向量嵌入

NUWA 提供兼容 OpenAI 标准的高效向量嵌入方案

调用指引

NUWA 的嵌入模型能够高效地将文本或文档内容转化为可检索的向量数据，广泛应用于 RAG 问答系统和智能客服。无论文本还是文档，均可一键生成嵌入，显著提升语义处理表现。

通用 Embedding

通用Embedding

Python

from openai import OpenAI
import os

client = OpenAI(
    api_key="NUWA_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://api.nuwaapi.com/v1"
)

response = client.embeddings.create(
    input="Your text string goes here",
    model="gemini-embedding-001"
)

print(response.data[0].embedding)

JavaScript

async function main() {
  const response = await fetch("https://api.nuwaapi.com/v1/embeddings", {
    method: "POST",
    headers: {
      "Authorization": "sk-***",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: "text-embedding-3-large",
      input: "Your text string goes here"
    })
  });

  const data = await response.json();
  console.log(data.data?.[0]?.embedding ?? JSON.stringify(data, null, 2));
}

main().catch(e => console.error("❌ 出错：", e.message));

TypeScript

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "sk-***",
  baseURL: "https://api.nuwaapi.com/v1"
});

async function main(): Promise<void> {
  const response = await client.embeddings.create({
    model: "text-embedding-3-large",
    input: "Your text string goes here"
  });

  console.log(response.data[0].embedding);
}

main().catch(e => console.error("❌ 出错：", e.message));

Curl

curl https://api.nuwaapi.com/v1/embeddings \
  -H "Authorization: sk-***" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-large",
    "input": "Your text string goes here"
  }'

文档读取Embedding

Python

from openai import OpenAI
import os

client = OpenAI(
    api_key="NUWA_API_KEY", # 换成你在后台生成的 Key "sk-***"
    base_url="https://api.nuwaapi.com/v1"
)

# Read file
def read_whimery_file():
    try:
        with open('yourpath/file.md', 'r', encoding='utf-8') as file:
            return file.read()
    except Exception as e:
        print(f"Error reading file: {e}")
        return None

# Read the content and create embeddings
content = read_whimery_file()
if content:
    response = client.embeddings.create(
        input=content,
        model="gemini-embedding-001"
    )
    
    print("File content successfully processed into embeddings")
    print(f"Embedding dimensions: {len(response.data[0].embedding)}")
    print("First 10 embedding values:", response.data[0].embedding)
else:
    print("Failed to read file content")

JavaScript

const fs = require("fs");

async function main() {
  let content;

  try {
    content = fs.readFileSync("yourpath/file.md", "utf-8");
  } catch (e) {
    console.error("❌ 读取文件失败：", e.message);
    return;
  }

  const response = await fetch("https://api.nuwaapi.com/v1/embeddings", {
    method: "POST",
    headers: {
      "Authorization": "sk-***",
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      model: "text-embedding-3-large",
      input: content
    })
  });

  const data = await response.json();

  if (data.data?.[0]?.embedding) {
    console.log("✅ 文件内容已成功生成 Embedding");
    console.log(`Embedding 维度：${data.data[0].embedding.length}`);
    console.log("前 10 个值：", data.data[0].embedding.slice(0, 10));
  } else {
    console.log(JSON.stringify(data, null, 2));
  }
}

main().catch(e => console.error("❌ 出错：", e.message));

TypeScript

import OpenAI from "openai";
import fs from "fs";

const client = new OpenAI({
  apiKey: "sk-***",
  baseURL: "https://api.nuwaapi.com/v1"
});

async function main(): Promise<void> {
  let content: string;

  try {
    content = fs.readFileSync("yourpath/file.md", "utf-8");
  } catch (e) {
    console.error("❌ 读取文件失败：", e);
    return;
  }

  const response = await client.embeddings.create({
    model: "text-embedding-3-large",
    input: content
  });

  console.log("✅ 文件内容已成功生成 Embedding");
  console.log(`Embedding 维度：${response.data[0].embedding.length}`);
  console.log("前 10 个值：", response.data[0].embedding.slice(0, 10));
}

main().catch(e => console.error("❌ 出错：", e.message));

Curl

FILE_CONTENT=$(cat "yourpath/file.md")

curl https://api.nuwaapi.com/v1/embeddings \
  -H "Authorization: sk-***" \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"text-embedding-3-large\",
    \"input\": $(echo \"$FILE_CONTENT\" | python -c \"import sys,json; print(json.dumps(sys.stdin.read()))\")
  }"

可用模型

• gemini-embedding-001
• gemini-embedding-exp-03-07
• text-embedding-3-large
• text-embedding-3-small
• text-embedding-ada-002
• jina-embeddings-v4
• jina-embeddings-v3
• jina-embeddings-v2-base-code
• text-embedding-v4
• Qwen/Qwen3-Embedding-0.6B
• doubao-embedding-large-text-240915
• doubao-embedding-text-240715