BigQuery-Shopify RAG with LangGraph

Ecommerce platforms like Shopify generate valuable customer behavior data through purchase histories. In this playbook, we'll build a simple product recommendation system using BigQuery's vector search capabilities combined with LangGraph.

I'll walk you through creating a Retrieval Augmented Generation (RAG) system that:

1. Transforms raw purchase data into meaningful customer journey narratives
2. Stores and indexes these journeys in BigQuery's vector store
3. Uses LangGraph to orchestrate recommendations based on semantic similarity

The result is a recommender that can provide product suggestions by understanding purchase patterns and product relationships. In the following sections, we'll build this system step by step, from data preparation to the final RAG implementation. Each section includes working code and clear explanations of the key concepts.