Iris Coleman
February 26, 2025 10:55
NVIDIA introduces a VLM -based multi -mode information search system that utilizes NIM micro services to improve data processing in a variety of ways, such as text and images.
The constantly evolving environment of artificial intelligence continues to push the boundaries of data processing and search. According to the company’s official blog, NVIDIA used NIM Microservices to solve the complexity of various data form processing to unveil a new approach to multi -mode information search using NIM micro service.
Multimodal AI model: New border
The multimodal AI model is designed to handle various data types, including text, images, and tables in a cohesive manner. NVIDIA’s VLM (Vision Language Model) -based system aims to simplify accurate information search by integrating these data types into integrated frameworks. This approach greatly improves the ability to generate comprehensive and consistent output across different forms.
Distributed to NVIDIA NIM
NVIDIA NIM micro service facilitates the deployment of AI basic models across language, computer visions and other domains. The service is designed to be placed in the NVIDIA-ACCELERATED infrastructure, providing an industry standard API that can perfectly integrate with popular AI development frameworks such as Langchain and LlamainDex. This infrastructure supports the distribution of a non -language model -based system that can respond to complex queries related to multiple data types.
Langgraph and LLMS integration
The system uses state-of-the-art frameworks, Langgraph and LLAMA-3.2-90B-Vision VLM and Mistral-Small-24B Large Language Models (LLM). This combination allows you to process and understand text, images and tables, allowing you to efficiently handle complex queries.
Advantages to traditional systems
VLM NIM micro service offers some advantages compared to traditional information search systems. It does not lose its consistency and processes long and complex visual documents to improve the understanding of the situation. In addition, integrating the tool eligibility of Langchain to dynamically select and use the external tool to improve data extraction and analysis precision.
Structural output for enterprise applications
This system is especially advantageous for enterprise applications and generates structured outputs that guarantee the consistency and reliability of the response. This structured output is important for automating and integrating with other systems and reduces the ambiguity that can occur in un structured data.
Challenge and solution
As the amount of data increases, problems associated with scalability and calculation costs occur. NVIDIA solves these tasks through hierarchical document reporting approach, which optimizes processing by dividing document summary into managed batches. This method improves all documents, considering all documents without exceeding the capacity of the model.
Future prospect
The current system includes significant calculation resources, but is expected to develop smaller and more efficient models. This development is more accessible and cost -effective to a system for a wide range of applications, promising to provide a similar performance level when reducing costs.
NVIDIA’s approach to composite information search shows an important step in handling complex data environments. NVIDIA uses advanced AI models and powerful infrastructure to set up new standards for efficient and effective data processing and search systems.
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