Embeddings

POST https://api.synthetic.new/openai/v1/embeddings

Create a vector representation of a given input for similarity, retrieval, clustering, and other applications.

Tip

/embeddings requests do not count against your subscription limits!

Supported Embeddings Models

Model	Context length	Status
hf:nomic-ai/nomic-embed-text-v1.5	8k tokens	✓ Included

Request Body

Parameter	Type	Required	Description
`model`	string	Yes	Model ID (must be prefixed with `hf:`)
`input`	string/array	Yes	Input text to embed, encoded as a string or array of strings
`dimensions`	number	No	Number of dimensions the resulting output embeddings should have

Example Request

Python
TypeScript
curl

import openai

client = openai.OpenAI(
  api_key="SYNTHETIC_API_KEY",
  base_url="https://api.synthetic.new/openai/v1"
)

response = client.embeddings.create(
  model="hf:nomic-ai/nomic-embed-text-v1.5",
  input="The quick brown fox jumps over the lazy dog."
)

embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")

Example Response

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        0.0023064255,
        -0.009327292,
        -0.0028842222,
        "... (768 more values)"
      ]
    }
  ],
  "model": "hf:nomic-ai/nomic-embed-text-v1.5",
  "usage": {
    "prompt_tokens": 8,
    "total_tokens": 8
  }
}

Multiple Inputs

You can embed multiple texts in a single request:

Python
TypeScript
curl

response = client.embeddings.create(
  model="hf:nomic-ai/nomic-embed-text-v1.5",
  input=[
    "The quick brown fox jumps over the lazy dog.",
    "Pack my box with five dozen liquor jugs.",
    "How vexingly quick daft zebras jump!"
  ]
)

embeddings = [data.embedding for data in response.data]
print(f"Created {len(embeddings)} embeddings")

Multiple Inputs Response

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, "..."]
    },
    {
      "object": "embedding", 
      "index": 1,
      "embedding": [0.0019234521, -0.007891234, "..."]
    },
    {
      "object": "embedding",
      "index": 2, 
      "embedding": [0.0021456789, -0.008765432, "..."]
    }
  ],
  "model": "hf:nomic-ai/nomic-embed-text-v1.5",
  "usage": {
    "prompt_tokens": 24,
    "total_tokens": 24
  }
}

Model

Context length

Status

hf:nomic-ai/nomic-embed-text-v1.5

8k tokens

✓ Included

Parameter

Type

Required

Description

model

string

Yes

Model ID (must be prefixed with hf:)

input

string/array

Yes

Input text to embed, encoded as a string or array of strings

dimensions

number

Number of dimensions the resulting output embeddings should have

import openai client = openai.OpenAI( api_key="SYNTHETIC_API_KEY", base_url="https://api.synthetic.new/openai/v1" ) response = client.embeddings.create( model="hf:nomic-ai/nomic-embed-text-v1.5", input="The quick brown fox jumps over the lazy dog." ) embedding = response.data[0].embedding print(f"Embedding dimensions: {len(embedding)}")

{ "object": "list", "data": [ { "object": "embedding", "index": 0, "embedding": [ 0.0023064255, -0.009327292, -0.0028842222, "... (768 more values)" ] } ], "model": "hf:nomic-ai/nomic-embed-text-v1.5", "usage": { "prompt_tokens": 8, "total_tokens": 8 } }

response = client.embeddings.create( model="hf:nomic-ai/nomic-embed-text-v1.5", input=[ "The quick brown fox jumps over the lazy dog.", "Pack my box with five dozen liquor jugs.", "How vexingly quick daft zebras jump!" ] ) embeddings = [data.embedding for data in response.data] print(f"Created {len(embeddings)} embeddings")

{ "object": "list", "data": [ { "object": "embedding", "index": 0, "embedding": [0.0023064255, -0.009327292, "..."] }, { "object": "embedding", "index": 1, "embedding": [0.0019234521, -0.007891234, "..."] }, { "object": "embedding", "index": 2, "embedding": [0.0021456789, -0.008765432, "..."] } ], "model": "hf:nomic-ai/nomic-embed-text-v1.5", "usage": { "prompt_tokens": 24, "total_tokens": 24 } }