Alibaba Cloud Model Studio:Embedding

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # Replace with your API Key if you have not configured environment variables
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url for Model Studio
)

completion = client.embeddings.create(
    model="text-embedding-v3",
    input='The quality of the clothes is excellent, very beautiful, worth the wait, I like it and will buy here again',
    dimensions=1024,
    encoding_format="float"
)

print(completion.model_dump_json())

import OpenAI from "openai";
import process from 'process';

// Initialize OpenAI client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

async function getEmbedding() {
    try {
        const completion = await openai.embeddings.create({
            model: "text-embedding-v3",
            input: 'The quality of the clothing is excellent, very beautiful. It was worth the long wait. I like it, and I will come back here to buy again.',
            dimensions: 1024, // Specify vector dimension (only supported by text-embedding-v3)
            encoding_format: "float"
        });

        console.log(JSON.stringify(completion, null, 2));
    } catch (error) {
        console.error('Error:', error);
    }
}

getEmbedding();

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": "The quality of the clothes is excellent, very beautiful, worth the wait, I like it and will buy here again",  
    "dimension": "1024",  
    "encoding_format": "float"
}'

import dashscope
from http import HTTPStatus

dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
resp = dashscope.TextEmbedding.call(
    model=dashscope.TextEmbedding.Models.text_embedding_v3,
    input='The quality of the clothes is excellent, very beautiful, worth the wait, I like it and will buy here again',
    dimension=1024,
    output_type="dense&sparse"
)

print(resp) if resp.status_code == HTTPStatus.OK else print(resp)

import java.util.Arrays;
import java.util.concurrent.Semaphore;
import com.alibaba.dashscope.common.ResultCallback;
import com.alibaba.dashscope.embeddings.TextEmbedding;
import com.alibaba.dashscope.embeddings.TextEmbeddingParam;
import com.alibaba.dashscope.embeddings.TextEmbeddingResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.Constants;

public final class Main {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    public static void basicCall() throws ApiException, NoApiKeyException{
        TextEmbeddingParam param = TextEmbeddingParam
        .builder()
        .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
        .texts(Arrays.asList("Shall I compare thee to a summers day", "Thou art more lovely and more temperate", "Rough winds do shake the darling buds of May", "And summers lease hath all too short a date")).build();
        TextEmbedding textEmbedding = new TextEmbedding();
        TextEmbeddingResult result = textEmbedding.call(param);
        System.out.println(result);
    }
  
    public static void callWithCallback() throws ApiException, NoApiKeyException, InterruptedException{
        TextEmbeddingParam param = TextEmbeddingParam
        .builder()
        .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
        .texts(Arrays.asList("Shall I compare thee to a summers day", "Thou art more lovely and more temperate", "Rough winds do shake the darling buds of May", "And summers lease hath all too short a date")).build();
        TextEmbedding textEmbedding = new TextEmbedding();
        Semaphore sem = new Semaphore(0);
        textEmbedding.call(param, new ResultCallback<TextEmbeddingResult>() {

          @Override
          public void onEvent(TextEmbeddingResult message) {
            System.out.println(message);
          }
          @Override
          public void onComplete(){
            sem.release();
          }

          @Override
          public void onError(Exception err){
            System.out.println(err.getMessage());
            err.printStackTrace();
            sem.release();
          }
          
        });
        sem.acquire();
    }

  public static void main(String[] args){
    try{
      callWithCallback();
    }catch(ApiException|NoApiKeyException|InterruptedException e){
      e.printStackTrace();
      System.out.println(e);

    }
      try {
        basicCall();
    } catch (ApiException | NoApiKeyException e) {
        System.out.println(e.getMessage());
    }
    System.exit(0);
  }
}

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": [
        "The quality of the clothes is excellent, very beautiful, worth the wait, I like it and will buy here again"
        ]
    },
    "parameters": {
        "dimension": 1024
    }
}'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # Replace with your API Key if you have not configured environment variables
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url for DashScope service
)

completion = client.embeddings.create(
    model="text-embedding-v3",
    input=['Shall I compare thee to a summers day', 'Thou art more lovely and more temperate', 'Rough winds do shake the darling buds of May', 'And summers lease hath all too short a date'],
    encoding_format="float"
)

print(completion.model_dump_json())

import OpenAI from "openai";
import process from 'process';

// Initialize OpenAI client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

async function getMultipleEmbeddings() {
    try {
        const completion = await openai.embeddings.create({
            model: "text-embedding-v3",
            input: [
                'Shall I compare thee to a summers day?', 
                'Thou art more lovely and more temperate.', 
                'Rough winds do shake the darling buds of May,', 
                'And summers lease hath all too short a date.'
            ],
            encoding_format: "float"
        });

        console.log(JSON.stringify(completion, null, 2));
    } catch (error) {
        console.error('Error:', error);
    }
}

getMultipleEmbeddings();

curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": [
        "Shall I compare thee to a summers day",
        "Thou art more lovely and more temperate", 
        "Rough winds do shake the darling buds of May", 
        "And summers lease hath all too short a date"
        ],
    "encoding_format": "float"
}'

import dashscope
from http import HTTPStatus

dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
DASHSCOPE_MAX_BATCH_SIZE = 25

inputs = ['Shall I compare thee to a summers day', 'Thou art more lovely and more temperate', 'Rough winds do shake the darling buds of May', 'And summers lease hath all too short a date']

result = None
batch_counter = 0
for i in range(0, len(inputs), DASHSCOPE_MAX_BATCH_SIZE):
    batch = inputs[i:i + DASHSCOPE_MAX_BATCH_SIZE]
    resp = dashscope.TextEmbedding.call(
        model=dashscope.TextEmbedding.Models.text_embedding_v3,
        input=batch,
        dimension=1024
    )
    if resp.status_code == HTTPStatus.OK:
        if result is None:
            result = resp
        else:
            for emb in resp.output['embeddings']:
                emb['text_index'] += batch_counter
                result.output['embeddings'].append(emb)
            result.usage['total_tokens'] += resp.usage['total_tokens']
    else:
        print(resp)
    batch_counter += len(batch)

print(result)

import java.util.Arrays;
import java.util.List;
import com.alibaba.dashscope.embeddings.TextEmbedding;
import com.alibaba.dashscope.embeddings.TextEmbeddingParam;
import com.alibaba.dashscope.embeddings.TextEmbeddingResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.Constants;

public final class Main {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    private static final int DASHSCOPE_MAX_BATCH_SIZE = 25;

    public static void main(String[] args) {
        List<String> inputs = Arrays.asList(
                "Shall I compare thee to a summers day",
                "Thou art more lovely and more temperate",
                "Rough winds do shake the darling buds of May",
                "And summers lease hath all too short a date"
        );

        TextEmbeddingResult result = null;
        int batchCounter = 0;

        for (int i = 0; i < inputs.size(); i += DASHSCOPE_MAX_BATCH_SIZE) {
            List<String> batch = inputs.subList(i, Math.min(i + DASHSCOPE_MAX_BATCH_SIZE, inputs.size()));
            TextEmbeddingParam param = TextEmbeddingParam.builder()
                    .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
                    .texts(batch)
                    .build();

            TextEmbedding textEmbedding = new TextEmbedding();
            try {
                TextEmbeddingResult resp = textEmbedding.call(param);
                if (resp != null) {
                    if (result == null) {
                        result = resp;
                    } else {
                        for (var emb : resp.getOutput().getEmbeddings()) {
                            emb.setTextIndex(emb.getTextIndex() + batchCounter);
                            result.getOutput().getEmbeddings().add(emb);
                        }
                        result.getUsage().setTotalTokens(result.getUsage().getTotalTokens() + resp.getUsage().getTotalTokens());
                    }
                } else {
                    System.out.println(resp);
                }
            } catch (ApiException | NoApiKeyException e) {
                e.printStackTrace();
            }
            batchCounter += batch.size();
        }

        System.out.println(result);
    }
}

curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": [
          "Shall I compare thee to a summers day",
          "Thou art more lovely and more temperate", 
          "Rough winds do shake the darling buds of May", 
          "And summers lease hath all too short a date"
        ]
    },
    "parameters": {
        "dimension": 1024
    }
}'

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.getenv("DASHSCOPE_API_KEY"),  # Replace with your API Key if you have not configured environment variables
    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"  # base_url for Model Studio
)
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    completion = client.embeddings.create(
        model="text-embedding-v3",
        input=f
    )
print(completion.model_dump_json())

import OpenAI from "openai";
import process from 'process';
import fs from 'fs/promises';

// Initialize OpenAI client
const openai = new OpenAI({
    apiKey: process.env.DASHSCOPE_API_KEY, // Read from environment variable
    baseURL: 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'
});

async function getEmbeddingsFromFile() {
    try {
        // Read the content of the file
        const fileContent = await fs.readFile('texts_to_embedding.txt', 'utf-8');
        
        // Create embedding vectors
        const completion = await openai.embeddings.create({
            model: "text-embedding-v3",
            input: fileContent
        });

        console.log(JSON.stringify(completion, null, 2));
    } catch (error) {
        console.error('Error:', error);
    }
}

getEmbeddingsFromFile();

FILE_CONTENT=$(cat texts_to_embedding.txt | jq -Rs .)
curl --location 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1/embeddings' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": ['"$FILE_CONTENT"']
}'

from http import HTTPStatus
import dashscope
from dashscope import TextEmbedding

dashscope.base_http_api_url = 'https://dashscope-intl.aliyuncs.com/api/v1'
with open('texts_to_embedding.txt', 'r', encoding='utf-8') as f:
    resp = TextEmbedding.call(
        model=TextEmbedding.Models.text_embedding_v3,
        input=f
    )

    if resp.status_code == HTTPStatus.OK:
        print(resp)
    else:
        print(resp)

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import com.alibaba.dashscope.embeddings.TextEmbedding;
import com.alibaba.dashscope.embeddings.TextEmbeddingParam;
import com.alibaba.dashscope.embeddings.TextEmbeddingResult;
import com.alibaba.dashscope.exception.ApiException;
import com.alibaba.dashscope.exception.NoApiKeyException;
import com.alibaba.dashscope.utils.Constants;

public final class Main {
    static {
        Constants.baseHttpApiUrl="https://dashscope-intl.aliyuncs.com/api/v1";
    }
    public static void main(String[] args) {
        try (BufferedReader reader = new BufferedReader(new FileReader(""))) {
            StringBuilder content = new StringBuilder();
            String line;
            while ((line = reader.readLine()) != null) {
                content.append(line).append("\n");
            }

            TextEmbeddingParam param = TextEmbeddingParam.builder()
                    .model(TextEmbedding.Models.TEXT_EMBEDDING_V3)
                    .text(content.toString())
                    .build();

            TextEmbedding textEmbedding = new TextEmbedding();
            TextEmbeddingResult result = textEmbedding.call(param);

            if (result != null) {
                System.out.println(result);
            } else {
                System.out.println("Failed to get embedding: " + result);
            }
        } catch (IOException | ApiException | NoApiKeyException e) {
            e.printStackTrace();
        }
    }
}

FILE_CONTENT=$(cat texts_to_embedding.txt | jq -Rs .)
curl --location 'https://dashscope-intl.aliyuncs.com/api/v1/services/embeddings/text-embedding/text-embedding' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "text-embedding-v3",
    "input": {
        "texts": ['"$FILE_CONTENT"']
    }
}'