API Qwen bersifat tanpa status (stateless). Untuk mengimplementasikan percakapan multi-putaran, sertakan riwayat percakapan dalam setiap permintaan. Gunakan pemotongan (truncation), ringkasan (summarization), atau pengambilan (retrieval) untuk mengelola konteks dan mengurangi konsumsi token.
Topik ini mencakup antarmuka Chat Completion yang kompatibel dengan OpenAI dan DashScope. Untuk alternatif yang lebih sederhana, lihat Kompatibel dengan OpenAI - Respons.
Cara kerja
Untuk mengimplementasikan percakapan multi-putaran, pertahankan array messages. Setelah setiap putaran, tambahkan pertanyaan pengguna dan respons model, lalu gunakan array yang telah diperbarui untuk permintaan berikutnya.
Contoh berikut menunjukkan bagaimana keadaan array messages berubah selama percakapan multi-putaran:
-
Putaran pertama
Tambahkan pertanyaan pengguna ke array
messages.// Gunakan model teks [ {"role": "user", "content": "Recommend a sci-fi movie about space exploration."} ] // Gunakan model multimodal, misalnya Qwen-VL // {"role": "user", // "content": [{"type": "image_url","image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"}}, // {"type": "text", "text": "What products are shown in the image?"}] // } -
Putaran kedua
Tambahkan respons model dan pertanyaan terbaru pengguna ke array
messages.// Gunakan model teks [ {"role": "user", "content": "Recommend a sci-fi movie about space exploration."}, {"role": "assistant", "content": "I recommend 'XXX'. It is a classic sci-fi work."}, {"role": "user", "content": "Who is the director of this movie?"} ] // Gunakan model multimodal, misalnya Qwen-VL //[ // {"role": "user", "content": [ // {"type": "image_url","image_url": {"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"}}, // {"type": "text", "text": "What products are shown in the image?"}]}, // {"role": "assistant", "content": "The image shows three items: a pair of light blue overalls, a blue and white striped short-sleeve shirt, and a pair of white sneakers."}, // {"role": "user", "content": "What style are they?"} //]
Mulai
Kompatibel dengan OpenAI
Python
import os
from openai import OpenAI
def get_response(messages):
client = OpenAI(
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
# Untuk daftar model, lihat https://www.alibabacloud.com/help/en/model-studio/getting-started/models
completion = client.chat.completions.create(model="qwen-plus", messages=messages)
return completion
# Inisialisasi array messages
messages = [
{
"role": "system",
"content": """You are a salesperson at the Bailian phone store. You are responsible for recommending phones to users. The phones have two parameters: screen size (including 6.1-inch, 6.5-inch, and 6.7-inch) and resolution (including 2K and 4K).
You can only ask the user for one parameter at a time. If the user does not provide complete information, you need to ask a follow-up question to get the missing parameter. When all parameters are collected, you must say: I have understood your purchase intention. Please wait.""",
}
]
assistant_output = "Welcome to the Bailian phone store. What screen size are you looking for?"
print(f"Model output: {assistant_output}\n")
while "I have understood your purchase intention" not in assistant_output:
user_input = input("Please enter: ")
# Tambahkan pertanyaan pengguna ke daftar messages
messages.append({"role": "user", "content": user_input})
assistant_output = get_response(messages).choices[0].message.content
# Tambahkan respons model ke daftar messages
messages.append({"role": "assistant", "content": assistant_output})
print(f"Model output: {assistant_output}")
print("\n")
Node.js
import OpenAI from "openai";
import { createInterface } from 'readline/promises';
// URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
const BASE_URL = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1";
// Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
const openai = new OpenAI({
apiKey: process.env.DASHSCOPE_API_KEY,
baseURL: BASE_URL
});
async function getResponse(messages) {
try {
const completion = await openai.chat.completions.create({
// Untuk daftar model, lihat https://www.alibabacloud.com/help/en/model-studio/getting-started/models
model: "qwen-plus",
messages: messages,
});
return completion.choices[0].message.content;
} catch (error) {
console.error("Error fetching response:", error);
throw error; // Melempar kembali pengecualian untuk ditangani oleh lapisan atas
}
}
// Inisialisasi array messages
const messages = [
{
"role": "system",
"content": `You are a salesperson at the Bailian phone store. You are responsible for recommending phones to users. The phones have two parameters: screen size (including 6.1-inch, 6.5-inch, and 6.7-inch) and resolution (including 2K and 4K).
You can only ask the user for one parameter at a time. If the user does not provide complete information, you need to ask a follow-up question to get the missing parameter. When all parameters are collected, you must say: I have understood your purchase intention. Please wait.`,
}
];
let assistant_output = "Welcome to the Bailian phone store. What screen size are you looking for?";
console.log(assistant_output);
const readline = createInterface({
input: process.stdin,
output: process.stdout
});
(async () => {
while (!assistant_output.includes("I have understood your purchase intention")) {
const user_input = await readline.question("Please enter: ");
messages.push({ role: "user", content: user_input});
try {
const response = await getResponse(messages);
assistant_output = response;
messages.push({ role: "assistant", content: assistant_output });
console.log(assistant_output);
console.log("\n");
} catch (error) {
console.error("An error occurred while fetching the response:", error);
}
}
readline.close();
})();curl
# ======= Penting =======
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"messages":[
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello, I am Qwen."
},
{
"role": "user",
"content": "What can you do?"
}
]
}'
DashScope
Python
Kode contoh menyediakan contoh penjual toko ponsel yang melakukan percakapan multi-putaran dengan pelanggan untuk menentukan niat pembelian mereka, lalu mengakhiri sesi.
import os
from dashscope import Generation
import dashscope
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
def get_response(messages):
response = Generation.call(
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx",
api_key=os.getenv("DASHSCOPE_API_KEY"),
# Untuk daftar model, lihat https://www.alibabacloud.com/help/en/model-studio/getting-started/models
model="qwen-plus",
messages=messages,
result_format="message",
)
return response
messages = [
{
"role": "system",
"content": """You are a salesperson at the Bailian phone store. You are responsible for recommending phones to users. The phones have two parameters: screen size (including 6.1-inch, 6.5-inch, and 6.7-inch) and resolution (including 2K and 4K).
You can only ask the user for one parameter at a time. If the user does not provide complete information, you need to ask a follow-up question to get the missing parameter. When all parameters are collected, you must say: I have understood your purchase intention. Please wait.""",
}
]
assistant_output = "Welcome to the Bailian phone store. What screen size are you looking for?"
print(f"Model output: {assistant_output}\n")
while "I have understood your purchase intention" not in assistant_output:
user_input = input("Please enter: ")
# Tambahkan pertanyaan pengguna ke daftar messages
messages.append({"role": "user", "content": user_input})
assistant_output = get_response(messages).output.choices[0].message.content
# Tambahkan respons model ke daftar messages
messages.append({"role": "assistant", "content": assistant_output})
print(f"Model output: {assistant_output}")
print("\n")
Java
import java.util.ArrayList;
import java.util.List;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import java.util.Scanner;
import com.alibaba.dashscope.protocol.Protocol;
public class Main {
public static GenerationParam createGenerationParam(List<Message> messages) {
return GenerationParam.builder()
// Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
// Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
// Untuk daftar model, lihat https://www.alibabacloud.com/help/en/model-studio/getting-started/models
.model("qwen-plus")
.messages(messages)
.resultFormat(GenerationParam.ResultFormat.MESSAGE)
.build();
}
public static GenerationResult callGenerationWithMessages(GenerationParam param) throws ApiException, NoApiKeyException, InputRequiredException {
// URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
return gen.call(param);
}
public static void main(String[] args) {
try {
List<Message> messages = new ArrayList<>();
messages.add(createMessage(Role.SYSTEM, "You are a helpful assistant."));
for (int i = 0; i < 3;i++) {
Scanner scanner = new Scanner(System.in);
System.out.print("Please enter: ");
String userInput = scanner.nextLine();
if ("exit".equalsIgnoreCase(userInput)) {
break;
}
messages.add(createMessage(Role.USER, userInput));
GenerationParam param = createGenerationParam(messages);
GenerationResult result = callGenerationWithMessages(param);
System.out.println("Model output: "+result.getOutput().getChoices().get(0).getMessage().getContent());
messages.add(result.getOutput().getChoices().get(0).getMessage());
}
} catch (ApiException | NoApiKeyException | InputRequiredException e) {
e.printStackTrace();
}
System.exit(0);
}
private static Message createMessage(Role role, String content) {
return Message.builder().role(role.getValue()).content(content).build();
}
}
curl
# ======= Penting =======
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"input":{
"messages":[
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello, I am Qwen."
},
{
"role": "user",
"content": "What can you do?"
}
]
}
}'
Untuk model multimodal
Model multimodal mendukung gambar dan audio dalam percakapan. Implementasinya berbeda dari model teks sebagai berikut:
-
Konstruksi pesan pengguna: Pesan pengguna untuk model multimodal dapat berisi informasi multimodal, seperti gambar dan audio, selain teks.
-
Antarmuka SDK DashScope: Saat menggunakan SDK Python DashScope, panggil antarmuka
MultiModalConversation. Saat menggunakan SDK Java DashScope, panggil kelasMultiModalConversation.
Untuk model multimodal, lihat: Pemahaman gambar dan video, dan Kimi. Untuk Qwen-Omni, lihat Non-real-time (Qwen-Omni). Qwen-VL-OCR dan Qwen3-Omni-Captioner dirancang untuk tugas single-turn spesifik dan tidak mendukung percakapan multi-putaran.
Kompatibel dengan OpenAI
Python
from openai import OpenAI
import os
client = OpenAI(
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx"
api_key=os.getenv("DASHSCOPE_API_KEY"),
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
messages = [
{"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
},
},
{"type": "text", "text": "What products are shown in the image?"},
],
}
]
completion = client.chat.completions.create(
model="qwen3-vl-plus", # Anda dapat mengganti ini dengan model multimodal lain dan memodifikasi messages sesuai kebutuhan
messages=messages,
)
print(f"First round output: {completion.choices[0].message.content}")
assistant_message = completion.choices[0].message
messages.append(assistant_message.model_dump())
messages.append({
"role": "user",
"content": [
{
"type": "text",
"text": "What style are they?"
}
]
})
completion = client.chat.completions.create(
model="qwen3-vl-plus",
messages=messages,
)
print(f"Second round output: {completion.choices[0].message.content}")Node.js
import OpenAI from "openai";
const openai = new OpenAI(
{
// Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
// Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: apiKey: "sk-xxx",
apiKey: process.env.DASHSCOPE_API_KEY,
// URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
baseURL: "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
}
);
let messages = [
{
role: "user", content: [
{ type: "image_url", image_url: { "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png" } },
{ type: "text", text: "What products are shown in the image?" },
]
}]
async function main() {
let response = await openai.chat.completions.create({
model: "qwen3-vl-plus", // Anda dapat mengganti ini dengan model multimodal lain dan memodifikasi messages sesuai kebutuhan
messages: messages
});
console.log(`First round output: ${response.choices[0].message.content}`);
messages.push(response.choices[0].message);
messages.push({"role": "user", "content": "Write a poem describing this scene"});
response = await openai.chat.completions.create({
model: "qwen3-vl-plus",
messages: messages
});
console.log(`Second round output: ${response.choices[0].message.content}`);
}
main()curl
# ======= Penting =======
# Kunci API bervariasi menurut wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "qwen3-vl-plus",
"messages": [
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
}
},
{
"type": "text",
"text": "Produk apa saja yang ditampilkan di gambar?"
}
]
},
{
"role": "assistant",
"content": [
{
"type": "text",
"text": "Gambar tersebut menampilkan tiga item: satu setel overall biru muda, kemeja lengan pendek bergaris biru dan putih, dan sepasang sepatu kets putih."
}
]
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Apa gaya pakaian tersebut?"
}
]
}
]
}'DashScope
Python
import os
from dashscope import MultiModalConversation
import dashscope
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
dashscope.base_http_api_url = 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1'
messages = [
{
"role": "user",
"content": [
{
"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"
},
{"text": "What products are shown in the image?"},
],
}
]
response = MultiModalConversation.call(
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen3-vl-plus', # Anda dapat mengganti ini dengan model multimodal lain dan memodifikasi messages sesuai kebutuhan
messages=messages
)
print(f"Model first round output {response.output.choices[0].message.content[0]['text']}")
messages.append(response['output']['choices'][0]['message'])
user_msg = {"role": "user", "content": [{"text": "What style are they?"}]}
messages.append(user_msg)
response = MultiModalConversation.call(
# Jika variabel lingkungan tidak dikonfigurasi, harap ganti baris berikut dengan: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
model='qwen3-vl-plus',
messages=messages
)
print(f"Model second round output {response.output.choices[0].message.content[0]['text']}")Java
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversation;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationParam;
import com.alibaba.dashscope.aigc.multimodalconversation.MultiModalConversationResult;
import com.alibaba.dashscope.common.MultiModalMessage;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.exception.UploadFileException;
import com.alibaba.dashscope.utils.Constants;
public class Main {
static {
// URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
Constants.baseHttpApiUrl="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1";
}
private static final String modelName = "qwen3-vl-plus"; // Anda dapat mengganti ini dengan model multimodal lain dan memodifikasi messages sesuai kebutuhan
public static void MultiRoundConversationCall() throws ApiException, NoApiKeyException, UploadFileException {
MultiModalConversation conv = new MultiModalConversation();
MultiModalMessage userMessage = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("image", "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"),
Collections.singletonMap("text", "What products are shown in the image?"))).build();
List<MultiModalMessage> messages = new ArrayList<>();
messages.add(userMessage);
MultiModalConversationParam param = MultiModalConversationParam.builder()
// Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
// Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
.model(modelName)
.messages(messages)
.build();
MultiModalConversationResult result = conv.call(param);
System.out.println("First round output: "+result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text")); // tambahkan hasil ke percakapan
messages.add(result.getOutput().getChoices().get(0).getMessage());
MultiModalMessage msg = MultiModalMessage.builder().role(Role.USER.getValue())
.content(Arrays.asList(Collections.singletonMap("text", "What style are they?"))).build();
messages.add(msg);
param.setMessages((List)messages);
result = conv.call(param);
System.out.println("Second round output: "+result.getOutput().getChoices().get(0).getMessage().getContent().get(0).get("text")); }
public static void main(String[] args) {
try {
MultiRoundConversationCall();
} catch (ApiException | NoApiKeyException | UploadFileException e) {
System.out.println(e.getMessage());
}
System.exit(0);
}
}curl
# ======= Penting =======
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/multimodal-generation/generation \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
"model": "qwen3-vl-plus",
"input":{
"messages":[
{
"role": "user",
"content": [
{"image": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20251031/ownrof/f26d201b1e3f4e62ab4a1fc82dd5c9bb.png"},
{"text": "What products are shown in the image?"}
]
},
{
"role": "assistant",
"content": [
{"text": "The image shows three items: a pair of light blue overalls, a blue and white striped short-sleeve shirt, and a pair of white sneakers."}
]
},
{
"role": "user",
"content": [
{"text": "What style are they?"}
]
}
]
}
}'Untuk model pemikiran
Model pemikiran mengembalikan reasoning_content (proses berpikir) dan content (respons). Saat memperbarui messages, simpan hanya content dan abaikan reasoning_content.
[
{"role": "user", "content": "Rekomendasikan film fiksi ilmiah tentang eksplorasi luar angkasa."},
{"role": "assistant", "content": "Saya merekomendasikan 'XXX'. Ini adalah karya fiksi ilmiah klasik."}, # Jangan tambahkan bidang reasoning_content saat Anda menambahkan ke konteks
{"role": "user", "content": "Siapa sutradara film ini?"}
]
Untuk informasi lebih lanjut tentang model pemikiran, lihat Pemikiran mendalam, Pemahaman gambar dan video, dan Penalaran visual.
Untuk informasi lebih lanjut tentang mengimplementasikan percakapan multi-putaran dengan Qwen3-Omni-Flash (mode berpikir), lihat omni-modal.
Kompatibel dengan OpenAI
Python
Kode contoh
from openai import OpenAI
import os
# Inisialisasi klien OpenAI
client = OpenAI(
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx"
api_key = os.getenv("DASHSCOPE_API_KEY"),
base_url="https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1"
)
messages = []
conversation_idx = 1
while True:
reasoning_content = "" # Definisikan proses berpikir lengkap
answer_content = "" # Definisikan respons lengkap
is_answering = False # Tentukan apakah akan mengakhiri proses berpikir dan mulai merespons
print("="*20+f"Putaran Percakapan {conversation_idx}"+"="*20)
conversation_idx += 1
user_msg = {"role": "user", "content": input("Masukkan pesan Anda: ")}
messages.append(user_msg)
# Buat permintaan penyelesaian chat
completion = client.chat.completions.create(
# Anda dapat mengganti ini dengan model pemikiran mendalam lain sesuai kebutuhan
model="qwen-plus",
messages=messages,
extra_body={"enable_thinking": True},
stream=True,
# stream_options={
# "include_usage": True
# }
)
print("\n" + "=" * 20 + "Proses Berpikir" + "=" * 20 + "\n")
for chunk in completion:
# Jika chunk.choices kosong, cetak penggunaan
if not chunk.choices:
print("\nPenggunaan:")
print(chunk.usage)
else:
delta = chunk.choices[0].delta
# Cetak proses berpikir
if hasattr(delta, 'reasoning_content') and delta.reasoning_content != None:
print(delta.reasoning_content, end='', flush=True)
reasoning_content += delta.reasoning_content
else:
# Mulai merespons
if delta.content != "" and is_answering is False:
print("\n" + "=" * 20 + "Respons Lengkap" + "=" * 20 + "\n")
is_answering = True
# Cetak proses respons
print(delta.content, end='', flush=True)
answer_content += delta.content
# Tambahkan konten respons model ke konteks
messages.append({"role": "assistant", "content": answer_content})
print("\n")
Node.js
Kode contoh
import OpenAI from "openai";
import process from 'process';
import readline from 'readline/promises';
// Inisialisasi antarmuka readline
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout
});
// Inisialisasi klien openai
const openai = new OpenAI({
// Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
apiKey: process.env.DASHSCOPE_API_KEY, // Baca dari variabel lingkungan
baseURL: 'https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1'
});
let reasoningContent = '';
let answerContent = '';
let isAnswering = false;
let messages = [];
let conversationIdx = 1;
async function main() {
while (true) {
console.log("=".repeat(20) + `Putaran Percakapan ${conversationIdx}` + "=".repeat(20));
conversationIdx++;
// Baca input pengguna
const userInput = await rl.question("Masukkan pesan Anda: ");
messages.push({ role: 'user', content: userInput });
// Reset state
reasoningContent = '';
answerContent = '';
isAnswering = false;
try {
const stream = await openai.chat.completions.create({
// Anda dapat mengganti ini dengan model pemikiran mendalam lain sesuai kebutuhan
model: 'qwen-plus',
messages: messages,
enable_thinking: true,
stream: true,
// stream_options:{
// include_usage: true
// }
});
console.log("\n" + "=".repeat(20) + "Proses Berpikir" + "=".repeat(20) + "\n");
for await (const chunk of stream) {
if (!chunk.choices?.length) {
console.log('\nPenggunaan:');
console.log(chunk.usage);
continue;
}
const delta = chunk.choices[0].delta;
// Proses proses berpikir
if (delta.reasoning_content) {
process.stdout.write(delta.reasoning_content);
reasoningContent += delta.reasoning_content;
}
// Proses respons formal
if (delta.content) {
if (!isAnswering) {
console.log('\n' + "=".repeat(20) + "Respons Lengkap" + "=".repeat(20) + "\n");
isAnswering = true;
}
process.stdout.write(delta.content);
answerContent += delta.content;
}
}
// Tambahkan respons lengkap ke riwayat pesan
messages.push({ role: 'assistant', content: answerContent });
console.log("\n");
} catch (error) {
console.error('Error:', error);
}
}
}
// Jalankan program
main().catch(console.error);
HTTP
Kode contoh
curl
# ======= Penting =======
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1/chat/completions \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "qwen-plus",
"messages": [
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello! Nice to meet you. Is there anything I can help you with?"
},
{
"role": "user",
"content": "Who are you?"
}
],
"stream": true,
"stream_options": {
"include_usage": true
},
"enable_thinking": true
}'
DashScope
Python
Kode contoh
import os
import dashscope
# URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
dashscope.base_http_api_url = "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/"
messages = []
conversation_idx = 1
while True:
print("=" * 20 + f"Putaran Percakapan {conversation_idx}" + "=" * 20)
conversation_idx += 1
user_msg = {"role": "user", "content": input("Masukkan pesan Anda: ")}
messages.append(user_msg)
response = dashscope.Generation.call(
# Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: api_key="sk-xxx",
api_key=os.getenv('DASHSCOPE_API_KEY'),
# Contoh ini menggunakan qwen-plus. Anda dapat menggantinya dengan model pemikiran mendalam lain sesuai kebutuhan
model="qwen-plus",
messages=messages,
enable_thinking=True,
result_format="message",
stream=True,
incremental_output=True
)
# Definisikan proses berpikir lengkap
reasoning_content = ""
# Definisikan respons lengkap
answer_content = ""
# Tentukan apakah akan mengakhiri proses berpikir dan mulai merespons
is_answering = False
print("=" * 20 + "Proses Berpikir" + "=" * 20)
for chunk in response:
# Jika proses berpikir dan respons keduanya kosong, abaikan
if (chunk.output.choices[0].message.content == "" and
chunk.output.choices[0].message.reasoning_content == ""):
pass
else:
# Jika saat ini sedang dalam proses berpikir
if (chunk.output.choices[0].message.reasoning_content != "" and
chunk.output.choices[0].message.content == ""):
print(chunk.output.choices[0].message.reasoning_content, end="",flush=True)
reasoning_content += chunk.output.choices[0].message.reasoning_content
# Jika saat ini sedang dalam respons
elif chunk.output.choices[0].message.content != "":
if not is_answering:
print("\n" + "=" * 20 + "Respons Lengkap" + "=" * 20)
is_answering = True
print(chunk.output.choices[0].message.content, end="",flush=True)
answer_content += chunk.output.choices[0].message.content
# Tambahkan konten respons model ke konteks
messages.append({"role": "assistant", "content": answer_content})
print("\n")
# Untuk mencetak proses berpikir lengkap dan respons lengkap, hapus komentar dan jalankan kode berikut
# print("=" * 20 + "Proses Berpikir Lengkap" + "=" * 20 + "\n")
# print(f"{reasoning_content}")
# print("=" * 20 + "Respons Lengkap" + "=" * 20 + "\n")
# print(f"{answer_content}")
Java
Kode contoh
// Versi SDK DashScope >= 2.19.4
import java.util.Arrays;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import com.alibaba.dashscope.aigc.generation.Generation;
import com.alibaba.dashscope.aigc.generation.GenerationParam;
import com.alibaba.dashscope.aigc.generation.GenerationResult;
import com.alibaba.dashscope.common.Message;
import com.alibaba.dashscope.common.Role;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.InputRequiredException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import io.reactivex.Flowable;
import java.lang.System;
import java.util.List;
import com.alibaba.dashscope.protocol.Protocol;
public class Main {
private static final Logger logger = LoggerFactory.getLogger(Main.class);
private static StringBuilder reasoningContent = new StringBuilder();
private static StringBuilder finalContent = new StringBuilder();
private static boolean isFirstPrint = true;
private static void handleGenerationResult(GenerationResult message) {
if (message != null && message.getOutput() != null
&& message.getOutput().getChoices() != null
&& !message.getOutput().getChoices().isEmpty()
&& message.getOutput().getChoices().get(0) != null
&& message.getOutput().getChoices().get(0).getMessage() != null) {
String reasoning = message.getOutput().getChoices().get(0).getMessage().getReasoningContent();
String content = message.getOutput().getChoices().get(0).getMessage().getContent();
if (reasoning != null && !reasoning.isEmpty()) {
reasoningContent.append(reasoning);
if (isFirstPrint) {
System.out.println("====================Proses Berpikir====================");
isFirstPrint = false;
}
System.out.print(reasoning);
}
if (content != null && !content.isEmpty()) {
finalContent.append(content);
if (!isFirstPrint) {
System.out.println("\n====================Respons Lengkap====================");
isFirstPrint = true;
}
System.out.print(content);
}
}
}
private static GenerationParam buildGenerationParam(List<Message> messages) {
return GenerationParam.builder()
// Jika Anda belum mengonfigurasi variabel lingkungan, ganti baris berikut dengan Kunci API Studio Model Anda: .apiKey("sk-xxx")
.apiKey(System.getenv("DASHSCOPE_API_KEY"))
// Contoh ini menggunakan qwen-plus. Anda dapat menggantinya dengan nama model lain sesuai kebutuhan.
.model("qwen-plus")
.enableThinking(true)
.messages(messages)
.incrementalOutput(true)
.resultFormat("message")
.build();
}
public static void streamCallWithMessage(Generation gen, List<Message> messages)
throws NoApiKeyException, ApiException, InputRequiredException {
GenerationParam param = buildGenerationParam(messages);
Flowable<GenerationResult> result = gen.streamCall(param);
result.doOnError(throwable -> logger.error("Error occurred in stream processing: {}", throwable.getMessage(), throwable))
.blockingForEach(Main::handleGenerationResult);
}
public static void main(String[] args) {
try {
// URL wilayah Singapura. Ganti {WorkspaceId} dengan ID ruang kerja aktual Anda. URL bervariasi berdasarkan wilayah.
Generation gen = new Generation(Protocol.HTTP.getValue(), "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1");
Message userMsg1 = Message.builder()
.role(Role.USER.getValue())
.content("Hello")
.build();
Message assistantMsg = Message.builder()
.role(Role.ASSISTANT.getValue())
.content("Hello! Nice to meet you. Is there anything I can help you with?")
.build();
Message userMsg2 = Message.builder()
.role(Role.USER.getValue())
.content("Who are you")
.build();
List<Message> messages = Arrays.asList(userMsg1, assistantMsg, userMsg2);
streamCallWithMessage(gen, messages);
} catch (ApiException | NoApiKeyException | InputRequiredException e) {
logger.error("An exception occurred: {}", e.getMessage(), e);
} catch (Exception e) {
logger.error("Unexpected error occurred: {}", e.getMessage(), e);
} finally {
// Pastikan program keluar secara normal
System.exit(0);
}
}
}
HTTP
Kode contoh
curl
# ======= Penting =======
# Kunci API bervariasi berdasarkan wilayah. Untuk mendapatkan kunci API, lihat https://www.alibabacloud.com/help/en/model-studio/get-api-key
# === Hapus komentar ini sebelum eksekusi ===
curl -X POST "https://{WorkspaceId}.ap-southeast-1.maas.aliyuncs.com/api/v1/services/aigc/text-generation/generation" \
-H "Authorization: Bearer $DASHSCOPE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-DashScope-SSE: enable" \
-d '{
"model": "qwen-plus",
"input":{
"messages":[
{
"role": "user",
"content": "Hello"
},
{
"role": "assistant",
"content": "Hello! Nice to meet you. Is there anything I can help you with?"
},
{
"role": "user",
"content": "Who are you?"
}
]
},
"parameters":{
"enable_thinking": true,
"incremental_output": true,
"result_format": "message"
}
}'
Tayang
Percakapan multi-putaran dapat mengonsumsi banyak token dan melebihi panjang konteks model, menyebabkan error. Gunakan strategi berikut untuk mengelola konteks dan mengendalikan biaya.
1. Manajemen konteks
Array messages bertambah panjang setiap putaran dan mungkin melebihi batas token model. Gunakan metode berikut untuk mengelola panjang konteks:
1.1. Pemotongan konteks
Pertahankan hanya N putaran terbaru ketika riwayat menjadi terlalu panjang. Ini mudah diimplementasikan tetapi kehilangan informasi percakapan sebelumnya.
1.2. Ringkasan bergulir
Ringkas konteks seiring berjalannya percakapan untuk memadatkan riwayat dan mengontrol panjang tanpa kehilangan informasi inti:
a. Ketika riwayat mencapai 70% dari panjang konteks maksimum, ekstrak bagian awal (misalnya separuh pertama) dan buat panggilan API terpisah untuk menghasilkan "ringkasan memori".
b. Pada permintaan berikutnya, ganti riwayat panjang dengan "ringkasan memori" dan tambahkan putaran terbaru.
1.3. Pengambilan Tervektorisasi
Ringkasan bergulir dapat kehilangan beberapa informasi. Untuk memungkinkan model mengingat kembali informasi relevan dari riwayat percakapan yang panjang, gunakan pengambilan berdasarkan permintaan alih-alih meneruskan konteks secara linear:
a. Setelah setiap putaran percakapan, simpan percakapan dalam database vektor.
b. Ketika pengguna mengajukan pertanyaan, ambil catatan percakapan yang relevan berdasarkan kemiripan.
c. Gabungkan catatan percakapan yang diambil dengan input pengguna terbaru dan kirimkan konten gabungan tersebut ke model.
2. Pengendalian biaya
Token input meningkat setiap putaran, sehingga secara signifikan menaikkan biaya. Gunakan strategi manajemen biaya berikut:
2.1. Kurangi token input
Gunakan strategi manajemen konteks yang dijelaskan sebelumnya untuk mengurangi token input dan menurunkan biaya.
2.2. Gunakan model yang mendukung cache konteks
Dalam permintaan multi-putaran, array messages diproses dan ditagih berulang kali. Model Studio menyediakan cache konteks untuk model seperti qwen-max dan qwen-plus, yang mengurangi biaya dan meningkatkan kecepatan respons. Utamakan model yang mendukung cache konteks.
Cache konteks diaktifkan secara otomatis—tidak diperlukan perubahan kode.
Kode error
Jika pemanggilan model gagal dan mengembalikan pesan error, lihat Kode error untuk penyelesaian.