Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2411.19804v1
• Date: Fri, 29 Nov 2024 16:09:43 GMT
• Title: Advanced System Integration: Analyzing OpenAPI Chunking for Retrieval-Augmented Generation
• Authors: Robin D. Pesl, Jerin G. Mathew, Massimo Mecella, Marco Aiello,
• Abstract summary: We analyze the usage of Retrieval Augmented Generation for endpoint discovery and chunking of OpenAPIs.<n>We propose a Discovery Agent that only receives a summary of the most relevant endpoints and retrieves details on demand.<n>Our experiments show that for preprocessing, LLM-based and format-specific approaches outperform na"ive chunking methods.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integrating multiple (sub-)systems is essential to create advanced Information Systems (ISs). Difficulties mainly arise when integrating dynamic environments across the IS lifecycle. A traditional approach is a registry that provides the API documentation of the systems' endpoints. Large Language Models (LLMs) have shown to be capable of automatically creating system integrations (e.g., as service composition) based on this documentation but require concise input due to input token limitations, especially regarding comprehensive API descriptions. Currently, it is unknown how best to preprocess these API descriptions. Within this work, we (i) analyze the usage of Retrieval Augmented Generation (RAG) for endpoint discovery and the chunking, i.e., preprocessing, of OpenAPIs to reduce the input token length while preserving the most relevant information. To further reduce the input token length for the composition prompt and improve endpoint retrieval, we propose (ii) a Discovery Agent that only receives a summary of the most relevant endpoints and retrieves details on demand. We evaluate RAG for endpoint discovery using the RestBench benchmark, first, for the different chunking possibilities and parameters measuring the endpoint retrieval recall, precision, and F1 score. Then, we assess the Discovery Agent using the same test set. With our prototype, we demonstrate how to successfully employ RAG for endpoint discovery to reduce the token count. While revealing high values for recall, precision, and F1, further research is necessary to retrieve all requisite endpoints. Our experiments show that for preprocessing, LLM-based and format-specific approaches outperform na\"ive chunking methods. Relying on an agent further enhances these results as the agent splits the tasks into multiple fine granular subtasks, improving the overall RAG performance in the token count, precision, and F1 score.

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