docs: style nits (#21666)

docs/docs/how_to/add_scores_retriever.ipynb

@@ -11,11 +11,11 @@
     "1. From [vectorstore retrievers](/docs/how_to/vectorstore_retriever);\n",
     "2. From higher-order LangChain retrievers, such as [SelfQueryRetriever](/docs/how_to/self_query) or [MultiVectorRetriever](/docs/how_to/multi_vector).\n",
     "\n",
-    "For (1), we will implement a short wrapper function around the corresponding vectorstore. For (2), we will update a method of the corresponding class.\n",
+    "For (1), we will implement a short wrapper function around the corresponding vector store. For (2), we will update a method of the corresponding class.\n",
     "\n",
-    "## Create vectorstore\n",
+    "## Create vector store\n",
     "\n",
-    "First we populate a vectorstore with some data. We will use a [PineconeVectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vectorstore that implements a `.similarity_search_with_score` method."
+    "First we populate a vector store with some data. We will use a [PineconeVectorStore](https://api.python.langchain.com/en/latest/vectorstores/langchain_pinecone.vectorstores.PineconeVectorStore.html), but this guide is compatible with any LangChain vector store that implements a `.similarity_search_with_score` method."
    ]
   },
   {
@@ -73,7 +73,7 @@
    "source": [
     "## Retriever\n",
     "\n",
-    "To obtain scores from a vectorstore retriever, we wrap the underlying vectorstore's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n",
+    "To obtain scores from a vector store retriever, we wrap the underlying vector store's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n",
     "\n",
     "We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/#langchain-expression-language) that can be used similarly to a typical retriever."
    ]
@@ -142,7 +142,7 @@
     "\n",
     "`SelfQueryRetriever` will use a LLM to generate a query that is potentially structured-- for example, it can construct filters for the retrieval on top of the usual semantic-similarity driven selection. See [this guide](/docs/how_to/self_query) for more detail.\n",
     "\n",
-    "`SelfQueryRetriever` includes a short (1 - 2 line) method `_get_docs_with_query` that executes the vectorstore search. We can subclass `SelfQueryRetriever` and override this method to propagate similarity scores.\n",
+    "`SelfQueryRetriever` includes a short (1 - 2 line) method `_get_docs_with_query` that executes the `vectorstore` search. We can subclass `SelfQueryRetriever` and override this method to propagate similarity scores.\n",
     "\n",
     "First, following the [how-to guide](/docs/how_to/self_query), we will need to establish some metadata on which to filter:"
    ]
@@ -187,7 +187,7 @@
    "id": "0a6c6fa8-1e2f-45ee-83e9-a6cbd82292d2",
    "metadata": {},
    "source": [
-    "We then override the `_get_docs_with_query` to use the `similarity_search_with_score` method of the underlying vectorstore: "
+    "We then override the `_get_docs_with_query` to use the `similarity_search_with_score` method of the underlying vector store: "
    ]
   },
   {
@@ -259,7 +259,7 @@
    "source": [
     "## MultiVectorRetriever\n",
     "\n",
-    "`MultiVectorRetriever` allows you to associate multiple vectors with a single document. This can be useful in a number of applications. For example, we can index small chunks of a larger document and run the retrieval on the chunks, but return the larger \"parent\" document when invoking the retriever. [ParentDocumentRetriever](/docs/how_to/parent_document_retriever/), a subclass of `MultiVectorRetriever`, includes convenience methods for populating a vectorstore to support this. Further applications are detailed in this [how-to guide](/docs/how_to/multi_vector/).\n",
+    "`MultiVectorRetriever` allows you to associate multiple vectors with a single document. This can be useful in a number of applications. For example, we can index small chunks of a larger document and run the retrieval on the chunks, but return the larger \"parent\" document when invoking the retriever. [ParentDocumentRetriever](/docs/how_to/parent_document_retriever/), a subclass of `MultiVectorRetriever`, includes convenience methods for populating a vector store to support this. Further applications are detailed in this [how-to guide](/docs/how_to/multi_vector/).\n",
     "\n",
     "To propagate similarity scores through this retriever, we can again subclass `MultiVectorRetriever` and override a method. This time we will override `_get_relevant_documents`.\n",
     "\n",
@@ -290,7 +290,7 @@
    "id": "453b7415-4a6d-45d4-a329-9c1d7271d1b2",
    "metadata": {},
    "source": [
-    "Next we will add some fake \"sub-documents\" to our vectorstore. We can link these sub-documents to the parent documents by populating the `\"doc_id\"` key in its metadata."
+    "Next we will add some fake \"sub-documents\" to our vector store. We can link these sub-documents to the parent documents by populating the `\"doc_id\"` key in its metadata."
    ]
   },
   {
@@ -338,7 +338,7 @@
    "source": [
     "To propagate the scores, we subclass `MultiVectorRetriever` and override its `_get_relevant_documents` method. Here we will make two changes:\n",
     "\n",
-    "1. We will add similarity scores to the metadata of the corresponding \"sub-documents\" using the `similarity_search_with_score` method of the underlying vectorstore as above;\n",
+    "1. We will add similarity scores to the metadata of the corresponding \"sub-documents\" using the `similarity_search_with_score` method of the underlying vector store as above;\n",
     "2. We will include a list of these sub-documents in the metadata of the retrieved parent document. This surfaces what snippets of text were identified by the retrieval, together with their corresponding similarity scores."
    ]
   },