Update README.md (#7604)

GitOrigin-RevId: 9ad1421ef8f2efee5bf0b5aec3083e5212276855

README.md

@@ -12,7 +12,7 @@
 [![follow on Twitter](https://img.shields.io/twitter/follow/pathway_com?style=social&logo=twitter)](https://twitter.com/intent/follow?screen_name=pathway_com)
 </div>
 
-Pathway's **LLM (Large Language Model) Apps** allow you to quickly put in production AI applications which offer **high-accuracy RAG at scale** using the most **up-to-date knowledge** available in your data sources.
+Pathway's **LLM (Large Language Model) App Templates** allow you to quickly put in production AI applications which offer **high-accuracy RAG and AI enterprise search at scale** using the most **up-to-date knowledge** available in your data sources. You can test them on your own machine and deploy on-cloud (GCP, AWS, Azure, Render,...) or on-premises.
 
 The apps connect and sync (all new data additions, deletions, updates) with data sources on your **file system, Google Drive, Sharepoint, S3, Kafka, PostgreSQL, real-time data APIs**. They come with no infrastructure dependencies that would need a separate setup. They include **built-in data indexing** enabling vector search, hybrid search, and full-text search - all done in-memory, with cache.
 
@@ -36,7 +36,7 @@ The application templates provided in this repo scale up to **millions of pages
 
 The apps can be run as **Docker containers**, and expose an **HTTP API** to connect the frontend. To allow quick testing and demos, some app templates also include an optional Streamlit UI which connects to this API. 
 
-The apps rely on the [Pathway framework](https://github.com/pathwaycom/pathway) for data source synchronization and for serving API requests (Pathway is a standalone Python library with a Rust engine built into it). They bring you a **simple and unified application logic** for back-end, embedding, retrieval, LLM tech stack. There is no need to integrate and maintain separate modules for your Gen AI app: ~Vector Database (e.g. Pinecone/Weaviate/Qdrant) + Cache (e.g. Redis) + API Framework (e.g. Fast API)~. Pathway's default choice of **built-in vector index** is based on the lightning-fast [Tantivy](https://github.com/quickwit-oss/tantivy) library, and works out of the box.
+The apps rely on the [Pathway framework](https://github.com/pathwaycom/pathway) for data source synchronization and for serving API requests (Pathway is a standalone Python library with a Rust engine built into it). They bring you a **simple and unified application logic** for back-end, embedding, retrieval, LLM tech stack. There is no need to integrate and maintain separate modules for your Gen AI app: ~Vector Database (e.g. Pinecone/Weaviate/Qdrant) + Cache (e.g. Redis) + API Framework (e.g. Fast API)~. Pathway's default choice of **built-in vector index** is based on the lightning-fast [usearch](https://github.com/unum-cloud/usearch) library, and **hybrid full-text indexes** make use of [Tantivy](https://github.com/quickwit-oss/tantivy) library. Everything works out of the box.
 
 ## Getting started