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Co-authored-by: jacoblee93 <jacoblee93@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
70 lines
3.0 KiB
Plaintext
70 lines
3.0 KiB
Plaintext
# Hugging Face
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This page covers how to use the Hugging Face ecosystem (including the [Hugging Face Hub](https://huggingface.co)) within LangChain.
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It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.
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## Installation and Setup
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If you want to work with the Hugging Face Hub:
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- Install the Hub client library with `pip install huggingface_hub`
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- Create a Hugging Face account (it's free!)
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- Create an [access token](https://huggingface.co/docs/hub/security-tokens) and set it as an environment variable (`HUGGINGFACEHUB_API_TOKEN`)
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If you want work with the Hugging Face Python libraries:
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- Install `pip install transformers` for working with models and tokenizers
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- Install `pip install datasets` for working with datasets
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## Wrappers
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### LLM
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There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub.
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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)
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To use the local pipeline wrapper:
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```python
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from langchain.llms import HuggingFacePipeline
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```
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To use a the wrapper for a model hosted on Hugging Face Hub:
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```python
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from langchain.llms import HuggingFaceHub
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```
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For a more detailed walkthrough of the Hugging Face Hub wrapper, see [this notebook](/docs/modules/model_io/models/llms/integrations/huggingface_hub.html)
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### Embeddings
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There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub.
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Note that these wrappers only work for [`sentence-transformers` models](https://huggingface.co/models?library=sentence-transformers&sort=downloads).
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To use the local pipeline wrapper:
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```python
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from langchain.embeddings import HuggingFaceEmbeddings
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```
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To use a the wrapper for a model hosted on Hugging Face Hub:
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```python
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from langchain.embeddings import HuggingFaceHubEmbeddings
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```
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For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/text_embedding/integrations/huggingfacehub.html)
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### Tokenizer
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There are several places you can use tokenizers available through the `transformers` package.
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By default, it is used to count tokens for all LLMs.
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You can also use it to count tokens when splitting documents with
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```python
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from langchain.text_splitter import CharacterTextSplitter
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CharacterTextSplitter.from_huggingface_tokenizer(...)
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```
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For a more detailed walkthrough of this, see [this notebook](/docs/modules/data_connection/document_transformers/text_splitters/huggingface_length_function.html)
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### Datasets
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The Hugging Face Hub has lots of great [datasets](https://huggingface.co/datasets) that can be used to evaluate your LLM chains.
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For a detailed walkthrough of how to use them to do so, see [this notebook](../use_cases/evaluation/huggingface_datasets.html)
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