diff --git a/docs/docs/integrations/platforms/huggingface.mdx b/docs/docs/integrations/platforms/huggingface.mdx
index b558f8947fc..da7d39c1c7a 100644
--- a/docs/docs/integrations/platforms/huggingface.mdx
+++ b/docs/docs/integrations/platforms/huggingface.mdx
@@ -12,7 +12,7 @@ pip install langchain-huggingface
 
 ## Chat models
 
-### Models from Hugging Face
+### ChatHuggingFace
 
 We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
 
@@ -24,7 +24,16 @@ from langchain_huggingface import ChatHuggingFace
 
 ## LLMs
 
-### Hugging Face Local Pipelines
+### HuggingFaceEndpoint
+
+
+See a [usage example](/docs/integrations/llms/huggingface_endpoint).
+
+```python
+from langchain_huggingface import HuggingFaceEndpoint
+```
+
+### HuggingFacePipeline
 
 Hugging Face models can be run locally through the `HuggingFacePipeline` class.
 
@@ -44,6 +53,22 @@ See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
 from langchain_huggingface import HuggingFaceEmbeddings
 ```
 
+### HuggingFaceEndpointEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
+
+```python
+from langchain_huggingface import HuggingFaceEndpointEmbeddings
+```
+
+### HuggingFaceInferenceAPIEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
+
+```python
+from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
+```
+
 ### HuggingFaceInstructEmbeddings
 
 See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
@@ -63,25 +88,6 @@ See a [usage example](/docs/integrations/text_embedding/bge_huggingface).
 from langchain_community.embeddings import HuggingFaceBgeEmbeddings
 ```
 
-### Hugging Face Text Embeddings Inference (TEI)
-
->[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source
-> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,
->including `FlagEmbedding`, `Ember`, `GTE` and `E5`.
-
-We need to install `huggingface-hub` python package.
-
-```bash
-pip install huggingface-hub
-```
-
-See a [usage example](/docs/integrations/text_embedding/text_embeddings_inference).
-
-```python
-from langchain_community.embeddings import HuggingFaceHubEmbeddings
-```
-
-
 ## Document Loaders
 
 ### Hugging Face dataset
@@ -104,7 +110,34 @@ See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
 from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
 ```
 
+### Hugging Face model loader
 
+>Load model information from `Hugging Face Hub`, including README content.
+>
+>This loader interfaces with the `Hugging Face Models API` to fetch 
+> and load model metadata and README files. 
+> The API allows you to search and filter models based on 
+> specific criteria such as model tags, authors, and more.
+
+```python
+from langchain_community.document_loaders import HuggingFaceModelLoader
+```
+
+### Image captions
+
+It uses the Hugging Face models to generate image captions.
+
+We need to install several python packages.
+
+```bash
+pip install transformers pillow
+```
+
+See a [usage example](/docs/integrations/document_loaders/image_captions).
+
+```python
+from langchain_community.document_loaders import ImageCaptionLoader
+```
 
 ## Tools
 
@@ -124,3 +157,12 @@ See a [usage example](/docs/integrations/tools/huggingface_tools).
 ```python
 from langchain_community.agent_toolkits.load_tools import load_huggingface_tool
 ```
+
+### Hugging Face Text-to-Speech Model Inference.
+
+> It is a wrapper around `OpenAI Text-to-Speech API`.
+
+```python
+from langchain_community.tools.audio import HuggingFaceTextToSpeechModelInference
+```
+
diff --git a/docs/docs/integrations/providers/apple.mdx b/docs/docs/integrations/providers/apple.mdx
new file mode 100644
index 00000000000..5a87afeb6c5
--- /dev/null
+++ b/docs/docs/integrations/providers/apple.mdx
@@ -0,0 +1,22 @@
+# Apple
+
+>[Apple Inc. (Wikipedia)](https://en.wikipedia.org/wiki/Apple_Inc.) is an American 
+> multinational corporation and technology company.
+>
+> [iMessage (Wikipedia)](https://en.wikipedia.org/wiki/IMessage) is an instant 
+> messaging service developed by Apple Inc. and launched in 2011. 
+> `iMessage` functions exclusively on Apple platforms.
+
+## Installation and Setup
+
+See [setup instructions](/docs/integrations/chat_loaders/imessage).
+
+## Chat loader
+
+It loads chat sessions from the `iMessage` `chat.db` `SQLite` file.
+
+See a [usage example](/docs/integrations/chat_loaders/imessage).
+
+```python
+from langchain_community.chat_loaders.imessage import IMessageChatLoader
+```
diff --git a/docs/docs/integrations/providers/nomic.ipynb b/docs/docs/integrations/providers/nomic.ipynb
deleted file mode 100644
index b1f4861612a..00000000000
--- a/docs/docs/integrations/providers/nomic.ipynb
+++ /dev/null
@@ -1,69 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Nomic\n",
-    "\n",
-    "Nomic currently offers two products:\n",
-    "\n",
-    "- Atlas: their Visual Data Engine\n",
-    "- GPT4All: their Open Source Edge Language Model Ecosystem\n",
-    "\n",
-    "The Nomic integration exists in its own [partner package](https://pypi.org/project/langchain-nomic/). You can install it with:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install -qU langchain-nomic"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Currently, you can import their hosted [embedding model](/docs/integrations/text_embedding/nomic) as follows:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "id": "y8ku6X96sebl"
-   },
-   "outputs": [],
-   "source": [
-    "from langchain_nomic import NomicEmbeddings"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/docs/docs/integrations/providers/nomic.mdx b/docs/docs/integrations/providers/nomic.mdx
new file mode 100644
index 00000000000..f825e3c74e3
--- /dev/null
+++ b/docs/docs/integrations/providers/nomic.mdx
@@ -0,0 +1,58 @@
+# Nomic
+
+>[Nomic](https://www.nomic.ai/) builds tools that enable everyone to interact with AI scale datasets and run AI models on consumer computers.
+>
+>`Nomic` currently offers two products:
+>
+>- `Atlas`: the Visual Data Engine
+>- `GPT4All`: the Open Source Edge Language Model Ecosystem
+
+The Nomic integration exists in two partner packages: [langchain-nomic](https://pypi.org/project/langchain-nomic/)
+and in [langchain-community](https://pypi.org/project/langchain-community/). 
+
+## Installation
+
+You can install them with:
+
+```bash
+pip install -U langchain-nomic
+pip install -U langchain-community
+```
+
+## LLMs
+
+### GPT4All
+
+See [a usage example](/docs/integrations/llms/gpt4all).
+
+```python
+from langchain_community.llms import GPT4All
+```
+
+## Embedding models
+
+### NomicEmbeddings
+
+See [a usage example](/docs/integrations/text_embedding/nomic).
+
+```python
+from langchain_nomic import NomicEmbeddings
+```
+
+### GPT4All
+
+See [a usage example](/docs/integrations/text_embedding/gpt4all).
+
+```python
+from langchain_community.embeddings import GPT4AllEmbeddings
+```
+
+## Vector store
+
+### Atlas
+
+See [a usage example and installation instructions](/docs/integrations/vectorstores/atlas).
+
+```python
+from langchain_community.vectorstores import AtlasDB
+```
diff --git a/docs/docs/integrations/providers/transwarp.mdx b/docs/docs/integrations/providers/transwarp.mdx
new file mode 100644
index 00000000000..4406f885bab
--- /dev/null
+++ b/docs/docs/integrations/providers/transwarp.mdx
@@ -0,0 +1,34 @@
+# Transwarp
+
+>[Transwarp](https://www.transwarp.cn/en/introduction) aims to build 
+> enterprise-level big data and AI infrastructure software, 
+> to shape the future of data world. It provides enterprises with 
+> infrastructure software and services around the whole data lifecycle, 
+> including integration, storage, governance, modeling, analysis, 
+> mining and circulation. 
+> 
+> `Transwarp` focuses on technology research and 
+> development and has accumulated core technologies in these aspects: 
+> distributed computing, SQL compilations, database technology, 
+> unification for multi-model data management, container-based cloud computing, 
+> and big data analytics and intelligence.
+
+## Installation
+
+You have to install several python packages:
+
+```bash
+pip install -U tiktoken hippo-api
+```
+
+and get the connection configuration.
+
+## Vector stores
+
+### Hippo
+
+See [a usage example and installation instructions](/docs/integrations/vectorstores/hippo).
+
+```python
+from langchain_community.vectorstores.hippo import Hippo
+```
diff --git a/docs/docs/integrations/providers/upstage.ipynb b/docs/docs/integrations/providers/upstage.ipynb
index b43bfe163d2..9dfce63e351 100644
--- a/docs/docs/integrations/providers/upstage.ipynb
+++ b/docs/docs/integrations/providers/upstage.ipynb
@@ -6,45 +6,18 @@
    "source": [
     "# Upstage\n",
     "\n",
-    "[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components. \n"
+    ">[Upstage](https://upstage.ai) is a leading artificial intelligence (AI) company specializing in delivering above-human-grade performance LLM components.\n",
+    ">\n",
+    ">**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
+    "\n",
+    ">Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. \n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Solar LLM\n",
-    "\n",
-    "**Solar Mini Chat** is a fast yet powerful advanced large language model focusing on English and Korean. It has been specifically fine-tuned for multi-turn chat purposes, showing enhanced performance across a wide range of natural language processing tasks, like multi-turn conversation or tasks that require an understanding of long contexts, such as RAG (Retrieval-Augmented Generation), compared to other models of a similar size. This fine-tuning equips it with the ability to handle longer conversations more effectively, making it particularly adept for interactive applications.\n",
-    "\n",
-    "Other than Solar, Upstage also offers features for real-world RAG (retrieval-augmented generation), such as **Groundedness Check** and **Layout Analysis**. "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Installation and Setup\n",
-    "\n",
-    "Install `langchain-upstage` package:\n",
-    "\n",
-    "```bash\n",
-    "pip install -qU langchain-core langchain-upstage\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Upstage LangChain integrations\n",
+    "### Upstage LangChain integrations\n",
     "\n",
     "| API | Description | Import | Example usage |\n",
     "| --- | --- | --- | --- |\n",
@@ -60,9 +33,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Quick Examples\n",
+    "## Installation and Setup\n",
     "\n",
-    "### Environment Setup"
+    "Install `langchain-upstage` package:\n",
+    "\n",
+    "```bash\n",
+    "pip install -qU langchain-core langchain-upstage\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get [API Keys](https://console.upstage.ai) and set environment variable `UPSTAGE_API_KEY`."
    ]
   },
   {
@@ -80,8 +64,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Chat models\n",
     "\n",
-    "### Chat\n"
+    "### Solar LLM\n",
+    "\n",
+    "See [a usage example](/docs/integrations/chat/upstage)."
    ]
   },
   {
@@ -101,10 +88,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Embedding models\n",
     "\n",
-    "\n",
-    "### Text embedding\n",
-    "\n"
+    "See [a usage example](/docs/integrations/text_embedding/upstage)."
    ]
   },
   {
@@ -134,7 +120,45 @@
     }
    },
    "source": [
-    "### Groundedness Check"
+    "## Document loader\n",
+    "\n",
+    "### Layout Analysis\n",
+    "\n",
+    "See [a usage example](/docs/integrations/document_loaders/upstage)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
+    "\n",
+    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
+    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
+    "\n",
+    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
+    "docs = layzer.load()  # or layzer.lazy_load()\n",
+    "\n",
+    "for doc in docs[:3]:\n",
+    "    print(doc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "source": [
+    "## Tools\n",
+    "\n",
+    "### Groundedness Check\n",
+    "\n",
+    "See [a usage example](/docs/integrations/tools/upstage_groundedness_check)."
    ]
   },
   {
@@ -159,36 +183,6 @@
     "response = groundedness_check.invoke(request_input)\n",
     "print(response)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "### Layout Analysis"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from langchain_upstage import UpstageLayoutAnalysisLoader\n",
-    "\n",
-    "file_path = \"/PATH/TO/YOUR/FILE.pdf\"\n",
-    "layzer = UpstageLayoutAnalysisLoader(file_path, split=\"page\")\n",
-    "\n",
-    "# For improved memory efficiency, consider using the lazy_load method to load documents page by page.\n",
-    "docs = layzer.load()  # or layzer.lazy_load()\n",
-    "\n",
-    "for doc in docs[:3]:\n",
-    "    print(doc)"
-   ]
   }
  ],
  "metadata": {
@@ -210,7 +204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,