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Transform text into high-dimensional vector representations for semantic search, similarity matching, and AI-powered applications. Generate text embeddings:
  • CLI
  • Python SDK
  • JavaScript SDK
gravixlayer chat --mode embeddings --model "text-embedding-ada-002" --text "Why is the sky blue?"
Example Output:
{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [
        -0.006929283495992422,
        -0.005336422007530928,
        ...
      ],
      "index": 0
    }
  ],
  "model": "text-embedding-ada-002",
  "usage": {
    "prompt_tokens": 6,
    "total_tokens": 6
  }
}

​
Batch Embeddings

Generate embeddings for multiple texts:
  • Python SDK
  • JavaScript SDK
import os
from gravixlayer import GravixLayer

client = GravixLayer()

texts = [
    "The weather is nice today",
    "I love programming",
    "Machine learning is fascinating"
]

embeddings = client.embeddings.create(
    model="text-embedding-ada-002",
    input=texts
)

for i, embedding in enumerate(embeddings.data):
    print(f"Text {i+1}: {len(embedding.embedding)} dimensions")
⌘I