嵌入

创建嵌入

POST /embeddings

使用选定的大型语言模型（LLM）生成嵌入。

Embeddings

post

Authorizations

Portkey-Key & Virtual-KeyPortkey-Key & Provider-Auth & Provider-NamePortkey-Key & ConfigPortkey-Key & Provider-Auth & Provider-Name & Custom-Host

x-portkey-api-keystringRequired

x-portkey-virtual-keystringRequired

Body

application/json

inputone ofRequired

Input text to embed, encoded as a string or array of tokens. To embed multiple inputs in a single request, pass an array of strings or array of token arrays. The input must not exceed the max input tokens for the model (8192 tokens for text-embedding-ada-002), cannot be an empty string, and any array must be 2048 dimensions or less. Example Python code for counting tokens.

Example: The quick brown fox jumped over the lazy dog

stringOptional

The string that will be turned into an embedding.

Default: ""Example: This is a test.

or

string[] · min: 1 · max: 2048Optional

The array of strings that will be turned into an embedding.

Example: ['This is a test.']

or

integer[] · min: 1 · max: 2048Optional

The array of integers that will be turned into an embedding.

Example: [1212, 318, 257, 1332, 13]

or

modelany ofRequired

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

Example: text-embedding-3-small

stringOptional

or

string · enumOptionalPossible values:

encoding_formatstring · enumOptional

The format to return the embeddings in. Can be either float or base64.

Default: floatExample: floatPossible values:

dimensionsinteger · min: 1Optional

The number of dimensions the resulting output embeddings should have. Only supported in text-embedding-3 and later models.

userstringOptional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example: user-1234

Responses

200

OK

application/json

post

/embeddings

DefaultDefaultDefaultSelf-HostedSelf-HostedSelf-Hosted

curl https://api.portkey.ai/v1/embeddings \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-virtual-key: $PORTKEY_PROVIDER_VIRTUAL_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The food was delicious and the waiter...",
    "model": "text-embedding-ada-002",
    "encoding_format": "float"
  }'

200

OK

{
  "data": [
    {
      "index": 1,
      "embedding": [
        1
      ],
      "object": "embedding"
    }
  ],
  "model": "text",
  "object": "list",
  "usage": {
    "prompt_tokens": 1,
    "total_tokens": 1
  }
}

此端点允许您使用特定模型为文本输入生成嵌入。响应将是与 OpenAI 的嵌入对象 格式一致的嵌入对象。

SDK 使用

Portkey SDK 中的 embeddings.create 方法便于使用各种 LLM 生成嵌入。该方法提供了一个类似于 OpenAI API 的简单接口，用于生成嵌入。

方法签名

NodeJS

portkey.embeddings.create(requestParams[, configParams]);

Python

参数

1. requestParams (Object): 嵌入请求的参数。支持所有 OpenAI 参数。这些参数包括输入文本和模型，并由 Portkey 自动转换为其他非 OpenAI 的 LLM。其他 LLM 不支持的参数将被省略。

2. configParams (Object): 请求的附加配置选项。这是一个可选参数，可以包含此特定请求的自定义配置选项。这些选项将覆盖在 Portkey 客户端中设置的配置。

示例用法

NodeJS

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
    virtualKey: "VIRTUAL_KEY"   // Optional: For virtual key management
});

// Generate embeddings
async function getEmbeddings() {
    const embeddings = await portkey.embeddings.create({
        input: "embed this",
        model: "text-embedding-3-large",
    });

    console.log(embeddings);
}
await getEmbeddings();

// Generate embeddings with config params
async function getEmbeddingsWithConfig() {
    const embeddings = await portkey.embeddings.create({
        input: "embed this",
        model: "text-embedding-3-large",
    }, {config: "custom-config-123"});

    console.log(embeddings);
}
await getEmbeddingsWithConfig();

Python

响应格式

响应将符合Portkey API的嵌入对象架构，通常包括与OpenAI提供的嵌入对象格式一致的嵌入向量列表。