mirror of
https://github.com/qdrant/landing_page.git
synced 2026-09-28 23:48:31 +02:00
74 lines
2.2 KiB
Markdown
74 lines
2.2 KiB
Markdown
---
|
|
title: "Quickstart"
|
|
weight: 2
|
|
---
|
|
|
|
# How to Generate Text Embedings with FastEmbed
|
|
|
|
## Install FastEmbed
|
|
```python
|
|
pip install fastembed
|
|
```
|
|
Just for demo purposes, you will use Lists and NumPy to work with sample data.
|
|
```python
|
|
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`.
|
|
```python
|
|
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
|
|
```python
|
|
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.
|
|
|
|
```python
|
|
embedding_model = TextEmbedding()
|
|
print("The model BAAI/bge-small-en-v1.5 is ready to use.")
|
|
```
|
|
## Embed data
|
|
|
|
Generate embeddings for both documents.
|
|
```python
|
|
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.
|
|
|
|
```bash
|
|
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
|
|
```python
|
|
print("Embeddings:\n", embeddings_list)
|
|
```
|
|
The embeddings don't look too interesting, but here is a visual.
|
|
|
|
```bash
|
|
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]]
|
|
``` |