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<img src="https://repository-images.githubusercontent.com/208728772/998c09ca-cfa6-4c01-ac75-3dfad7f4862b" alt="milvus banner">
<img src="https://github.com/user-attachments/assets/51e33300-7f85-43ff-a05a-3a0317a961f3" alt="milvus banner">
<div class="column" align="middle">
<a href="https://discord.com/invite/8uyFbECzPX"><img height="20" src="https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white" alt="license"/></a>
<img src="https://img.shields.io/github/license/milvus-io/milvus" alt="license"/>
<img src="https://img.shields.io/docker/pulls/milvusdb/milvus" alt="docker-pull-count" />
<a href="https://github.com/milvus-io/milvus/blob/master/LICENSE"><img height="20" src="https://img.shields.io/github/license/milvus-io/milvus" alt="license"/></a>
<a href="https://milvus.io/docs/install_standalone-docker.md"><img src="https://img.shields.io/docker/pulls/milvusdb/milvus" alt="docker-pull-count"/></a>
<a href="https://discord.com/invite/8uyFbECzPX"><img height="20" src="https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white" alt="discord"/></a>
</div>
## What is Milvus?
<img src="https://github.com/milvus-io/artwork/blob/master/horizontal/color/milvus-horizontal-color.png" alt="milvus-logo"/>
[Milvus](https://milvus.io/) is a high-performance vector database built for scale. It is used by AI applications to organize and search through large amount of unstructured data, such as text, images, and multi-modal information.
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 is implemented with Go and C++ and employs CPU/GPU instruction-level optimization for best vector search performance. With [fully-distributed architecture on K8s](https://milvus.io/docs/overview.md#What-Makes-Milvus-so-Scalable), it can handle tens of thousands of search queries on billions of vectors, scale horizontally and maintain data freshness by processing streaming updates in real-time. For smaller use cases, Milvus supports [Standalone mode](https://milvus.io/docs/install_standalone-docker.md) that can run on Docker. In addition, [Milvus Lite](https://milvus.io/docs/milvus_lite.md) is a lightweight version suitable for quickstart in python, with simply `pip install`.
Milvus 2.0 is a cloud-native vector database with storage and computation separated by design. All components in this refactored version of Milvus are stateless to enhance elasticity and flexibility. For more architecture details, see [Milvus Architecture Overview](https://milvus.io/docs/architecture_overview.md).
The easiest way to try out Milvus is to use [Zilliz Cloud with free trial](https://cloud.zilliz.com/signup). Milvus is available as a fully managed service on [Zilliz Cloud](https://zilliz.com/cloud), with Serverless, Dedicated and BYOC options available.
Milvus was released under the [open-source Apache License 2.0](https://github.com/milvus-io/milvus/blob/master/LICENSE) in October 2019. It is currently a graduate project under [LF AI & Data Foundation](https://lfaidata.foundation/).
The Milvus open-source project is
under [LF AI & Data Foundation](https://lfaidata.foundation/projects/milvus/), distributed with [Apache 2.0](https://github.com/milvus-io/milvus/blob/master/LICENSE) License.
## Key features
## Quickstart
<details>
<summary><b>Millisecond search on trillion vector datasets</b></summary>
Average latency measured in milliseconds on trillion vector datasets.
</details>
<details>
<summary><b>Simplified unstructured data management</b></summary>
<li>Rich APIs designed for data science workflows.</li><li>Consistent user experience across laptop, local cluster, and cloud.</li><li>Embed real-time search and analytics into virtually any application.</li>
</details>
<details>
<summary><b>Reliable, always on vector database</b></summary>
Milvus’ built-in replication and failover/failback features ensure data and applications can maintain business continuity in the event of a disruption.
</details>
<details>
<summary><b>Highly scalable and elastic</b></summary>
Component-level scalability makes it possible to scale up and down on demand. Milvus can autoscale at a component level according to the load type, making resource scheduling much more efficient.
</details>
<details>
<summary><b>Hybrid search</b></summary>
Since Milvus 2.4, we introduced multi-vector support and a hybrid search framework, which means users can bring in several vector fields (up to 10) into a single collection. These vectors in different columns represent diverse facets of data, originating from different embedding models or undergoing distinct processing methods. The results of hybrid searches are integrated using reranking strategies, such as Reciprocal Rank Fusion (RRF) and Weighted Scoring.
This feature is particularly useful in comprehensive search scenarios, such as identifying the most similar person in a vector library based on various attributes like pictures, voice, fingerprints, etc. For details, refer to [Hybrid Search](https://milvus.io/docs/multi-vector-search.md) for more.
</details>
<details>
<summary><b>Unified Lambda structure</b></summary>
Milvus combines stream and batch processing for data storage to balance timeliness and efficiency. Its unified interface makes vector similarity search a breeze.
</details>
<details>
<summary><b>Community supported, industry recognized</b></summary>
With over 1,000 enterprise users, 27,000+ stars on GitHub, and an active open-source community, you’re not alone when you use Milvus. As a graduate project under the <a href="https://lfaidata.foundation/">LF AI & Data Foundation</a>, Milvus has institutional support.
</details>
## Quick start
### Start with Zilliz Cloud
Zilliz Cloud is a fully managed service on cloud and the simplest way to deploy LF AI Milvus®, See [Zilliz Cloud](https://zilliz.com/) and start your [free trial](https://cloud.zilliz.com/signup).
### Install Milvus
- [Standalone Quick Start Guide](https://milvus.io/docs/install_standalone-docker.md)
- [Cluster Quick Start Guide](https://milvus.io/docs/install_cluster-docker.md)
- [Advanced Deployment](https://github.com/milvus-io/milvus/wiki)
### Build Milvus from source code
Check the requirements first.
Linux systems (Ubuntu 20.04 or later recommended):
```bash
go: >= 1.21
cmake: >= 3.26.4
gcc: 9.5
python: > 3.8 and <= 3.11
```python
$ pip install -U pymilvus
```
This installs `pymilvus`, the Python SDK for Milvus. Use `MilvusClient` to create a client:
```python
from pymilvus import MilvusClient
```
MacOS systems with x86_64 (Big Sur 11.5 or later recommended):
```bash
go: >= 1.21
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and <= 3.11
* `pymilvus` also includes Milvus Lite for quickstart. To create a local vector database, simply instantiate a client with a local file name for persisting data:
```python
client = MilvusClient("milvus_demo.db")
```
* You can also specify the credentials to connect to your deployed [Milvus server](https://milvus.io/docs/authenticate.md?tab=docker) or [Zilliz Cloud](https://docs.zilliz.com/docs/quick-start):
```python
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:
```python
client.create_collection(
collection_name="demo_collection",
dimension=768, # The vectors we will use in this demo has 768 dimensions
)
```
MacOS systems with Apple Silicon (Monterey 12.0.1 or later recommended):
```bash
go: >= 1.21 (Arch=ARM64)
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and <= 3.11
Ingest data:
```python
res = client.insert(collection_name="demo_collection", data=data)
```
Clone Milvus repo and build.
Perform vector search:
```bash
# Clone github repository.
$ git clone https://github.com/milvus-io/milvus.git
# Install third-party dependencies.
$ cd milvus/
$ ./scripts/install_deps.sh
# Compile Milvus.
$ make
```python
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
)
```
For the full story, see [developer's documentation](https://github.com/milvus-io/milvus/blob/master/DEVELOPMENT.md).
## Why Milvus
> **IMPORTANT** The master branch is for the development of Milvus v2.0. On March 9th, 2021, we released Milvus v1.0, the first stable version of Milvus with long-term support. To use Milvus v1.0, switch to [branch 1.0](https://github.com/milvus-io/milvus/tree/1.0).
Milvus is designed to handle vector search at scale. Users can store vectors, which are numerical representations of unstructured data, together with other scalar data types such as integers, strings, and JSON objects, to conduct efficient vector search with metadata filtering or hybrid search. Here are why users choose Milvus as vector database:
## Milvus 2.0 vs. 1.x: Cloud-native, distributed architecture, highly scalable, and more
**High Performance at Scale and High Availability**
* Milvus features a [distributed architecture](https://milvus.io/docs/architecture_overview.md ) that separates [compute](https://milvus.io/docs/data_processing.md#Data-query) and [storage](https://milvus.io/docs/data_processing.md#Data-insertion). 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](https://milvus.io/docs/coordinator_ha.md#Coordinator-HA) from failure, ensuring high availability. The support for [replicas](https://milvus.io/docs/replica.md) further enhances fault tolerance and throughput by loading data segments on multiple query nodes. See [benchmark](https://zilliz.com/vector-database-benchmark-tool) for performance comparison.
See [Milvus 2.0 vs. 1.x](https://github.com/milvus-io/milvus/blob/master/milvus20vs1x.md) for more information.
## Real world demos
**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](https://milvus.io/docs/index.md?tab=floating#IVFPQ) variations and [mmap](https://milvus.io/docs/mmap.md). Milvus optimizes vector search for advanced features such as [metadata filtering](https://milvus.io/docs/scalar_index.md#Scalar-Index) and [range search](https://milvus.io/docs/single-vector-search.md#Range-search). Additionally, Milvus implements hardware acceleration to enhance vector search performance and supports GPU indexing, such as NVIDIA's [CAGRA](https://github.com/rapidsai/raft).
**Flexible Multi-tenancy and Hot/Cold Storage**
* Milvus supports [multi-tenancy](https://milvus.io/docs/multi_tenancy.md#Multi-tenancy-strategies) with flexible strategies for organizing data in AI applications such as Retrieval-Augmented Generation (RAG). By using databases, collections, partitions, and partition keys, Milvus can handle hundreds to millions of tenants in a single instance. This helps businesses to save resources while handling many tenant, ensuring data isolation, optimized search performance, and flexible access control. Incorporating hot/cold data storage further enhances cost efficiency and performance. Users can config storing frequently accessed hot data on memory or SSD for better performance while less accessed cold data is kept on cost-effective, slower storage. This separation optimizes resource allocation, reduces costs, and maintains high performance for critical tasks. By combining flexible multi-tenancy with hot/cold storage, Milvus helps businesses scale, optimize resources, and manage data efficiently, leading to significant cost savings while still keep high performance.
**Sparse Vector for Full Text Search and Hybrid Search**
* Milvus supports full text search with [sparse vector](https://milvus.io/docs/sparse_vector.md). Users can combine sparse vector and dense vector in the same collection, and define functions to rerank results from multiple search requests. For details, refer to [Hybrid Search](https://milvus.io/docs/multi-vector-search.md).
**Data Security and Fine-grain Access Control**
* Milvus ensures data security by implementing mandatory user authentication, TLS encryption, and Role-Based Access Control (RBAC). User authentication ensures that only authorized users with valid credentials can access the database, while TLS encryption secures all communications within the network. Additionally, RBAC allows for fine-grained access control by assigning specific permissions to users based on their roles. These features make Milvus a robust and secure choice for enterprise applications, protecting sensitive data from unauthorized access and potential breaches.
Milvus is trusted by AI developers to build applications such as text and image search, Retrieval-Augmented Generation (RAG), and recommendation systems. Milvus powers [many mission-critical business]((https://milvus.io/use-cases)) for startups and enterprises.
## Demos and Tutorials
Here is a selection of demos and tutorials to show how to build various types of AI applications made with Milvus:
| Tutorial | Use Case | Related Milvus Features |
| -------- | -------- | --------- |
| [Build RAG with Milvus](build-rag-with-milvus.md) | RAG | vector search |
| [Multimodal RAG with Milvus](multimodal_rag_with_milvus.md) | RAG | vector search, dynamic field |
| [Image Search with Milvus](image_similarity_search.md) | Semantic Search | vector search, dynamic field |
| [Hybrid Search with Milvus](hybrid_search_with_milvus.md) | Hybrid Search | hybrid search, multi vector, dense embedding, sparse embedding |
| [Multimodal Search using Multi Vectors](multimodal_rag_with_milvus.md) | Semantic Search | multi vector, hybrid search |
| [Recommender System](recommendation_system.md) | Recommendation System | vector search |
| [Video Similarity Search](video_similarity_search.md) | Semantic Search | vector search |
| [Audio Similarity Search](audio_similarity_search.md) | Semantic Search | vector search |
| [DNA Classification](dna_sequence_classification.md) | Classification | vector search |
| [Graph RAG with Milvus](graph_rag_with_milvus.md) | RAG | vector search |
| [Contextual Retrieval with Milvus](contextual_retrieval_with_milvus.md) | RAG, Semantic Search | vector search |
| [HDBSCAN Clustering with Milvus](hdbscan_clustering_with_milvus.md) | Clustering | vector search |
| [Use ColPali for Multi-Modal Retrieval with Milvus](use_ColPali_with_milvus.md) | RAG, Semantic Search | vector search |
| [Vector Visualization](vector_visualization.md) | Data Visualization | vector search |
<table>
<tr>
@ -140,479 +129,73 @@ See [Milvus 2.0 vs. 1.x](https://github.com/milvus-io/milvus/blob/master/milvus2
</tr>
<tr>
<th>
<a href="https://milvus.io/milvus-demos">Image search</a>
<a href="https://milvus.io/milvus-demos">Image Search</a>
</th>
<th>
<a href="https://milvus.io/milvus-demos">Chatbots</a>
<a href="https://milvus.io/milvus-demos">RAG</a>
</th>
<th>
<a href="https://milvus.io/milvus-demos">Chemical structure search</a>
<a href="https://milvus.io/milvus-demos">Drug Discovery</a>
</th>
</tr>
</table>
#### Image Search
## Ecosystem and Integration
Milvus integrates with a comprehensive suite of [AI development tools](https://milvus.io/docs/integrations_overview.md), such as LangChain, LlamaIndex, OpenAI and HuggingFace, making it an ideal vector store for GenAI applications such as Retrieval-Augmented Generation (RAG). Milvus works with both open-source embedding models and embedding service, in text, image and video modalities. Milvus also provides a convenient util [`pymilvus[model]`](https://milvus.io/docs/embeddings.md), users can use the simple wrapper code to transform unstructured data into vector embeddings and leverage reranking models for optimized search results. The Milvus ecosystem also includes [Attu](https://github.com/zilliztech/attu?tab=readme-ov-file#attu) for GUI-based administration, [Birdwatcher](https://milvus.io/docs/birdwatcher_overview.md) for system debugging, [Prometheus/Grafana](https://milvus.io/docs/monitor_overview.md) for monitoring, [Milvus CDC](https://milvus.io/docs/milvus-cdc-overview.md) for data synchronization, [VTS](https://github.com/zilliztech/vts?tab=readme-ov-file#vts) for data migration and data connectors for [Spark](https://milvus.io/docs/integrate_with_spark.md#Spark-Milvus-Connector-User-Guide), [Kafka](https://github.com/zilliztech/kafka-connect-milvus?tab=readme-ov-file#kafka-connect-milvus-connector), [Fivetran](https://fivetran.com/docs/destinations/milvus), and [Airbyte](https://milvus.io/docs/integrate_with_airbyte.md) to build search pipelines.
Images made searchable. Instantaneously return the most similar images from a massive database.
#### Chatbots
Interactive digital customer service that saves users time and businesses money.
#### Chemical Structure Search
Blazing fast similarity search, substructure search, or superstructure search for a specified molecule.
## Bootcamps
Milvus [bootcamp](https://github.com/milvus-io/bootcamp) is designed to expose users to both the simplicity and depth of the vector database. Discover how to run benchmark tests as well as build similarity search applications spanning chatbots, recommendation systems, reverse image search, molecular search, and much more.
## Contributing
Contributions to Milvus are welcome from everyone. See [Guidelines for Contributing](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) for details on submitting patches and the contribution workflow. See our [community repository](https://github.com/milvus-io/community) to learn about our governance and access more community resources.
### All contributors
<br><!-- Do not remove start of hero-bot -->
<img src="https://img.shields.io/badge/all--contributors-415-orange"><br>
<a href="https://github.com/0xflotus"><img src="https://avatars.githubusercontent.com/u/26602940?v=4" width="30px" /></a>
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Check out https://milvus.io/docs/integrations_overview.md for more details.
## Documentation
For guidance on installation, development, deployment, and administration, check out [Milvus Docs](https://milvus.io/docs). For technical milestones and enhancement proposals, check out [milvus confluence](https://wiki.lfaidata.foundation/display/MIL/Milvus+Home)
For guidance on installation, usage, deployment, and administration, check out [Milvus Docs](https://milvus.io/docs). For technical milestones and enhancement proposals, check out [issues on GitHub](https://github.com/milvus-io/milvus/issues).
### SDK
## Contributing
The implemented SDK and its API documentation are listed below:
The Milvus open-source project accepts contribution from everyone. See [Guidelines for Contributing](https://github.com/milvus-io/milvus/blob/master/CONTRIBUTING.md) for details on submitting patches and the development workflow. See our [community repository](https://github.com/milvus-io/community) to learn about project governance and access more community resources.
- [PyMilvus SDK](https://github.com/milvus-io/pymilvus)
- [Java SDK](https://github.com/milvus-io/milvus-sdk-java)
- [Go SDK](https://github.com/milvus-io/milvus-sdk-go)
- [Cpp SDK](https://github.com/milvus-io/milvus-sdk-cpp)(under development)
- [Node SDK](https://github.com/milvus-io/milvus-sdk-node)
- [Rust SDK](https://github.com/milvus-io/milvus-sdk-rust)(under development)
- [CSharp SDK](https://github.com/milvus-io/milvus-sdk-csharp)(under development)
### Build Milvus from Source Code
### Attu
Requirements:
Attu provides an intuitive and efficient GUI for Milvus.
* Linux systems (Ubuntu 20.04 or later recommended):
```bash
go: >= 1.21
cmake: >= 3.26.4
gcc: 9.5
python: > 3.8 and <= 3.11
```
- [Quick start](https://github.com/zilliztech/milvus-insight#quick-start)
* MacOS systems with x86_64 (Big Sur 11.5 or later recommended):
```bash
go: >= 1.21
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and <= 3.11
```
* MacOS systems with Apple Silicon (Monterey 12.0.1 or later recommended):
```bash
go: >= 1.21 (Arch=ARM64)
cmake: >= 3.26.4
llvm: >= 15
python: > 3.8 and <= 3.11
```
Clone Milvus repo and build.
```bash
# Clone github repository.
$ git clone https://github.com/milvus-io/milvus.git
# Install third-party dependencies.
$ cd milvus/
$ ./scripts/install_deps.sh
# Compile Milvus.
$ make
```
For full instructions, see [developer's documentation](https://github.com/milvus-io/milvus/blob/master/DEVELOPMENT.md).
## Community
@ -628,9 +211,10 @@ Subscribe to Milvus mailing lists:
Follow Milvus on social media:
- [Medium](https://medium.com/@milvusio)
- [X](https://twitter.com/milvusio)
- [LinkedIn](https://www.linkedin.com/company/the-milvus-project)
- [Youtube](https://www.youtube.com/channel/UCMCo_F7pKjMHBlfyxwOPw-g)
- [Medium](https://medium.com/@milvusio)
## Reference
@ -656,20 +240,3 @@ Reference to cite when you use Milvus in a research paper:
publisher={VLDB Endowment}
}
```
## Acknowledgments
Milvus adopts dependencies from the following:
- Thanks to [FAISS](https://github.com/facebookresearch/faiss) for the excellent search library.
- Thanks to [etcd](https://github.com/coreos/etcd) for providing great open-source key-value store tools.
- Thanks to [Pulsar](https://github.com/apache/pulsar) for its wonderful distributed pub-sub messaging system.
- Thanks to [Tantivy](https://github.com/quickwit-oss/tantivy) for its full-text search engine library written in Rust.
- Thanks to [RocksDB](https://github.com/facebook/rocksdb) for the powerful storage engines.
Milvus is adopted by following opensource project:
- [Towhee](https://github.com/towhee-io/towhee) a flexible, application-oriented framework for computing embedding vectors over unstructured data.
- [Haystack](https://github.com/deepset-ai/haystack) an open source NLP framework that leverages Transformer models
- [Langchain](https://github.com/hwchase17/langchain) Building applications with LLMs through composability
- [LLamaIndex](https://github.com/run-llama/llama_index) a data framework for your LLM applications
- [GPTCache](https://github.com/zilliztech/GPTCache) a library for creating semantic cache to store responses from LLM queries.