llama-index-embeddings-voxell 0.1.0

llama-index embeddings voxell integration

LlamaIndex Embeddings Integration: Voxell

Voxell Forge is a hosted text-embedding API with three tiers (turbo, pro, and ultra) on an OpenAI-compatible endpoint.

Voxell's Ingot-8B-R3 ranks #1 for English on the public MTEB leaderboard (English v2), with a 75.98 mean task score across 41 tasks. It is the top usable English embedding model. See the model card, or try Forge with no signup on the playground.

Installation

pip install llama-index-embeddings-voxell

Setup

Create a free API key at dash.voxell.ai (new accounts include 10M free tokens), then set it in your environment:

export FORGE_API_KEY="your_api_key_here"

Usage

from llama_index.embeddings.voxell import VoxellEmbedding

emb = VoxellEmbedding(model="turbo")  # reads FORGE_API_KEY from the environment

vector = emb.get_text_embedding("Voxell Forge turns text into vectors.")
print(len(vector))  # 1024

query_vector = emb.get_query_embedding("How do I turn text into vectors?")

Tiers

Pick your point on the quality and cost curve:

Tier	Dimensions
turbo	1024
pro	2560
ultra	4096

Shorter vectors with Matryoshka

Pass dimensions to get a shorter, re-normalized vector, which means a smaller index with minimal quality loss.

emb = VoxellEmbedding(model="turbo", dimensions=256)
short_vector = emb.get_query_embedding("a compact embedding")

Async

vector = await emb.aget_text_embedding("async text")