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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

TopicDescriptionResource
LangChain LCELA notebook that defines a language program with LangChain LCEL, compiles it with DSPy, and converts it back to LangChain LCEL.Notebook
LangChain and Multi-TenancyBuild a multi-language RAG by multiple PDFs per tenant with Langchain, OpenAI, and Weaviate.Notebook
Multi-Language RAGSimple notebook showing you how to build a RAG application using LangChain and Weaviate.Notebook
LangChain and Weaviate Query AgentUse the Weaviate Query Agent as a tool with LangChain.Notebook

Read and Listen

TopicDescriptionResource
Combining LangChain and WeaviateLearn about how Weaviate is integrated in LangChain and the different CombineDocuments techniques.Blog
Weaviate Podcast #36LangChain and Weaviate with Harrison Chase and Bob van LuijtPodcast
Weaviate + LangChain for LLM appsAn overview of how LangChain and Weaviate work together.Video