Praktik Terbaik Explicit Cache

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Ganti WorkspaceId dengan Workspace ID Anda yang sebenarnya.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# Prompt system: manual produk (harus melebihi 1024 token)
product_manual = """You are the support assistant for "BaiLian SmartHome" smart home controller. Here is the complete product manual:

## Product Overview
BaiLian SmartHome is a whole-home smart controller supporting voice control, scene automation, and energy management...

## Installation Guide
1. Install at a central location with good WiFi coverage...
2. Connect the power adapter (5V/2A)...

## FAQ
Q: Cannot connect to WiFi? A: Make sure your router supports 2.4GHz...
""" * 80  # Ulangi hingga melebihi 1024 token

messages = [{"role": "system", "content": product_manual}]

def chat(user_input):
    # Kunci: tambahkan cache_control ke pesan pengguna terakhir
    messages.append({
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": user_input,
                "cache_control": {"type": "ephemeral"},
            }
        ],
    })
    completion = client.chat.completions.create(
        model="qwen3.7-max",
        messages=messages,
        extra_body={"enable_thinking": False},
    )
    assistant_msg = completion.choices[0].message.content
    messages.append({"role": "assistant", "content": assistant_msg})

    usage = completion.usage
    created = usage.prompt_tokens_details.cache_creation_input_tokens
    cached = usage.prompt_tokens_details.cached_tokens
    print(f"  [Cache] Created: {created} tokens, Hit: {cached} tokens")
    return assistant_msg

# Simulasikan percakapan multi-putaran
print("User: What voice assistants does BaiLian SmartHome support?")
print(f"Agent: {chat('What voice assistants does BaiLian SmartHome support?')[:80]}...\n")

print("User: What if I cannot connect to WiFi?")
print(f"Agent: {chat('What if I cannot connect to WiFi?')[:80]}...\n")

print("User: How many devices can it control simultaneously?")
print(f"Agent: {chat('How many devices can it control simultaneously?')[:80]}...")

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Ganti WorkspaceId dengan Workspace ID Anda yang sebenarnya.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# Lapisan 1: Persona system (jarang berubah)
system_persona = """You are the senior AI support agent for "Model Studio Electronics". Your guidelines:
1. Answer questions based on the knowledge base
2. For information not in the knowledge base, say "Let me transfer you to a human agent"
3. Maintain a professional and friendly tone
4. If the user is unhappy, apologize first then resolve the issue

Below is your complete service specification and script guide:
""" + "Detailed service specification..." * 200  # Pastikan > 1024 token

# Lapisan 2: Definisi tool/keterampilan (kadang berubah, misal saat fitur baru diluncurkan)
tools_description = """### Available Tools
- search_product(query): Search product information
- check_inventory(sku, color): Check stock status
- create_ticket(type, description): Create a support ticket
- transfer_to_human(reason): Transfer to a human agent

### Tool Usage Rules
1. When user asks about product details, use search_product first
2. When user asks about stock/shipping, use check_inventory
3. When user requests return/exchange, use create_ticket
4. When a tool returns an error, apologize and transfer_to_human
""" + "Detailed tool usage examples..." * 150  # Pastikan > 1024 token

# Lapisan 3: Basis pengetahuan proyek (semi-stabil, berubah saat pengguna berganti produk)
knowledge_base_product_a = """### Current product: Model Studio Pro Max Wireless Earbuds
- SKU: BL-PM-2024
- Price: CNY 599
- Colors: Night Black / Nebula White / Ice Blue
- Battery: 8 hours (ANC on), 12 hours (ANC off)
- Water resistance: IPX5
- Warranty: 1 year, 7-day no-questions-asked return
- Stock: Night Black (in stock) / Nebula White (low) / Ice Blue (out of stock)
""" * 50  # Pastikan > 1024 token

def ask_agent(user_question, history=None):
    if history is None:
        history = []
    messages = [
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": system_persona + "\n\n" + tools_description,
                    "cache_control": {"type": "ephemeral"},  # Penanda 1: persona system + tool
                }
            ],
        },
        {
            "role": "user",
            "content": [
                {
                    "type": "text",
                    "text": f"Here is the knowledge base for the current product:\n{knowledge_base_product_a}",
                    "cache_control": {"type": "ephemeral"},  # Penanda 2: basis pengetahuan
                }
            ],
        },
        {"role": "assistant", "content": "Got it. I have the product details ready. How can I help you?"},
    ]
    messages.extend(history)
    # Tambahkan pertanyaan saat ini dengan penanda 3
    messages.append({
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": user_question,
                "cache_control": {"type": "ephemeral"},  # Penanda 3: riwayat percakapan
            }
        ],
    })

    completion = client.chat.completions.create(
        model="qwen3.7-max",
        messages=messages,
        extra_body={"enable_thinking": False},
    )
    usage = completion.usage
    print(f"  Created: {usage.prompt_tokens_details.cache_creation_input_tokens}, "
          f"Hit: {usage.prompt_tokens_details.cached_tokens}")
    return completion.choices[0].message.content

# Permintaan pertama
print("Q1: Is the Ice Blue color available?")
a1 = ask_agent("Is the Ice Blue color available?")
print(f"A1: {a1}\n")

# Permintaan kedua: produk sama (persona + tool + basis pengetahuan semua hit)
history = [
    {"role": "user", "content": "Is the Ice Blue color available?"},
    {"role": "assistant", "content": a1},
]
print("Q2: When will it be back in stock?")
a2 = ask_agent("When will it be back in stock?", history)
print(f"A2: {a2}")

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Ganti WorkspaceId dengan Workspace ID Anda yang sebenarnya.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# Prompt system panjang: aturan klasifikasi detail (harus melebihi 1024 token)
classification_prompt = """You are a product review classifier. Classify each review into one of these categories:
- Positive
- Negative
- Neutral
- Question
- Complaint

Output only the category name, nothing else.

Detailed classification rules and examples:
""" + """Rules:
1. Positive: Contains positive sentiment words (e.g., "great", "excellent", "recommend"), or expresses satisfaction.
2. Negative: Contains negative sentiment words (e.g., "terrible", "disappointed", "return"), or expresses dissatisfaction.
3. Neutral: No clear sentiment, merely states facts.
4. Question: Phrased as a question asking for product information.
5. Complaint: Expresses suggestions for improvement or lodges a complaint.
""" * 100

# Ulasan untuk diklasifikasikan (mensimulasikan pemrosesan batch)
reviews = [
    "This product is amazing, great quality, highly recommended!",
    "Shipping took a week and the packaging was damaged",
    "Does this come in red? Does it run large or small?",
    "You should add more size options, medium is too big for me",
    "It's okay I guess, nothing special, does what it says",
]

print("=== Batch Classification (Explicit Cache) ===")
for i, review in enumerate(reviews):
    completion = client.chat.completions.create(
        model="qwen3.7-max",
        messages=[
            {
                "role": "system",
                "content": [
                    {
                        "type": "text",
                        "text": classification_prompt,
                        "cache_control": {"type": "ephemeral"},  # Cache aturan klasifikasi
                    }
                ],
            },
            {"role": "user", "content": review},
        ],
    )
    result = completion.choices[0].message.content
    cached = completion.usage.prompt_tokens_details.cached_tokens
    created = completion.usage.prompt_tokens_details.cache_creation_input_tokens
    print(f"Review {i+1}: \"{review[:40]}...\" -> {result}")
    print(f"  Created: {created}, Hit: {cached}")

from openai import OpenAI
import os

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    # Ganti WorkspaceId dengan Workspace ID Anda yang sebenarnya.
    base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)

# Teks panjang untuk memenuhi minimum 1024 token
long_text_content = "<Your Code Here>" * 400

# Definisi tool: harus benar-benar identik di berbagai permintaan
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather for a given city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name"},
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]}
                },
                "required": ["city"]
            }
        }
    },
    {
        "type": "function",
        "function": {
            "name": "search_flights",
            "description": "Search flights between two cities",
            "parameters": {
                "type": "object",
                "properties": {
                    "origin": {"type": "string", "description": "Departure city"},
                    "destination": {"type": "string", "description": "Destination city"},
                    "date": {"type": "string", "description": "Departure date in YYYY-MM-DD format"}
                },
                "required": ["origin", "destination", "date"]
            }
        }
    }
]

def ask(user_input):
    messages = [
        {
            "role": "system",
            "content": [
                {
                    "type": "text",
                    "text": long_text_content,
                    # cache_control hanya dapat ditambahkan ke konten pesan, bukan ke tool
                    "cache_control": {"type": "ephemeral"},
                }
            ],
        },
        {"role": "user", "content": user_input},
    ]
    completion = client.chat.completions.create(
        model="qwen3.7-max",
        messages=messages,
        tools=tools,
        extra_body={"enable_thinking": False},
    )
    usage = completion.usage
    print(f"  Created: {usage.prompt_tokens_details.cache_creation_input_tokens}, "
          f"Hit: {usage.prompt_tokens_details.cached_tokens}")
    tool_calls = completion.choices[0].message.tool_calls
    if tool_calls:
        print(f"  Tools called: {[t.function.name for t in tool_calls]}")
    return completion

# Permintaan pertama: membuat cache (termasuk definisi tool)
print("Q1: What's the weather in Beijing today?")
ask("What's the weather in Beijing today?")

# Permintaan kedua: mengenai cache
print("\nQ2: Find flights from Shanghai to Beijing tomorrow")
ask("Find flights from Shanghai to Beijing tomorrow")