Apply suggestions from code review

Co-authored-by: Prashanth Rao <35005448+prrao87@users.noreply.github.com>

Author: prrao87

docs/integrations/embedding/superlinked.mdx

@@ -8,11 +8,9 @@ sidebarTitle: Superlinked
 ## Installation
 
 <CodeGroup>
-```bash Python
 pip install sie-lancedb
 ```
 
-```bash TypeScript
 npm install @superlinked/sie-lancedb @lancedb/lancedb
 ```
 </CodeGroup>
@@ -35,7 +33,6 @@ Supported parameters on `.create()`:
 
 ## Usage
 
-```python
 import lancedb
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector
@@ -68,7 +65,6 @@ LanceDB handles embedding generation for both inserts and queries automatically,
 
 `SIEReranker` plugs into LanceDB's hybrid search pipeline. It uses SIE's cross-encoder `score()` to rerank combined vector + full-text search results. You need a full-text search index on the column first:
 
-```python
 from sie_lancedb import SIEReranker
 
 # Create FTS index for hybrid search
@@ -91,7 +87,6 @@ The reranker also works with pure vector or pure FTS search via `.rerank()`.
 
 `SIEMultiVectorEmbeddingFunction` (registered as `"sie-multivector"`) works with LanceDB's native `MultiVector` type and MaxSim scoring for ColBERT and ColPali models:
 
-```python
 from lancedb.pydantic import MultiVector
 
 sie_colbert = get_registry().get("sie-multivector").create(