Enhancing product search
Large language models (LLMs) can be used to harness the rapidly growing range of content and goods to ensure customer searches yield the desired results. With the Databricks Lakehouse for Retail, organizations can:
Unify product, query and label data within a retailer’s product catalog
Enable rapid search with analytics against numerical arrays
Train and deploy an LLM model with Databricks Model Serving
Build an LLM-enabled chatbot
Use this Solution Accelerator to build a context-enabled LLM-based chatbot solution using content taken from our own knowledge base (made publicly available so that users can recreate our work).
The step-by-step code behind this work includes data preparation, agent development and deployment to a microservice that allows you to integrate the agent into any number of applications, and provides sufficient comments and documentation to help your organization understand the solution and get started with their own.
Automate product review summarization
Use this Solution Accelerator to streamline the summarization of customer feedback, allowing your organization to:
Process a high volume of reviews at a lower cost
Collect feedback from a wider range of products and summarize these on a regular basis
Task an LLM to extract different sets of information from each high-level category of reviews