✨ التخزين المؤقت الذكي

User A: "What is the capital of France?"
User B: "Tell me the capital city of France"
→ Same cached response (high semantic similarity)

# Skip cache lookup, always call the model
curl https://api.lemondata.cc/v1/chat/completions \
  -H "Authorization: Bearer sk-your-key" \
  -H "Cache-Control: no-cache" \
  -d '{"model": "gpt-4o", "messages": [...]}'

X-Cache: HIT           # Response served from cache
X-Cache: MISS          # Fresh response from model
X-Cache-Entry-Id: abc  # Cache entry ID (for feedback)

from openai import OpenAI

client = OpenAI(
    api_key="sk-your-key",
    base_url="https://api.lemondata.cc/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What is 2+2?"}]
)

# Check cache status from response headers
# (Available in raw HTTP response)
print(f"Cache: {response._raw_response.headers.get('X-Cache')}")

# Disable caching for this request
curl https://api.lemondata.cc/v1/chat/completions \
  -H "Cache-Control: no-store" \
  -d '...'

curl -X POST https://api.lemondata.cc/v1/cache/feedback \
  -H "Authorization: Bearer sk-your-key" \
  -H "Content-Type: application/json" \
  -d '{
    "cache_entry_id": "abc123",
    "feedback_type": "wrong_answer",
    "description": "Response was outdated"
  }'

# For time-sensitive queries
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "What's the current stock price of AAPL?"}],
    extra_headers={"Cache-Control": "no-cache"}
)