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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>
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Hugging Face
This page covers how to use the Hugging Face ecosystem (including the Hugging Face Hub) 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 Python SDK with
pip install huggingface_hub - Get an OpenAI api key and set it as an environment variable (
HUGGINGFACEHUB_API_TOKEN)
If you want work with Hugging Face python libraries:
- Install
pip install transformersfor working with models and tokenizers - Install
pip install datasetsfor 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 the following tasks: text2text-generation, text-generation
To use the local pipeline wrapper:
from langchain.llms import HuggingFacePipeline
To use a the wrapper for a model hosted on Hugging Face Hub:
from langchain.llms import HuggingFaceHub
For a more detailed walkthrough of the Hugging Face Hub wrapper, see this notebook
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.
To use the local pipeline wrapper:
from langchain.embeddings import HuggingFaceEmbeddings
To use a the wrapper for a model hosted on Hugging Face Hub:
from langchain.embeddings import HuggingFaceHubEmbeddings
For a more detailed walkthrough of this, see this notebook
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
from langchain.text_splitter import CharacterTextSplitter
CharacterTextSplitter.from_huggingface_tokenizer(...)
For a more detailed walkthrough of this, see this notebook
Datasets
Hugging Face has lots of great 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