seems linkchecker isn't catching them because it runs on generated html. at that point the links are already missing. the generation process seems to strip invalid references when they can't be re-written from md to html. I used https://github.com/tcort/markdown-link-check to check the doc source directly. There are a few false positives on localhost for development.
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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 Hub client library with
pip install huggingface_hub
- Create a Hugging Face account (it's free!)
- Create an access token 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
, 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
The Hugging Face Hub 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