OpenAI 双语文档参考 Embeddings

Embeddings

What are embeddings? 什么是嵌入？

OpenAI’s text embeddings measure the relatedness of text strings. Embeddings are commonly used for:
OpenAI 的文本嵌入衡量文本字符串的相关性。嵌入通常用于：

• Search (where results are ranked by relevance to a query string)
  搜索（结果按与查询字符串的相关性排序）
• Clustering (where text strings are grouped by similarity)
  聚类（其中文本字符串按相似性分组）
• Recommendations (where items with related text strings are recommended)
  推荐（推荐具有相关文本字符串的项目）
• Anomaly detection (where outliers with little relatedness are identified)
  异常检测（识别出相关性很小的异常值）
• Diversity measurement (where similarity distributions are analyzed)
  多样性测量（分析相似性分布）
• Classification (where text strings are classified by their most similar label)
  分类（其中文本字符串按其最相似的标签分类）

An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.
嵌入是浮点数的向量（列表）。两个向量之间的距离衡量它们的相关性。小距离表示高相关性，大距离表示低相关性。

Visit our pricing page to learn about Embeddings pricing. Requests are billed based on the number of tokens in the input sent.
访问我们的定价页面以了解嵌入定价。请求根据发送的输入中的令牌数量计费。

**To see embeddings in action, check out our code samples

要查看嵌入的实际效果，请查看我们的代码示例**

• Classification
• Topic clustering
• Search
• Recommendations

Browse Samples‍

How to get embeddings 如何获得嵌入

To get an embedding, send your text string to the embeddings API endpoint along with a choice of embedding model ID (e.g., text-embedding-ada-002). The response will contain an embedding, which you can extract, save, and use.
要获得嵌入，请将您的文本字符串连同选择的嵌入模型 ID（例如 text-embedding-ada-002 ）一起发送到嵌入 API 端点。响应将包含一个嵌入，您可以提取、保存和使用它。

Example requests:

Example: Getting embeddings 示例：获取嵌入

python

response = openai.Embedding.create(
    input="Your text string goes here",
    model="text-embedding-ada-002"
)
embeddings = response['data'][0]['embedding']

Example response:

{
   
   
  "data": [
    {
   
   
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        ...
        -4.547132266452536e-05,
        -0.024047505110502243
      ],
      "index": 0,
      "object": "embedding"
    }
  ],
  "model": "text-embedding-ada-002",
  "object": "list",
  "usage": {
   
   
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

See more Python code examples in the OpenAI Cookbook.
在 OpenAI Cookbook 中查看更多 Python 代码示例。

When using OpenAI embeddings, please keep in mind their limitations and risks.
使用 OpenAI 嵌入时，请牢记它们的局限性和风险。

Embedding models

OpenAI offers one second-generation embedding model (denoted by -002 in the model ID) and 16 first-generation models (denoted by -001 in the model ID).
OpenAI 提供了一个二代嵌入模型（在模型 ID 中用 -002 表示）和 16 个第一代模型（在模型 ID 中用 -001 表示）。

We recommend using text-embedding-ada-002 for nearly all use cases. It’s better, cheaper, and simpler to use. Read the blog post announcement.
我们建议对几乎所有用例使用 text-embedding-ada-002。它更好、更便宜、更易于使用。阅读博文公告。

MODEL GENERATION	TOKENIZER	MAX INPUT TOKENS 最大输入代币	KNOWLEDGE CUTOFF
V2	cl100k_base	8191	Sep 2021
V1	GPT-2/GPT-3	2046	Aug 2020

Usage is priced per input token, at a rate of $0.0004 per 1000 tokens, or about ~3,000 pages per US dollar (assuming ~800 tokens per page):
使用量按输入令牌定价，每 1000 个令牌 0.0004 美元，或每美元约 3,000 页（假设每页约 800 个令牌）：

MODEL	ROUGH PAGES PER DOLLAR 每美元粗略页数	EXAMPLE PERFORMANCE ON BEIR SEARCH EVAL BEIR SEARCH EVAL 的性能示例
text-embedding-ada-002 文本嵌入-ada-002	3000	53.9
-davinci--001	6	52.8
-curie--001	60	50.9
-babbage--001	240	50.4
-ada--001	300	49.0

Second-generation models 二代机型

MODEL NAME	TOKENIZER	MAX INPUT TOKENS 最大输入代币	OUTPUT DIMENSIONS
text-embedding-ada-002 文本嵌入-ada-002	cl100k_base	8191	1536

First-generation models (not recommended)
第一代机型（不推荐）