Use Alibaba Cloud Model Studio in LangChain

OpenAI

Only some models from Alibaba Cloud Model Studio are supported. For a complete list, see List of models supported in OpenAI compatibility mode. For information about calling fees, input limits, and output limits, see Model overview.

Before you start, install the following dependency:

pip install langchain_openai

Call the model:

from langchain_openai import ChatOpenAI
import os

chatLLM = ChatOpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),
    base_url="https://dashscope.aliyuncs.com/compatible-mode/v1",
    model="qwen-plus",  # This example uses qwen-plus. You can change the model name as needed. Model list: https://help.aliyun.com/en/model-studio/getting-started/models
    # other params...
)
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Who are you?"}]
response = chatLLM.invoke(messages)
print(response.json())

For advanced techniques such as tool calling, see the official LangChain ChatOpenAI documentation. For the complete API reference, see the official LangChain ChatOpenAI API Reference.

DashScope

All text generation models from Alibaba Cloud Model Studio are supported. For a complete list and information on calling fees, see Model overview. (This also supports models after deployment.)

Before you start, install the following dependencies:

pip install langchain-community
pip install dashscope

Call the model:

from langchain_community.chat_models.tongyi import ChatTongyi
from langchain_core.messages import HumanMessage

chatLLM = ChatTongyi(
    model="qwen-plus",   # This example uses qwen-plus. You can change the model name as needed. Model list: https://help.aliyun.com/en/model-studio/getting-started/models
    streaming=True,
    # other params...
)
res = chatLLM.stream([HumanMessage(content="hi")], streaming=True)
for r in res:
    print("chat resp:", r.content)

For advanced techniques such as multi-modal calling and tool calling, see the official LangChain ChatTongyi documentation. For the complete API reference, see the official LangChain ChatTongyi API Reference.

OpenAI

Only some models from Alibaba Cloud Model Studio are supported. For a complete list, see List of models supported in OpenAI compatibility mode. For information about calling fees, input limits, and output limits, see Model overview.

Before you start, install the following dependencies:

npm install @langchain/openai @langchain/core

Call the model:

import { ChatOpenAI } from "@langchain/openai";

const llm = new ChatOpenAI({
  // This example uses qwen-plus. You can change the model name as needed. Model list: https://help.aliyun.com/en/model-studio/getting-started/models
  model: "qwen-plus",
  apiKey: process.env.DASHSCOPE_API_KEY,
  configuration: {
    baseURL: "https://dashscope.aliyuncs.com/compatible-mode/v1",
    // other params...
  },
  // other params...
});

const aiMsg = await llm.invoke([
  {
    role: "system",
    content:
      "You are a helpful assistant that translates English to French. Translate the user sentence.",
  },
  {
    role: "user",
    content: "I love programming.",
  },
]);
console.log('---------------------------');
console.log(aiMsg.content);

For advanced techniques such as tool calling, see the official LangChain ChatOpenAI documentation. For the complete API reference, see the official LangChain ChatOpenAI API Reference.

DashScope

All text generation models from Alibaba Cloud Model Studio are supported. For a complete list and information on calling fees, see Model overview. (This also supports models after deployment.)

Before you start, install the following dependencies:

npm install @langchain/community @langchain/core

Call the model:

import { ChatAlibabaTongyi } from "@langchain/community/chat_models/alibaba_tongyi";
import { HumanMessage } from "@langchain/core/messages";

// Default model is qwen-turbo
const qwenTurbo = new ChatAlibabaTongyi({
  alibabaApiKey: process.env.DASHSCOPE_API_KEY,
  // other params...
});

// Use qwen-plus
const qwenPlus = new ChatAlibabaTongyi({
// This example uses qwen-plus. You can change the model name as needed. Model list: https://help.aliyun.com/en/model-studio/getting-started/models
  model: "qwen-plus",
  temperature: 1,
  alibabaApiKey: process.env.DASHSCOPE_API_KEY,
  // other params...
});

const messages = [new HumanMessage("Hello")];

const res = await qwenTurbo.invoke(messages);

const res2 = await qwenPlus.invoke(messages);

console.log('---------------------------');
console.log(res.content);
console.log('---------------------------');
console.log(res2.content);

For advanced techniques such as multi-modal calling and tool calling, see the official LangChain ChatTongyi documentation. For the complete API reference, see the official LangChain ChatTongyi API Reference.

OpenAI

Only some models from Alibaba Cloud Model Studio are supported. For a complete list, see List of models supported in OpenAI compatibility mode. For information about calling fees, input limits, and output limits, see Model overview.

You can use the open source LangChain4j library to implement these features in Java. Two implementation methods are available: Plain Java and Spring Boot. For more details and code examples, see the official LangChain4j OpenAI website.

DashScope

All text generation models from Alibaba Cloud Model Studio are supported. For a complete list and information on calling fees, see Model overview. (This also supports models after deployment.)

You can use the open source LangChain4j library to implement these features in Java. Two implementation methods are available: Plain Java and Spring Boot. For more details and code examples, see the official LangChain4j DashScope website.

DashScope

Before you start, install the following dependencies:

pip install langchain-community
pip install dashscope

Call the model:

from langchain_community.embeddings import DashScopeEmbeddings
embeddings = DashScopeEmbeddings(
    model="text-embedding-v4",
    # other params...
)

text = "This is a test document."

query_result = embeddings.embed_query(text)
print("Text vector length:", len(query_result), sep='')

doc_results = embeddings.embed_documents(
    [
        "Hi there!",
        "Oh, hello!",
        "What's your name?",
        "My friends call me World",
        "Hello World!"
    ])
print("Number of text vectors:", len(doc_results), "，Text vector length:", len(doc_results[0]), sep='')

For a detailed introduction and usage examples, see the official LangChain DashScope Embeddings documentation. For the complete API reference, see the official LangChain Embedding API Reference.

DashScope

Before you start, install the following dependencies:

npm install @langchain/community @langchain/core

Call the model:

import { AlibabaTongyiEmbeddings } from "@langchain/community/embeddings/alibaba_tongyi";

const model = new AlibabaTongyiEmbeddings({ 
  apiKey: process.env.DASHSCOPE_API_KEY,
  modelName: "text-embedding-v4",
  // other params...
  });
const res = await model.embedQuery(
  "What would be a good company name a company that makes colorful socks?",
);
console.log('---------------------------');
console.log({ res });

For a detailed introduction and usage examples, see the official LangChain DashScope Embeddings documentation. For the complete API reference, see the official LangChain Embedding API Reference.

DashScope

You can use the open source LangChain4j library to implement features in Java. For more information, including interface details and code examples, see the official LangChain4j DashScope website.

1. Add dependencies

   For example, in Maven, add the following dependency to the pom.xml file:

   <!-- https://mvnrepository.com/artifact/dev.langchain4j/langchain4j-community-dashscope -->
   <dependency>
       <groupId>dev.langchain4j</groupId>
       <artifactId>langchain4j-community-dashscope</artifactId>
       <version>1.0.0-beta3</version>
   </dependency>

2. Write code and call the model

   import dev.langchain4j.community.model.dashscope.QwenEmbeddingModel;
   import dev.langchain4j.data.embedding.Embedding;
   
   import java.util.List;
   
   import static dev.langchain4j.data.segment.TextSegment.textSegment;
   import static java.util.Arrays.asList;
   
   public class LangChainEmbeddingsTest {
       public static void main(String[] args) {
           QwenEmbeddingModel model = QwenEmbeddingModel.builder()
                   .apiKey(System.getenv("DASHSCOPE_API_KEY"))
                   .modelName("text-embedding-v4")
                   .build();
   
           List<Embedding> embeddings = model.embedAll(asList(textSegment("hello"), textSegment("how are you?")))
                   .content();
           System.out.println(embeddings.size());
       }
   }

DashScope

Before you start, install the following dependencies:

pip install langchain-community
pip install dashscope

Call the model:

from langchain_community.document_compressors.dashscope_rerank import DashScopeRerank

sequence = ["text1", "text2", "text3"]

reranker = DashScopeRerank(
    model="gte-rerank-v2",
    # other params...
)
print(reranker.rerank(documents=sequence, query="text3", top_n=2))

For a detailed introduction and more usage examples, see the official LangChain DashScope Rerank documentation. For the complete API reference, see the official LangChain Rerank API Reference.