OpenAI Embeddings API - Generate text-embedding-3-large, ada-002 vectors through UniterRouter. Build semantic search, RAG applications with unified embedding API.

Embeddings

Create vector embeddings from text input. Embeddings are numerical representations of text that can be used for semantic search, clustering, and similarity comparisons.

Endpoint

POST https://api.uniterouter.ai/v1/embeddings

Request Body

Parameter	Type	Required	Description
`model`	string	Yes	ID of the embedding model to use
`input`	string \| array	Yes	Text to embed. Can be a string or array of strings
`encoding_format`	string	No	Format of embeddings: `float` (default) or `base64`
`dimensions`	integer	No	Number of dimensions for the output (model-dependent)

Supported Models

Model	Provider	Dimensions	Max Tokens
`text-embedding-3-small`	OpenAI	1536	8191
`text-embedding-3-large`	OpenAI	3072	8191
`text-embedding-ada-002`	OpenAI	1536	8191
`voyage-3`	Voyage	1024	32000
`voyage-3-lite`	Voyage	512	32000

Example Request

curl https://api.uniterouter.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $UNITEROUTER_API_KEY" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

from openai import OpenAI

client = OpenAI(
    base_url="https://api.uniterouter.ai/v1",
    api_key="your_api_key",
)

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 dimensions: {len(embedding)}")

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.uniterouter.ai/v1",
  apiKey: process.env.UNITEROUTER_API_KEY,
});

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

const embedding = response.data[0].embedding;
console.log(`Embedding dimensions: ${embedding.length}`);

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0023064255, -0.009327292, ...]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 9,
    "total_tokens": 9
  }
}

Batch Embeddings

Embed multiple texts in a single request for better efficiency:

response = client.embeddings.create(
    model="text-embedding-3-small",
    input=[
        "First document to embed",
        "Second document to embed",
        "Third document to embed"
    ]
)

for item in response.data:
    print(f"Index {item.index}: {len(item.embedding)} dimensions")

Use Cases

Semantic Search

import numpy as np

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

# Embed query and documents
query_embedding = get_embedding("What is machine learning?")
doc_embeddings = [get_embedding(doc) for doc in documents]

# Find most similar
similarities = [cosine_similarity(query_embedding, doc) for doc in doc_embeddings]
most_similar_idx = np.argmax(similarities)

RAG (Retrieval-Augmented Generation)

Combine embeddings with chat completions for context-aware responses:

# 1. Embed user query
query_embedding = client.embeddings.create(
    model="text-embedding-3-small",
    input=user_query
).data[0].embedding

# 2. Search vector database for relevant context
relevant_docs = vector_db.search(query_embedding, top_k=5)

# 3. Generate response with context
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": f"Context: {relevant_docs}"},
        {"role": "user", "content": user_query}
    ]
)

Tip: Use text-embedding-3-small for most use cases. It offers the best balance of cost and performance.

Embeddings

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