add document detail

docker/Dockerfile_tf210

@@ -27,6 +27,7 @@ RUN pip3 install -r /EasyRec/requirements/runtime.txt   -i http://mirrors.aliyun
 RUN pip3 install -r /EasyRec/requirements/extra.txt   -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
 RUN pip3 install https://easyrec.oss-cn-beijing.aliyuncs.com/3rdparty/graphlearn-1.1.0-cp38-cp38-linux_x86_64.whl -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
 # RUN pip3 install http://easyrec.oss-cn-beijing.aliyuncs.com/releases/pai_automl-0.0.1rc1-py3-none-any.whl -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
+RUN pip3 install tensorflow_probability==0.18.0
 RUN pip3 install https://dlc-task.oss-cn-hangzhou.aliyuncs.com/whl/common_io-0.4.1%2Btunnel-py2.py3-none-any.whl -i http://mirrors.aliyun.com/pypi/simple --trusted-host mirrors.aliyun.com
 RUN cd /EasyRec && python setup.py install
 RUN rm -rf /EasyRec

docs/source/feature/feature.rst

@@ -130,6 +130,17 @@ RawFeature：连续值特征
     -DossHost=oss-cn-beijing-internal.aliyuncs.com
     -Dwith_evaluator=1;
 
+.. code:: protobuf
+
+  feature_config:{
+    features {
+      input_names: "ctr"
+      feature_type: RawFeature
+    }
+  }
+
+也可以为每个RawFeature添加一个Field Embedding(原始特征值乘上一个可学习embedding参数)，如下：
+
 .. code:: protobuf
 
   feature_config:{

docs/source/quick_start/local_tutorial.md

@@ -8,20 +8,39 @@
 
 #### 本地Anaconda安装
 
+温馨提示：**在搭载Apple M系列芯片的MacBook上必须使用TensorFlow 2.5或更高版本**，安装方法请查看TF官方文档。
+
 Demo实验中使用的环境为 `python=3.6.8` + `tenserflow=1.12.0`
 
 ```bash
 conda create -n py36_tf12 python=3.6.8
 conda activate py36_tf12
 pip install tensorflow==1.12.0
+pip install tensorflow_probability==0.5.0
 ```
 
+注意：必须要安装`tensorflow_probability`包，需要根据tensorflow的版本安装对应版本的`tensorflow_probability`包。
+
+常见版本对应关系：
+
+| TensorFlow版本 | TensorFlowProbability版本 |
+|--------------|-------------------------|
+| 1.12         | 0.5.0                   |
+| 1.15         | 0.8.0                   |
+| 2.5.0        | 0.13.0                  |
+| 2.6.0        | 0.14.0                  |
+| 2.7.0        | 0.15.0                  |
+| 2.8.0        | 0.16.0                  |
+| 2.10         | 0.18.0                  |
+| 2.12         | 0.20.0                  |
+
+其他版本对应关系请查看链接：[Releases · tensorflow/probability](https://github.com/tensorflow/probability/releases)。
+
 ```bash
 git clone https://github.com/alibaba/EasyRec.git
 cd EasyRec
 bash scripts/init.sh
 python setup.py install
-
 ```
 
 #### Docker镜像启动
@@ -33,13 +52,22 @@ Docker的环境为`python=3.6.9` + `tenserflow=1.15.5`
 ```bash
 git clone https://github.com/alibaba/EasyRec.git
 cd EasyRec
-docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-0.7.4
-docker run -td --network host -v /local_path/EasyRec:/docker_path/EasyRec mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-0.7.4
+docker pull mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-0.8.5
+docker run -td --network host -v /local_path/EasyRec:/docker_path/EasyRec mybigpai-public-registry.cn-beijing.cr.aliyuncs.com/easyrec/easyrec:py36-tf1.15-0.8.5
 docker exec -it <CONTAINER_ID> bash
 ```
 
 ##### 方法二：自行构建Docker镜像
 
+我们提供四个版本的tensorflow镜像构建示例，对应的脚本路径如下：
+
+- scripts/build_docker_tf112.sh
+- scripts/build_docker_tf115.sh
+- scripts/build_docker_tf210.sh
+- scripts/build_docker_tf212.sh
+
+默认使用`tensorflow 1.15`的版本，示例脚本如下，请根据需要替换脚本路径：
+
 ```bash
 git clone https://github.com/alibaba/EasyRec.git
 cd EasyRec

docs/source/vector_retrieve.md

@@ -37,7 +37,7 @@ pai -name easy_rec_ext -project algo_public_dev
 
 ## 使用示例
 
-### 1. 创建查询表
+### 1. 创建索引表
 
 ```sql
 create table doc_table(pk BIGINT,vector string) partitioned by (pt string);
@@ -53,7 +53,7 @@ VALUES
 ;
 ```
 
-### 2. 创建索引表
+### 2. 创建查询表
 
 ```sql
 create table query_table(pk BIGINT,vector string) partitioned by (pt string);