student
Russian Federation
employee
Russian Federation
employee
Russian Federation
VAK Russia 1.2.1
UDC 004.041
UDC 004.056
A specialized solution is offered for analyzing documents used in corporate document management systems. Purpose: to develop and implement a RAG architecture for automated verification of corporate documents using locally deployed large language models that identify missing required fields, data format errors, and substantive contradictions in documents of transport and logistics systems. Methods: Java application was designed and implemented programmatically, integrating a text extraction module from PDF and DOCX documents based on Apache libraries, a vector storage with simplified embeddings based on frequency analysis of words, a semantic search algorithm through cosine similarity calculation and an LLM interaction client via the Ollama server API. Results: the developed system has demonstrated the ability to contextually analyze the content of documents and adaptability to variable information presentation formats, which makes it possible to overcome the limitations of traditional systems. The experimental verification was performed on a test set of corporate documents with intentionally introduced errors of various types; the effectiveness was assessed by the metrics of completeness, accuracy and their average, as well as by system response time. The llama3.2:latest model showed the best results, while completely excluding the transfer of confidential data outside the organization’s infrastructure. Practical significance: the proposed solution is applicable for automation of documentation quality control in corporate document management systems of transport enterprises, government agencies and industrial organizations. The modular architecture provides scalability to other types of documents and the ability to integrate with existing information systems at minimal cost of adaptation. Using open models and a local Ollama server reduces dependence on third-party cloud services and ensures compliance with information security requirements.
large language models, automated document verification, vector search, natural language processing, corporate systems, Ollama, text extraction, semantic analysis
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