polars-fastembed 0.1.16

A Polars plugin for text embeddings in DataFrames

Polars FastEmbed

A Polars plugin for embedding DataFrames

Installation

uv pip install polars-fastembed[cpu]

Change the [cpu] package extras according to your system:

• for use with GPU: [cuda]
• for backcompatibility with older CPUs: [rtcompat]

Features

• Embed from a DataFrame by specifying the source column(s)
• Re-order/filter rows by semantic similarity to a query
• Efficiently reuse loaded models via a global registry (no repeated model loads)

Demo

See demo.py

import polars as pl
from polars_fastembed import register_model

# Create a sample DataFrame
df = pl.DataFrame(
    {
        "id": [1, 2, 3],
        "text": [
            "Hello world",
            "Deep Learning is amazing",
            "Polars and FastEmbed are well integrated",
        ],
    }
)

model_id = "Xenova/bge-small-en-v1.5"

# 1) Register a model
#    Optionally specify GPU: providers=["CUDAExecutionProvider"]
#    Or omit it for CPU usage
register_model(model_id, providers=["CPUExecutionProvider"])

# 2) Embed your text
df_emb = df.fastembed.embed(
    columns="text",
    model_name=model_id,
    output_column="embedding",
)

# Inspect embeddings
print(df_emb)

# 3) Perform retrieval
result = df_emb.fastembed.retrieve(
    query="Tell me about deep learning",
    model_name=model_id,
    embedding_column="embedding",
    k=3,
)
print(result)

shape: (3, 3)
┌─────┬─────────────────────────────────┬─────────────────────────────────┐
│ id  ┆ text                            ┆ embedding                       │
│ --- ┆ ---                             ┆ ---                             │
│ i64 ┆ str                             ┆ array[f32, 384]                 │
╞═════╪═════════════════════════════════╪═════════════════════════════════╡
│ 1   ┆ Hello world                     ┆ [0.015196, -0.022571, … 0.0260… │
│ 2   ┆ Deep Learning is amazing        ┆ [-0.016128, -0.018325, … -0.06… │
│ 3   ┆ Polars and FastEmbed are well … ┆ [-0.086584, 0.026477, … 0.0399… │
└─────┴─────────────────────────────────┴─────────────────────────────────┘
shape: (3, 4)
┌─────┬─────────────────────────────────┬─────────────────────────────────┬────────────┐
│ id  ┆ text                            ┆ embedding                       ┆ similarity │
│ --- ┆ ---                             ┆ ---                             ┆ ---        │
│ i64 ┆ str                             ┆ array[f32, 384]                 ┆ f64        │
╞═════╪═════════════════════════════════╪═════════════════════════════════╪════════════╡
│ 2   ┆ Deep Learning is amazing        ┆ [-0.016128, -0.018325, … -0.06… ┆ 0.825373   │
│ 3   ┆ Polars and FastEmbed are well … ┆ [-0.086584, 0.026477, … 0.0399… ┆ 0.543264   │
│ 1   ┆ Hello world                     ┆ [0.015196, -0.022571, … 0.0260… ┆ 0.52316    │
└─────┴─────────────────────────────────┴─────────────────────────────────┴────────────┘

Note:

• This will download a 133 MB model to your working directory under .fastembed_cache
• In the original version this was a 384-dimensional array of f64 and here it is a list of f32. This will become an array as well in future versions (watch this space).

Contributing

Feel free to open issues or submit pull requests for improvements or bug fixes.

License

MIT License