代码示例 / Python

python 使用Embeddings 向量化

嵌入(Embeddings) API 指南

概述

新模型发布

特点：更低成本、更好的多语言性能、可控制维度

主要应用场景

基础使用

获取嵌入向量


from openai import OpenAI

client = OpenAI(

    base_url="https://www.cokeapi.com/v1",

    api_key=key

)



response = client.embeddings.create(

    input="Your text string goes here",

    model="text-embedding-3-small"

)



print(response.data[0].embedding)

响应格式


{

  "object": "list",

  "data": [

    {

      "object": "embedding",

      "index": 0,

      "embedding": [

        -0.006929283495992422,

        -0.005336422007530928,

        // ... 更多数值

      ],

    }

  ],

  "model": "text-embedding-3-small",

  "usage": {

    "prompt_tokens": 5,

    "total_tokens": 5

  }

}

模型对比

模型每美元页面数MTEB性能评估最大输入
text-embedding-3-small62,50062.3%8191
text-embedding-3-large9,61564.6%8191
text-embedding-ada-00212,50061.0%8191

实际应用示例

处理评论数据


from openai import OpenAI

client = OpenAI(

    base_url="https://www.cokeapi.com/v1",

    api_key=key

)



def get_embedding(text, model="text-embedding-3-small"):

   text = text.replace("\n", " ")

   return client.embeddings.create(input = [text], model=model).data[0].embedding



# 处理数据框

df['ada_embedding'] = df.combined.apply(lambda x: get_embedding(x, model='text-embedding-3-small'))

df.to_csv('output/embedded_1k_reviews.csv', index=False)



# 加载保存的嵌入

import pandas as pd

import numpy as np



df = pd.read_csv('output/embedded_1k_reviews.csv')

df['ada_embedding'] = df.ada_embedding.apply(eval).apply(np.array)

技术细节

维度说明

注意事项