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Embedding API

Stima API provides the Embedding API for developers to convert text into vectors and find similar text through vector search.

Usage (Example in Python)​

import http.client
import json

conn = http.client.HTTPSConnection("api.stima.tech")
payload = json.dumps({
   "model": "text-embedding-3-large",
   "input": "The food was delicious and the waiter..."
})
headers = {
   'Authorization': 'Bearer <STIMA_API_KEY>',
   'Content-Type': 'application/json'
}
conn.request("POST", "/v1/embeddings", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))

Parameters​

  • model: The model to use, currently supports text-embedding-3-large, text-embedding-3-small, text-embedding-ada-002 from OpenAI and jina-embeddings-v3, jina-clip-v2, jina-colbert-v2, jina-embeddings-v2-base-code, jina-embeddings-v2-base-zh, jina-embeddings-v2-base-en from Jina AI.
  • input: The text to convert
  • STIMA_API_KEY: Your API key

Jina AI Embedding Model Usage (Example in Python)​

curl -X POST "https://api.stima.tech/v1/embeddings" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer <STIMA_API_KEY>" \
  -d '{
    "model": "jina-embeddings-v3",
    "input": ["你好，世界", "Hello, World"],
    "task": "retrieval.passage"
  }'

Parameters​

  • model: The model to use, currently supports jina-embeddings-v3, jina-clip-v2, jina-colbert-v2, jina-embeddings-v2-base-code, jina-embeddings-v2-base-zh, jina-embeddings-v2-base-en, jina-embeddings-v2-base-de, jina-clip-v1, jina-embeddings-v2-base-es, jina-colbert-v1-en from Jina AI.
  • input: The text to convert
  • task: The task to use, currently supports retrieval.query, retrieval.passage, separation, classification, text-matching and none from Jina AI with model jina-embeddings-v3.
  • STIMA_API_KEY: Your API key