> ## Documentation Index
> Fetch the complete documentation index at: https://apidoc.cometapi.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Create embeddings
> Use CometAPI POST /v1/embeddings to create vector embeddings for text input with a selected embedding model.
CometAPI supports embedding models from multiple providers through a single endpoint. Pass one or more text strings and receive numerical vectors for semantic search, clustering, classification, or retrieval-augmented generation (RAG). See the [model list](/overview/models) for available embedding models and pricing.
The `text-embedding-3-*` models support the `dimensions` parameter, which shortens the embedding vector without significant accuracy loss. This can reduce storage costs while retaining most semantic information.
To embed multiple texts in a single request, pass an array of strings to the `input` parameter. Batch input is significantly more efficient than making individual requests.
***
## OpenAPI
````yaml api/openapi/text/post-embeddings.openapi.json POST /v1/embeddings
openapi: 3.1.0
info:
title: Embeddings API
version: 1.0.0
servers:
- url: https://api.cometapi.com
security:
- bearerAuth: []
paths:
/v1/embeddings:
post:
summary: Create Embeddings
operationId: createEmbeddings
requestBody:
required: true
content:
application/json:
schema:
type: object
required:
- model
- input
properties:
model:
type: string
description: >-
The embedding model to use. See the [Models
page](/overview/models) for current embedding model IDs.
example: text-embedding-3-small
input:
oneOf:
- type: string
description: A single text string to embed.
- type: array
description: >-
An array of strings to embed in a single request. Each
string can be up to 8,191 tokens.
items:
type: string
- type: array
description: An array of token arrays.
items:
type: array
items:
type: integer
description: >-
The text to embed. Can be a single string, an array of
strings, or an array of token arrays. Each input must not
exceed the model's maximum token limit (8,191 tokens for
`text-embedding-3-*` models).
encoding_format:
type: string
description: >-
The format of the returned embedding vectors. `float`
returns an array of floating-point numbers. `base64` returns
a base64-encoded string representation, which can reduce
response size for large batches.
enum:
- float
- base64
default: float
dimensions:
type: integer
description: >-
The number of dimensions for the output embedding vector.
Only supported by `text-embedding-3-*` models. Reducing
dimensions can lower storage costs while maintaining most of
the embedding's utility.
minimum: 1
user:
type: string
description: >-
A unique identifier for your end-user, which can help
monitor and detect abuse.
examples:
Single Text:
summary: Single Text
value:
model: text-embedding-3-small
input: The food was delicious and the waiter was friendly.
Batch Input:
summary: Batch Input
value:
model: text-embedding-3-small
input:
- Hello world
- How are you?
- Embedding example
encoding_format: float
With Dimensions:
summary: Reduced Dimensions
value:
model: text-embedding-3-small
input: Search query text
dimensions: 256
responses:
'200':
description: A list of embedding vectors for the input text(s).
content:
application/json:
schema:
type: object
properties:
object:
type: string
description: The object type, always `list`.
enum:
- list
example: list
data:
type: array
description: >-
An array of embedding objects, one per input text. When
multiple inputs are provided, results are returned in the
same order as the input.
items:
type: object
properties:
object:
type: string
description: The object type, always `embedding`.
enum:
- embedding
example: embedding
index:
type: integer
description: >-
The index of this embedding in the input array
(starting from 0).
example: 0
embedding:
type: array
description: >-
The embedding vector as an array of floating-point
numbers. The length depends on the model and
`dimensions` parameter.
items:
type: number
example:
- -0.0021
- -0.0491
- 0.0209
- 0.0314
- -0.0453
model:
type: string
description: The model used to generate the embeddings.
example: text-embedding-3-small
usage:
type: object
description: Token usage statistics for this request.
properties:
prompt_tokens:
type: integer
description: The number of tokens in the input text(s).
example: 2
total_tokens:
type: integer
description: >-
The total number of tokens processed (same as
`prompt_tokens` for embeddings).
example: 2
example:
object: list
data:
- object: embedding
index: 0
embedding:
- -0.0021
- -0.0491
- 0.0209
- 0.0314
- -0.0453
model: text-embedding-3-small
usage:
prompt_tokens: 2
total_tokens: 2
x-codeSamples:
- lang: Python
label: Single Text
source: |
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
response = client.embeddings.create(
model="text-embedding-3-small",
input="The food was delicious and the waiter was friendly.",
)
print(response.data[0].embedding[:5]) # First 5 dimensions
print(f"Dimensions: {len(response.data[0].embedding)}")
- lang: Python
label: Batch Input
source: |
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
response = client.embeddings.create(
model="text-embedding-3-small",
input=["Hello world", "How are you?", "Embedding example"],
)
for item in response.data:
print(f"Index {item.index}: {len(item.embedding)} dimensions")
- lang: Python
label: Reduced Dimensions
source: |
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.cometapi.com/v1",
api_key=os.environ["COMETAPI_KEY"],
)
# Use fewer dimensions to reduce storage costs
response = client.embeddings.create(
model="text-embedding-3-small",
input="Search query text",
dimensions=256,
)
print(f"Dimensions: {len(response.data[0].embedding)}") # 256
- lang: JavaScript
label: Single Text
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.embeddings.create({
model: "text-embedding-3-small",
input: "The food was delicious and the waiter was friendly.",
});
console.log(response.data[0].embedding.slice(0, 5));
console.log(`Dimensions: ${response.data[0].embedding.length}`);
- lang: JavaScript
label: Batch Input
source: |
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
const response = await client.embeddings.create({
model: "text-embedding-3-small",
input: ["Hello world", "How are you?", "Embedding example"],
});
for (const item of response.data) {
console.log(`Index ${item.index}: ${item.embedding.length} dimensions`);
}
- lang: JavaScript
label: Reduced Dimensions
source: >
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api.cometapi.com/v1",
apiKey: process.env.COMETAPI_KEY,
});
// Use fewer dimensions to reduce storage costs
const response = await client.embeddings.create({
model: "text-embedding-3-small",
input: "Search query text",
dimensions: 256,
});
console.log(`Dimensions: ${response.data[0].embedding.length}`); //
256
- lang: Shell
label: Single Text
source: |
curl https://api.cometapi.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "text-embedding-3-small",
"input": "The food was delicious and the waiter was friendly."
}'
- lang: Shell
label: Batch Input
source: |
curl https://api.cometapi.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "text-embedding-3-small",
"input": ["Hello world", "How are you?", "Embedding example"]
}'
- lang: Shell
label: Reduced Dimensions
source: |
curl https://api.cometapi.com/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $COMETAPI_KEY" \
-d '{
"model": "text-embedding-3-small",
"input": "Search query text",
"dimensions": 256
}'
components:
securitySchemes:
bearerAuth:
type: http
scheme: bearer
description: Bearer token authentication. Use your CometAPI key.
````