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POST
/
v1
/
embeddings
Embeddings
curl --request POST \
  --url https://api.savegate.ai/v1/embeddings \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "<string>",
  "input": {},
  "encoding_format": "<string>"
}
'
{
  "object": "<string>",
  "data": [
    {
      "object": "<string>",
      "embedding": [
        {}
      ],
      "index": 123
    }
  ],
  "model": "<string>",
  "usage": {}
}

​
Overview

Create embeddings for text inputs. Useful for semantic search, clustering, and RAG applications.

​
Request

​
Body Parameters

​
model
string
required
Embedding model ID (e.g., text-embedding-3-small, text-embedding-ada-002)
​
input
string or array
required
Text or array of texts to embed
​
encoding_format
string
default:"float"
Format for embeddings (float or base64)

​
Response

​
object
string
Object type (always list)
​
data
array
Array of embedding objects
​
model
string
Model used
​
usage
object
Token usage

​
Examples

from openai import OpenAI

client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="Your text here"
)

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

​
Batch Embeddings

# Embed multiple texts at once
texts = [
    "First document",
    "Second document",
    "Third document"
]

response = client.embeddings.create(
    model="text-embedding-3-small",
    input=texts
)

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

​
Supported Models

ModelDimensionDescription
text-embedding-3-small1536Latest, most efficient
text-embedding-3-large3072Highest quality
text-embedding-ada-0021536Previous generation

​
Use Cases

Semantic Search

Find relevant documents based on meaning

Clustering

Group similar texts together

RAG Applications

Retrieval-augmented generation

Recommendations

Content recommendation systems