Polish reference docs (#7045)

This PR fixes broken links in the reference docs.

docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx

@@ -10,7 +10,7 @@ for you.
 
 ## Get started
 
-This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/model_io/models/embeddings.html) interfaces before diving into this.
+This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this.
 
 import GetStarted from "@snippets/modules/data_connection/vectorstores/get_started.mdx"
 

docs/extras/guides/evaluation/openapi_eval.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Evaluating an OpenAPI Chain\n",
     "\n",
-    "This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additiona/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
+    "This notebook goes over ways to semantically evaluate an [OpenAPI Chain](/docs/modules/chains/additional/openapi.html), which calls an endpoint defined by the OpenAPI specification using purely natural language."
    ]
   },
   {

docs/snippets/modules/data_connection/text_embedding/get_started.mdx

@@ -31,7 +31,7 @@ embeddings_model = OpenAIEmbeddings()
 #### Embed list of texts
 
 ```python
-embeddings = embedding_model.embed_documents(
+embeddings = embeddings_model.embed_documents(
     [
         "Hi there!",
         "Oh, hello!",
@@ -56,7 +56,7 @@ len(embeddings), len(embeddings[0])
 Embed a single piece of text for the purpose of comparing to other embedded pieces of texts.
 
 ```python
-embedded_query = embedding_model.embed_query("What was the name mentioned in the conversation?")
+embedded_query = embeddings_model.embed_query("What was the name mentioned in the conversation?")
 embedded_query[:5]
 ```
 

docs/snippets/modules/data_connection/vectorstores/get_started.mdx

@@ -26,7 +26,8 @@ raw_documents = TextLoader('../../../state_of_the_union.txt').load()
 text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
 documents = text_splitter.split_documents(raw_documents)
 
-db = FAISS.from_documents(documents, OpenAIEmbeddings())
+embeddings = OpenAIEmbeddings()
+db = FAISS.from_documents(documents, embeddings)
 ```
 
 ### Similarity search