Embeddings — POST /v1/embeddings

The /v1/embeddings endpoint generates vector embeddings for text input. Embeddings represent the semantic meaning of text as a high-dimensional float array, enabling similarity search, clustering, and retrieval-augmented generation (RAG). The endpoint is compatible with the OpenAI Embeddings API.

Endpoint

POST https://router.flatkey.ai/v1/embeddings

Supported embedding models

Model	Dimensions	Provider
`gemini-embedding-001`	3072	Google
`text-embedding-3-small`	1536	OpenAI
`text-embedding-3-large`	3072	OpenAI

See the Model Directory for current availability and pricing.

Request

Headers

Header	Value
`Authorization`	`Bearer $FLATKEY_API_KEY`
`Content-Type`	`application/json`

Body parameters

model

string

required

Embedding model ID. Example: "gemini-embedding-001", "text-embedding-3-small".

input

string | array

required

Text to embed. Can be a single string or an array of strings for batch embedding.

encoding_format

string

"float" (default) returns a float array. "base64" returns a base64-encoded string.

dimensions

integer

Number of dimensions for the output embedding. Not supported by all models.

Example requests

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["FLATKEY_API_KEY"],
    base_url="https://router.flatkey.ai/v1",
)

# Single text
response = client.embeddings.create(
    model="gemini-embedding-001",
    input="The quick brown fox jumps over the lazy dog",
)

embedding = response.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["FLATKEY_API_KEY"],
    base_url="https://router.flatkey.ai/v1",
)

# Batch embedding
texts = [
    "What is machine learning?",
    "How does neural network training work?",
    "Explain gradient descent",
]

response = client.embeddings.create(
    model="gemini-embedding-001",
    input=texts,
)

for i, item in enumerate(response.data):
    print(f"{texts[i]}: {len(item.embedding)} dimensions")

curl https://router.flatkey.ai/v1/embeddings \
  -H "Authorization: Bearer $FLATKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-embedding-001",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, ...]
    }
  ],
  "model": "gemini-embedding-001",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

Response fields

data

array

Array of embedding objects, one per input string.

Show embedding object

index

integer

Position in the input array.

embedding

array

Float array of the embedding vector.

usage.prompt_tokens

integer

Number of tokens processed.

Cosine similarity example

python

import os
import numpy as np
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["FLATKEY_API_KEY"],
    base_url="https://router.flatkey.ai/v1",
)

def embed(text):
    return client.embeddings.create(
        model="gemini-embedding-001",
        input=text,
    ).data[0].embedding

def cosine_similarity(a, b):
    a, b = np.array(a), np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

q = embed("capital city of France")
doc1 = embed("Paris is the capital and most populous city of France.")
doc2 = embed("The Eiffel Tower is a famous landmark.")

print(cosine_similarity(q, doc1))  # higher — more relevant
print(cosine_similarity(q, doc2))  # lower

​Endpoint

​Supported embedding models

​Request

​Headers

​Body parameters

​Example requests

​Response

​Response fields

​Cosine similarity example

Endpoint

Supported embedding models

Request

Headers

Body parameters

Example requests

Response

Response fields

Cosine similarity example