LangChain Automate

Target Audience

• Data Analysts: Individuals who need to analyze supply chain data and generate insights using SQL queries.
• Business Intelligence Professionals: Users who require automated data retrieval and reporting from Google BigQuery.
• Developers: Those interested in integrating AI capabilities into their applications for data processing.
• Supply Chain Managers: Professionals looking to monitor and optimize shipment performance using data-driven insights.

Problem Solved

This workflow automates the process of querying a Google BigQuery database for supply chain analytics. It addresses the need for quick data retrieval without exposing SQL queries, ensuring that users can focus on results rather than query syntax. The AI agent simplifies the interaction by interpreting user requests and executing relevant SQL commands, making data analysis more accessible and efficient.

Workflow Steps

1. Chat Trigger: The workflow begins with a chat interface where users can input their queries.
2. AI Control Tower Agent: An AI agent interprets user requests and prepares SQL queries based on predefined rules and the schema of the transport.shipments table.
3. Sanitizing the Query: The generated SQL query is cleaned to remove any unnecessary formatting before execution.
4. Executing the Query: The cleaned query is sent to the bigquery_tool to fetch results from the Google BigQuery database.
5. Returning Results: The results are formatted and sent back to the user through the chat interface, providing a clear and structured response.