Create Contextualized Embeddings

POST/v1/contextualizedembeddingsCreate Contextualized Embeddings

Generate contextualized embeddings for document chunks. Chunks from the same document share context awareness, improving retrieval quality for document-based applications.

Request body

required

application/json

objectContextualizedEmbeddingsRequest

Contextualized Embeddings Request

Request body for creating contextualized embeddings

dimensionsinteger

Number of dimensions for output embeddings (Matryoshka). Range: 128-1024 for pplx-embed-context-v1-0.6b, 128-2560 for pplx-embed-context-v1-4b. Defaults to full dimensions (1024 or 2560).

maximum 2560 · minimum 128

encoding_formatstring

Output encoding format for embeddings. base64_int8 returns base64-encoded signed int8 values. base64_binary returns base64-encoded packed binary (1 bit per dimension).

one of "base64_int8", "base64_binary" · default "base64_int8"

inputarray of array of stringrequired

Nested array structure where each inner array contains chunks from a single document. Chunks within the same document are encoded with document-level context awareness. Maximum 512 documents. Total chunks across all documents must not exceed 16,000. Total tokens per document must not exceed 32K. All chunks in a single request must not exceed 120,000 tokens combined. Empty strings are not allowed.

maxItems 512 · minItems 1

Show child attributes

maxItems 512 · minItems 1

Show array items

minItems 1

modelstringrequired

The contextualized embedding model to use

one of "pplx-embed-context-v1-0.6b", "pplx-embed-context-v1-4b"

Example request

{
  "dimensions": 0,
  "encoding_format": "base64_int8",
  "input": [
    [
      "string"
    ]
  ],
  "model": "pplx-embed-context-v1-0.6b"
}

Responses

objectContextualizedEmbeddingsResponse

Contextualized Embeddings Response

Response body for contextualized embeddings request

dataarray of object

List of contextualized embedding objects

Show child attributes

Show array items

A single contextualized embedding result

dataarray of object

List of embedding objects for chunks in this document

Show child attributes

Show array items

A single embedding result

embeddingstring

Base64-encoded embedding vector. For base64_int8: decode to signed int8 array (length = dimensions). For base64_binary: decode to packed bits (length = dimensions / 8 bytes).

indexinteger

The index of the input text this embedding corresponds to

objectstring

The object type

indexinteger

The index of the document this chunk belongs to

objectstring

The object type

modelstring

The model used to generate embeddings

objectstring

The object type

usageobject

Token usage for the embeddings request

Show child attributes

costobject

Cost breakdown for the request

Show child attributes

currencystring

Currency of the cost values

one of "USD"

input_costnumber

Cost for input tokens in USD

total_costnumber

Total cost for the request in USD

prompt_tokensinteger

Number of tokens in the input texts

total_tokensinteger

Total number of tokens processed

Example response

{
  "data": [
    {
      "data": [
        {
          "embedding": "string",
          "index": 0,
          "object": "embedding"
        }
      ],
      "index": 0,
      "object": "list"
    }
  ],
  "model": "string",
  "object": "list",
  "usage": {
    "cost": {
      "currency": "USD",
      "input_cost": 0,
      "total_cost": 0
    },
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

objectHTTPValidationError

HTTPValidationError

detailarray of object

Show child attributes

Show array items

locarray of valuerequired

Show child attributes

Show array items

anyOf · 2 options

Option 1string

Option 2integer

msgstringrequired

typestringrequired

Example response

{
  "detail": [
    {
      "loc": [
        0
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

Examples

curl -X POST 'https://api.perplexity.ai/v1/contextualizedembeddings' \
  -H 'Content-Type: application/json' \
  -d '{"dimensions":0,"encoding_format":"base64_int8","input":[["string"]],"model":"pplx-embed-context-v1-0.6b"}'

const response = await fetch('https://api.perplexity.ai/v1/contextualizedembeddings', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({"dimensions":0,"encoding_format":"base64_int8","input":[["string"]],"model":"pplx-embed-context-v1-0.6b"}),
});
const data = await response.json();

const response = await fetch('https://api.perplexity.ai/v1/contextualizedembeddings', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: "{\"dimensions\":0,\"encoding_format\":\"base64_int8\",\"input\":[[\"string\"]],\"model\":\"pplx-embed-context-v1-0.6b\"}" });
console.log(await response.json());

import requests

response = requests.request(
    "POST",
    "https://api.perplexity.ai/v1/contextualizedembeddings",
    headers={"Content-Type": "application/json"},
    data='{"dimensions":0,"encoding_format":"base64_int8","input":[["string"]],"model":"pplx-embed-context-v1-0.6b"}',
)
print(response.json())

package main

import (
	"context"
	"net/http"
	"strings"
)

func main() {
	ctx := context.Background()
	body := strings.NewReader(`{"dimensions":0,"encoding_format":"base64_int8","input":[["string"]],"model":"pplx-embed-context-v1-0.6b"}`)
	req, err := http.NewRequestWithContext(ctx, "POST", "https://api.perplexity.ai/v1/contextualizedembeddings", body)
	if err != nil {
		panic(err)
	}
	req.Header.Set("Content-Type", "application/json")
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		panic(err)
	}
	defer resp.Body.Close()
}

<?php
$request = curl_init("https://api.perplexity.ai/v1/contextualizedembeddings");
curl_setopt($request, CURLOPT_CUSTOMREQUEST, "POST");
curl_setopt($request, CURLOPT_POSTFIELDS, "{\"dimensions\":0,\"encoding_format\":\"base64_int8\",\"input\":[[\"string\"]],\"model\":\"pplx-embed-context-v1-0.6b\"}");
curl_setopt($request, CURLOPT_HTTPHEADER, ['Content-Type: application/json']);
$response = curl_exec($request);
curl_close($request);

var request = HttpRequest.newBuilder(URI.create("https://api.perplexity.ai/v1/contextualizedembeddings"))
    .method("POST", HttpRequest.BodyPublishers.noBody())
    .header("Content-Type", "application/json")
    .method("POST", HttpRequest.BodyPublishers.ofString("{\"dimensions\":0,\"encoding_format\":\"base64_int8\",\"input\":[[\"string\"]],\"model\":\"pplx-embed-context-v1-0.6b\"}"))
    .build();
var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());

require 'net/http'

uri = URI("https://api.perplexity.ai/v1/contextualizedembeddings")
request = Net::HTTPGenericRequest.new("POST", true, true, uri)
request['Content-Type'] = 'application/json'
request.body = "{\"dimensions\":0,\"encoding_format\":\"base64_int8\",\"input\":[[\"string\"]],\"model\":\"pplx-embed-context-v1-0.6b\"}"
response = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |http| http.request(request) }

using var client = new HttpClient();
using var request = new HttpRequestMessage(HttpMethod.Post, "https://api.perplexity.ai/v1/contextualizedembeddings");
request.Content = new StringContent("{\"dimensions\":0,\"encoding_format\":\"base64_int8\",\"input\":[[\"string\"]],\"model\":\"pplx-embed-context-v1-0.6b\"}", Encoding.UTF8, "application/json");
using var response = await client.SendAsync(request);

{
  "data": [
    {
      "data": [
        {
          "embedding": "string",
          "index": 0,
          "object": "embedding"
        }
      ],
      "index": 0,
      "object": "list"
    }
  ],
  "model": "string",
  "object": "list",
  "usage": {
    "cost": {
      "currency": "USD",
      "input_cost": 0,
      "total_cost": 0
    },
    "prompt_tokens": 0,
    "total_tokens": 0
  }
}

{
  "detail": [
    {
      "loc": [
        0
      ],
      "msg": "string",
      "type": "string"
    }
  ]
}

Search documentation

Skills

Operations

Create Contextualized Embeddings

Request body

Responses

Skills

Operations