docs: clean up prose flagged by the shared Vale rules (#4596)

The existing docs have 254 findings from our shared Vale rules.
Contributors would otherwise inherit these when editing the same lines.

Clean up the prose across 80 pages: correct product names and a repeated
word, simplify a few phrases, and replace dashes with punctuation that
fits each sentence. The pinned Vale scan now reports zero findings, with
no suppressions.

docs/web/tutorials/search/multivector-needle-in-a-haystack.mdx

@@ -53,7 +53,7 @@ A bi-encoder maps an entire piece of content (a query, a document page) to a *si
 
 ### Multi-vector (Late-Interaction) Models {#multi-vector-late-interaction-models}
 
-Multi-vector models, pioneered by ColBERT, take a different approach. Instead of one vector per page, they generate a *set of vectors* for each page—one for every token (or image patch).
+Multi-vector models, pioneered by ColBERT, take a different approach. Instead of one vector per page, they generate a *set of vectors* for each page: one for every token (or image patch).
 
 *   **Mechanism (MaxSim):** The search process is more sophisticated. For each token in the query, the system finds the most similar token on the page. These maximum similarity scores are then summed up to get the final relevance score. This "late-interaction" preserves fine-grained, token-level details.
 *   **The Models:** We used several vision-language models adapted for this architecture, including `ColPali`, `ColQwen2`, and `ColSmol`. While their underlying transformer backbones differ, they all share the ColBERT philosophy of representing documents as a bag of contextualized token embeddings.

docs/web/tutorials/search/ner-search.mdx

@@ -4,8 +4,8 @@ sidebarTitle: "NER Search"
 description: "Extract named entities and embed them to sharpen retrieval."
 ---
 
-Uses Named Entity Recognition to power vector search. Extracting entities --
-people, places, organizations -- and embedding them alongside the text improves
+Uses Named Entity Recognition to power vector search. Extracting entities
+(people, places, organizations) and embedding them alongside the text improves
 accuracy when queries are about specific things rather than general topics.
 
 <Note>

docs/web/tutorials/search/video-search.mdx

@@ -6,7 +6,7 @@ description: "Embed video with V-JEPA and search it by similarity."
 
 Builds a video search application on V-JEPA (Video Joint Embedding Predictive
 Architecture). Generates vector embeddings for video content and uses
-[vector search](/search/vector-search) to find visually similar clips -- a
+[vector search](/search/vector-search) to find visually similar clips: a
 multimodal case where the searchable column is not text.
 
 <Note>