docs: `Hugging Face` platform page (#13831)

`Hugging Face` is definitely a platform. It includes many integrations
for many modules (LLM, Embedding, DocumentLoader, Tool)
So, a doc page was added that defines Hugging Face as a platform.

docs/docs/integrations/platforms/huggingface.mdx

@@ -0,0 +1,136 @@
+# Hugging Face
+
+All functionality related to the [Hugging Face Platform](https://huggingface.co/).
+
+## LLMs
+
+### Hugging Face Hub
+
+>The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform 
+> with over 350k models, 75k datasets, and 150k demo apps (Spaces), all open source 
+> and publicly available, in an online platform where people can easily 
+> collaborate and build ML together. The Hub works as a central place where anyone 
+> can explore, experiment, collaborate, and build technology with Machine Learning.
+
+To use, we should have the `huggingface_hub` python [package installed](https://huggingface.co/docs/huggingface_hub/installation).
+
+```bash
+pip install huggingface_hub
+```
+
+See a [usage example](/docs/integrations/llms/huggingface_hub).
+
+```python
+from langchain.llms import HuggingFaceHub
+```
+
+### Hugging Face Local Pipelines
+
+Hugging Face models can be run locally through the `HuggingFacePipeline` class.
+
+We need to install `transformers` python package.
+
+```bash
+pip install transformers
+```
+
+See a [usage example](/docs/integrations/llms/huggingface_pipelines).
+
+```python
+from langchain.llms.huggingface_pipeline import HuggingFacePipeline
+```
+
+### Hugging Face TextGen Inference
+
+>[Text Generation Inference](https://github.com/huggingface/text-generation-inference) is 
+> a Rust, Python and gRPC server for text generation inference. Used in production at 
+> [HuggingFace](https://huggingface.co/) to power LLMs api-inference widgets.
+
+We need to install `text_generation` python package.
+
+```bash
+pip install text_generation
+```
+
+See a [usage example](/docs/integrations/llms/huggingface_textgen_inference).
+
+```python
+from langchain.llms import HuggingFaceTextGenInference
+```
+
+
+
+## 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.document_loaders.hugging_face_dataset import HuggingFaceDatasetLoader
+```
+
+
+## Embedding Models
+
+### Hugging Face Hub
+
+>The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform 
+> with over 350k models, 75k datasets, and 150k demo apps (Spaces), all open source 
+> and publicly available, in an online platform where people can easily 
+> collaborate and build ML together. The Hub works as a central place where anyone 
+> can explore, experiment, collaborate, and build technology with Machine Learning.
+
+We need to install the `sentence_transformers` python package.
+
+```bash
+pip install sentence_transformers
+```
+
+
+#### HuggingFaceEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/huggingfacehub).
+
+```python
+from langchain.embeddings import HuggingFaceEmbeddings
+```
+#### HuggingFaceInstructEmbeddings
+
+See a [usage example](/docs/integrations/text_embedding/instruct_embeddings).
+
+```python
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+```
+
+
+## Tools
+
+### Hugging Face Hub Tools
+
+>[Hugging Face Tools](https://huggingface.co/docs/transformers/v4.29.0/en/custom_tools) 
+> support text I/O and are loaded using the `load_huggingface_tool` function.
+
+We need to install several python packages.
+
+```bash
+pip install transformers huggingface_hub
+```
+
+See a [usage example](/docs/integrations/tools/huggingface_tools).
+
+```python
+from langchain.agents import load_huggingface_tool
+```

docs/docs/integrations/providers/huggingface.mdx

@@ -1,69 +0,0 @@
-# Hugging Face
-
-This page covers how to use the Hugging Face ecosystem (including the [Hugging Face Hub](https://huggingface.co)) within LangChain.
-It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.
-
-## Installation and Setup
-
-If you want to work with the Hugging Face Hub:
-- Install the Hub client library with `pip install huggingface_hub`
-- Create a Hugging Face account (it's free!)
-- Create an [access token](https://huggingface.co/docs/hub/security-tokens) and set it as an environment variable (`HUGGINGFACEHUB_API_TOKEN`)
-
-If you want work with the Hugging Face Python libraries:
-- Install `pip install transformers` for working with models and tokenizers
-- Install `pip install datasets` for working with datasets
-
-## Wrappers
-
-### LLM
-
-There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub.
-Note that these wrappers only work for models that support the following tasks: [`text2text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text2text-generation&sort=downloads), [`text-generation`](https://huggingface.co/models?library=transformers&pipeline_tag=text-classification&sort=downloads)
-
-To use the local pipeline wrapper:
-```python
-from langchain.llms import HuggingFacePipeline
-```
-
-To use a the wrapper for a model hosted on Hugging Face Hub:
-```python
-from langchain.llms import HuggingFaceHub
-```
-For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/integrations/llms/huggingface_hub)
-
-
-### Embeddings
-
-There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub.
-Note that these wrappers only work for [`sentence-transformers` models](https://huggingface.co/models?library=sentence-transformers&sort=downloads).
-
-To use the local pipeline wrapper:
-```python
-from langchain.embeddings import HuggingFaceEmbeddings
-```
-
-To use a the wrapper for a model hosted on Hugging Face Hub:
-```python
-from langchain.embeddings import HuggingFaceHubEmbeddings
-```
-For a more detailed walkthrough of this, see [this notebook](/docs/integrations/text_embedding/huggingfacehub)
-
-### Tokenizer
-
-There are several places you can use tokenizers available through the `transformers` package.
-By default, it is used to count tokens for all LLMs.
-
-You can also use it to count tokens when splitting documents with 
-```python
-from langchain.text_splitter import CharacterTextSplitter
-CharacterTextSplitter.from_huggingface_tokenizer(...)
-```
-For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/document_transformers/text_splitters/huggingface_length_function)
-
-
-### Datasets
-
-The Hugging Face Hub has lots of great [datasets](https://huggingface.co/datasets) that can be used to evaluate your LLM chains.
-
-For a detailed walkthrough of how to use them to do so, see [this notebook](/docs/integrations/document_loaders/hugging_face_dataset)

docs/vercel.json

@@ -304,6 +304,10 @@
       "source":  "/docs/integrations/providers/amazon_api_gateway",
       "destination": "/docs/integrations/platforms/aws"
     },
+    {
+      "source":  "/docs/integrations/providers/huggingface",
+      "destination": "/docs/integrations/platforms/huggingface"
+    },
     {
       "source":  "/docs/integrations/providers/azure_blob_storage",
       "destination": "/docs/integrations/platforms/microsoft"