# Milvus — Scalable Vector Database for AI at Scale

> Cloud-native vector database built for billion-scale AI search. Milvus offers GPU-accelerated indexing, hybrid search, multi-tenancy, and Kubernetes-native deployment.

## Install

Copy the content below into your project:

## Quick Use

```bash
# Docker Compose (standalone)
curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh | bash
```

```python
from pymilvus import MilvusClient

client = MilvusClient("http://localhost:19530")

# Create collection
client.create_collection(
    collection_name="docs",
    dimension=1536,
)

# Insert vectors
client.insert(
    collection_name="docs",
    data=[
        {"id": 1, "vector": [0.1, 0.2, ...], "text": "AI is transforming software"},
        {"id": 2, "vector": [0.3, 0.4, ...], "text": "Python is popular for ML"},
    ],
)

# Search
results = client.search(
    collection_name="docs",
    data=[[0.15, 0.25, ...]],
    limit=5,
    output_fields=["text"],
)
```

## What is Milvus?

Milvus is a cloud-native vector database designed for billion-scale similarity search. Built in Go and C++, it provides GPU-accelerated indexing, hybrid dense+sparse search, multi-tenancy, and Kubernetes-native deployment. Milvus is the backbone of production AI search at scale — used by companies processing billions of vectors with sub-second latency.

**Answer-Ready**: Milvus is a cloud-native vector database for billion-scale AI search. GPU-accelerated indexing, hybrid search (dense+sparse+full-text), multi-tenancy, and K8s deployment. Used by 10,000+ organizations. Zilliz Cloud for managed hosting. 32k+ GitHub stars.

**Best for**: Enterprise teams needing vector search at massive scale. **Works with**: OpenAI, Cohere, HuggingFace embeddings, LangChain, LlamaIndex. **Setup time**: Under 5 minutes.

## Core Features

### 1. Multiple Index Types

| Index | Best For | Speed |
|-------|----------|-------|
| IVF_FLAT | Small-medium datasets | Good |
| IVF_SQ8 | Memory-efficient | Good |
| HNSW | Low latency | Fastest |
| GPU_IVF_FLAT | GPU-accelerated | Very fast |
| SCANN | Balanced | Very good |

### 2. Hybrid Search

```python
# Dense + Sparse + Full-text in one query
results = client.hybrid_search(
    collection_name="docs",
    reqs=[
        AnnSearchRequest(data=[[0.1, ...]], anns_field="dense_vector", limit=10),
        AnnSearchRequest(data=sparse_vector, anns_field="sparse_vector", limit=10),
    ],
    ranker=RRFRanker(),  # Reciprocal Rank Fusion
    limit=10,
)
```

### 3. Filtering

```python
results = client.search(
    collection_name="docs",
    data=[[0.1, ...]],
    filter='category == "ai" and year >= 2024',
    limit=10,
)
```

### 4. Multi-Tenancy

```python
# Partition key for tenant isolation
client.create_collection(
    collection_name="multi_tenant",
    dimension=1536,
    partition_key_field="tenant_id",
)
```

### 5. Deployment Options

| Mode | Scale | Use Case |
|------|-------|----------|
| Lite (in-process) | Dev/test | Prototyping |
| Standalone | Single node | Small production |
| Distributed | Multi-node K8s | Billion-scale |
| Zilliz Cloud | Managed | Zero-ops production |

## Milvus vs Alternatives

| Feature | Milvus | Qdrant | Pinecone | Weaviate |
|---------|--------|--------|----------|----------|
| Scale | Billions | Millions | Billions | Millions |
| GPU indexing | Yes | No | No | No |
| Hybrid search | Yes | Yes | No | Yes |
| Multi-tenancy | Native | Namespace | Namespace | Class |
| Self-hosted | Yes | Yes | No | Yes |
| Managed cloud | Zilliz | Qdrant Cloud | Yes | WCS |

## FAQ

**Q: How big can it scale?**
A: Billions of vectors across distributed nodes. Zilliz has customers with 10B+ vectors.

**Q: Is there a managed version?**
A: Yes, Zilliz Cloud offers fully managed Milvus with free tier.

**Q: Does it support GPU?**
A: Yes, GPU-accelerated indexing (IVF_FLAT, IVF_PQ) for 10x faster index building.

## Source & Thanks

> Created by [Zilliz](https://github.com/milvus-io). Licensed under Apache 2.0.
>
> [milvus-io/milvus](https://github.com/milvus-io/milvus) — 32k+ stars

<!-- ZH -->

## 快速使用

```bash
curl -sfL https://raw.githubusercontent.com/milvus-io/milvus/master/scripts/standalone_embed.sh | bash
```

一键启动亿级向量搜索数据库。

## 什么是 Milvus？

云原生向量数据库，为十亿级 AI 搜索设计。GPU 加速索引、混合搜索、多租户、K8s 原生部署。

**一句话总结**：云原生向量数据库，十亿级规模，GPU 加速 + 混合搜索 + 多租户 + K8s 部署，10000+ 企业使用，Zilliz Cloud 托管，32k+ stars。

**适合人群**：需要大规模向量搜索的企业团队。

## 核心功能

### 1. 多种索引 — IVF/HNSW/GPU/SCANN
### 2. 混合搜索 — Dense + Sparse + 全文
### 3. 多租户 — Partition Key 隔离

## 常见问题

**Q: 能扩展多大？**
A: 十亿级向量，分布式多节点。

**Q: 有托管版？**
A: Zilliz Cloud，免费层可用。

## 来源与致谢

> [milvus-io/milvus](https://github.com/milvus-io/milvus) — 32k+ stars, Apache 2.0

---
Source: https://tokrepo.com/en/workflows/a614800d-bb1c-4fbe-87c3-d349627362cb
Author: AI Open Source