- CLI
- Python SDK
- JavaScript SDK
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gravixlayer chat --mode embeddings --model "baai/bge-large-en-v1.5" --text "Why is the sky blue?"
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{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
-0.006929283495992422,
-0.005336422007530928,
...
],
"index": 0
}
],
"model": "baai/bge-large-en-v1.5",
"usage": {
"prompt_tokens": 6,
"total_tokens": 6
}
}
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import os
from gravixlayer import GravixLayer
client = GravixLayer()
embedding = client.embeddings.create(
model="baai/bge-large-en-v1.5",
input="Why is the sky blue?"
)
print(f"Embedding dimension: {len(embedding.data[0].embedding)}")
print(f"First few values: {embedding.data[0].embedding[:5]}")
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import { GravixLayer } from 'gravixlayer';
const client = new GravixLayer({
apiKey: process.env.GRAVIXLAYER_API_KEY,
});
const embedding = await client.embeddings.create({
model: "baai/bge-large-en-v1.5",
input: "Why is the sky blue?"
});
console.log(`Embedding dimension: ${embedding.data[0].embedding.length}`);
console.log(`First few values: ${embedding.data[0].embedding.slice(0, 5)}`);
Batch Embeddings
Generate embeddings for multiple texts:- Python SDK
- JavaScript SDK
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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="baai/bge-large-en-v1.5",
input=texts
)
for i, embedding in enumerate(embeddings.data):
print(f"Text {i+1}: {len(embedding.embedding)} dimensions")
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import { GravixLayer } from 'gravixlayer';
const client = new GravixLayer({
apiKey: process.env.GRAVIXLAYER_API_KEY,
});
const texts = [
"The weather is nice today",
"I love programming",
"Machine learning is fascinating"
];
const embeddings = await client.embeddings.create({
model: "baai/bge-large-en-v1.5",
input: texts
});
embeddings.data.forEach((embedding, i) => {
console.log(`Text ${i+1}: ${embedding.embedding.length} dimensions`);
});