This playbook will walk you through building a repeat purchase analytics library. We'll cover everything from writing SQL queries to creating a Python class for your analysis tools. We'll then create a suite of LangChain tools out of the class and let LLMs figure out how to use the different tools.
In this playbook, we'll implement a market basket recommender to find products that can be used for cross-selling. We'll then use this with the Next Product Recommender and LangChain to optimize CLV.
In this playbook, we'll build a simple product recommendation system using BigQuery's vector search capabilities combined with LangGraph.
In this playbook, we will develop the analytical infrastructure for building robust recommendation engines with SQL. I'll show how to use the recommenders as LLM tools, allowing you to incorporate advanced natural language capabilities into your recommendation systems.
In this tutorial, we will build high-performance real-time Retrieval Augmented Generation (RAG) chains using Llama 3, GroqCloud, LangChain, and Redis.
