improve readme (#57)

Signed-off-by: Jinjing.Zhou <allenzhou@tensorchord.ai>

README.md

@@ -8,7 +8,7 @@
 <a href="https://github.com/tensorchord/pgvecto.rs#contributors-"><img alt="all-contributors" src="https://img.shields.io/github/all-contributors/tensorchord/pgvecto.rs/main"></a>
 </p>
 
-pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on [pgrx](https://github.com/tcdi/pgrx). It is currently ⚠️**under heavy development**⚠️, please take care when using it in production. Read more at [📝our launch blog](https://modelz.ai/blog/pgvecto-rs).
+pgvecto.rs is a Postgres extension that provides vector similarity search functions. It is written in Rust and based on [pgrx](https://github.com/tcdi/pgrx). It is currently in the beta status, we invite you to try it out in production and provide us with feedback. Read more at [📝our launch blog](https://modelz.ai/blog/pgvecto-rs).
 
 ## Why use pgvecto.rs
 
@@ -251,6 +251,11 @@ We utilize TOML syntax to express the index's configuration. Here's what each ke
 | algorithm.hnsw.m       | integer | (Optional) Maximum degree of the node.                                                                                |
 | algorithm.hnsw.ef      | integer | (Optional) Search scope in building.                                                                                  |
 
+## Limitations
+- The index is constructed and persisted using a memory map file (mmap) instead of PostgreSQL's shared buffer. As a result, physical replication or logical replication may not function correctly. Additionally, vector indexes are not automatically loaded when PostgreSQL restarts. To load or unload the index, you can utilize the `vectors_load` and `vectors_unload` commands.
+- The filtering process is not yet optimized. To achieve optimal performance, you may need to manually experiment with different strategies. For example, you can try searching without a vector index or implementing post-filtering techniques like the following query: `select * from (select * from items ORDER BY embedding <-> '[3,2,1]' LIMIT 100 ) where category = 1`. This involves using approximate nearest neighbor (ANN) search to obtain enough results and then applying filtering afterwards.
+
+
 ## Why not a specialty vector database?
 
 Imagine this, your existing data is stored in a Postgres database, and you want to use a vector database to do some vector similarity search. You have to move your data from Postgres to the vector database, and you have to maintain two databases at the same time. This is not a good idea.