Create Text Embeddings for a Vector Store using LangChain

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you learn how to use LangChain to store documents as embeddings in a vector store. You will use the LangChain framework to split a set of documents into chunks, vectorize (embed) each chunk and then store the embeddings in a vector database.