ist beschädigt. Ich möchte mein Geld zurück!"
# Generate response
response, metadata = await engine.generate_response(context, customer_message)
print("=" * 60)
print("CUSTOMER:", customer_message)
print("-" * 60)
print("AGENT:", response)
print("-" * 60)
print("METADATA:", json.dumps(metadata, indent=2, ensure_ascii=False))
print("=" * 60)
if __name__ == "__main__":
asyncio.run(full_after_sales_flow())3. Gemini Multimodale Bildanalyse für Qualitätskontrolle
"""
HolySheep Gemini Image Analysis für Community Group Buying
===========================================================
Automatische Produktqualitätsprüfung und Schadenserkennung
"""
import base64
import aiohttp
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import hashlib
import json
class DamageType(Enum):
PHYSICAL_DAMAGE = "physical"
QUALITY_ISSUE = "quality"
WRONG_ITEM = "wrong_item"
MISSING_ITEMS = "missing"
EXPIRED = "expired"
NO_DAMAGE = "none"
@dataclass
class ImageAnalysisResult:
damage_type: DamageType
confidence: float
severity: str # "none", "minor", "moderate", "severe"
refund_recommended: bool
refund_amount_ratio: float # 0.0 - 1.0
description: str
detected_issues: List[str]
class GeminiImageAnalyzer:
"""
Gemini 2.5 Flash-powered image analysis for product quality verification.
Supports batch processing and caching for cost optimization.
"""
ANALYSIS_PROMPT = """分析这张社区团购产品的图片。
请检测并报告以下内容：
1. 产品损伤类型 (physical_damage, quality_issue, wrong_item, missing, expired)
2. 损伤严重程度 (none, minor, moderate, severe)
3. 退款建议 (是/否)
4. 如果建议退款，金额比例 (0.0-1.0)
产品类型：{product_category}
订单金额：¥{order_amount}
请以JSON格式回复，包含：
- damage_type: 字符串
- confidence: 0.0-1.0
- severity: 字符串
- refund_recommended: 布尔值
- refund_amount_ratio: 浮点数
- description: 字符串
- detected_issues: 字符串数组
"""
CACHE_TTL_SECONDS = 3600 # 1 hour for image analysis
SIMILARITY_THRESHOLD = 0.92 # For duplicate detection
def __init__(self, holy_sheep_client):
self.client = holy_sheep_client
self._cache: Dict[str, ImageAnalysisResult] = {}
def _get_image_hash(self, image_data: bytes) -> str:
"""Generate hash for image deduplication"""
return hashlib.sha256(image_data[:10000]).hexdigest()[:16]
async def analyze_image(
self,
image_data: bytes,
product_category: str,
order_amount: float,
force_refresh: bool = False
) -> ImageAnalysisResult:
"""
Analyze product image using Gemini 2.5 Flash.
Args:
image_data: Raw image bytes (JPEG, PNG)
product_category: Product category for context
order_amount: Order amount for refund calculation
force_refresh: Bypass cache
Returns:
ImageAnalysisResult with damage classification
"""
image_hash = self._get_image_hash(image_data)
# Check cache
if not force_refresh and image_hash in self._cache:
cached = self._cache[image_hash]
if cached.confidence > self.SIMILARITY_THRESHOLD:
return cached
# Encode image for API
base64_image = base64.b64encode(image_data).decode("utf-8")
# Build prompt
prompt = self.ANALYSIS_PROMPT.format(
product_category=product_category,
order_amount=order_amount
)
# Call Gemini via HolySheep
response = await self._call_gemini(
base64_image=base64_image,
prompt=prompt
)
# Parse result
result = self._parse_response(response, order_amount)
# Cache result
self._cache[image_hash] = result
return result
async def _call_gemini(
self,
base64_image: str,
prompt: str
) -> Dict:
"""
Call Gemini 2.5 Flash via HolySheep API.
Note: HolySheep uses unified API - model specified in request body
"""
payload = {
"model": "gemini-2.5-flash",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": prompt
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{base64_image}"
}
}
]
}
],
"max_tokens": 800,
"temperature": 0.3
}
async with self.client._session.post(
f"{self.client.base_url}/chat/completions",
json=payload
) as response:
if response.status != 200:
error_body = await response.text()
raise Exception(f"Gemini API error: {error_body}")
return await response.json()
def _parse_response(
self,
response: Dict,
order_amount: float
) -> ImageAnalysisResult:
"""Parse Gemini response into structured result"""
content = response["choices"][0]["message"]["content"]
# Try to parse as JSON
try:
# Extract JSON from response (Claude/Gemini sometimes wrap in markdown)
json_str = content
if "```json" in content:
json_str = content.split("``json")[1].split("``")[0]
elif "```" in content:
json_str = content.split("``")[1].split("``")[0]
data = json.loads(json_str.strip())
return ImageAnalysisResult(
damage_type=DamageType(data.get("damage_type", "none")),
confidence=float(data.get("confidence", 0.0)),
severity=data.get("severity", "none"),
refund_recommended=bool(data.get("refund_recommended", False)),
refund_amount_ratio=float(data.get("refund_amount_ratio", 0.0)),
description=data.get("description", ""),
detected_issues=data.get("detected_issues", [])
)
except json.JSONDecodeError:
# Fallback: Basic text parsing
content_lower = content.lower()
damage_type = DamageType.NO_DAMAGE
if "physical" in content_lower or "beschädigt" in content_lower:
damage_type = DamageType.PHYSICAL_DAMAGE
elif "quality" in content_lower or "质量" in content_lower:
damage_type = DamageType.QUALITY_ISSUE
elif "wrong" in content_lower or "falsch" in content_lower:
damage_type = DamageType.WRONG_ITEM
severity = "none"
if "severe" in content_lower or "严重" in content_lower:
severity = "severe"
elif "moderate" in content_lower or "中等" in content_lower:
severity = "moderate"
elif "minor" in content_lower or "轻微" in content_lower:
severity = "minor"
return ImageAnalysisResult(
damage_type=damage_type,
confidence=0.75, # Conservative fallback
severity=severity,
refund_recommended="refund" in content_lower or "退款" in content_lower,
refund_amount_ratio=0.5 if severity in ["moderate", "severe"] else 0.0,
description=content[:500],
detected_issues=[damage_type.value]
)
async def batch_analyze(
self,
images: List[Tuple[bytes, str, float]],
max_concurrent: int = 5
) -> List[ImageAnalysisResult]:
"""
Analyze multiple images concurrently with rate limiting.
Args:
images: List of (image_data, product_category, order_amount)
max_concurrent: Maximum concurrent API calls
Returns:
List of ImageAnalysisResult in same order as input
"""
semaphore = asyncio.Semaphore(max_concurrent)
async def analyze_with_limit(img_data, category, amount):
async with semaphore:
return await self.analyze_image(img_data, category, amount)
tasks = [
analyze_with_limit(data, cat, amount)
for data, cat, amount in images
]
return await asyncio.gather(*tasks, return_exceptions=True)
============================================================
PRODUCTION INTEGRATION EXAMPLE
============================================================
async def quality_control_pipeline():
"""
Full quality control pipeline integrating Claude and Gemini.
Demonstrates end-to-end after-sales automation.
"""
async with HolySheepClient() as client:
# Initialize analyzers
script_engine = AfterSalesScriptEngine(client)
image_analyzer = GeminiImageAnalyzer(client)
# Load test image
with open("damaged_strawberries.jpg", "rb") as f:
test_image = f.read()
# Step 1: Analyze product image
print("📸 Analyzing product image...")
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