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text-embedding-ada-002

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openai/text-embedding-ada-002

An earlier-generation OpenAI text embedding model for search, clustering and recommendations. Returns vectors with 1,536 coordinates. The dimensions option is not supported.

Input → Output
Text Embeddings
Access
Token Exchange
Context window
8,192 tokens
Default dimensions
1,536
License: proprietary

From model to application

Quick Start

API reference
  1. Get your API key

    Create a key in the ShareAI Console, then save it as an environment variable on your server.

    Create API key
    Environment variable
    export SHAREAI_API_KEY="your-api-key"
  2. Choose your endpoint

    Send a POST request with your API key in the Authorization header.

    POSThttps://api.shareai.now/api/v1/embeddings
  3. Make your first request

    The request below uses your selected tag. Run it on your server to keep your API key private.

    cURL
    curl --request POST 'https://api.shareai.now/api/v1/embeddings' \
      --header "Authorization: Bearer ${SHAREAI_API_KEY}" \
      --header 'Content-Type: application/json' \
      --data-raw '{
      "model": "openai/text-embedding-ada-002",
      "input": "Hello!"
    }'
    Python
    import json
    import os
    import urllib.request
    
    payload = json.loads("{\n  \"model\": \"openai/text-embedding-ada-002\",\n  \"input\": \"Hello!\"\n}")
    request = urllib.request.Request("https://api.shareai.now/api/v1/embeddings",
        data=json.dumps(payload).encode(),
        headers={
            "Authorization": f"Bearer {os.environ['SHAREAI_API_KEY']}",
            "Content-Type": "application/json",
        },
        method="POST",
    )
    with urllib.request.urlopen(request) as response:
        print(json.load(response))
    TypeScript
    const response = await fetch("https://api.shareai.now/api/v1/embeddings", {
      method: 'POST',
      headers: {
        Authorization: `Bearer ${process.env.SHAREAI_API_KEY}`,
        'Content-Type': 'application/json',
      },
      body: JSON.stringify({
        "model": "openai/text-embedding-ada-002",
        "input": "Hello!"
      }),
    });
    if (!response.ok) throw new Error(await response.text());
    console.log(await response.json());

Token Exchange

Available through Token Exchange

Use the network with your token balance. Your account’s consumption settings determine how requests are billed.

A little more detail

Frequently asked questions

What is text-embedding-ada-002?

An earlier-generation OpenAI text embedding model for search, clustering and recommendations. Returns vectors with 1,536 coordinates. The dimensions option is not supported.

How much does text-embedding-ada-002 cost?

This tag is available through Token Exchange. Your token balance and account consumption settings determine access and billing. Check your Console for your balance and eligibility.

What inputs and outputs does text-embedding-ada-002 support?

Inputs: Text. Outputs: Embeddings. These capabilities apply to the selected model tag. Check the endpoint documentation for supported request formats.

What is the context length of text-embedding-ada-002?

This tag has a context window of 8,192 tokens. Context size and pricing thresholds are separate. Request limits can be lower for a particular deployment.

How do I use text-embedding-ada-002 with ShareAI?

Create an API key in the ShareAI Console, choose a model tag, and send an authenticated request to the endpoint in Quick Start. Your key must have access to the chosen model. Keep your key on your server.