Embedding

The embedding API allows you to get a vector representation of the input to be used from machine learning models or algorithms, leveraging models like gte-Qwen2.

API Call Parameters

• input: A string describing the sentence, such as "A white cat resting in Rome."
• model: The identifier for the model used in image generation, e.g., "gte-Qwen2."

Using Regolo ClientPythonCURL

import regolo

regolo.default_key = "<YOUR_REGOLO_KEY>"
regolo.default_embedder_model = "gte-Qwen2"


embeddings = regolo.static_embeddings(input_text=["A white cat resting in Rome", "A white cat resting in Paris"])

print(embeddings)

import requests
import json

url = 'https://api.regolo.ai/v1/embeddings'
headers = {
    'Authorization': 'Bearer YOUR_REGOLO_KEY',
    'Content-Type': 'application/json'
}

data = {
    "prompt": "A white cat resting in Rome",
    "model": "gte-Qwen2",
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    with open("./embedding.json", 'w') as _file:
        json.dump(response.json(), _file)
else:
    print("Failed embedding request:", response.status_code, response.text)

curl -X POST https://api.regolo.ai/v1/embeddings
-H "Content-Type: application/json"
-H "Authorization: Bearer YOUR_REGOLO_KEY"
-d '{
    "model": "gte-Qwen2",
    "input": "The quick brown fox jumps over the lazy dog"
}'

For the exhaustive API's endpoints documentation visit docs.api.regolo.ai.