隨著 LLM（大型語言模型）和 RAG（檢索增強生成）的興起，像 Milvus 和 Pinecone 這樣的向量資料庫受到了廣泛的關注。傳統資料庫也透過第三方擴充功能在向量搜尋支援方面迎頭趕上，例如用於 PostgreSQL 的 pgvector 和用於 SQLite 的 sqlite-vss。
在本文中，我將介紹 Vectorlite，這是我為 SQLite 編寫的另一個快速且可調的向量搜尋擴展，它剛剛發布了第一個測試版。現在可以使用 pip 安裝
pip install vectorlite-py
SQLite 擴充功能是一個動態函式庫，可以由 SQLite 在執行時載入。以下是在 SQLite CLI shell 中使用 Vectorlite 的範例：
-- Load vectorlite
.load path/to/vectorlite.[so|dll|dylib]
-- shows vectorlite version and build info.
select vectorlite_info();
-- Calculate vector l2(squared) distance
select vector_distance(vector_from_json('[1,2,3]'), vector_from_json('[3,4,5]'), 'l2');
-- Create a virtual table named my_table with one vector column my_embedding with a dimention of 3 using default HNSW parameters.
create virtual table my_table using vectorlite(my_embedding float32[3], hnsw(max_elements=100));
-- Insert vectors into my_table. rowid can be used to relate to a vector's metadata stored elsewhere, e.g. another table.
insert into my_table(rowid, my_embedding) values (0, vector_from_json('[1,2,3]'));
insert into my_table(rowid, my_embedding) values (1, vector_from_json('[2,3,4]'));
insert into my_table(rowid, my_embedding) values (2, vector_from_json('[7,7,7]'));
-- Find 2 approximate nearest neighbors of vector [3,4,5] with distances
select rowid, distance from my_table where knn_search(my_embedding, knn_param(vector_from_json('[3,4,5]'), 2));
-- Find the nearest neighbor of vector [3,4,5] among vectors with rowid 0 and 1. (requires sqlite_version>=3.38)
-- It is called metadata filter in vectorlite, because you could get rowid set beforehand based on vectors' metadata and then perform vector search.
-- Metadata filter is pushed down to the underlying index when traversing the HNSW graph.
select rowid, distance from my_table where knn_search(my_embedding, knn_param(vector_from_json('[3,4,5]'), 1)) and rowid in (0, 1) ;
有關完整的 API 參考，請查看此處。
首先，SQLite 對於 LLM 支援的應用程式來說是很有價值的工具，因為它是輕量級的並且在本地存儲數據，使您的數據更加安全。
雖然已經有 sqlite-vss 可以支援 SQLite 的向量搜索，但它的一些技術決策值得討論，這就是我寫 Vectorlite 的原因。 sqlite-vss 的作者已經轉向另一個向量搜尋擴展項目，並寫了一篇文章從他的角度解釋了 sqlite-vss 的問題所在。我來分享一下我的。
Sqlite-vss 使用 faiss 進行向量搜尋。它是 Meta（facebook）開源的一個很棒的庫，提供了廣泛的向量搜尋演算法。但是，它針對大型資料集上的批次操作進行了最佳化，這使得 CPU 上的單一向量查詢和增量要求速度變慢。但是，SQLite 的可擴充性模型（稱為虛擬表）不提供用於批次作業的 API，僅公開用於一次插入/更新/刪除一行的 API。另外sqlite-vss只支援單向量搜索，faiss不擅長。因此，sqlite-vss 無法充分發揮 faiss 的效能。
Vectorlite 使用 hnswlib，它提供了 HNSW 演算法的快速實現，並針對增量索引建置和單向量查詢進行了最佳化，與 SQLite 的虛擬表 API 配合良好。
在我的基準測試中，與sqlite-vss 相比，Vectorlite 在插入向量方面快了10 倍，在搜尋方面快了2 倍到40 倍（取決於具有速度精度權衡的HNSW 參數），這主要是因為向量搜尋庫的選擇不同.
sqlite-vss 錯過的另一個重要功能是向量元資料過濾。在現實場景中，向量總是被標記一些元數據，包括日期、流派、類別、名稱、類型、內容等。毫無疑問，在執行向量搜尋之前根據其標籤過濾向量是向量搜尋的必備功能。一個功能向量資料庫。
Vectorlite 自首次發布以來就支援向量元資料過濾器（需要 sqlite 版本 >= 3.38）。請檢查這個例子。
sqlite-vss 和 vectorlite 都利用 ANN（近似最近鄰）演算法進行向量搜索，這意味著結果不能保證 100% 正確，但搜索速度相對較快。在生產過程中，需要根據您的工作負載、嵌入和特定需求對 ANN 演算法的效能進行基準測試和調整。例如，一個人可能需要快速向量查詢，但代價是較低的召回率，因為他們正在構建實時語義搜尋應用程序，而另一個人可能需要高召回率，但不關心搜尋速度，因為他們正在做一些離線分析。
In a typical recommendation system, its vector index is often built and tuned offline using libraries like faiss and hnswlib for acceptable speed and accuracy, and then served in production.
With sqlite-vss, you have to build vector index using it, making performance tuning very difficult. One cannot build the vector index offline with faiss and then serve the index with sqlite-vss. Though you do get to pass faiss strings to it to tune the index, non-default indexes are so slow that they barely work (probably due to the nature of the algorithm or improper use of faiss I mentioned above).
With vectorlite, you have full control over the HNSW paramters to tune the search speed and recall rate. Please check this example and the result.
┏━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┓ ┃ distance ┃ vector ┃ ef ┃ ┃ ef ┃ insert_time ┃ search_time ┃ recall ┃ ┃ type ┃ dimension ┃ construction ┃ M ┃ search ┃ per vector ┃ per query ┃ rate ┃ ┡━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━┩ │ l2 │ 256 │ 200 │ 32 │ 10 │ 291.13 us │ 35.70 us │ 31.60% │ │ l2 │ 256 │ 200 │ 32 │ 50 │ 291.13 us │ 99.50 us │ 72.30% │ │ l2 │ 256 │ 200 │ 32 │ 100 │ 291.13 us │ 168.80 us │ 88.60% │ │ l2 │ 256 │ 200 │ 32 │ 150 │ 291.13 us │ 310.53 us │ 95.50% │ │ l2 │ 256 │ 200 │ 48 │ 10 │ 286.92 us │ 37.79 us │ 37.30% │ │ l2 │ 256 │ 200 │ 48 │ 50 │ 286.92 us │ 117.73 us │ 80.30% │ │ l2 │ 256 │ 200 │ 48 │ 100 │ 286.92 us │ 196.01 us │ 93.80% │ │ l2 │ 256 │ 200 │ 48 │ 150 │ 286.92 us │ 259.88 us │ 98.20% │ │ l2 │ 256 │ 200 │ 64 │ 10 │ 285.82 us │ 50.26 us │ 42.60% │ │ l2 │ 256 │ 200 │ 64 │ 50 │ 285.82 us │ 138.83 us │ 84.00% │ │ l2 │ 256 │ 200 │ 64 │ 100 │ 285.82 us │ 253.18 us │ 95.40% │ │ l2 │ 256 │ 200 │ 64 │ 150 │ 285.82 us │ 316.45 us │ 98.70% │ │ l2 │ 1024 │ 200 │ 32 │ 10 │ 1395.02 us │ 158.75 us │ 23.50% │ │ l2 │ 1024 │ 200 │ 32 │ 50 │ 1395.02 us │ 564.27 us │ 60.30% │ │ l2 │ 1024 │ 200 │ 32 │ 100 │ 1395.02 us │ 919.26 us │ 79.30% │ │ l2 │ 1024 │ 200 │ 32 │ 150 │ 1395.02 us │ 1232.40 us │ 88.20% │ │ l2 │ 1024 │ 200 │ 48 │ 10 │ 1489.91 us │ 252.91 us │ 28.50% │ │ l2 │ 1024 │ 200 │ 48 │ 50 │ 1489.91 us │ 848.13 us │ 69.40% │ │ l2 │ 1024 │ 200 │ 48 │ 100 │ 1489.91 us │ 1294.02 us │ 86.80% │ │ l2 │ 1024 │ 200 │ 48 │ 150 │ 1489.91 us │ 1680.97 us │ 94.20% │ │ l2 │ 1024 │ 200 │ 64 │ 10 │ 1412.03 us │ 273.36 us │ 33.30% │ │ l2 │ 1024 │ 200 │ 64 │ 50 │ 1412.03 us │ 899.13 us │ 75.50% │ │ l2 │ 1024 │ 200 │ 64 │ 100 │ 1412.03 us │ 1419.61 us │ 90.10% │ │ l2 │ 1024 │ 200 │ 64 │ 150 │ 1412.03 us │ 1821.85 us │ 96.00% │ │ cosine │ 256 │ 200 │ 32 │ 10 │ 255.22 us │ 28.66 us │ 38.60% │ │ cosine │ 256 │ 200 │ 32 │ 50 │ 255.22 us │ 85.39 us │ 75.90% │ │ cosine │ 256 │ 200 │ 32 │ 100 │ 255.22 us │ 137.31 us │ 91.10% │ │ cosine │ 256 │ 200 │ 32 │ 150 │ 255.22 us │ 190.87 us │ 95.30% │ │ cosine │ 256 │ 200 │ 48 │ 10 │ 259.62 us │ 57.31 us │ 46.60% │ │ cosine │ 256 │ 200 │ 48 │ 50 │ 259.62 us │ 170.54 us │ 84.80% │ │ cosine │ 256 │ 200 │ 48 │ 100 │ 259.62 us │ 221.11 us │ 94.80% │ │ cosine │ 256 │ 200 │ 48 │ 150 │ 259.62 us │ 239.90 us │ 97.90% │ │ cosine │ 256 │ 200 │ 64 │ 10 │ 273.21 us │ 49.34 us │ 48.10% │ │ cosine │ 256 │ 200 │ 64 │ 50 │ 273.21 us │ 139.07 us │ 88.00% │ │ cosine │ 256 │ 200 │ 64 │ 100 │ 273.21 us │ 242.51 us │ 96.30% │ │ cosine │ 256 │ 200 │ 64 │ 150 │ 273.21 us │ 296.21 us │ 98.40% │ │ cosine │ 1024 │ 200 │ 32 │ 10 │ 1192.27 us │ 146.86 us │ 27.40% │ │ cosine │ 1024 │ 200 │ 32 │ 50 │ 1192.27 us │ 451.61 us │ 66.10% │ │ cosine │ 1024 │ 200 │ 32 │ 100 │ 1192.27 us │ 826.40 us │ 83.30% │ │ cosine │ 1024 │ 200 │ 32 │ 150 │ 1192.27 us │ 1199.33 us │ 90.00% │ │ cosine │ 1024 │ 200 │ 48 │ 10 │ 1337.96 us │ 200.14 us │ 33.10% │ │ cosine │ 1024 │ 200 │ 48 │ 50 │ 1337.96 us │ 654.35 us │ 72.60% │ │ cosine │ 1024 │ 200 │ 48 │ 100 │ 1337.96 us │ 1091.57 us │ 88.90% │ │ cosine │ 1024 │ 200 │ 48 │ 150 │ 1337.96 us │ 1429.51 us │ 94.50% │ │ cosine │ 1024 │ 200 │ 64 │ 10 │ 1287.88 us │ 257.67 us │ 38.20% │ │ cosine │ 1024 │ 200 │ 64 │ 50 │ 1287.88 us │ 767.61 us │ 77.00% │ │ cosine │ 1024 │ 200 │ 64 │ 100 │ 1287.88 us │ 1250.36 us │ 92.10% │ │ cosine │ 1024 │ 200 │ 64 │ 150 │ 1287.88 us │ 1699.57 us │ 96.50% │ └──────────┴───────────┴──────────────┴────┴────────┴─────────────┴─────────────┴────────┘
The same benchmark is also run for sqlite-vss using its default index:
┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓ ┃ vector dimension ┃ insert_time(per vector) ┃ search_time(per query) ┃ recall_rate ┃ ┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩ │ 256 │ 3644.42 us │ 1483.18 us │ 55.00% │ │ 1024 │ 18466.91 us │ 3412.92 us │ 52.20% │ └──────────────────┴─────────────────────────┴────────────────────────┴─────────────┘
Vectorlite is not only much faster, but also offers much better recall rate if properly tuned. Besides, you can also build the index using hnswlib directly and serve the index using vectorlite as vectorlite can be considered a thin wrapper around hnswlib with a SQL API.
The quickest way to get started is to install vectorlite using python.
# Note: vectorlite-py not vectorlite. vectorlite is another project. pip install vectorlite-py apsw numpy
Vectorlite's metadata filter feature requires sqlite>=3.38. Python's builtin sqlite module is usually built with old sqlite versions. So apsw is used here as the SQLite driver, as it provides bindings to newer SQLite. Vectorlite still works with old SQLite versions if metadata filter support is not required.
Below is a minimal example of using vectorlite.
import vectorlite_py
import apsw
import numpy as np
"""
Quick start of using vectorlite extension.
"""
conn = apsw.Connection(':memory:')
conn.enable_load_extension(True) # enable extension loading
conn.load_extension(vectorlite_py.vectorlite_path()) # load vectorlite
cursor = conn.cursor()
# check if vectorlite is loaded
print(cursor.execute('select vectorlite_info()').fetchall())
# Vector distance calculation
for distance_type in ['l2', 'cosine', 'ip']:
v1 = "[1, 2, 3]"
v2 = "[4, 5, 6]"
# Note vector_from_json can be used to convert a JSON string to a vector
distance = cursor.execute(f'select vector_distance(vector_from_json(?), vector_from_json(?), "{distance_type}")', (v1, v2)).fetchone()
print(f'{distance_type} distance between {v1} and {v2} is {distance[0]}')
# generate some test data
DIM = 32 # dimension of the vectors
NUM_ELEMENTS = 10000 # number of vectors
data = np.float32(np.random.random((NUM_ELEMENTS, DIM))) # Only float32 vectors are supported by vectorlite for now
# Create a virtual table using vectorlite using l2 distance (default distance type) and default HNSW parameters
cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}], hnsw(max_elements={NUM_ELEMENTS}))')
# Vector distance type can be explicitly set to cosine using:
# cursor.execute(f'create virtual table my_table using vectorlite(my_embedding float32[{DIM}] cosine, hnsw(max_elements={NUM_ELEMENTS}))')
# Insert the test data into the virtual table. Note that the rowid MUST be explicitly set when inserting vectors and cannot be auto-generated.
# The rowid is used to uniquely identify a vector and serve as a "foreign key" to relate to the vector's metadata.
# Vectorlite takes vectors in raw bytes, so a numpy vector need to be converted to bytes before inserting into the table.
cursor.executemany('insert into my_table(rowid, my_embedding) values (?, ?)', [(i, data[i].tobytes()) for i in range(NUM_ELEMENTS)])
# Query the virtual table to get the vector at rowid 12345. Note the vector needs to be converted back to json using vector_to_json() to be human-readable.
result = cursor.execute('select vector_to_json(my_embedding) from my_table where rowid = 1234').fetchone()
print(f'vector at rowid 1234: {result[0]}')
# Find 10 approximate nearest neighbors of data[0] and there distances from data[0].
# knn_search() is used to tell vectorlite to do a vector search.
# knn_param(V, K, ef) is used to pass the query vector V, the number of nearest neighbors K to find and an optional ef parameter to tune the performance of the search.
# If ef is not specified, ef defaults to 10. For more info on ef, please check https://github.com/nmslib/hnswlib/blob/v0.8.0/ALGO_PARAMS.md
result = cursor.execute('select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10))', [data[0].tobytes()]).fetchall()
print(f'10 nearest neighbors of row 0 is {result}')
# Find 10 approximate nearest neighbors of the first embedding in vectors with rowid within [1000, 2000) using metadata(rowid) filtering.
rowids = ','.join([str(rowid) for rowid in range(1000, 2000)])
result = cursor.execute(f'select rowid, distance from my_table where knn_search(my_embedding, knn_param(?, 10)) and rowid in ({rowids})', [data[0].tobytes()]).fetchall()
print(f'10 nearest neighbors of row 0 in vectors with rowid within [1000, 2000) is {result}')
conn.close()
More examples can be found in examples and integration_test folder.
Vectorlite is created with the hope that it could be the go-to vector search solution for SQLite like pgvector for PostgreSQL.
It is still in early stage. Any suggestions are welcome and appreciated.
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