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

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)

文档读取Embedding

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")

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