Leveraging Generative AI for Data Analysis with Langchain and OpenAI

Building an Automated Data Analysis Assistant Using BigQuery, DLP, Langchain and OpenAI Tools

Since businesses generate massive amounts of data daily, pulling out useful insights from all this information can be tough, especially with complex datasets and huge volumes of data. But with generative AI, we can streamline and automate data analysis, making it efficient and accessible. In this article, I’ll show you how to set up and use an AI data Analysis assistant using Google Langchain, OpenAI, BigQuery, and Data Loss Prevention (DLP).

Use Case: Automating Data Analysis with BigQuery

Solution Design

The solution involves setting up a Streamlit app using a Langchain agent and OpenAI that interacts with the BigQuery dataset to automate data analysis. This agent will use custom tools for specific tasks such as masking PII customer attributes and visualizing data. Additionally, the agent will be configured to retain chat history, ensuring contextually accurate responses.

Here’s a diagram of the solution architecture:

Let’s consider a scenario where we have a BigQuery dataset containing the following tables:

Customer Table: Contains customer data.

Contact Table: Contains customer contact details.

Customer Address Table: Links customers to addresses.

Address Table: Contains address information.

Job Stats Table: Logs ETL batch job summaries that truncate and load data into the customer profile tables

Set-up Langchain

What is Langchain?

LangChain provides AI developers with tools to connect language models with external data sources. It is open-source and supported by an active community. Organizations…