fix url in readme

Signed-off-by: Keming <kemingyang@tensorchord.ai>

README.md

@@ -6,8 +6,8 @@
 <p align=center>
 <a href="https://discord.gg/KqswhpVgdU"><img alt="discord invitation link" src="https://dcbadge.vercel.app/api/server/KqswhpVgdU?style=flat"></a>
 <a href="https://twitter.com/TensorChord"><img src="https://img.shields.io/twitter/follow/tensorchord?style=social" alt="trackgit-views" /></a>
-<a href="https://hub.docker.com/r/tensorchord/vcvec"><img src="https://img.shields.io/docker/pulls/tensorchord/vcvec" /></a>
-<a href="https://github.com/tensorchord/vcvec#contributors-"><img alt="all-contributors" src="https://img.shields.io/github/all-contributors/tensorchord/vcvec/main"></a>
+<a href="https://hub.docker.com/r/tensorchord/vchord-postgres"><img src="https://img.shields.io/docker/pulls/tensorchord/vchord-postgres" /></a>
+<a href="https://github.com/tensorchord/VectorChord#contributors-"><img alt="all-contributors" src="https://img.shields.io/github/all-contributors/tensorchord/VectorChord/main"></a>
 </p>
 
 VectorChord (vchord) is a PostgreSQL extension designed for scalable, high-performance, and disk-efficient vector similarity search. It serves as the successor to the [pgvecto.rs](https://github.com/tensorchord/pgvecto.rs) project. 

scripts/index.py

@@ -128,7 +128,7 @@ async def add_centroids(conn, name, centroids):
         copy.set_types(["integer", "integer", "vector"])
         await copy.write_row((0, None, root))
         for i, centroid in tqdm(enumerate(centroids), desc="Adding centroids", total=n):
-            await copy.write_row((i+1, 0, centroid))
+            await copy.write_row((i + 1, 0, centroid))
         while conn.pgconn.flush() == 1:
             await asyncio.sleep(0)
 

scripts/train.py

@@ -129,7 +129,9 @@ def kmeans_cluster(
     if n > MAX_POINTS_PER_CLUSTER * k and not mmap:
         train = reservoir_sampling(iter(data), MAX_POINTS_PER_CLUSTER * args.lists)
     elif n > MAX_POINTS_PER_CLUSTER * k and mmap:
-        reservoir_sampling_np(data, file_path, MAX_POINTS_PER_CLUSTER * args.lists, chunks)
+        reservoir_sampling_np(
+            data, file_path, MAX_POINTS_PER_CLUSTER * args.lists, chunks
+        )
         train = np.array(
             np.memmap(
                 "index.mmap",
@@ -196,6 +198,8 @@ def kmeans_cluster(
         args.mmap,
         args.chunks,
     )
-    print(f"K-means (k=({args.lists}, {args.lists_2})): {perf_counter() - start_time:.2f}s")
+    print(
+        f"K-means (k=({args.lists}, {args.lists_2})): {perf_counter() - start_time:.2f}s"
+    )
 
     np.save(Path(args.output), centroids, allow_pickle=False)