Sure! Here’s the translation to American English:
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Companies across various sectors, including healthcare, finance, manufacturing, and legal services, are facing increasing challenges in managing large volumes of multimodal information that combines text, images, and complex technical formats. As organizations generate this type of content at an unprecedented rate, traditional document processing methods are falling behind, struggling to adapt to the complexities inherent in specialized domains. Technical terminology, data interrelationships, and industry-specific formats create operational bottlenecks that hinder both productivity and decision-making.
A clear example of this issue is seen in the oil and gas industry, where vast amounts of technical data are generated from drilling operations. Documents such as well completion reports and drilling logs contain vital information that guides operational and strategic decisions. To address these challenges, an innovative Advanced Information Retrieval (RAG) solution has been developed, leveraging Amazon Bedrock and Infosys Topaz™’s artificial intelligence capabilities. This tool is specifically designed for the oil and gas sector, enabling the effective management of multimodal data sources, processing texts, diagrams, and numerical data while maintaining the context of relationships between various elements.
The solution has been built using several AWS services, including Amazon Bedrock Nova Pro and Amazon OpenSearch Serverless, providing scalability and cost efficiency. Additionally, a real-time indexing system has been implemented, ensuring that information is always up-to-date and incorporating new documents as they become available. These components guarantee effective handling of a large volume of requests without compromising performance.
Thanks to an advanced approach to RAG exploration, multichannel vector pairings have been developed to deeply understand the visual and textual content of technical reports. A hierarchical chunking strategy has been employed to preserve document structure and contextual relationships, facilitating the retrieval of specific technical information without losing context.
The final solution combines hybrid search capabilities and optimized chunking, resulting in a system that understands both context and specific references. This not only improves the accuracy of document searches but also optimizes response time to under two seconds, achieving a 92% accuracy rate in information retrieval, which has generated high satisfaction among field engineers and geologists.
The implementation of this advanced RAG solution has provided significant value to operations in the oil and gas industry. It has maximized operational efficiency, reducing manual processing costs by 40% to 50%, and cutting down the time professionals spend searching for technical information by 60%. This case demonstrates the potential of advanced technologies and machine learning to transform knowledge management in sectors with technical complexities. Continuous innovation promises great opportunities, with the integration of real-time sensor data and predictive analytics capabilities on the horizon.
via: MiMub in Spanish
