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Big docs refactor! Motivation is to make it easier for people to find resources they are looking for. To accomplish this, there are now three main sections: - Getting Started: steps for getting started, walking through most core functionality - Modules: these are different modules of functionality that langchain provides. Each part here has a "getting started", "how to", "key concepts" and "reference" section (except in a few select cases where it didnt easily fit). - Use Cases: this is to separate use cases (like summarization, question answering, evaluation, etc) from the modules, and provide a different entry point to the code base. There is also a full reference section, as well as extra resources (glossary, gallery, etc) Co-authored-by: Shreya Rajpal <ShreyaR@users.noreply.github.com>
69 lines
2.5 KiB
Markdown
69 lines
2.5 KiB
Markdown
# Hugging Face
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This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) 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 Python SDK with `pip install huggingface_hub`
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- Get an OpenAI api key and set it as an environment variable (`HUGGINGFACEHUB_API_TOKEN`)
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If you want work with 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 the following tasks: `text2text-generation`, `text-generation`
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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](../modules/llms/integrations/huggingface_hub.ipynb)
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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.
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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](../modules/utils/combine_docs_examples/embeddings.ipynb)
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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](../modules/utils/combine_docs_examples/textsplitter.ipynb)
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### Datasets
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Hugging Face has lots of great 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.ipynb)
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