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/integrations/data/dlt.mdx

@@ -40,7 +40,7 @@ In this example, we will be fetching movie information from the [Open Movie Data
 
 3. **Specify necessary credentials and/or embedding model details:**  
     
-    In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB Enterprise, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/secrets.toml`. 
+    To fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB Enterprise, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/secrets.toml`.
 
     dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/secrets.toml`:
     ```toml

docs/web/integrations/data/voxel51.mdx

@@ -37,7 +37,7 @@ You can then sort your samples by similarity or use this information to find pot
 
 We'll be doing the following : 
 
-1. **Create Index** - In order to run similarity queries against our media, we need to **index** the data. We can do this via the `compute_similarity()` function. 
+1. **Create Index** - To run similarity queries against our media, we need to **index** the data. We can do this via the `compute_similarity()` function.
   -  In the function, specify the **model** you want to use to generate the embedding vectors, and what **vector search engine** you want to use on the **backend** (here LanceDB). 
   <Tip>
   You can also give the similarity index a name(`brain_key`), which is useful if you want to run vector searches against multiple indexes.