AI Embeddings API in Python
Complete Python integration guide for the AI Embeddings API. Copy the code below, add your RapidAPI key, and start building.
Prerequisites
- 1.Sign up for a free account on RapidAPI
- 2.Subscribe to the AI Embeddings API (free tier available)
- 3.Copy your
X-RapidAPI-Keyfrom the dashboard - 4.Install the dependency:
pip install requests
Complete Python Example
import requests
url = "https://ai-embeddings-by-helix-api.p.rapidapi.com/embed"
headers = {
"Content-Type": "application/json",
"X-RapidAPI-Key": "YOUR_API_KEY",
"X-RapidAPI-Host": "ai-embeddings-by-helix-api.p.rapidapi.com"
}
payload = {"texts": ["semantic search", "vector database"]}
response = requests.post(url, json=payload, headers=headers)
data = response.json()
print(f"Status: {data.get('status')}")
print(f"Result: {data.get('data')}")Response Format
All Helix-API endpoints return a consistent JSON envelope:
{
"status": "ok",
"data": { ... },
"meta": {
"request_id": "req_abc123",
"latency_ms": 42
}
}On errors, status becomes "error" and a message field explains what went wrong.
Error Handling
| Status | Meaning | Action |
|---|---|---|
200 | Success | Parse the response body normally |
400 | Bad request | Check your request parameters |
401 | Unauthorized | Verify your X-RapidAPI-Key header |
429 | Rate limited | Wait and retry with exponential backoff |
500 | Server error | Retry after a short delay |
Python Best Practices
Use a session for multiple calls
Create a requests.Session() and set headers once. This reuses TCP connections and is faster when making many calls to the AI Embeddings API.
Handle rate limits gracefully
Check for HTTP 429 responses and implement exponential backoff. The Retry-After header tells you how long to wait.
Type your responses
Use Pydantic models or TypedDict to validate API responses. This catches schema changes early and gives you autocomplete in your IDE.
Async for high throughput
Use aiohttp or httpx for async calls when you need to make many concurrent requests. Perfect for batch processing.
AI Embeddings API Endpoints
/embedGenerate embeddings for text(s)
/similarityCompute cosine similarity between texts
/modelsList available models
Other Languages
View the AI Embeddings API integration guide in another language: