milvus-lite 2.4.3

A lightweight version of Milvus wrapped with Python.

Introduction

Milvus is an open-source vector database built to power embedding similarity search and AI applications. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment. Milvus Lite is a lightweight version of Milvus that can be embedded into your Python application, integrating the core vector search functionality of Milvus. Please note that it is not suggested to use Milvus Lite in a production environment. Consider using Milvus clustered or the fully managed Milvus on Cloud.

Requirements

Milvus Lite is available in:

• Google Colab
• Jupyter Notebook

Here's also a list of verified OS types where Milvus Lite can successfully build and run:

• Ubuntu >= 20.04 (x86_64)
• MacOS >= 11.0 (Apple Silicon)

Installation

Note that milvus-lite is included in pymilvus since version 2.4.2.

pip install "pymilvus>=2.4.2"

Usage

In pymilvus, specify a local file name as uri parameter of MilvusClient to use Milvus Lite.

from pymilvus import MilvusClient
client = MilvusClient("./milvus_demo.db")

Or with old connections.connect API (not recommended):

from pymilvus import connections
connections.connect(uri="./milvus_demo.db")

Examples

from pymilvus import MilvusClient
import numpy as np

client = MilvusClient("./milvus_demo.db")
client.create_collection(
    collection_name="demo_collection",
    dimension=384  # The vectors we will use in this demo has 384 dimensions
)

# Text strings to search from.
docs = [
    "Artificial intelligence was founded as an academic discipline in 1956.",
    "Alan Turing was the first person to conduct substantial research in AI.",
    "Born in Maida Vale, London, Turing was raised in southern England.",
]
# For illustration, here we use fake vectors with random numbers (384 dimension).

vectors = [[ np.random.uniform(-1, 1) for _ in range(384) ] for _ in range(len(docs)) ]
data = [ {"id": i, "vector": vectors[i], "text": docs[i], "subject": "history"} for i in range(len(vectors)) ]
res = client.insert(
    collection_name="demo_collection",
    data=data
)

# This will exclude any text in "history" subject despite close to the query vector.
res = client.search(
    collection_name="demo_collection",
    data=[vectors[0]],
    filter="subject == 'history'",
    limit=2,
    output_fields=["text", "subject"],
)
print(res)

# a query that retrieves all entities matching filter expressions.
res = client.query(
    collection_name="demo_collection",
    filter="subject == 'history'",
    output_fields=["text", "subject"],
)
print(res)

# delete
res = client.delete(
    collection_name="demo_collection",
    filter="subject == 'history'",
)
print(res)

Contributing

If you want to contribute to Milvus Lite, please read the Contributing Guide first.

Report a bug

When you use or develop milvus-lite, if you find any bug, please report it to us. You could submit an issue in milvus-lite or report you milvus repo if you think it is a Milvus issue.

License

Milvus Lite is under the Apache 2.0 license. See the LICENSE file for details.