---
title: sqlite-vec in Python
---

# Using `sqlite-vec` in Python

[![PyPI](https://img.shields.io/pypi/v/sqlite-vec.svg?color=blue&logo=python&logoColor=white)](https://pypi.org/project/sqlite-vec/)

To use `sqlite-vec` from Python, install the
[`sqlite-vec` PyPi package](https://pypi.org/project/sqlite-vec/) using your
favorite Python package manager:

```bash
pip install sqlite-vec
```

Once installed, use the `sqlite_vec.load()` function to load `sqlite-vec` SQL
functions into a SQLite connection.

```python
import sqlite3
import sqlite_vec

db = sqlite3.connect(":memory:")
db.enable_load_extension(True)
sqlite_vec.load(db)
db.enable_load_extension(False)

vec_version, = db.execute("select vec_version()").fetchone()
print(f"vec_version={vec_version}")
```

See
[`simple-python/demo.py`](https://github.com/asg017/sqlite-vec/blob/main/examples/simple-python/demo.py)
for a more complete Python demo.

## Working with Vectors

### Lists

If your vectors in Python are provided as a list of floats, you can
convert them into the compact BLOB format that `sqlite-vec` uses with
`serialize_float32()`. This will internally call [`struct.pack()`](https://docs.python.org/3/library/struct.html#struct.pack).

```python
from sqlite_vec import serialize_float32

embedding = [0.1, 0.2, 0.3, 0.4]
result = db.execute('select vec_length(?)', [serialize_float32(embedding)])

print(result.fetchone()[0]) # 4
```

### NumPy Arrays

If your vectors are NumPy arrays, the Python SQLite package allows you to
pass it along as-is, since NumPy arrays implement [the Buffer protocol](https://docs.python.org/3/c-api/buffer.html). Make sure you cast your array elements to 32-bit floats
with
[`.astype(np.float32)`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html),
as some embeddings will use `np.float64`.

```python
import numpy as np
embedding = np.array([0.1, 0.2, 0.3, 0.4])
db.execute(
    "SELECT vec_length(?)", [embedding.astype(np.float32)]
) # 4
```


## Using an up-to-date version of SQLite

Some features of `sqlite-vec` will require an up-to-date SQLite library. You can
see what version of SQLite your Python environment uses with
[`sqlite3.sqlite_version`](https://docs.python.org/3/library/sqlite3.html#sqlite3.sqlite_version),
or with this one-line command:

```bash
python -c 'import sqlite3; print(sqlite3.sqlite_version)'
```

Currently, **SQLite version 3.41 or higher** is recommended but not required.
`sqlite-vec` will work with older versions, but certain features and queries will
only work correctly in >=3.41.

To "upgrade" the SQLite version your Python installation uses, you have a few
options.

### Compile your own SQLite version

You can compile an up-to-date version of SQLite and use some system environment
variables (like `LD_PRELOAD` and `DYLD_LIBRARY_PATH`) to force Python to use a
different SQLite library.
[This guide](https://til.simonwillison.net/sqlite/sqlite-version-macos-python)
goes into this approach in more details.

Although compiling SQLite can be straightforward, there are a lot of different
compilation options to consider, which makes it confusing. This also doesn't
work with Windows, which statically compiles its own SQLite library.

### Use `pysqlite3`

[`pysqlite3`](https://github.com/coleifer/pysqlite3) is a 3rd party PyPi package
that bundles an up-to-date SQLite library as a separate pip package.

While it's mostly compatible with the Python `sqlite3` module, there are a few
rare edge cases where the APIs don't match.

### Upgrading your Python version

Sometimes installing a latest version of Python will "magically" upgrade your
SQLite version as well. This is a nuclear option, as upgrading Python
installations can be quite the hassle, but most Python 3.12 builds will have a
very recent SQLite version.