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POST /v1/embeddings
Creates an embedding vector representing the input text. Embeddings are useful for semantic search, clustering, and similarity comparison.

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Request Body

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model
string
required
The embedding model to use (e.g., text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002).
​
input
string | array
required
The text to embed. Can be a single string or an array of strings for batch processing.
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encoding_format
string
default:"float"
The format to return embeddings in. Options: float or base64.
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dimensions
integer
The number of dimensions for the output embeddings. Only supported by some models.

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Response

​
object
string
Always list.
​
data
array
Array of embedding objects.
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model
string
The model used to generate embeddings.
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usage
object
Token usage statistics.

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Examples

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Single Text Embedding

from openai import OpenAI

client = OpenAI(
    api_key="sk-voidai-your_key_here",
    base_url="https://api.voidai.app/v1"
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="The quick brown fox jumps over the lazy dog."
)

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

​
Batch Embeddings

texts = [
    "How do I reset my password?",
    "What are your pricing plans?",
    "How can I contact support?"
]

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

for i, data in enumerate(response.data):
    print(f"Text {i}: {len(data.embedding)} dimensions")

​
Custom Dimensions

# Use smaller dimension for efficiency
response = client.embeddings.create(
    model="text-embedding-3-large",
    input="Your text here",
    dimensions=256
)

embedding = response.data[0].embedding
print(f"Reduced dimension: {len(embedding)}")  # 256

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Response Example

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023, -0.0095, 0.0152, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}

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Use Cases

Semantic Search

Find similar content by comparing embedding distances.

Clustering

Group similar documents together based on embeddings.

Classification

Use embeddings as features for ML classifiers.

Recommendations

Find similar items for recommendation systems.

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Similarity Search Example

import numpy as np
from openai import OpenAI

client = OpenAI(
    api_key="sk-voidai-your_key_here",
    base_url="https://api.voidai.app/v1"
)

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

# Create embeddings for documents
documents = [
    "Python is a programming language",
    "JavaScript runs in the browser",
    "Machine learning uses neural networks"
]

doc_embeddings = client.embeddings.create(
    model="text-embedding-3-small",
    input=documents
).data

# Search query
query = "How do I code in Python?"
query_embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input=query
).data[0].embedding

# Find most similar document
similarities = [
    cosine_similarity(query_embedding, doc.embedding)
    for doc in doc_embeddings
]

best_match_idx = np.argmax(similarities)
print(f"Best match: {documents[best_match_idx]}")
print(f"Similarity: {similarities[best_match_idx]:.4f}")

​
Model Comparison

ModelDimensionsBest For
text-embedding-3-small1536Cost-effective, general use
text-embedding-3-large3072Highest quality, customizable dimensions
text-embedding-ada-0021536Legacy, broad compatibility