LangChain was started by Harrison Chase in October 2022 as a platform for developing applications that seamlessly brought together other databases and machine learning models under one roof. On the other hand, Hugging Face is an open-source collection of datasets and models needed to build, test, or train systems based on Artificial Intelligence. Together, LangChain and Hugging Face can provide programmers with the means to develop advanced applications that are run by large language models that are trained and tested on terabytes of data.
What is LangChain and What are its Key Features?
Primarily, LangChain is a framework for creating applications that utilize “Language Models” to produce text or images from given prompts. LangChain makes the coding process simpler with its many features. Some of them are as follows:
- It helps connect Language Models to data resources such as online datasets or cloud storage.
- It provides a lot of options to handle the data as required.
- Different Language Models available online can be accessed on a singular platform with LangChain.
- LangChain can easily handle various data types such as text or images.
- The caches of data help increase processing speeds when running large models.
- It can also process the output from natural language models by grading them on a scale of 0 to 1 such as in a n-gram overlap.
How to Integrate Hugging Face and LangChain?
It can even be said that Hugging Face and LangChain are made for each other. Here, we will present ways in which they can be integrated to develop applications.
- Hugging Face provides two wrappers for hosting “Large Language Models (LLM)”. This can be done on LangChain via the pipeline wrapper, “HuggingFacePipeline” and on the Hugging Face hub itself by the “HuggingFaceHub” wrapper. These are used for tasks that involve text generation.
- Hugging Face models can be embedded into LangChain such as “Sentence Transformers” by using wrappers. For a local embedding on LangChain, the “LangChain.Embeddings” wrapper can be used whereas the “HuggingFaceEmbeddings” wrapper is used for a model hosted on the Hugging Face Hub.
- Developers can utilize the “Datasets” library of Hugging Face on LangChain. There are thousands of datasets available on the Hugging Face platform that are free to use. These are uploaded by users all over the world.
- “Tokenizers” from Hugging Face “Transformers” can also be used on LangChain via the “CharacterTextSplitter.from_huggingface_tokenizer()” method.
- Hugging Face and LangChain can be combined to create many applications that run on Artificial Intelligence such as chatbots, code-generators, and image-creators.
Conclusion
LangChain and Hugging Face are the latest joint venture in the swiftly developing world of Artificial Intelligence and Machine Learning models. The feature-packed environment of LangChain is ideal for developers to create AI applications that benefit from the large databases available on the Hugging Face platform. If you would like to read more about the collaboration between Hugging Face and LangChain, you can do so here.
