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]]

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