0.5.3 (#145)

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

Author: usamoi

src/vectorchord/admin/kubernetes.md

@@ -150,7 +150,7 @@ ttensorchord=> \dx
   Name   | Version |   Schema   |                                         Description                                         
 ---------+---------+------------+---------------------------------------------------------------------------------------------
  plpgsql | 1.0     | pg_catalog | PL/pgSQL procedural language
- vchord  | 0.5.2   | public     | vchord: Vector database plugin for Postgres, written in Rust, specifically designed for LLM
+ vchord  | 0.5.3   | public     | vchord: Vector database plugin for Postgres, written in Rust, specifically designed for LLM
  vector  | 0.8.1   | public     | vector data type and ivfflat and hnsw access methods
 (3 rows)
 ```

src/vectorchord/admin/migration.md

@@ -75,7 +75,7 @@ postgres=# \dx
  plpgsql | 1.0     | pg_catalog | PL/pgSQL procedural language
  vchord  | 0.1.0   | public     | vchord: Vector database plugin for Postgres, written in Rust, specifically designed for LLM
  vector  | 0.8.0   | public     | vector data type and ivfflat and hnsw access methods
- vectors | 0.5.2   | vectors    | vectors: Vector database plugin for Postgres, written in Rust, specifically designed for LLM
+ vectors | 0.4.0   | vectors    | vectors: Vector database plugin for Postgres, written in Rust, specifically designed for LLM
 ```
 
 :::

src/vectorchord/getting-started/installation.md

@@ -3,13 +3,13 @@
 VectorChord is tested on the following operating system:
 
 * Ubuntu (x86_64, aarch64)
-* MacOS (aarch64)
+* MacOS (x86_64, aarch64)
 * Windows (x86_64)
 * Alpine Linux (x86_64, aarch64) [^1]
 * Debian (powerpc64le, s390x, riscv64) [^2]
 
 [^1]: VectorChord is tested with PostgreSQL 15 in `community` repository, 16 and 17 in `main` repository on Alpine Linux 3.22.
-[^2]: VectorChord is tested with PostgreSQL 13, 14, 15, 16, and 17 in PostgreSQL's official repository on `powerpc64le`, and PostgreSQL 15 and 17 in Debian's official repository on `s390x` and `riscv64`, with QEMU user space emulator.
+[^2]: VectorChord is tested with PostgreSQL 13, 14, 15, 16, 17 and 18 in PostgreSQL's official repository on `powerpc64le`, and PostgreSQL 15 and 17 in Debian's official repository on `s390x` and `riscv64`, with QEMU user space emulator.
 
 Please report a bug if you encounter issues on any of the above operating systems, or submit a feature request for additional platform support.
 
@@ -28,7 +28,7 @@ docker run \
   --name vchord-demo \
   -e POSTGRES_PASSWORD=mysecretpassword \
   -p 5432:5432 \
-  -d tensorchord/vchord-postgres:pg17-v0.5.2
+  -d tensorchord/vchord-postgres:pg18-v0.5.3
 ```
 
 2. Connect to the database using the `psql` command line tool. The default username is `postgres`.
@@ -96,8 +96,8 @@ If you have not installed PostgreSQL yet, please install PostgreSQL. If you have
 1. Download the source code, build and install it with `make`.
 
 ```sh
-curl -fsSL https://github.com/tensorchord/VectorChord/archive/refs/tags/0.5.2.tar.gz | tar -xz
-cd VectorChord-0.5.2
+curl -fsSL https://github.com/tensorchord/VectorChord/archive/refs/tags/0.5.3.tar.gz | tar -xz
+cd VectorChord-0.5.3
 make build
 make install # or `sudo make install`
 ```
@@ -139,34 +139,21 @@ Please note that binaries generated with a specific `target-cpu` are not compati
 You can also do it by using Cargo's configuration.
 
 ```sh
-cd VectorChord-0.5.2
+cd VectorChord-0.5.3
 mkdir -p .cargo
 touch .cargo/config.toml
 echo 'build.rustflags = ["-Ctarget-cpu=icelake"]' >> ./cargo/config.toml
 ```
 
-### s390x
-
-This section applies only to version 0.5.1 and 0.5.2 and may change in the next version.
-
-When the target is S390X, you need to use the patch we provide for the upstream crate. You can do it by using Cargo's configuration.
-
-```sh
-cd VectorChord-0.5.2
-mkdir -p .cargo
-touch .cargo/config.toml
-echo 'patch.crates-io.pgrx = { git = "https://github.com/tensorchord/pgrx.git", branch = "big-endian" }' >> ./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.
 
 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/0.5.2/postgresql-17-vchord_0.5.2-1_$(dpkg --print-architecture).deb
-sudo apt install ./postgresql-17-vchord_0.5.2-1_$(dpkg --print-architecture).deb
+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
 ```
 
 2. Configure your PostgreSQL by modifying the `shared_preload_libraries` to include the extension. And then restart the PostgreSQL cluster.
@@ -189,8 +176,8 @@ If you have not installed PostgreSQL yet, please install PostgreSQL following ht
 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/0.5.2/postgresql-17-vchord_0.5.2-1_$(dpkg --print-architecture).deb
-sudo apt install ./postgresql-17-vchord_0.5.2-1_$(dpkg --print-architecture).deb
+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
 ```
 
 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

@@ -37,7 +37,7 @@ docker run \
   --name vectorchord-demo \
   -e POSTGRES_PASSWORD=mysecretpassword \
   -p 5432:5432 \
-  -d tensorchord/vchord-postgres:pg17-v0.5.2
+  -d tensorchord/vchord-postgres:pg18-v0.5.3
 ```
 > In addition to the base image with the VectorChord extension, we provide an all-in-one image, `tensorchord/vchord-suite:pg17-latest`. This comprehensive image includes all official TensorChord extensions. Developers should select an image tag that is compatible with their extension's version, as indicated in [the support matrix](https://github.com/tensorchord/VectorChord-images?tab=readme-ov-file#support-matrix).
 

src/vectorchord/usage/indexing.md

@@ -156,6 +156,10 @@ The operator classes for `MaxSim` are available since version `0.3.0`.
     - `build.pin = false` means that shared memory is not used.
     - `build.pin = true` means that shared memory is used.
 
+### Default Build Options <badge type="tip" text="since v0.5.3" />
+
+This is the default value of index building. The index will not be partitioned. In terms of semantics, `build.default = {}` is similar to `build.internal.lists = []`.
+
 ### Internal Build Options <badge type="info" text="vchordrq" />
 
 #### `build.internal.lists`

src/vectorchord/usage/monitoring.md

@@ -12,14 +12,15 @@ SELECT command, phase, round(100.0 * blocks_done / nullif(blocks_total, 0), 1) A
 
 There are 5 steps in the index construction process of `vchordrq`. The phase name of each step are as follows:
 
-| Step | Message                                 | Description                                                                                         | Waiting time |
-| ---- | --------------------------------------- | --------------------------------------------------------------------------------------------------- | ------------ |
-| 1    | `initializing`                          | Start building index                                                                                | Short        |
-| 2    | `initializing index, by internal build` | This step is only for internal build. It involves performing K-means clustering to obtain centroids | Long         |
-| 2    | `initializing index, by external build` | This step is only for external build. It loads centroids from a specified table                     | Short        |
-| 3    | `initializing index`                    | Initialize the data structure and storage of the index                                              | Short        |
-| 4    | `inserting tuples from table to index`  | Assign all vectors to their corresponding lists                                                     | Long         |
-| 5    | `compacting tuples in index`            | Optimize the structure of the vector index to enhance performance                                   | Medium       |
+| Step | Message                                 | Description                                                                                                            | Waiting time |
+| ---- | --------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- | ------------ |
+| 1    | `initializing`                          | Start building index                                                                                                   | Short        |
+| 2    | `initializing index, by default build`  | This step is only for default build                                                                                    | Short        |
+| 2    | `initializing index, by internal build` | This step is only for internal build. It involves table sampling and performing K-means clustering to obtain centroids | Long         |
+| 2    | `initializing index, by external build` | This step is only for external build. It loads centroids from a specified table                                        | Short        |
+| 3    | `initializing index`                    | Initialize the data structure and storage of the index                                                                 | Short        |
+| 4    | `inserting tuples from table to index`  | Assign all vectors to their corresponding lists                                                                        | Long         |
+| 5    | `compacting tuples in index`            | Optimize the structure of the vector index to enhance performance                                                      | Medium       |
 
 The 4th step, `inserting tuples from table to index`, takes up the majority of the time during index construction. The `tuples_done`, `blocks_done` and `blocks_total` columns indicate the progress of this step.