docs: `Hugging Face` update (#16490)

- added missed integrations to the platform page
- updated integration examples: added links and fixed formats

docs/docs/integrations/chat/huggingface.ipynb

@@ -4,9 +4,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Hugging Face Chat Wrapper\n",
+    "# Hugging Face\n",
     "\n",
-    "This notebook shows how to get started using Hugging Face LLM's as chat models.\n",
+    "This notebook shows how to get started using `Hugging Face` LLM's as chat models.\n",
     "\n",
     "In particular, we will:\n",
     "1. Utilize the [HuggingFaceTextGenInference](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_text_gen_inference.py), [HuggingFaceEndpoint](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_endpoint.py), or [HuggingFaceHub](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/llms/huggingface_hub.py) integrations to instantiate an `LLM`.\n",
@@ -49,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### `HuggingFaceTextGenInference`"
+    "### `HuggingFaceTextGenInference`"
    ]
   },
   {
@@ -93,7 +93,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### `HuggingFaceEndpoint`"
+    "### `HuggingFaceEndpoint`"
    ]
   },
   {
@@ -121,7 +121,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### `HuggingFaceHub`"
+    "### `HuggingFaceHub`"
    ]
   },
   {
@@ -291,7 +291,7 @@
    "source": [
     "## 3. Take it for a spin as an agent!\n",
     "\n",
-    "Here we'll test out `Zephyr-7B-beta` as a zero-shot ReAct Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
+    "Here we'll test out `Zephyr-7B-beta` as a zero-shot `ReAct` Agent. The example below is taken from [here](https://python.langchain.com/docs/modules/agents/agent_types/react#using-chat-models).\n",
     "\n",
     "> Note: To run this section, you'll need to have a [SerpAPI Token](https://serpapi.com/) saved as an environment variable: `SERPAPI_API_KEY`"
    ]
@@ -448,7 +448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.5"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/platforms/huggingface.mdx

@@ -58,31 +58,24 @@ See a [usage example](/docs/integrations/llms/huggingface_textgen_inference).
 from langchain_community.llms import HuggingFaceTextGenInference
 ```
 
+## Chat models
 
+### Models from Hugging Face
 
-## Document Loaders
+We can use the `Hugging Face` LLM classes or directly use the `ChatHuggingFace` class.
-
-### Hugging Face dataset
 
->[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000 
+We need to install several python packages.
-> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages 
-> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
-> They used for a diverse range of tasks such as translation, automatic speech 
-> recognition, and image classification.
-
-We need to install `datasets` python package.
 
 ```bash
-pip install datasets
+pip install huggingface_hub
+pip install transformers
 ```
-
+See a [usage example](/docs/integrations/chat/huggingface).
-See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
 
 ```python
-from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
+from langchain_community.chat_models.huggingface import ChatHuggingFace
 ```
 
-
 ## Embedding Models
 
 ### Hugging Face Hub
@@ -126,6 +119,48 @@ 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
+
+>[Hugging Face Hub](https://huggingface.co/docs/hub/index) is home to over 75,000 
+> [datasets](https://huggingface.co/docs/hub/index#datasets) in more than 100 languages 
+> that can be used for a broad range of tasks across NLP, Computer Vision, and Audio.
+> They used for a diverse range of tasks such as translation, automatic speech 
+> recognition, and image classification.
+
+We need to install `datasets` python package.
+
+```bash
+pip install datasets
+```
+
+See a [usage example](/docs/integrations/document_loaders/hugging_face_dataset).
+
+```python
+from langchain_community.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
+```
+
+
 
 ## Tools
 

docs/docs/integrations/text_embedding/text_embeddings_inference.ipynb

@@ -7,7 +7,9 @@
    "source": [
     "# Text Embeddings Inference\n",
     "\n",
-    "Text Embeddings Inference (TEI) 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.\n",
+    ">[Hugging Face Text Embeddings Inference (TEI)](https://huggingface.co/docs/text-generation-inference/index) is a toolkit for deploying and serving open-source\n",
+    "> text embeddings and sequence classification models. `TEI` enables high-performance extraction for the most popular models,\n",
+    ">including `FlagEmbedding`, `Ember`, `GTE` and `E5`.\n",
     "\n",
     "To use it within langchain, first install `huggingface-hub`."
    ]
@@ -21,7 +23,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  huggingface-hub -q"
+    "%pip install --upgrade huggingface-hub"
    ]
   },
   {
@@ -146,9 +148,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "conda_python3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda_python3"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -160,7 +162,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,