> ## Documentation Index
> Fetch the complete documentation index at: https://docs.requesty.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Create Embedding
> Create vector embeddings for text input using embedding models.
```bash cURL theme={"dark"}
curl https://router.requesty.ai/v1/embeddings \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_REQUESTY_API_KEY" \
-d '{
"model": "openai/text-embedding-3-small",
"input": "Requesty is a unified LLM gateway."
}'
```
```python Python theme={"dark"}
from openai import OpenAI
client = OpenAI(
api_key="YOUR_REQUESTY_API_KEY",
base_url="https://router.requesty.ai/v1",
)
response = client.embeddings.create(
model="openai/text-embedding-3-small",
input="Requesty is a unified LLM gateway.",
)
print(response.data[0].embedding[:5])
```
```typescript TypeScript theme={"dark"}
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.REQUESTY_API_KEY,
baseURL: "https://router.requesty.ai/v1",
});
const response = await client.embeddings.create({
model: "openai/text-embedding-3-small",
input: "Requesty is a unified LLM gateway.",
});
console.log(response.data[0].embedding.slice(0, 5));
```
```json Response 200 theme={"dark"}
{
"object": "list",
"data": [
{
"object": "embedding",
"index": 0,
"embedding": [0.0023, -0.0091, 0.0153, -0.0028, 0.0074]
}
],
"model": "openai/text-embedding-3-small",
"usage": {
"prompt_tokens": 8,
"total_tokens": 8
}
}
```
Create vector embeddings for text input. Embeddings are useful for semantic search, similarity matching, clustering, and retrieval-augmented generation (RAG).
## Supported Models
Browse the full catalog on the [Embedding model library](https://app.requesty.ai/model-library/embeddings).
| Model | Dimensions | Best for |
| ------------------------------- | ---------- | ------------------------------- |
| `openai/text-embedding-3-small` | 1536 | Cost-efficient, general purpose |
| `openai/text-embedding-3-large` | 3072 | Higher accuracy, multilingual |
| `openai/text-embedding-ada-002` | 1536 | Legacy compatibility |
## OpenAPI
````yaml POST /v1/embeddings
openapi: 3.0.3
info:
title: Requesty API
description: Requesty API for AI model routing and key management
version: 1.0.0
servers:
- url: https://api-v2.requesty.ai
description: Management API endpoint
- url: https://router.requesty.ai
description: Inference router endpoint
security:
- BearerAuth: []
paths:
/v1/embeddings:
servers:
- url: https://router.requesty.ai
description: Inference router endpoint
post:
summary: Create embedding
description: Create vector embeddings for text input using embedding models.
operationId: createEmbedding
requestBody:
required: true
content:
application/json:
schema:
$ref: '#/components/schemas/EmbeddingRequest'
example:
model: openai/text-embedding-3-small
input: Requesty is a unified LLM gateway.
responses:
'200':
description: Embedding response
content:
application/json:
schema:
$ref: '#/components/schemas/EmbeddingResponse'
example:
object: list
data:
- object: embedding
index: 0
embedding:
- 0.0023
- -0.0091
- 0.0153
- -0.0028
- 0.0074
model: openai/text-embedding-3-small
usage:
prompt_tokens: 8
total_tokens: 8
'400':
description: Bad request - malformed payload or invalid parameters.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'401':
description: Unauthorized - missing or empty Authorization header.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'402':
description: Payment required - organization balance exhausted.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'403':
description: Forbidden - invalid token or model not in access list.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'404':
description: Not found - provider/model not supported.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'429':
description: Rate limit exceeded. Retry after the Retry-After header value.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'500':
description: Internal server error.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
'502':
description: Bad gateway - upstream provider returned an invalid response.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
components:
schemas:
EmbeddingRequest:
type: object
required:
- input
- model
properties:
input:
oneOf:
- type: string
description: A single text string to embed
- type: array
items:
type: string
description: An array of text strings to embed
- type: array
items:
type: integer
format: int64
description: An array of token integers to embed
- type: array
items:
type: array
items:
type: integer
format: int64
description: An array of token arrays to embed
description: >-
Input text to embed, encoded as a string, array of strings, array of
tokens, or array of token arrays
model:
type: string
description: The model name to use for embedding generation
example: openai/text-embedding-3-small
dimensions:
type: integer
format: int64
description: The number of dimensions the resulting output embeddings should have
encoding_format:
type: string
enum:
- float
- base64
description: >-
The format to return the embeddings in. Can be either float or
base64.
default: float
user:
type: string
description: A unique identifier representing your end-user.
EmbeddingResponse:
type: object
required:
- data
- model
- object
- usage
properties:
data:
type: array
items:
$ref: '#/components/schemas/EmbeddingData'
description: The list of embeddings generated by the model
model:
type: string
description: The name of the model used to generate the embedding
object:
type: string
description: The object type, which is always 'list'
usage:
$ref: '#/components/schemas/EmbeddingUsage'
ErrorResponse:
type: object
required:
- error
properties:
error:
type: object
required:
- origin
- message
properties:
origin:
type: string
enum:
- router
- provider
description: >-
Whether the error originated from Requesty's router or an
upstream provider.
message:
type: string
description: Human-readable error description.
EmbeddingData:
type: object
required:
- embedding
- index
- object
properties:
embedding:
oneOf:
- type: array
items:
type: number
format: float
description: The embedding vector as an array of floats
- type: string
description: The embedding vector as a base64-encoded string
description: >-
The embedding vector, the format of which is determined by the
encoding_format parameter
index:
type: integer
format: int64
description: The index of the embedding in the list
object:
type: string
description: The object type, which is always 'embedding'
EmbeddingUsage:
type: object
required:
- prompt_tokens
- total_tokens
properties:
prompt_tokens:
type: integer
format: int64
description: The number of tokens used by the prompt
total_tokens:
type: integer
format: int64
description: The total number of tokens used by the request
securitySchemes:
BearerAuth:
type: http
scheme: bearer
description: API key for authentication
````