LangChain

LangChain is a framework for building applications that use large language models (LLMs).

LangChain and Weaviate

Weaviate is a supported vector store in LangChain. You will need a running Weaviate cluster to use the integration.

Connect LangChain to your Weaviate cluster:

weaviate_client = weaviate.connect_to_local()
db = WeaviateVectorStore.from_documents(docs, embeddings, client=weaviate_client)

Our Resources

The resources are broken into two categories:

1. Hands on Learning: Build your technical understanding with end-to-end tutorials.

2. Read and Listen: Develop your conceptual understanding of these technologies.

Hands on Learning

Topic	Description	Resource
LangChain LCEL	A notebook that defines a language program with LangChain LCEL, compiles it with DSPy, and converts it back to LangChain LCEL.	Notebook
LangChain and Multi-Tenancy	Build a multi-language RAG by multiple PDFs per tenant with Langchain, OpenAI, and Weaviate.	Notebook
Multi-Language RAG	Simple notebook showing you how to build a RAG application using LangChain and Weaviate.	Notebook
LangChain and Weaviate Query Agent	Use the Weaviate Query Agent as a tool with LangChain.	Notebook

Read and Listen

Topic	Description	Resource
Combining LangChain and Weaviate	Learn about how Weaviate is integrated in LangChain and the different CombineDocuments techniques.	Blog
Weaviate Podcast #36	LangChain and Weaviate with Harrison Chase and Bob van Luijt	Podcast
Weaviate + LangChain for LLM apps	An overview of how LangChain and Weaviate work together.	Video