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Merge pull request #86 from milvus-io/0.5.0

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![Milvuslogo](https://github.com/milvus-io/docs/blob/0.5.0/assets/milvus_logo.png)
![Milvuslogo](https://github.com/milvus-io/docs/blob/master/assets/milvus_logo.png)
![LICENSE](https://img.shields.io/badge/license-Apache--2.0-brightgreen)
![Language](https://img.shields.io/badge/language-C%2B%2B-blue)
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# Welcome to Milvus
Firstly, welcome, and thanks for your interest in [Milvus](https://milvus.io)! ​​No matter who you are, what you do, we greatly appreciate your contribution to help us reinvent data science with Milvus.​ :beers:
## What is Milvus
Milvus is an open source vector search engine which provides state-of-the-art similarity search and analysis for billion-scale feature vectors.
Milvus is an open source similarity search engine for massive feature vectors. Designed with heterogeneous computing architecture for the best cost efficiency. Searches over billion-scale vectors take only milliseconds with minimum computing resources.
Milvus provides stable Python, Java and C++ APIs.
Keep up-to-date with newest releases and latest updates by reading Milvus [release notes](https://milvus.io/docs/en/Releases/v0.4.0/).
Keep up-to-date with newest releases and latest updates by reading Milvus [release notes](https://milvus.io/docs/en/Releases/v0.5.0/).
- GPU-accelerated search engine
- Heterogeneous computing
Milvus uses CPU/GPU heterogeneous computing architecture to process feature vectors, and are orders of magnitudes faster than traditional databases.
Milvus is designed with heterogeneous computing architecture for the best performance and cost efficiency.
- Various indexes
- Multiple indexes
Milvus supports quantization indexing, tree-based indexing, and graph indexing algorithms.
Milvus supports a variety of indexing types that employs quantization, tree-based, and graph indexing techniques.
- Intelligent scheduling
- Intelligent resource management
Milvus optimizes the search computation and index building according to your data size and available resources.
Milvus automatically adapts search computation and index building processes based on your datasets and available resources.
- Horizontal scalability
Milvus expands computation and storage by adding nodes during runtime, which allows you to scale the data size without redesigning the system.
Milvus supports online / offline expansion to scale both storage and computation resources with simple commands.
- High availability
Milvus is integrated with Kubernetes framework so that all single point of failures could be avoided.
- High compatibility
Milvus is compatible with almost all deep learning models and major programming languages such as Python, Java and C++, etc.
- Ease of use
Milvus can be easily installed in a few steps and enables you to exclusively focus on feature vectors.
- Visualized monitor
You can track system performance on Prometheus-based GUI monitor dashboards.
## Architecture
![Milvus_arch](https://github.com/milvus-io/docs/blob/0.5.0/assets/milvus_arch.jpg)
![Milvus_arch](https://github.com/milvus-io/docs/blob/master/assets/milvus_arch.png)
## Get started
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#### Run Python example code
Make sure [Python 3.4](https://www.python.org/downloads/) or higher is already installed and in use.
Make sure [Python 3.5](https://www.python.org/downloads/) or higher is already installed and in use.
Install Milvus Python SDK.
```shell
# Install Milvus Python SDK
$ pip install pymilvus==0.2.0
$ pip install pymilvus==0.2.3
```
Create a new file `example.py`, and add [Python example code](https://github.com/milvus-io/pymilvus/blob/master/examples/AdvancedExample.py) to it.
Run the example code.
```python
```shell
# Run Milvus Python example
$ python3 example.py
```