fastembed 0.0.5a1

Fast, light, accurate library built for retrieval embedding generation

⚡️ What is FastEmbed?

FastEmbed is an easy to use -- lightweight, fast, Python library built for retrieval embedding generation.

The default embedding supports "query" and "passage" prefixes for the input text. The default model is Flag Embedding, which is top of the MTEB leaderboard. Here is an example for Retrieval Embedding Generation

1. Light

   • Quantized model weights
   • ONNX Runtime for inference
   • No hidden dependencies on PyTorch or TensorFlow via Huggingface Transformers

2. Accuracy/Recall

   • Better than OpenAI Ada-002
   • Default is Flag Embedding, which is top of the MTEB leaderboard

3. Fast

   • About 2x faster than Huggingface (PyTorch) transformers on single queries
   • Lot faster for batches!
   • ONNX Runtime allows you to use dedicated runtimes for even higher throughput and lower latency

🚀 Installation

To install the FastEmbed library, pip works:

pip install fastembed

📖 Usage

from fastembed.embedding import FlagEmbedding as Embedding

documents: List[str] = [
    "passage: Hello, World!",
    "query: Hello, World!", # these are two different embedding
    "passage: This is an example passage.",
    # You can leave out the prefix but it's recommended
    "fastembed is supported by and maintained by Qdrant." 
]
embedding_model = Embedding(model_name="BAAI/bge-base-en", max_length=512) 
embeddings: List[np.ndarray] = list(embedding_model.embed(documents))

Why fast?

It's important we justify the "fast" in FastEmbed. FastEmbed is fast because:

1. Quantized model weights
2. ONNX Runtime which allows for fast inference on CPU and other dedicated runtimes

Why light?

1. No hidden dependencies on PyTorch or TensorFlow via Huggingface Transformers
2. We do use the tokenizer from Huggingface Transformers, but it's a light dependency

Why accurate?

1. Better than OpenAI Ada-002
2. Top of the Embedding leaderboards e.g. MTEB

Similar Work

Ilyas M. wrote about using FlagEmbeddings with Optimum over CUDA.