add roadmap

Author: ZiyiXia

README.md

@@ -22,6 +22,7 @@
         <a href=#news>News</a> |
         <a href=#installation>Installation</a> |
         <a href=#quick-start>Quick Start</a> |
+        <a href=#community>Community</a> |
         <a href="#projects">Projects</a> |
         <a href=#model-list>Model List</a> |
         <a href="#contributor">Contributor</a> |
@@ -117,6 +118,24 @@ similarity = embeddings_1 @ embeddings_2.T
 print(similarity)
 ```
 
+
+## Community
+
+We are actively maintaining the community of BGE and FlagEmbedding. Let us know if you have any suggessions or ideas!
+
+Currently we are updating the [tutorials](./Tutorials/), we aim to create a comprehensive and detailed tutorial for beginners on text retrieval and RAG. Stay tuned!
+
+The following contents are releasing in the upcoming weeks:
+
+- BGE Intro
+- Evaluation on MTEB tasks
+
+<details>
+  <summary>The whole tutorial roadmap</summary>
+    <img src="./Tutorials/tutorial_map.png"/>
+</details>
+
+
 ## Projects
 
 ### BGE-M3 ([Paper](https://arxiv.org/pdf/2402.03216.pdf), [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))

README_zh.md

@@ -22,6 +22,7 @@
         <a href=#更新>更新</a> |
         <a href=#安装>安装</a> |
         <a href=#快速开始>快速开始</a> |
+        <a href=#社区>社区</a> |
         <a href="#项目">项目</a> |
         <a href="#模型列表">模型列表</a> |
         <a href=#贡献者>贡献者</a> |
@@ -113,6 +114,22 @@ similarity = embeddings_1 @ embeddings_2.T
 print(similarity)
 ```
 
+## 社区
+
+我们将持续维护BGE及FlagEmbedding社区，有任何想法建议都欢迎告诉我们！
+
+近期会持续更新[教学](./Tutorials/)中的内容，希望为文本检索以及RAG打造出完整且详细的教学，欢迎持续关注！
+
+在未来两周左右将会更新以下内容：
+
+- BGE模型介绍
+- 在MTEB的任务中的评测
+
+<details>
+  <summary>教程规划</summary>
+    <img src="./Tutorials/tutorial_map.png"/>
+</details>
+
 
 ## 项目
 

Tutorials/README.md

@@ -1,7 +1,12 @@
-# Tutorials
+# FlagEmbedding_tutorial
 
 If you are new to here, check out the 5 minute [quick start](./quick_start.ipynb)!
 
+<details>
+  <summary>Tutorial roadmap</summary>
+    <img src="./tutorial_map.png"/>
+</details>
+
 ## [Embedding](./1_Embedding)
 
 This module includes tutorials and demos showing how to use BGE and Sentence Transformers, as well as other embedding related topics.
@@ -18,4 +23,6 @@ Although not included in the quick start, indexing is a very important part in p
 
 In this module, we'll show the full pipeline of evaluating an embedding model, as well as popular benchmarks like MTEB and C-MTEB.
 
-## Reranking
+## [Reranking](./5_Reranking/)
+
+To balance accuracy and efficiency tradeoff, many retrieval system use a more efficient retriever to quickly narrow down the candidates. Then use more accurate models do reranking for the final results.