diff --git a/docs/docs/integrations/text_embedding/together.ipynb b/docs/docs/integrations/text_embedding/together.ipynb
index 6f3cc393d1..16dd5aa3a1 100644
--- a/docs/docs/integrations/text_embedding/together.ipynb
+++ b/docs/docs/integrations/text_embedding/together.ipynb
@@ -12,101 +12,244 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e49f1e0d",
+   "id": "9a3d6f34",
    "metadata": {},
    "source": [
     "# TogetherEmbeddings\n",
     "\n",
-    "This notebook covers how to get started with open source embedding models hosted in the Together AI API.\n",
+    "This will help you get started with Together embedding models using LangChain. For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n",
     "\n",
-    "## Installation"
+    "## Overview\n",
+    "### Integration details\n",
+    "\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
+    "\n",
+    "<ItemTable category=\"text_embedding\" item=\"Together\" />\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "To access Together embedding models you'll need to create a/an Together account, get an API key, and install the `langchain-together` integration package.\n",
+    "\n",
+    "### Credentials\n",
+    "\n",
+    "Head to [https://api.together.xyz/](https://api.together.xyz/) to sign up to Together and generate an API key. Once you've done this set the TOGETHER_API_KEY environment variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
+   "execution_count": 1,
+   "id": "36521c2a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# install package\n",
-    "%pip install --upgrade --quiet  langchain-together"
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "if not os.getenv(\"TOGETHER_API_KEY\"):\n",
+    "    os.environ[\"TOGETHER_API_KEY\"] = getpass.getpass(\"Enter your Together API key: \")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "2b4f3e15",
+   "id": "c84fb993",
    "metadata": {},
    "source": [
-    "## Environment Setup\n",
-    "\n",
-    "Make sure to set the following environment variables:\n",
-    "\n",
-    "- `TOGETHER_API_KEY`\n",
-    "\n",
-    "## Usage\n",
-    "\n",
-    "First, select a supported model from [this list](https://docs.together.ai/docs/embedding-models). In the following example, we will use `togethercomputer/m2-bert-80M-8k-retrieval`."
+    "If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "62e0dbc3",
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 2,
+   "id": "39a4953b",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain_together.embeddings import TogetherEmbeddings\n",
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9664366",
+   "metadata": {},
+   "source": [
+    "### Installation\n",
     "\n",
-    "embeddings = TogetherEmbeddings(model=\"togethercomputer/m2-bert-80M-8k-retrieval\")"
+    "The LangChain Together integration lives in the `langchain-together` package:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "12fcfb4b",
+   "execution_count": 3,
+   "id": "64853226",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
-    "embeddings.embed_query(\"My query to look up\")"
+    "%pip install -qU langchain-together"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45dd1724",
+   "metadata": {},
+   "source": [
+    "## Instantiation\n",
+    "\n",
+    "Now we can instantiate our model object and generate chat completions:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "1f2e6104",
+   "execution_count": 5,
+   "id": "9ea7a09b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "embeddings.embed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
+    "from langchain_together import TogetherEmbeddings\n",
+    "\n",
+    "embeddings = TogetherEmbeddings(\n",
+    "    model=\"togethercomputer/m2-bert-80M-8k-retrieval\",\n",
     ")"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "46739f68",
+   "cell_type": "markdown",
+   "id": "77d271b6",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# async embed query\n",
-    "await embeddings.aembed_query(\"My query to look up\")"
+    "## Indexing and Retrieval\n",
+    "\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "\n",
+    "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e48632ea",
+   "execution_count": 6,
+   "id": "d817716b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LangChain is the framework for building context-aware reasoning applications'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# async embed documents\n",
-    "await embeddings.aembed_documents(\n",
-    "    [\"This is a content of the document\", \"This is another document\"]\n",
-    ")"
+    "# Create a vector store with a sample text\n",
+    "from langchain_core.vectorstores import InMemoryVectorStore\n",
+    "\n",
+    "text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
+    "\n",
+    "vectorstore = InMemoryVectorStore.from_texts(\n",
+    "    [text],\n",
+    "    embedding=embeddings,\n",
+    ")\n",
+    "\n",
+    "# Use the vectorstore as a retriever\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "# Retrieve the most similar text\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
+    "\n",
+    "# show the retrieved document's content\n",
+    "retrieved_documents[0].page_content"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e02b9855",
+   "metadata": {},
+   "source": [
+    "## Direct Usage\n",
+    "\n",
+    "Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
+    "\n",
+    "You can directly call these methods to get embeddings for your own use cases.\n",
+    "\n",
+    "### Embed single texts\n",
+    "\n",
+    "You can embed single texts or documents with `embed_query`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "0d2befcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n"
+     ]
+    }
+   ],
+   "source": [
+    "single_vector = embeddings.embed_query(text)\n",
+    "print(str(single_vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b5a7d03",
+   "metadata": {},
+   "source": [
+    "### Embed multiple texts\n",
+    "\n",
+    "You can embed multiple texts with `embed_documents`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "2f4d6e97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.3812227, -0.052848946, -0.10564975, 0.03480297, 0.2878488, 0.0084609175, 0.11605915, 0.05303011, \n",
+      "[0.066308185, -0.032866564, 0.115751594, 0.19082588, 0.14017, -0.26976448, -0.056340694, -0.26923394\n"
+     ]
+    }
+   ],
+   "source": [
+    "text2 = (\n",
+    "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
+    ")\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
+    "for vector in two_vectors:\n",
+    "    print(str(vector)[:100])  # Show the first 100 characters of the vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98785c12",
+   "metadata": {},
+   "source": [
+    "## API Reference\n",
+    "\n",
+    "For detailed documentation on `TogetherEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_together.embeddings.TogetherEmbeddings.html).\n"
    ]
   }
  ],
@@ -126,7 +269,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,