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159 lines
5.2 KiB
Markdown
159 lines
5.2 KiB
Markdown
---
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title: sqlite-vec in Python
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---
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# Using `sqlite-vec` in Python
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[](https://pypi.org/project/sqlite-vec/)
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To use `sqlite-vec` from Python, install the
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[`sqlite-vec` PyPi package](https://pypi.org/project/sqlite-vec/) using your
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favorite Python package manager:
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```bash
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pip install sqlite-vec
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```
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Once installed, use the `sqlite_vec.load()` function to load `sqlite-vec` SQL
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functions into a SQLite connection.
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```python
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import sqlite3
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import sqlite_vec
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db = sqlite3.connect(":memory:")
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db.enable_load_extension(True)
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sqlite_vec.load(db)
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db.enable_load_extension(False)
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vec_version, = db.execute("select vec_version()").fetchone()
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print(f"vec_version={vec_version}")
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```
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## Working with Vectors
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### Lists
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If the vectors you are working with are provided as a list of floats, you can convert them into the compact BLOB format that `sqlite-vec` uses with [`struct.pack()`](https://docs.python.org/3/library/struct.html#struct.pack).
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```python
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import struct
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def serialize(vector: List[float]) -> bytes:
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""" serializes a list of floats into a compact "raw bytes" format """
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return struct.pack('%sf' % len(vector), *vector)
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embedding = [0.1, 0.2, 0.3, 0.4]
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result = db.execute('select vec_length(?)', [serialize(embedding)]).fetchone()[0]
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print(result) # 4
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```
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### NumPy Arrays
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If your vectors are from `numpy` arrays, the Python SQLite package allows you to pass it along as-is. Make sure you convert your array elements to 32-bit floats with [`.astype(np.float32)`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html), as some embedding services will use `np.float64` elements.
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```python
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import numpy as np
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import sqlite3
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import sqlite_vec
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db = sqlite3.connect(":memory:")
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db.enable_load_extension(True)
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sqlite_vec.load(db)
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db.enable_load_extension(False)
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db.execute("CREATE VIRTUAL TABLE vec_demo(sample_embedding float[4])")
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embedding = np.array([0.1, 0.2, 0.3, 0.4])
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db.execute(
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"INSERT INTO vec_demo(sample_embedding) VALUES (?)", [embedding.astype(np.float32)]
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)
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```
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## Recipes
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### OpenAI
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https://platform.openai.com/docs/guides/embeddings/what-are-embeddings?lang=python
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TODO
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```python
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from openai import OpenAI
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import sqlite3
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import sqlite_vec
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texts = [
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'Capri-Sun is a brand of juice concentrate–based drinks manufactured by the German company Wild and regional licensees.',
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'Shohei Ohtani is a Japanese professional baseball pitcher and designated hitter for the Los Angeles Dodgers of Major League Baseball.',
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'George V was King of the United Kingdom and the British Dominions, and Emperor of India, from 6 May 1910 until his death in 1936.',
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'Alan Mathison Turing was an English mathematician, computer scientist, logician, cryptanalyst, philosopher and theoretical biologist.',
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'Alaqua Cox is a Native American (Menominee) actress.'
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]
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# change ':memory:' to a filepath to persist data
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db = sqlite3.connect(':memory:')
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db.enable_load_extension(True)
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sqlite_vec.load(db)
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db.enable_load_extension(False)
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client = OpenAI()
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response = client.embeddings.create(
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input=[texts],
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model="text-embedding-3-small"
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)
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print(response.data[0].embedding)
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```
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### llamafile
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https://github.com/Mozilla-Ocho/llamafile
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TODO
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### llama-cpp-python
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https://github.com/abetlen/llama-cpp-python
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TODO
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### sentence-transformers (etc.)
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https://github.com/UKPLab/sentence-transformers
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TODO
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## Using an up-to-date version of SQLite
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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:
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```bash
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python -c 'import sqlite3; print(sqlite3.sqlite_version)'
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```
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Currently, **SQLite version 3.41 or higher** is recommended but not required. `sqlite-vec` will work with older version, but certain features and queries will only work correctly in >=3.41.
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To "upgrade" the SQLite version your Python installation uses, you have a few options.
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### Compile your own SQLite version
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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.
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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.
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### Use `pysqlite3`
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[`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.
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While it's mostly compatible with the Python `sqlite3` module, there are a few rare edge cases where the APIs don't match.
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### Upgrading your Python version
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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.
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