Transport & Logistics — Hybrid Index Creation for Enhanced Logistics Document Search

Overview

The purpose of this DAG is to create a hybrid index that significantly improves the search capabilities for logistics documents by integrating traditional search methods with advanced vector-based approaches. The data sources include ERP transaction logs, shipping manifests, and inventory records, which are essential for ensuring comprehensive coverage of logistics operations. The ingestion pipeline begins with data extraction from these varied sources, followed by preprocessing steps that inclu

The purpose of this DAG is to create a hybrid index that significantly improves the search capabilities for logistics documents by integrating traditional search methods with advanced vector-based approaches. The data sources include ERP transaction logs, shipping manifests, and inventory records, which are essential for ensuring comprehensive coverage of logistics operations. The ingestion pipeline begins with data extraction from these varied sources, followed by preprocessing steps that include data cleaning and normalization to ensure high-quality input. During the processing phase, the DAG employs both traditional indexing techniques and modern vectorization methods to create a robust hybrid index. Quality controls are implemented throughout the workflow to monitor data integrity and relevance, ensuring that only the most pertinent documents are indexed. The final output of this DAG is an API that exposes the hybrid index, allowing seamless integration with existing search tools used within the logistics sector. Monitoring KPIs such as search speed, accuracy of results, and user engagement metrics provide insights into the system's performance. The business value of this solution lies in its ability to enhance operational efficiency, reduce time spent searching for documents, and improve decision-making processes by providing quick access to relevant information.

Part of the Recommendations solution for the Transport & Logistics industry.

Use cases

• Reduces time spent searching for critical logistics documents
• Improves accuracy of search results, leading to better decisions
• Facilitates integration with existing logistics management tools
• Increases operational efficiency across logistics processes
• Supports scalability as data sources and volumes grow