Change absolute links to relative links.

Author: yamasite

site/en/aboutmilvus/overview.md

@@ -51,7 +51,7 @@ As an open source vector similarity search engine, Milvus is easy-to-use, highly
 
 ## Overall architecture
 
-![Milvus architecture](https://raw.githubusercontent.com/milvus-io/docs/master/assets/milvus_arch.png)
+![Milvus architecture](../../../assets/milvus_arch.png)
 
 ## What's next
 

site/en/guides/monitor.md

@@ -27,14 +27,14 @@ Milvus uses Prometheus to store and monitor its metrics, and it uses Grafana for
 
 The following graph shows how Prometheus works in Milvus:
 
-![prometheus](https://raw.githubusercontent.com/milvus-io/docs/v0.7.0/assets/monitoring/monitoring.png)
+![prometheus](../../../assets/monitoring/monitoring.png)
 
 
 - Grafana
 
   Grafana is an open source platform for time-series analytics and used in Milvus to visualize various performance metrics:
 
-  ![dashboard](https://raw.githubusercontent.com/milvus-io/docs/v0.7.0/assets/prometheus.png)
+  ![dashboard](../../../assets/prometheus.png)
 
 
 ### Events to create alert rules

site/en/reference/application.md

@@ -30,7 +30,7 @@ Milvus can be used to build intelligent systems in most AI application scenarios
 
 The application architecture of Milvus as a feature vector search engine is as follows:
 
-![MilvusApplication](https://raw.githubusercontent.com/milvus-io/docs/master/assets/application_arch.png)
+![MilvusApplication](../../../assets/application_arch.png)
 
 Unstructured data (images/videos/texts/audios) are transformed to feature vectors by feature extraction models, and saved to Milvus database. When you input a target vector, it is saved  to the current vector collection, and the search begins, until the most similar vectors are matched, and their IDs returned.
 
@@ -48,7 +48,7 @@ Recommend personalized content based on user persona.
 
 Take personalized advertising content recommendation as an example, the application architecture is:
 
-![Recommendation](https://raw.githubusercontent.com/milvus-io/docs/master/assets/ads_recommend.png)
+![Recommendation](../../../assets/ads_recommend.png)
 
 1. Create user persona by data analysis and key feature extraction
 

site/zh-CN/aboutmilvus/overview.md

@@ -51,7 +51,7 @@ Milvus 是一款开源的特征向量相似度搜索引擎，具有使用方便
 
 ## 整体架构
 
-![Milvus 架构](https://raw.githubusercontent.com/milvus-io/docs/master/assets/milvus_arch.png)
+![Milvus 架构](../../../assets/milvus_arch.png)
 
 ## 接下来您可以
 

site/zh-CN/guides/monitor.md

@@ -27,13 +27,13 @@ Milvus 使用 Prometheus 作为监控和性能指标存储方案，使用 Grafan
 
 其工作流程如下图所示:
 
-![1566787785125](https://raw.githubusercontent.com/milvus-io/docs/v0.7.0/assets/monitoring/monitoring.png)
+![1566787785125](../../../assets/monitoring/monitoring.png)
 
 - Grafana
 
   Grafana 是一个开源的时序数据分析及可视化平台。Milvus 使用 Grafana 来展示各项性能指标：
 
-  ![prometheus.png](https://raw.githubusercontent.com/milvus-io/docs/v0.7.0/assets/prometheus.png)
+  ![prometheus.png](../../../assets/prometheus.png)
 
 
 ### 需要报警的事件

site/zh-CN/reference/application.md

@@ -31,7 +31,7 @@ sidebar_label: Application Scenarios
 
 Milvus 做特征向量检索时典型应用架构如下：
 
-![MilvusApplication](https://raw.githubusercontent.com/milvus-io/docs/master/assets/application_arch_cn.png)
+![MilvusApplication](../../../assets/application_arch_cn.png)
 
 非结构化数据（图像/视频/文字/音频等）首先通过特征提取模型产生特征向量，然后存入Milvus数据库系统。查询的时候，待查询的非结构化数据，也需要通过特征提取模型，提取特征向量。然后用该向量到Milvus中已存入的向量集里，查询匹配度最高的向量集合。最后，使用返回的向量ID，找到对应非结构化数据，结合上层应用，实现对应功能。
 
@@ -49,7 +49,7 @@ Milvus 做特征向量检索时典型应用架构如下：
 
 以个性化广告内容推荐为例，Milvus 实现架构如下：
 
-![Recommendation](https://raw.githubusercontent.com/milvus-io/docs/master/assets/ads_recommend_cn.png)
+![Recommendation](../../../assets/ads_recommend_cn.png)
 
 具体实现步骤为：