JAPAN AI Unveils Advanced Multimodal RAG Implementation for Enhanced Data Utilization

Introduction

JAPAN AI has recently rolled out an innovative feature called Multimodal Retrieval-Augmented Generation (RAG) that significantly enhances the way artificial intelligence can process and understand data by integrating text and images. As industries, particularly manufacturing, grapple with the challenges of skill transfer and information utilization, this breakthrough aims to transform the operational landscape.

Implementation Background

In its ongoing support for AI adoption across various sectors, including manufacturing, JAPAN AI has identified pressing issues related to the transmission of crucial technical knowledge. Senior engineers often possess invaluable experience and know-how, but their departure from a company leads to the scattering of critical technical documents, such as design drafts, operational procedures, and quality control data. The challenge has been how to effectively access this dispersed information when it's needed.

Previously, JAPAN AI's RAG technology achieved remarkable text information retrieval with an impressive accuracy rate of 82.7%. However, there was a significant gap when it came to extracting valuable data from visual information like charts and images. This limitation often led to missed opportunities in leveraging critical information. As a solution, JAPAN AI has now implemented the Multimodal RAG to harness the potential of both text and visual data.

What is Multimodal RAG?

Multimodal RAG integrates various data formats—images, documents, and audio—and uses them to generate answers based on retrieved information. Unlike the traditional RAG that only focused on locating technical documents like drawings, the new model can now extract specific numerical and specification details from these documents. The system can seamlessly interpret both text and images from sources like PDF files, merging them into comprehensive insights.

Key Features

- Document Integration: The system can automatically identify and extract relevant text and image data, allowing for a holistic understanding of the information presented.
- Data Extraction Capabilities: It has advanced considerably from simple text extraction and OCR techniques, now enabling the understanding of detailed measurements, graphical data, and specifications.

Practical Use Cases

1. Drawing Search

When a drawing search is initiated, users can specify precise queries, such as “Find a vessel design with a total length of 50 meters and provide detailed specifications.” Upon finding related diagrams, the system can automatically output critical dimensional data, such as “Length: 52 meters, Width: 8 meters, Draft: 3 meters, Total Tonnage: 450 tons, Engine Power: 1200 HP.” This efficiency allows designers to quickly reference past cases for new designs.

2. Graph Interpretation

If a user asks, “What are the figures for Q3 2023 from the quarterly sales trend graph?” the system will analyze the graphic meticulously. It will extract values from axes and line positions to respond with precise data: “Q3 2023 sales saw an increase of 15% year-over-year, totaling 12 million yen, while also reflecting an 8% increase compared to the previous quarter.” This analytical capability provides comprehensive insights along with trend analysis.

Future Directions

JAPAN AI aims to extend functionalities such as proposing optimal business improvement strategies based on past designs, and enabling estimation creation by referencing historical drawings and quotations for similar product manufacturing. The company is determined to continuously evolve RAG technology to accommodate increasingly complex drawings and diverse graph formats, transitioning from mere information retrieval to a robust knowledge utilization support system. This proactive approach promises to enhance operational efficiency and foster technological innovation across various industries, especially manufacturing.

Overview of JAPAN AI Services

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