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87 lines
2.7 KiB
Markdown
87 lines
2.7 KiB
Markdown
---
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title: "Quickstart"
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weight: 2
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---
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# How to Generate Text Embedings with FastEmbed
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## Install FastEmbed
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```python
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pip install fastembed
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```
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Just for demo purposes, you will use Lists and NumPy to work with sample data.
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```python
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from typing import List
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import numpy as np
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```
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## Load default model
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In this example, you will use the default text embedding model, `BAAI/bge-small-en-v1.5`.
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```python
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from fastembed import TextEmbedding
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```
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## Add sample data
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Now, add two sample documents. Your documents must be in a list, and each document must be a string
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```python
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documents: List[str] = [
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"FastEmbed is lighter than Transformers & Sentence-Transformers.",
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"FastEmbed is supported by and maintained by Qdrant.",
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]
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```
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Download and initialize the model. Print a message to verify the process.
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```python
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embedding_model = TextEmbedding()
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print("The model BAAI/bge-small-en-v1.5 is ready to use.")
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```
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## Embed data
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Generate embeddings for both documents.
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```python
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embeddings_generator = embedding_model.embed(documents)
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embeddings_list = list(embeddings_generator)
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len(embeddings_list[0])
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```
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Here is the sample document list. The default model creates vectors with 384 dimensions.
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```bash
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Document: This is built to be faster and lighter than other embedding libraries e.g. Transformers, Sentence-Transformers, etc.
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Vector of type: <class 'numpy.ndarray'> with shape: (384,)
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Document: fastembed is supported by and maintained by Qdrant.
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Vector of type: <class 'numpy.ndarray'> with shape: (384,)
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```
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## Verify embedding shape
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Next, take the embeddings and convert them into a NumPy array.
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Then, check the shape of this array to confirm the dimensions of the generated embeddings.
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```python
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embeddings_list = np.array(list(embedding_model.embed(documents)))
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embeddings_list.shape
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```
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The shape will be a tuple indicating the number of documents and the dimensions of the embeddings. For example, if there are 2 documents and each embedding has 384 dimensions, the shape will be (2, 384).
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```python
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(2, 384)
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```
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## See the embeddings
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```python
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print("Embeddings:\n", embeddings_list)
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```
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The embeddings don't look too interesting, but here is a visual.
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```bash
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Embeddings:
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[[-0.11154681 0.00976555 0.00524559 0.01951888 -0.01934952 0.02943449
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-0.10519084 -0.00890122 0.01831438 0.01486796 -0.05642502 0.02561352
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-0.00120165 0.00637456 0.02633459 0.0089221 0.05313658 0.03955453
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-0.04400245 -0.02929407 0.04691846 -0.02515868 0.00778646 -0.05410657
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...
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-0.00243012 -0.01820582 0.02938612 0.02108984 -0.02178085 0.02971899
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-0.00790564 0.03561783 0.0652488 -0.04371546 -0.05550042 0.02651665
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-0.01116153 -0.01682246 -0.05976734 -0.03143916 0.06522726 0.01801389
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-0.02611006 0.01627177 -0.0368538 0.03968835 0.027597 0.03305927]]
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``` |