What is Milvus?
🐦 Milvus is a high-performance vector database built for scale. It powers AI applications by efficiently organizing and searching vast amounts of unstructured data, such as text, images, and multi-modal information.
🧑💻 Written in Go and C++, Milvus implements hardware acceleration for CPU/GPU to achieve best-in-class vector search performance. Thanks to its fully-distributed and K8s-native architecture, Milvus can scale horizontally, handle tens of thousands of search queries on billions of vectors, and keep data fresh with real-time streaming updates. Milvus also supports Standalone mode for single machine deployment. Milvus Lite is a lightweight version good for quickstart in python with pip install.
Want to use Milvus with zero setup? Try out Zilliz Cloud ☁️ for free. Milvus is available as a fully managed service on Zilliz Cloud, with Serverless, Dedicated and BYOC options available.
For questions about how to use Milvus, join the community on Discord to get help. For reporting problems, file bugs and feature requests in GitHub Issues or ask in Discussions.
The Milvus open-source project is
under LF AI & Data Foundation, distributed with Apache 2.0 License, with Zilliz as its major contributor.
Quickstart
$ pip install -U pymilvus
This installs pymilvus, the Python SDK for Milvus. Use MilvusClient to create a client:
from pymilvus import MilvusClient
-
You can also try Milvus Lite for quickstart by installing pymilvus[milvus-lite]. To create a local vector database, simply instantiate a client with a local file name for persisting data:
client = MilvusClient("milvus_demo.db")
-
You can also specify the credentials to connect to your deployed Milvus server or Zilliz Cloud:
client = MilvusClient(
uri="<endpoint_of_self_hosted_milvus_or_zilliz_cloud>",
token="<username_and_password_or_zilliz_cloud_api_key>")
With the client, you can create collection:
client.create_collection(
collection_name="demo_collection",
dimension=768, # The vectors we will use in this demo have 768 dimensions
)
Ingest data:
res = client.insert(collection_name="demo_collection", data=data)
Perform vector search:
query_vectors = embedding_fn.encode_queries(["Who is Alan Turing?", "What is AI?"])
res = client.search(
collection_name="demo_collection", # target collection
data=query_vectors, # a list of one or more query vectors, supports batch
limit=2, # how many results to return (topK)
output_fields=["vector", "text", "subject"], # what fields to return
)
Why Milvus
Milvus is designed to handle vector search at scale. It stores vectors, which are learned representations of unstructured data, together with other scalar data types such as integers, strings, and JSON objects. Users can conduct efficient vector search with metadata filtering or hybrid search. Here are why developers choose Milvus as the vector database for AI applications:
High Performance at Scale and High Availability
- Milvus features a distributed architecture that separates compute and storage. Milvus can horizontally scale and adapt to diverse traffic patterns, achieving optimal performance by independently increasing query nodes for read-heavy workload and data node for write-heavy workload. The stateless microservices on K8s allow quick recovery from failure, ensuring high availability. The support for replicas further enhances fault tolerance and throughput by loading data segments on multiple query nodes. See benchmark for performance comparison.
Support for Various Vector Index Types and Hardware Acceleration
- Milvus separates the system and core vector search engine, allowing it to support all major vector index types that are optimized for different scenarios, including HNSW, IVF, FLAT (brute-force), SCANN, and DiskANN, with quantization-based variations and mmap. Milvus optimizes vector search for advanced features such as metadata filtering and range search. Additionally, Milvus implements hardware acceleration to enhance vector search performance and supports GPU indexing, such as NVIDIA's CAGRA.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
