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