📄️ 👐 Load the dataset
First, let's download the dataset for the lab. We'll use a subset of MongoDB's technical documentation as the source data for the documentation chatbot.
📄️ 👐 Chunk and embed the data
Since we are working with large documents, we first need to break them up into smaller chunks. Then, to make each chunk searchable using vector search, we need to add embeddings to them.
📄️ 👐 Ingest data into MongoDB
The final step to build a MongoDB vector store for the chatbot is to ingest the embedded article chunks into MongoDB.