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]]