docs: Update Pinecone example notebook with embedded widget (#21719)

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -12,16 +12,7 @@
     "\n",
     "This notebook shows how to use functionality related to the `Pinecone` vector database.\n",
     "\n",
-    "To use Pinecone, you must have an API key. \n",
+    "Set the following environment variables to follow along in this doc:\n",
-    "Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n",
-    "\n",
-    "Set the following environment variables to make using the `Pinecone` integration easier:\n",
-    "\n",
-    "- `PINECONE_API_KEY`: Your Pinecone API key.\n",
-    "- `PINECONE_INDEX_NAME`: The name of the index you want to use.\n",
-    "\n",
-    "And to follow along in this doc, you should also set\n",
-    "\n",
     "- `OPENAI_API_KEY`: Your OpenAI API key, for using `OpenAIEmbeddings`"
    ]
   },
@@ -34,7 +25,11 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-pinecone langchain-openai langchain"
+    "%pip install --upgrade --quiet  \\\n",
+    "    langchain-pinecone \\\n",
+    "    langchain-openai \\\n",
+    "    langchain \\\n",
+    "    pinecone-notebooks"
    ]
   },
   {
@@ -72,14 +67,93 @@
     "embeddings = OpenAIEmbeddings()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ef6dc4de",
+   "metadata": {},
+   "source": [
+    "Now let's create a new Pinecone account, or sign into your existing one, and create an API key to use in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fdc3c36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pinecone_notebooks.colab import Authenticate\n",
+    "\n",
+    "Authenticate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54da1a39",
+   "metadata": {},
+   "source": [
+    "The newly created API key has been stored in the `PINECONE_API_KEY` environment variable. We will use it to setup the Pinecone client."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb554814",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "pinecone_api_key = os.environ.get(\"PINECONE_API_KEY\")\n",
+    "pinecone_api_key\n",
+    "\n",
+    "import time\n",
+    "\n",
+    "from pinecone import Pinecone, ServerlessSpec\n",
+    "\n",
+    "pc = Pinecone(api_key=pinecone_api_key)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "658706a3",
+   "metadata": {},
+   "source": [
+    "Next, let's connect to your Pinecone index. If one named `index_name` doesn't exist, it will be created."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "276a06dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "\n",
+    "index_name = \"langchain-index\"  # change if desired\n",
+    "\n",
+    "existing_indexes = [index_info[\"name\"] for index_info in pc.list_indexes()]\n",
+    "\n",
+    "if index_name not in existing_indexes:\n",
+    "    pc.create_index(\n",
+    "        name=index_name,\n",
+    "        dimension=1536,\n",
+    "        metric=\"cosine\",\n",
+    "        spec=ServerlessSpec(cloud=\"aws\", region=\"us-east-1\"),\n",
+    "    )\n",
+    "    while not pc.describe_index(index_name).status[\"ready\"]:\n",
+    "        time.sleep(1)\n",
+    "\n",
+    "index = pc.Index(index_name)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "3a4d377f",
    "metadata": {},
    "source": [
-    "Now let's assume you have your Pinecone index set up with `dimension=1536`.\n",
+    "Now that our Pinecone index is setup, we can upsert those chunked docs as contents with `PineconeVectorStore.from_documents`."
-    "\n",
-    "We can connect to our Pinecone index and insert those chunked docs as contents with `PineconeVectorStore.from_documents`."
    ]
   },
   {
@@ -91,8 +165,6 @@
    "source": [
     "from langchain_pinecone import PineconeVectorStore\n",
     "\n",
-    "index_name = \"langchain-test-index\"\n",
-    "\n",
     "docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)"
    ]
   },
@@ -315,14 +387,6 @@
     "for i, doc in enumerate(found_docs):\n",
     "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b0fd750b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -341,7 +405,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,