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docs: NiFi integration (#963)

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qdrant-landing/content/documentation/frameworks/langchain.md
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---
title: LangChain
title: Langchain
weight: 100
aliases:
- ../integrations/langchain/
- /documentation/overview/integrations/langchain/
---
# LangChain
# Langchain
LangChain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using LangChain, you can focus on the business value
Langchain is a library that makes developing Large Language Model-based applications much easier. It unifies the interfaces
to different libraries, including major embedding providers and Qdrant. Using Langchain, you can focus on the business value
instead of writing the boilerplate.
Langchain distributes their Qdrant integration in their community package. It might be installed with pip:
@@ -97,8 +97,8 @@ qdrant = Qdrant.from_documents(
## Next steps
If you'd like to know more about running Qdrant in a LangChain-based application, please read our article
[Question Answering with LangChain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
might also be found in the [LangChain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).
If you'd like to know more about running Qdrant in a Langchain-based application, please read our article
[Question Answering with Langchain and Qdrant without boilerplate](/articles/langchain-integration/). Some more information
might also be found in the [Langchain documentation](https://python.langchain.com/docs/integrations/vectorstores/qdrant).
- [Source Code](https://github.com/langchain-ai/langchain/tree/master/libs%2Fpartners%2Fqdrant)