Implement max_marginal_relevance_search in VectorStore of Pinecone (#6056)

This adds implementation of MMR search in pinecone; and I have two
semi-related observations about this vector store class:
- Maybe we should also have a
`similarity_search_by_vector_returning_embeddings` like in supabase, but
it's not in the base `VectorStore` class so I didn't implement
- Talking about the base class, there's
`similarity_search_with_relevance_scores`, but in pinecone it is called
`similarity_search_with_score`; maybe we should consider renaming it to
align with other `VectorStore` base and sub classes (or add that as an
alias for backward compatibility)

#### Who can review?

Tag maintainers/contributors who might be interested:
 - VectorStores / Retrievers / Memory - @dev2049

docs/modules/indexes/vectorstores/examples/pinecone.ipynb

@@ -24,7 +24,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install pinecone-client"
+    "!pip install pinecone-client openai tiktoken"
    ]
   },
   {
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "aac9563e",
    "metadata": {
     "tags": []
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a3c3999a",
    "metadata": {},
    "outputs": [],
@@ -135,13 +135,51 @@
     "print(docs[0].page_content)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d46d1452",
+   "metadata": {},
+   "source": [
+    "### Maximal Marginal Relevance Searches\n",
+    "\n",
+    "In addition to using similarity search in the retriever object, you can also use `mmr` as retriever.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "a359ed74",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
+    "matched_docs = retriever.get_relevant_documents(query)\n",
+    "for i, d in enumerate(matched_docs):\n",
+    "    print(f\"\\n## Document {i}\\n\")\n",
+    "    print(d.page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7c477287",
+   "metadata": {},
+   "source": [
+    "Or use `max_marginal_relevance_search` directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca82740",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n",
+    "for i, doc in enumerate(found_docs):\n",
+    "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
+   ]
   }
  ],
  "metadata": {