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This research investigates the efficacy of Artificial Intelligence Agents in processing and responding to personalized, private contextual information. We studied how to implement a system designed to augment an open-source Large Language Model (LLM), such as Llama, Claude, and Gemma, with domain-specific knowledge bases. This augmentation is intended to facilitate the generation of contextually coherent and accurate responses to user queries. The system was developed and tested using different versions of a clinical conversation transcripts dataset between patients and medical professionals, enabling specialized knowledge integration. The architectural framework, built upon LangChain, FAISS, Ollama, and Gradio, demonstrates a simple, modular, scalable, and extensible design. This work helped us to take our first steps into the development of robust AI agents capable of leveraging external knowledge for enhanced conversational intelligence in specialized domains.

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Context-Aware AI Agents for Clinical Dialogue Assistance Through Large Language Models

  • 会议论文

  • 发表日期：

    2026年

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  • Rodrigo Naranjo Pozas,
  • Pablo Doblado Mendoza,
  • Belén Vega-Márquez

摘要

This research investigates the efficacy of Artificial Intelligence Agents in processing and responding to personalized, private contextual information. We studied how to implement a system designed to augment an open-source Large Language Model (LLM), such as Llama, Claude, and Gemma, with domain-specific knowledge bases. This augmentation is intended to facilitate the generation of contextually coherent and accurate responses to user queries. The system was developed and tested using different versions of a clinical conversation transcripts dataset between patients and medical professionals, enabling specialized knowledge integration. The architectural framework, built upon LangChain, FAISS, Ollama, and Gradio, demonstrates a simple, modular, scalable, and extensible design. This work helped us to take our first steps into the development of robust AI agents capable of leveraging external knowledge for enhanced conversational intelligence in specialized domains.