[doc] Revamped README.md (#330)

* Minor editorial updates

* Updated readme

* Revamped README

* minor

README.md

@@ -8,51 +8,39 @@
 </p>
 
 <h4 align="center">
-  <a href="https://github.com/infiniflow/infinity/discussions">GitHub Discussions</a> |
   <a href="https://www.meilisearch.com/pricing?utm_campaign=oss&utm_source=github&utm_medium=meilisearch&utm_content=nav">Roadmap 2024</a> |
   <a href="https://twitter.com/infiniflowai">Twitter</a> |
   <a href="https://discord.gg/6Zex37FE">Discord</a> |
   <a href="https://www.youtube.com/@InfiniFlow-AI">YouTube</a> |
 </h4>
 
 
-Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as vectors, full-text, and structured data. It provides robust support for various LLM applications, including search, recommendations, question-answering, conversational AI, copilot, content generation, data management, and much RAG (Retrieval-augmented Generation) applications
-
-The vector search performance of Infinity on common datasets is exceptionally superior to all known open source vector databases, the higher the dimensionality the embeddings, the greater performance improvements achieved.
-
-Infinity was released under the [open-source Apache License 2.0](https://github.com/infiniflow/infinity/blob/master/LICENSE) on December 20, 2023.
+Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as vectors, full-text, and structured data. It provides robust support for various LLM applications, including search, recommendations, question-answering, conversational AI, copilot, content generation, and many more RAG (Retrieval-augmented Generation) applications.
 
 ## 🌟 Key Features
 
 Infinity comes with **performance**, **flexibility**, **ease-of-use**, and many features designed to address the challenges facing the next-gen RAG applications:
 
-### Incredibly fast
+### ⚡️ Incredibly fast
 
-0.1 milliseconds query latency with 10K QPS on million-scale vector datasets. See the [Benchmarking](https://www.example.com).
+- Achieves 0.1 milliseconds query latency on million-scale vector datasets.
+- Up to 10K QPS on million-scale vector datasets.
+- See the [Benchmarking](https://www.example.com).
 
 
-### Fused search
+### 🔮 Fused search
 
 Supports a fused search of multi-embeddings and full text, in addition to filtering.
 
-
-### Rich data types
+### 🍔 Rich data types
 
 Supports a wide range of data types including strings, numerics, vectors, and more.
 
-### Ease-of-use
+### 🎁 Ease-of-use
 
-- Intuitive Python API.
+- Intuitive Python API. See the [Python client]()
 - A single-binary architecture with no dependencies, making deployment a breeze.
 
-### 🧐 Benchmark
-
-See a Benchmark report [here]().
-
-### Clients
-
-- [Python client]()
-
 
 ## 🎮 Get Started
 
@@ -63,7 +51,7 @@ CONTENT MISSING HERE
 
 See [Build from Source](build_from_source.md).
 
-## 📑 Roadmap
+## 📜 Roadmap
 
 - [Infinity Roadmap 2024]()