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Documentation Index

Fetch the complete documentation index at: https://docs.langchain.com/llms.txt

Use this file to discover all available pages before exploring further.

This page covers all LangChain integrations with Hugging Face Hub and libraries like transformers, sentence transformers, and datasets.

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Chat models

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ChatHuggingFace

We can use the Hugging Face LLM classes or directly use the ChatHuggingFace class. See a usage example. 
from langchain_huggingface import ChatHuggingFace

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LLMs

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HuggingFaceEndpoint

We can use the HuggingFaceEndpoint class to run open source models via serverless Inference Providers or via dedicated Inference Endpoints. See a usage example. 
from langchain_huggingface import HuggingFaceEndpoint

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HuggingFacePipeline

We can use the HuggingFacePipeline class to run open source models locally. See a usage example. 
from langchain_huggingface import HuggingFacePipeline

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Embedding models

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HuggingFaceEmbeddings

We can use the HuggingFaceEmbeddings class to run open source embedding models locally. See a usage example. 
from langchain_huggingface import HuggingFaceEmbeddings

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HuggingFaceEndpointEmbeddings

We can use the HuggingFaceEndpointEmbeddings class to run open source embedding models via a dedicated Inference Endpoint. See a usage example. 
from langchain_huggingface import HuggingFaceEndpointEmbeddings

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Text Embeddings Inference (TEI)

For self-hosted production serving of Sentence Transformers models, Hugging Face publishes Text Embeddings Inference, a dedicated inference server with batching and GPU support. Point LangChain at a TEI deployment via HuggingFaceEndpointEmbeddings or see the dedicated TEI integration guide.

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BGE embedding models

BGE models on Hugging Face are a strong open-source embedding family from the Beijing Academy of Artificial Intelligence (BAAI).
BGE models are Sentence Transformers models, so use HuggingFaceEmbeddings with encode_kwargs={"normalize_embeddings": True}. See a usage example.
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