[doc] Editorial updates to readme

README.md

@@ -4,82 +4,64 @@
 </div>
 
 <p align="center">
-    <b>The AI-native database built for the next-gen Retrieval-Augmented Generation</b>
-    <p><b>Incredibly fast vector search</b></p>
-    <b></b>
+    <b>The AI-native database built for LLM applications, offering incredibly fast vector and full-text search</b>
 </p>
 
 <h4 align="center">
   <a href="https://github.com/infiniflow/infinity/discussions">GitHub Discussions</a> |
-  <a href="https://www.youtube.com/@InfiniFlow-AI">YouTube</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://github.com/infiniflow/infinity/blob/main/LICENSE">Apache 2.0</a> |
   <a href="https://discord.gg/6Zex37FE">Discord</a> |
+  <a href="https://www.youtube.com/@InfiniFlow-AI">YouTube</a> |
 </h4>
 
 
-Infinity is an open-source AI-native database designed to enhance retrieval-augmented generation (RAG) applications. As a natural partner to mainstream LLMs, Infinity solves primary challenges faced by B2B applications, such as internal enterprise search, industry-specific search, in-house AI assistants, chatbots, in-house knowledge management systems, and more. Infinity empowers these applications by supporting full-text search, multi-embedding search, multiple-collection query, and fused search. 
+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.
 
-### 🧐 Benchmark
-
-See a Benchmark report [here]().
-
-### Clients
-
-- [Python client]()
-
-
-## 🎮 Get Started
+## 🌟 Key Features
 
-CONTENT MISSING HERE
+Infinity comes with **performance**, **flexibility**, **ease-of-use**, and many features designed to address the challenges facing the next-gen RAG applications:
 
-## 🛠️ Build from Source
+### Incredibly fast
 
-See [Build from Source](build_from_source.md).
+0.1 milliseconds query latency with 10K QPS on million-scale vector datasets. See the [Benchmarking](https://www.example.com).
 
 
+### Fused search
 
-## 🌟 Key Features
+Supports a fused search of multi-embeddings and full text, in addition to filtering.
 
-Infinity comes with **performance**, **flexibility**, **ease-of-use**, and many features designed to address the challenges facing the next-gen RAG applications:
 
-### Incredibly fast
+### Rich data types
 
-- End-to-end latency as low as 0.1 ms. See the [Benchmarking](https://www.example.com).
-- 10K QPS on CPU: 
+Supports a wide range of data types including strings, numerics, vectors, and more.
 
+### Ease-of-use
 
-### Fused search
+- Intuitive Python API.
+- A single-binary architecture with no dependencies, making deployment a breeze.
 
-In addition to hybrid search, Infinity takes over the decision-making process previously owned by the upper-level applications, thereby simplifying complex queries considerably.
+### 🧐 Benchmark
 
-- Full-text search
-- KNN-based vector search
+See a Benchmark report [here]().
 
+### Clients
 
-### Rich data types
+- [Python client]()
 
-In addition to embeddings generated by LLMs, Infinity also stores structured and semi-structured data, offering support for mixed data type queries.
 
-- Numeric
-- String
-- Float
-- Date
-- Time
-- Geography
+## 🎮 Get Started
 
-### Easy-to-use
+CONTENT MISSING HERE
 
-- One binary to deploy
-- Intuitive API: 
-  - We carefully weighed the pros and cons of similar APIs in the market and designed our own.
 
+## 🛠️ Build from Source
 
+See [Build from Source](build_from_source.md).
 
 ## 📑 Roadmap
 
@@ -91,8 +73,3 @@ In addition to embeddings generated by LLMs, Infinity also stores structured and
 - [Twitter](https://twitter.com/infiniflowai)
 - [GitHub Discussions](https://github.com/infiniflow/infinity/discussions)
 - [YouTube](https://www.youtube.com/@InfiniFlow-AI)
-
-
-## 👩‍💻 Contributing
-
-To find out how to make a contribution to Infinity, see the [contribution guidelines](CONTRIBUTING.md).