update postgresql version in installation instruction (#153)

Signed-off-by: usamoi <usamoi@outlook.com>

Author: usamoi

src/pgvecto_rs/use-case/sparse-vector.md

@@ -59,7 +59,7 @@ For instance, both `rocket` and `SPACE X` are related to space exploration, but
 
 A vector embedding is the internal representation of input data in deep learning models, also known as embedding models. Most embedding models, such as `text-embedding-3-small`, only output dense embeddings. 
 
-[SPLADE](https://europe.naverlabs.com/research/computer-science/splade-a-sparse-bi-encoder-bert-based-model-achieves-effective-and-efficient-full-text-document-ranking/?utm_source=qdrant&utm_medium=website&utm_campaign=sparse-vectors&utm_content=article&utm_term=sparse-vectors) and [BGE-M3](https://arxiv.org/pdf/2402.03216.pdf) can generate sparse embeddings, sometimes called lexical weights. In this section, we will introduce the `BGE-M3` model for generating dense and sparse vectors.
+[SPLADE](https://europe.naverlabs.com/blog/splade-a-sparse-bi-encoder-bert-based-model-achieves-effective-and-efficient-first-stage-ranking/) and [BGE-M3](https://arxiv.org/pdf/2402.03216.pdf) can generate sparse embeddings, sometimes called lexical weights. In this section, we will introduce the `BGE-M3` model for generating dense and sparse vectors.
 
 `BGE-M3` is a multi-functionality model, which can simultaneously perform the three common retrieval functionalities of the embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. We will learn how to generate dense and sparse embeddings from it.
 

src/vectorchord/getting-started/installation.md

@@ -169,13 +169,13 @@ echo 'build.rustflags = ["-Ctarget-cpu=icelake"]' >> ./cargo/config.toml
 
 ## Debian
 
-If you have not installed PostgreSQL yet, please install PostgreSQL following https://www.postgresql.org/download/linux/debian/. If you have not installed pgvector yet, you could install pgvector by `apt install postgresql-17-pgvector` before the 3rd step.
+If you have not installed PostgreSQL yet, please install PostgreSQL following https://www.postgresql.org/download/linux/debian/. If you have not installed pgvector yet, you could install pgvector by `apt install postgresql-18-pgvector` before the 3rd step.
 
 1. Download Debian packages in [the release page](https://github.com/tensorchord/VectorChord/releases/latest), and install them by `apt`.
 
 ```sh
-wget https://github.com/tensorchord/VectorChord/releases/download/0.5.3/postgresql-17-vchord_0.5.3-1_$(dpkg --print-architecture).deb
-sudo apt install ./postgresql-17-vchord_0.5.3-1_$(dpkg --print-architecture).deb
+wget https://github.com/tensorchord/VectorChord/releases/download/0.5.3/postgresql-18-vchord_0.5.3-1_$(dpkg --print-architecture).deb
+sudo apt install ./postgresql-18-vchord_0.5.3-1_$(dpkg --print-architecture).deb
 ```
 
 2. Configure your PostgreSQL by modifying the `shared_preload_libraries` to include the extension. And then restart the PostgreSQL cluster.
@@ -193,13 +193,13 @@ CREATE EXTENSION IF NOT EXISTS vchord CASCADE;
 
 ## Ubuntu
 
-If you have not installed PostgreSQL yet, please install PostgreSQL following https://www.postgresql.org/download/linux/ubuntu/. If you have not installed pgvector yet, you could install pgvector by `apt install postgresql-17-pgvector` before the 3rd step.
+If you have not installed PostgreSQL yet, please install PostgreSQL following https://www.postgresql.org/download/linux/ubuntu/. If you have not installed pgvector yet, you could install pgvector by `apt install postgresql-18-pgvector` before the 3rd step.
 
 1. Download Debian packages in [the release page](https://github.com/tensorchord/VectorChord/releases/latest), and install them by `apt`.
 
 ```sh
-wget https://github.com/tensorchord/VectorChord/releases/download/0.5.3/postgresql-17-vchord_0.5.3-1_$(dpkg --print-architecture).deb
-sudo apt install ./postgresql-17-vchord_0.5.3-1_$(dpkg --print-architecture).deb
+wget https://github.com/tensorchord/VectorChord/releases/download/0.5.3/postgresql-18-vchord_0.5.3-1_$(dpkg --print-architecture).deb
+sudo apt install ./postgresql-18-vchord_0.5.3-1_$(dpkg --print-architecture).deb
 ```
 
 2. Configure your PostgreSQL by modifying the `shared_preload_libraries` to include the extension. And then restart the PostgreSQL cluster.

src/vectorchord/getting-started/overview.md

@@ -10,7 +10,7 @@ VectorChord introduces remarkable enhancements over pgvecto.rs and pgvector:
 
 **⚡ Enhanced Performance**: Delivering optimized operations with up to 5x faster queries, 16x higher insert throughput, and 16x quicker[^1] index building compared to pgvector's HNSW implementation.
 
-[^1]: Based on [MyScale Benchmark](https://myscale.github.io/benchmark/) with 768-dimensional vectors and 95% recall. Please checkout our [blog post](https://blog.vectorchord.ai/vectorchord-store-400k-vectors-for-1-in-postgresql) for more details.
+[^1]: Based on [MyScale Benchmark](https://myscale.github.io/benchmark/) with 768-dimensional vectors and 95% recall. Please check out our [blog post](https://blog.vectorchord.ai/vectorchord-store-400k-vectors-for-1-in-postgresql) for more details.
 
 **💰 Affordable Vector Search**: Query 100M 768-dimensional vectors using just 32GB of memory, achieving 35ms P50 latency with top10 recall@95%, helping you keep infrastructure costs down while maintaining high search quality.