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-v1.5

Setup

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

Model-name lookup in get_dimensions() is now case-insensitive and matches longest keys first as of PraisonAI PR #3027:

• all-MiniLM-L6-v2 and sentence-transformers/all-MiniLM-L6-v2 both resolve to 384 (was 1536).
• voyage/voyage-3-lite resolves to 512 (was 1024 — voyage-3 was matching first).

Earlier releases returned the wrong dimension for these names, producing dimension-mismatch errors on memory / RAG upsert.

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")

Related

• Embedding Providers Overview
• Ollama Embeddings