docs: Update broken vectorstore urls in retrievers.ipynb (#27838)

**Description**: Update outdated `VectorStore` api reference urls in
`retrievers.ipynb`

Co-authored-by: Erick Friis <[email protected]>

docs/docs/tutorials/retrievers.ipynb

@@ -151,13 +151,13 @@
    "id": "ff0f0b43-e5b8-4c79-b782-a02f17345487",
    "metadata": {},
    "source": [
-    "Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store--  e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
+    "Calling `.from_documents` here will add the documents to the vector store. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) implements methods for adding documents that can also be called after the object is instantiated. Most implementations will allow you to connect to an existing vector store--  e.g., by providing a client, index name, or other information. See the documentation for a specific [integration](/docs/integrations/vectorstores) for more detail.\n",
     "\n",
-    "Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html) includes methods for querying:\n",
+    "Once we've instantiated a `VectorStore` that contains documents, we can query it. [VectorStore](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html) includes methods for querying:\n",
     "- Synchronously and asynchronously;\n",
     "- By string query and by vector;\n",
     "- With and without returning similarity scores;\n",
-    "- By similarity and [maximum marginal relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
+    "- By similarity and [maximum marginal relevance](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html#langchain_core.vectorstores.base.VectorStore.max_marginal_relevance_search) (to balance similarity with query to diversity in retrieved results).\n",
     "\n",
     "The methods will generally include a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html#langchain_core.documents.base.Document) objects in their outputs.\n",
     "\n",
@@ -303,7 +303,7 @@
    "source": [
     "Learn more:\n",
     "\n",
-    "- [API reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html)\n",
+    "- [API reference](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStore.html)\n",
     "- [How-to guide](/docs/how_to/vectorstores)\n",
     "- [Integration-specific docs](/docs/integrations/vectorstores)\n",
     "\n",
@@ -348,7 +348,7 @@
    "id": "a36d3f64-a8bc-4baa-b2ea-07e324a0143e",
    "metadata": {},
    "source": [
-    "Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
+    "Vectorstores implement an `as_retriever` method that will generate a Retriever, specifically a [VectorStoreRetriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.base.VectorStoreRetriever.html). These retrievers include specific `search_type` and `search_kwargs` attributes that identify what methods of the underlying vector store to call, and how to parameterize them. For instance, we can replicate the above with the following:"
    ]
   },
   {