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On this page
  • What are Embeddings?
  • Pricing
  • Example: Generating Embeddings
  • Example in Python
  • All Available Embedding Models

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  1. API REFERENCES

Embedding Models

PreviousOFR: Optical Feature RecognitionNextAnthropic

Last updated 10 days ago

Was this helpful?

We support multiple embedding models. You can find the along with API reference links at the end of the page.

What are Embeddings?

Embeddings from AI/ML API quantify the similarity between text strings. These embeddings are particularly useful for:

  • Search: Rank search results by their relevance to a query.

  • Clustering: Group similar text strings together.

  • Recommendations: Suggest items based on related text strings.

  • Anomaly Detection: Identify outliers that differ significantly from the norm.

  • Diversity Measurement: Analyze the spread of similarities within a dataset.

  • Classification: Categorize text strings by comparing them to labeled examples.

An embedding is a vector (list) of floating-point numbers, where the distance between vectors indicates their relatedness. Smaller distances indicate higher similarity, while larger distances suggest lower similarity.

Pricing

For more information on Embeddings pricing, visit our . Costs are calculated based on the number of tokens in the input.

Example: Generating Embeddings

curl https://api.aimlapi.com/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $AIMLAPI_API_KEY" \
  -d '{
    "input": "Your text string goes here",
    "model": "text-embedding-3-small"
  }'

The response will include the embedding vector and additional metadata:

Response
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        // ...(omitted for spacing)
        -4.547132266452536e-05,
        -0.024047505110502243
      ]
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 5,
    "total_tokens": 5
  }
}

By default, the length of the embedding vector is 1536 for text-embedding-3-small or 3072 for text-embedding-3-large. You can reduce the dimensions of the embedding using the dimensions parameter without losing its ability to represent concepts. More details on embedding dimensions can be found in the embedding use case section.

Example in Python

Here's how to use the embeddings API in Python:

import os
import json
import openai

# Initialize the API client
client = openai.OpenAI(
    base_url="https://api.aimlapi.com/v1",
    api_key=os.getenv("<YOUR_AIMLAPI_KEY"),
)

# Define the text for which to generate an embedding
text = "Your text string goes here"

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

# Extract the embedding from the response
embedding = response['data'][0]['embedding']

# Print the embedding
print(json.dumps(embedding, indent=2))

This Python example shows how to set up an API client, send text to the embeddings API, and handle the response to extract and print the embedding vector.

All Available Embedding Models

Model ID
Developer
Context
Model Card

Open AI

8000

-

Open AI

8000

Open AI

8000

Together AI

32000

BAAI

Together AI

BAAI

Together AI

8000

Anthropic

16000

Anthropic

32000

-

Anthropic

32000

-

Anthropic

16000

-

Anthropic

16000

-

Anthropic

16000

-

Anthropic

4000

-

Google

2000

Google

2000

Google

2000

-

pricing page
complete list
text-embedding-3-small
text-embedding-3-large
Text-embedding-3-large
text-embedding-ada-002
Text-embedding-ada-002
togethercomputer/m2-bert-80M-32k-retrieval
M2-BERT-Retrieval-32k
BAAI/bge-base-en-v1.5
BAAI-Bge-Base-1p5
togethercomputer/m2-bert-80M-2k-retrieval
M2-BERT-Retrieval-2K
BAAI/bge-large-en-v1.5
bge-large-en
togethercomputer/m2-bert-80M-8k-retrieval
M2-BERT-Retrieval-8k
voyage-large-2-instruct
Voyage Large 2 Instruct
voyage-finance-2
voyage-multilingual-2
voyage-law-2
voyage-code-2
voyage-large-2
voyage-2
textembedding-gecko@003
Textembedding-gecko@003
textembedding-gecko-multilingual@001
Textembedding-gecko-multilingual@001
text-multilingual-embedding-002