Support case analysis is a critical task for any product-driven company. Understanding customer issues, their frequency, and patterns can significantly enhance product development and customer satisfaction. With the rise of data engineering and AI, platforms like Snowflake with AI-powered language models are game-changers in automating and enhancing this process. In this guide, we will explore how to leverage these technologies to dissect, analyze, and summarize product support cases effectively.
By completing this QuickStart guide, you will deploy a Streamlit application that leverages Snowflake Cortex AI features like LLM Inference and Cortex Search for summarization, search and analysis, alongside the LangChain framework to chain together interoperable components for large language models (LLMs).By leveraging Snowflake Cortex, this solution not only processes but also meaningfully summarizes support tickets, aiding in more agile issue resolution and strategic planning.
Cortex Search provides low-latency, high-quality fuzzy search capabilities for Snowflake data, enabling a range of advanced search experiences, including Retrieval-Augmented Generation (RAG) applications that leverage Large Language Models (LLMs).
With Cortex Search, one can quickly set up a hybrid (vector and keyword) search engine on the text data in minutes, eliminating the need for embedding management, infrastructure maintenance, search quality adjustments, or index refreshes. This allows you to focus more on building robust chat and search applications and less on managing infrastructure and fine-tuning search quality.
This section will walk you through creating various objects
Step 1. - Clone GitHub repository.
Step 2. - Run the code under the scripts/setup.sql file in Snowsight SQL Worksheet. Once the objects are setup, now let us proceed to the notebook execution
Step 3. The Notebook is available to download from the /notebook folder in the git repository.
from langchain_core.prompts import PromptTemplate
map_template = PromptTemplate.from_template("""
Given the following support cases for an order, return a summary of each case.
Include details on the category of the issue, the errors or symptoms the customer noticed,
and any basic details about what the customer was looking to accomplish.
If multiple cases exist in the same category, you can group them together.
The summary will be used to understand overall case trends and causes that the team can
use to prioritize fixes and improvements.
### Cases ###
{cases}
""")
reduce_template = PromptTemplate.from_template("""
Given the following set of summaries for support case reports opened for an order,
distill it into a final, consolidated and detailed summary of trends and top pain points or blockers customers have been hitting.
Prioritize issue categories that show up in multiple summaries as they are likely to be the most impactful.
Include a description of the issue, the symptoms the customer noticed, what they were trying to do, and what led them to open the case.
### Case Chunk Summaries ###
{summaries}
""")
The app is built on open source Streamlit and can be hosted in one's own platform. The app allows the user carries the end to end processing that was carried in the Notebook with fine tuned granular control by the end user.
Here is the Streamlit source code repository and instructions for running the Streamlit app.
In the Support Case Summary page one can view support case summaries using Cortex LLMs. It retrieve the Summary tables created for different categories and lists them in a dropdown. The app displays the most recent record's summary from the selected table and includes an expandable section to show each step of intermediate analysis.
To help users get a chatbot experience, the Support cases Chatbot page helps users ask questions about the support cases logged using Snowflake Cortex. The Key components include :
This Streamlit page processes and summarizes Snowflake support cases to identify trends and key issues.
In this fast-paced world, providing timely, effective support is critical for customer satisfaction and operational success for a successful product evolution. Support Teams often receive a high volume of support requests ranging from routine inquiries to complex technical issues. Analyzing and summarizing these support cases helps uncover patterns, identify recurring issues, and ultimately improve product service quality. However, managing and synthesizing this data can be resource-intensive without the right tools.
By combining the power of Snowflake Cortex and AI-powered language models, we have created a sophisticated pipeline that automates the analysis of support cases. This approach not only saves time and resources but also provides deeper insights into customer issues, enabling organizations to make data-driven decisions for product improvements and customer service strategies.