docs: modify spark vector comparison document (#91)

Signed-off-by: jinweios <jinwei.peng@beingthink.com>

src/faqs/comparison-pgvector.md

@@ -40,9 +40,7 @@ Dense vectors are embeddings from neural networks. They are generated by text em
 
 $$D[1\times 256]=\left[ \begin{matrix} 0.342 & 1.774 & 0.087 & 0.870 & 0.001 & \cdots & 0.543 & 0.999 \end{matrix} \right]$$
 
-pgvector supports dense vector search well, but it [does not have plan to support sparse vector](https://github.com/pgvector/pgvector/issues/81).
-
-[pgvecto.rs](http://pgvecto.rs), on the other hand, supports both dense vector search and [sparse vector search](https://docs.pgvecto.rs/use-case/sparse-vector.html). It provides the ability to use the [`svector`](https://docs.pgvecto.rs/use-case/sparse-vector.html) data type to build sparse vector indexes and perform searches on them.
+[pgvecto.rs](http://pgvecto.rs) supports both dense vector search and [sparse vector search](https://docs.pgvecto.rs/use-case/sparse-vector.html). It provides the ability to use the [`svector`](https://docs.pgvecto.rs/use-case/sparse-vector.html) data type to build sparse vector indexes and perform searches on them.
 
 ```sql
 CREATE TABLE items (
@@ -127,7 +125,6 @@ Here we will show the main distinctions between pgvecto.rs and pgvector.
 | Feature | pgvecto.rs | pgvector |
 | --- | --- | --- |
 | Filtering | Introduces VBASE method for vector search and relational query (e.g. Single-Vector TopK + Filter + Join). | When filters are applied, the results may be incomplete. For example, if you originally intended to limit the results to 10, you might end up with only 5 results with filters. |
-| Sparse Vector Search | Supports both dense and sparse vector search. | Supports dense vector search. |
 | Vector Dimensions | Supports up to 65535 dimensions. | Supports up to 2000 dimensions. |
 | SIMD | SIMD instructions are dynamically dispatched at runtime to maximize performance based on the capabilities of the specific machine. | Relies on compiler-generated SIMD code at compile time. |
 | Data Types | Introduces additional data types: binary vectors, FP16 (16-bit floating point), and INT8 (8-bit integer). | \- |

src/getting-started/overview.md

@@ -13,7 +13,6 @@ Checkout [pgvecto.rs vs pgvector](https://docs.pgvecto.rs/faqs/comparison-pgvect
 | Feature | pgvecto.rs | pgvector |
 | --- | --- | --- |
 | Filtering | Introduces VBASE method for vector search and relational query (e.g. Single-Vector TopK + Filter + Join). | When filters are applied, the results may be incomplete. For example, if you originally intended to limit the results to 10, you might end up with only 5 results with filters. |
-| Sparse Vector Search | Supports both dense and sparse vector search. | Supports dense vector search. |
 | Vector Dimensions | Supports up to 65535 dimensions. | Supports up to 2000 dimensions. |
 | SIMD | SIMD instructions are dynamically dispatched at runtime to maximize performance based on the capabilities of the specific machine. | Added CPU dispatching for distance functions on Linux x86-64" in 0.7.0. |
 | Data Types | Introduces additional data types: binary vectors, FP16 (16-bit floating point), and INT8 (8-bit integer). | \- |