The Embeddings API encodes any text into a vector using state-of-the-art NLP machine learning models. It can be used to power semantic search, text comparison tools (also check out our Text Similarity API), recommendation engines, and much more.
https://api.api-ninjas.com/v1/embeddings
Returns a 768-dimensional vector as an array that encodes the meaning of any given input text.
text requiredQuery text to embed. Maximum 5000 characters.
X-Api-Key requiredAPI Key associated with your account.
https://api.api-ninjas.com/v1/embeddings1
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{
"embeddings": [
0.013939207419753075,
-0.07620275765657425,
-0.014649288728833199,
-0.00781314168125391,
-0.0740455836057663,
0.03170469030737877,
...
]
}1
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import requests
body = { 'text': 'This is an example sentence.' }
api_url = 'https://api.api-ninjas.com/v1/embeddings'
response = requests.post(api_url, headers={'X-Api-Key': 'YOUR_API_KEY'}, json=body)
if response.status_code == requests.codes.ok:
print(response.text)
else:
print("Error:", response.status_code, response.text)If your programming language is not listed in the Code Example above, you can still make API calls by using a HTTP request library written in your programming language and following the above documentation.