---
title: LangChain Embeddings
 | Developer Documentation
---

This guide shows you how to use embedding models from [LangChain](https://python.langchain.com/docs/integrations/text_embedding/).

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/embeddings/Langchain.ipynb)

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

```
%pip install llama-index-embeddings-langchain
```

```
!pip install llama-index
```

```
from langchain.embeddings import HuggingFaceEmbeddings
from llama_index.embeddings.langchain import LangchainEmbedding


lc_embed_model = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)
embed_model = LangchainEmbedding(lc_embed_model)
```

```
# Basic embedding example
embeddings = embed_model.get_text_embedding(
    "It is raining cats and dogs here!"
)
print(len(embeddings), embeddings[:10])
```

```
768 [-0.005906202830374241, 0.04911914840340614, -0.04757878929376602, -0.04320324584841728, 0.02837090566754341, -0.017371710389852524, -0.04422023147344589, -0.019035547971725464, 0.04941621795296669, -0.03839121758937836]
```