HuggingFace Embeddings
Generate embeddings using HuggingFace Inference API
Overview
HuggingFace provides access to thousands of open-source embedding models through their Inference API.
Quick Start
from praisonaiagents import embedding
result = embedding(
input="Hello world",
model="huggingface/BAAI/bge-large-en-v1.5"
)
print(f"Dimensions: {len(result.embeddings[0])}")CLI Usage
praisonai embed "Hello world" --model huggingface/BAAI/bge-large-en-v1.5Setup
export HUGGINGFACE_API_KEY="hf_..."Popular Models
| Model | Dimensions | Use Case |
|---|---|---|
huggingface/BAAI/bge-large-en-v1.5 | 1024 | High quality English |
huggingface/BAAI/bge-base-en-v1.5 | 768 | Balanced |
huggingface/BAAI/bge-small-en-v1.5 | 384 | Fast |
huggingface/sentence-transformers/all-MiniLM-L6-v2 | 384 | Fast, general |
huggingface/sentence-transformers/all-mpnet-base-v2 | 768 | High quality |
huggingface/intfloat/e5-large-v2 | 1024 | E5 model |
Batch Embeddings
from praisonaiagents import embedding
texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
input=texts,
model="huggingface/BAAI/bge-large-en-v1.5"
)
print(f"Generated {len(result.embeddings)} embeddings")