FastEmbed

FastEmbed is a lightweight Python library built for embedding generation. It supports popular embedding models and offers a user-friendly experience for embedding data into vector space.

By using FastEmbed, you can ensure that your embedding generation process is not only fast and efficient but also highly accurate, meeting the needs of various machine learning and natural language processing applications.

FastEmbed easily integrates with Qdrant for a variety of multimodal search purposes.

Why is FastEmbed useful?

• Light: Unlike other inference frameworks, such as PyTorch, FastEmbed requires very little external dependencies. Because it uses the ONNX runtime, it is perfect for serverless environments like AWS Lambda.
• Fast: By using ONNX, FastEmbed ensures high-performance inference across various hardware platforms.
• Accurate: FastEmbed aims for better accuracy and recall than models like OpenAI’s Ada-002. It always uses model which demonstrate strong results on the MTEB leaderboard.
• Support: FastEmbed supports a wide range of models, including multilingual ones, to meet diverse use case needs.

How to Generate Text Embedings with FastEmbed

Install FastEmbed

pip install fastembed

Just for demo purposes, you will use Lists and NumPy to work with sample data.

from typing import List
import numpy as np

Load default model

In this example, you will use the default text embedding model, BAAI/bge-small-en-v1.5.

from fastembed import TextEmbedding

Add sample data

Now, add two sample documents. Your documents must be in a list, and each document must be a string

documents: List[str] = [
    "FastEmbed is lighter than Transformers & Sentence-Transformers.",
    "FastEmbed is supported by and maintained by Qdrant.",
]

Download and initialize the model. Print a message to verify the process.

embedding_model = TextEmbedding()
print("The model BAAI/bge-small-en-v1.5 is ready to use.")

Embed data

Generate embeddings for both documents.

embeddings_generator = embedding_model.embed(documents)
embeddings_list = list(embeddings_generator)
len(embeddings_list[0])

Here is the sample document list. The default model creates vectors with 384 dimensions.

Document: This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.
Vector of type: <class 'numpy.ndarray'> with shape: (384,)
Document: fastembed is supported by and maintained by Qdrant.
Vector of type: <class 'numpy.ndarray'> with shape: (384,)

Visualize embeddings

print("Embeddings:\n", embeddings_list)

The embeddings don’t look too interesting, but here is a visual.

Embeddings:
 [[-0.11154681  0.00976555  0.00524559  0.01951888 -0.01934952  0.02943449
  -0.10519084 -0.00890122  0.01831438  0.01486796 -0.05642502  0.02561352
  -0.00120165  0.00637456  0.02633459  0.0089221   0.05313658  0.03955453
  -0.04400245 -0.02929407  0.04691846 -0.02515868  0.00778646 -0.05410657
...
  -0.00243012 -0.01820582  0.02938612  0.02108984 -0.02178085  0.02971899
  -0.00790564  0.03561783  0.0652488  -0.04371546 -0.05550042  0.02651665
  -0.01116153 -0.01682246 -0.05976734 -0.03143916  0.06522726  0.01801389
  -0.02611006  0.01627177 -0.0368538   0.03968835  0.027597    0.03305927]]