A pure Python-implemented, lightweight, server-optional, multi-end compatible, vector database deployable locally or remotely.
Project description
A pure Python-implemented, lightweight, server-optional, multi-end compatible, vector database deployable locally or remotely.
⚡ Serverless, simple parameters, simple API.
⚡ Fast, memory-efficient, easily scales to millions of vectors.
⚡ Supports cosine similarity and L2 distance, uses FLAT for exhaustive search or IVF-FLAT for inverted indexing.
⚡ Friendly caching technology stores recently queried vectors for accelerated access.
⚡ Based on a generic Python software stack, platform-independent, highly versatile.
WARNING: MinVectorDB is actively being updated, and API backward compatibility is not guaranteed. You should use version numbers as a strong constraint during deployment to avoid unnecessary feature conflicts and errors. Although our goal is to enable brute force search or inverted indexing on billion-scale vectors, we currently still recommend using it on a scale of millions of vectors or less for the best experience.
MinVectorDB is a vector database implemented purely in Python, designed to be lightweight, serverless, and easy to deploy locally. It offers straightforward and clear Python APIs, aiming to lower the entry barrier for using vector databases. In response to user needs and to enhance its practicality, we are planning to introduce new features, including but not limited to:
- Optimizing Global Search Performance: We are focusing on algorithm and data structure enhancements to speed up searches across the database, enabling faster retrieval of vector data.
- Enhancing Cluster Search with Inverted Indexes: Utilizing inverted index technology, we aim to refine the cluster search process for better search efficiency and precision.
- Refining Clustering Algorithms: By improving our clustering algorithms, we intend to offer more precise and efficient data clustering to support complex queries.
- Facilitating Vector Modifications and Deletions: We will introduce features to modify and delete vectors, allowing for more flexible data management.
- Implementing Rollback Strategies: To increase database robustness and data security, rollback strategies will be added, helping users recover from incorrect operations or system failures easily.
MinVectorDB focuses on achieving 100% recall, prioritizing recall accuracy over high-speed search performance. This approach ensures that users can reliably retrieve all relevant vector data, making MinVectorDB particularly suitable for applications that require responses within hundreds of milliseconds.
While the project has not yet been benchmarked against other systems, we believe these planned features will significantly enhance MinVectorDB's capabilities in managing and retrieving vector data, addressing a wide range of user needs.
Install Client API package (Mandatory)
pip install MinVectorDB
If you wish to use Docker (Optional)
docker pull birchkwok/minvectordb:latest
Qucik Start
import min_vec
print("MinVectorDB version is: ", min_vec.__version__)
MinVectorDB version is: 0.3.2
Initialize Database
MinVectorDB now supports HTTP API and Python local code API.
The HTTP API mode requires starting an HTTP server beforehand. You have two options:
-
start directly.
For direct startup, the default port is 7637. You can run the following command in the terminal to start the service:
min_vec run --host 127.0.0.1 --port 7637
-
within Docker
In Docker, the default port is 5403. You can run the following command in the terminal to start the service:
docker run -p 5403:7637 birchkwok/minvectordb:latest
from min_vec import MinVectorDB
# This method is for the Python local code API, recommended only for CI/CD testing or single-user local use.
# Specify database root directory
my_db = MinVectorDB('my_vec_db') # Judgment condition, root_path does not start with http or https
# or
# Use the HTTP API mode, it is suitable for use in production environments.
# For direct startup
my_db = MinVectorDB("http://127.0.0.1:7637")
# within Docker
my_db = MinVectorDB("http://127.0.0.1:5403")
from min_vec import MinVectorDB
# For direct startup
my_db = MinVectorDB("http://localhost:5403")
create a collection
collection = my_db.require_collection("test_collection", 4, drop_if_exists=True, scaler_bits=8)
Add vectors
When inserting vectors, collection requires manually running the commit
function or inserting within the insert_session
function context manager, which will run the commit
function in the background.
with collection.insert_session():
id = collection.add_item(vector=[0.01, 0.34, 0.74, 0.31], id=1, field={'field': 'test_1', 'order': 0}) # id = 0
id = collection.add_item(vector=[0.36, 0.43, 0.56, 0.12], id=2, field={'field': 'test_1', 'order': 1}) # id = 1
id = collection.add_item(vector=[0.03, 0.04, 0.10, 0.51], id=3, field={'field': 'test_2', 'order': 2}) # id = 2
id = collection.add_item(vector=[0.11, 0.44, 0.23, 0.24], id=4, field={'field': 'test_2', 'order': 3}) # id = 3
id = collection.add_item(vector=[0.91, 0.43, 0.44, 0.67], id=5, field={'field': 'test_2', 'order': 4}) # id = 4
id = collection.add_item(vector=[0.92, 0.12, 0.56, 0.19], id=6, field={'field': 'test_3', 'order': 5}) # id = 5
id = collection.add_item(vector=[0.18, 0.34, 0.56, 0.71], id=7, field={'field': 'test_1', 'order': 6}) # id = 6
id = collection.add_item(vector=[0.01, 0.33, 0.14, 0.31], id=8, field={'field': 'test_2', 'order': 7}) # id = 7
id = collection.add_item(vector=[0.71, 0.75, 0.91, 0.82], id=9, field={'field': 'test_3', 'order': 8}) # id = 8
id = collection.add_item(vector=[0.75, 0.44, 0.38, 0.75], id=10, field={'field': 'test_1', 'order': 9}) # id = 9
# If you do not use the insert_session function, you need to manually call the commit function to submit the data
# collection.commit()
# or use the bulk_add_items function
# with collection.insert_session():
# ids = collection.bulk_add_items([([0.01, 0.34, 0.74, 0.31], 0, {'field': 'test_1', 'order': 0}),
# ([0.36, 0.43, 0.56, 0.12], 1, {'field': 'test_1', 'order': 1}),
# ([0.03, 0.04, 0.10, 0.51], 2, {'field': 'test_2', 'order': 2}),
# ([0.11, 0.44, 0.23, 0.24], 3, {'field': 'test_2', 'order': 3}),
# ([0.91, 0.43, 0.44, 0.67], 4, {'field': 'test_2', 'order': 4}),
# ([0.92, 0.12, 0.56, 0.19], 5, {'field': 'test_3', 'order': 5}),
# ([0.18, 0.34, 0.56, 0.71], 6, {'field': 'test_1', 'order': 6}),
# ([0.01, 0.33, 0.14, 0.31], 7, {'field': 'test_2', 'order': 7}),
# ([0.71, 0.75, 0.91, 0.82], 8, {'field': 'test_3', 'order': 8}),
# ([0.75, 0.44, 0.38, 0.75], 9, {'field': 'test_1', 'order': 9})])
# print(ids) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
Query
collection.query(vector=[0.36, 0.43, 0.56, 0.12], k=10)
(array([ 2, 9, 1, 4, 6, 5, 10, 7, 8, 3]),
array([1. , 0.92355633, 0.86097705, 0.85727406, 0.81551266,
0.813797 , 0.78595245, 0.7741583 , 0.6871773 , 0.34695023]))
print(collection.query_report_)
* - MOST RECENT QUERY REPORT -
| - Collection Shape: (10, 4)
| - Query Time: 0.20518 s
| - Query Distance: cosine
| - Query K: 10
| - Top 10 Results ID: [ 2 9 1 4 6 5 10 7 8 3]
| - Top 10 Results Similarity: [1. 0.92355633 0.86097705 0.85727406 0.81551266 0.813797
0.78595245 0.7741583 0.6871773 0.34695023]
* - END OF REPORT -
Use Filter
import operator
from min_vec.structures.filter import Filter, FieldCondition, MatchField, IDCondition, MatchID
collection.query(
vector=[0.36, 0.43, 0.56, 0.12],
k=10,
query_filter=Filter(
must=[
FieldCondition(key='field', matcher=MatchField('test_1')), # Support for filtering fields
],
any=[
FieldCondition(key='order', matcher=MatchField(8, comparator=operator.ge)),
IDCondition(MatchID([1, 2, 3, 4, 5])), # Support for filtering IDs
]
)
)
print(collection.query_report_)
* - MOST RECENT QUERY REPORT -
| - Collection Shape: (10, 4)
| - Query Time: 0.11985 s
| - Query Distance: cosine
| - Query K: 10
| - Top 10 Results ID: [ 2 1 4 5 10 3]
| - Top 10 Results Similarity: [1. 0.86097705 0.85727406 0.813797 0.78595245 0.34695023]
* - END OF REPORT -
Drop a collection
print("Collection list before dropping:", my_db.show_collections())
my_db.drop_collection("test_collection")
print("Collection list after dropped:", my_db.show_collections())
Collection list before dropping: ['test_collection']
Collection list after dropped: []
Drop the database
my_db.drop_database()
my_db
MinVectorDB remote server at http://localhost:5403 does not exist.
my_db.database_exists()
{'status': 'success', 'params': {'exists': False}}
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