标题： BioModelsRAG：一种使用RAG（检索增强生成）的生物建模助手 显示英文标题

标题： BioModelsRAG: A Biological Modeling Assistant Using RAG (Retrieval Augmented Generation)

摘要： 生物模型数据库是系统生物学中计算模型的主要数据库之一。 该数据库包含超过1000个经过整理的模型和数量更多的非整理模型。 所有模型都以机器可读格式SBML存储。 尽管SBML可以转换为人类可读的Antimony格式，但分析这些模型仍然耗时。 为了弥合这一差距，创建了一个大型语言模型（LLM）助手来分析生物模型，并允许用户通过自然语言与模型进行交互。 通过这种方式，用户可以轻松快速地提取给定模型中的关键点。 我们的分析流程包括对生物模型进行“分块”处理，并使用llama3将其转换为纯文本，然后将它们嵌入到ChromaDB数据库中。 用户提供的查询也被嵌入，并在查询和ChromaDB中的生物模型之间进行相似性搜索，以提取最相关的生物模型。 然后将生物模型用作上下文，在用户和LLM之间的聊天中生成最准确的输出。 这种方法大大降低了幻觉的可能性，并使LLM专注于当前的问题。 显示英文摘要

摘要： The BioModels database is one of the premier databases for computational models in systems biology. The database contains over 1000 curated models and an even larger number of non-curated models. All the models are stored in the machine-readable format, SBML. Although SBML can be translated into the human readable Antimony format, analyzing the models can still be time consuming. In order to bridge this gap, a LLM (large language model) assistant was created to analyze the BioModels and allow interaction between the user and the model using natural language. By doing so, a user can easily and rapidly extract the salient points in a given model. Our analysis workflow involved 'chunking' BioModels and converting them to plain text using llama3, and then embedding them in a ChromaDB database. The user-provided query was also embedded, and a similarity search was performed between the query and the BioModels in ChromaDB to extract the most relevant BioModels. The BioModels were then used as context to create the most accurate output in the chat between the user and the LLM. This approach greatly minimized the chance of hallucination and kept the LLM focused on the problem at hand.