Nebius Embeddings¶

This notebook demonstrates how to use Nebius AI Studio Embeddings with LlamaIndex. Nebius AI Studio implements all state-of-the-art embeddings models, available for commercial use.

First, let's install LlamaIndex and dependencies of Nebius AI Studio.

In [ ]:

%pip install llama-index-embeddings-nebius llama-index

%pip install llama-index-embeddings-nebius llama-index

Upload your Nebius AI Studio key from system variables below or simply insert it. You can get it by registering for free at Nebius AI Studio and issuing the key at API Keys section.

In [ ]:

import os

NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY")  # NEBIUS_API_KEY = ""

import os NEBIUS_API_KEY = os.getenv("NEBIUS_API_KEY") # NEBIUS_API_KEY = ""

Now let's get embeddings using Nebius AI Studio

In [ ]:

from llama_index.embeddings.nebius import NebiusEmbedding

embed_model = NebiusEmbedding(api_key=NEBIUS_API_KEY)

from llama_index.embeddings.nebius import NebiusEmbedding embed_model = NebiusEmbedding(api_key=NEBIUS_API_KEY)

Basic usage¶

In [ ]:

text = "Everyone loves justice at another person's expense"
embeddings = embed_model.get_text_embedding(text)
assert len(embeddings) == 4096
print(len(embeddings), embeddings[:5], sep="\n")

text = "Everyone loves justice at another person's expense" embeddings = embed_model.get_text_embedding(text) assert len(embeddings) == 4096 print(len(embeddings), embeddings[:5], sep="\n")

4096
[-0.0024051666259765625, 0.0083770751953125, -0.005413055419921875, 0.007396697998046875, -0.022247314453125]

Asynchronous usage¶

In [ ]:

text = "Everyone loves justice at another person's expense"
embeddings = await embed_model.aget_text_embedding(text)
assert len(embeddings) == 4096
print(len(embeddings), embeddings[:5], sep="\n")

text = "Everyone loves justice at another person's expense" embeddings = await embed_model.aget_text_embedding(text) assert len(embeddings) == 4096 print(len(embeddings), embeddings[:5], sep="\n")

4096
[-0.0024051666259765625, 0.0083770751953125, -0.005413055419921875, 0.007396697998046875, -0.022247314453125]

Batched usage¶

In [ ]:

texts = [
    "As the hours pass",
    "I will let you know",
    "That I need to ask",
    "Before I'm alone",
]

embeddings = embed_model.get_text_embedding_batch(texts)
assert len(embeddings) == 4
assert len(embeddings[0]) == 4096
print(*[x[:3] for x in embeddings], sep="\n")

texts = [ "As the hours pass", "I will let you know", "That I need to ask", "Before I'm alone", ] embeddings = embed_model.get_text_embedding_batch(texts) assert len(embeddings) == 4 assert len(embeddings[0]) == 4096 print(*[x[:3] for x in embeddings], sep="\n")

[-0.0003848075866699219, 0.0004799365997314453, 0.011199951171875]
[-0.0037078857421875, 0.0114288330078125, 0.00878143310546875]
[0.005924224853515625, 0.005153656005859375, 0.001438140869140625]
[-0.009490966796875, -0.004852294921875, 0.004779815673828125]

Async batched usage¶

In [ ]:

texts = [
    "As the hours pass",
    "I will let you know",
    "That I need to ask",
    "Before I'm alone",
]

embeddings = await embed_model.aget_text_embedding_batch(texts)
assert len(embeddings) == 4
assert len(embeddings[0]) == 4096
print(*[x[:3] for x in embeddings], sep="\n")

texts = [ "As the hours pass", "I will let you know", "That I need to ask", "Before I'm alone", ] embeddings = await embed_model.aget_text_embedding_batch(texts) assert len(embeddings) == 4 assert len(embeddings[0]) == 4096 print(*[x[:3] for x in embeddings], sep="\n")

[-0.0003848075866699219, 0.0004799365997314453, 0.011199951171875]
[-0.0037078857421875, 0.0114288330078125, 0.00878143310546875]
[0.005924224853515625, 0.005153656005859375, 0.001438140869140625]
[-0.009490966796875, -0.004852294921875, 0.004779815673828125]