docs: misc retrievers fixes (#9791)

Various miscellaneous fixes to most pages in the 'Retrievers' section of
the documentation:
- "VectorStore" and "vectorstore" changed to "vector store" for
consistency
- Various spelling, grammar, and formatting improvements for readability

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>

docs/snippets/modules/data_connection/document_loaders/how_to/csv.mdx

@@ -19,7 +19,7 @@ print(data)
 
 </CodeOutputBlock>
 
-## Customizing the csv parsing and loading
+## Customizing the CSV parsing and loading
 
 See the [csv module](https://docs.python.org/3/library/csv.html) documentation for more information of what csv args are supported.
 

docs/snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx

@@ -1,4 +1,4 @@
-Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file.html)
+Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file.html).
 
 ```python
 from langchain.document_loaders import DirectoryLoader
@@ -121,7 +121,7 @@ len(docs)
 
 </CodeOutputBlock>
 
-## Auto detect file encodings with TextLoader
+## Auto-detect file encodings with TextLoader
 
 In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the `TextLoader` class.
 
@@ -212,7 +212,7 @@ loader.load()
 
 </HTMLOutputBlock>
 
-The file `example-non-utf8.txt` uses a different encoding the `load()` function fails with a helpful message indicating which file failed decoding. 
+The file `example-non-utf8.txt` uses a different encoding, so the `load()` function fails with a helpful message indicating which file failed decoding. 
 
 With the default behavior of `TextLoader` any failure to load any of the documents will fail the whole loading process and no documents are loaded. 
 

docs/snippets/modules/data_connection/document_loaders/how_to/pdf.mdx

@@ -139,9 +139,9 @@ data[0]
 
 ### Fetching remote PDFs using Unstructured
 
-This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/
+This covers how to load online PDFs into a document format that we can use downstream. This can be used for various online PDF sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/
 
-Note: all other pdf loaders can also be used to fetch remote PDFs, but `OnlinePDFLoader` is a legacy function, and works specifically with `UnstructuredPDFLoader`.
+Note: all other PDF loaders can also be used to fetch remote PDFs, but `OnlinePDFLoader` is a legacy function, and works specifically with `UnstructuredPDFLoader`.
 
 
 
@@ -208,7 +208,7 @@ data = loader.load()
 
 ### Using PDFMiner to generate HTML text
 
-This can be helpful for chunking texts semantically into sections as the output html content can be parsed via `BeautifulSoup` to get more structured and rich information about font size, page numbers, pdf headers/footers, etc.
+This can be helpful for chunking texts semantically into sections as the output html content can be parsed via `BeautifulSoup` to get more structured and rich information about font size, page numbers, PDF headers/footers, etc.
 
 
 ```python
@@ -222,7 +222,7 @@ loader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf")
 
 
 ```python
-data = loader.load()[0]   # entire pdf is loaded as a single Document
+data = loader.load()[0]   # entire PDF is loaded as a single Document
 ```
 
 
@@ -259,7 +259,7 @@ for c in content:
         cur_text = c.text
 snippets.append((cur_text,cur_fs))
 # Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as
-# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info)
+# headers/footers in a PDF appear on multiple pages so if we find duplicates it's safe to assume that it is redundant info)
 ```
 
 
@@ -285,7 +285,7 @@ for s in snippets:
         continue
     
     # if current snippet's font size > previous section's content but less than previous section's heading than also make a new 
-    # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections)
+    # section (e.g. title of a PDF will have the highest font size but we don't want it to subsume all sections)
     metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]}
     metadata.update(data.metadata)
     semantic_snippets.append(Document(page_content='',metadata=metadata))
@@ -358,7 +358,7 @@ loader = PyPDFDirectoryLoader("example_data/")
 docs = loader.load()
 ```
 
-## Using pdfplumber
+## Using PDFPlumber
 
 Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page.