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Perspicacité-AI

Lucas Pradi, Tao Jiang, Matthieu Féraud, Madina Bekbergenova, Yousouf Taghzouti, Louis-Félix Nothias

► To cite this version:

Lucas Pradi, Tao Jiang, Matthieu Féraud, Madina Bekbergenova, Yousouf Taghzouti, et al. Perspicacité-AI. Metabolomics Symposium, Jul 2025, Nice, France. hal-05233151

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https://hal.science/hal-05233151v1

Submitted on 1 Sep 2025

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# Perspicacité-Al: An AI-Pipeline for Accelerating Scientific Education and Research

**Lucas Pradi**¹,², Tao Jiang¹,², Matthieu Féraud¹,², Madina Bekbergenova¹,², Yousouf Taghzouti¹,², Louis-Félix Nothias¹,²*

1. Université Côte d'Azur, CNRS, ICN, Nice, France.

2. Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur, Nice

lucas.pradi@univ-cotedazur.fr

Recently, emerging tools such as LLM have gained attention in scientific research and education. However, their limitations — notably outdated knowledge and untrackable provenance¹,² — hinder their ability to keep pace with rapid advancements in scientific fields, like metabolomics. Some of these challenges can be addressed by coupling LLM with Retrieval-Augmented Generation (RAG)³ techniques, which combine relevant data retrieval with generative AI. Here, we developed Perspicacité-Al, a free and open source agentic literature assistant with advanced RAG capabilities designed for scientific research and education.

Perspicacité-Al required the development of the Bibt2KB python package, which seamlessly transforms open-access bibliographic references into a structured and searchable Facebook AI Similarity Search (FAISS)⁴ knowledge base (KB). The generated KB is used by the pipeline's framework to deploy an AI-assistant enhanced by RAG, ensuring that each question asked is grounded in relevant literature and documents. The framework features different advanced modes of document search and question-answering, including Perspicacité-Profound, an iterative, structured reasoning workflow suitable for answering complex scientific questions with the trackable facts and sources. Furthermore, the pipeline supports major open source and proprietary LLM providers, broadening the access for different users.

Perspicacité-Al also incorporates a novel reranking function for document selection, Sigmoid Weighted RRF (SW-RRF). The benchmarking of SW-RRF showed up to 3.5% improvement over the traditional Reciprocal Reranking Function (RRF) for all tested metrics: Recall, Precision, F1, Mean Reciprocal Rank (MRR), Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG) at 1, 3, 5 and 10 documents retrieved (p < 0.05) at no computational expenses.

Initial applications of Perspicacité-Al in computational metabolomics [metaboguide.holobiomicslab.eu] demonstrated effective chatbot responses with accurate citations, and while still ongoing, initial evaluations of the pipeline show promising results, not only mitigating some of the limitations of standalone LLMs but empowering scientific literature with generative AI.

## Acknowledgement:

This work was supported by the French government through the France 2030 investment plan managed by the National Research Agency (ANR), as part of the Initiative of Excellence Université Côte d'Azur under reference number ANR-15-IDEX-01. **Lucas Pradi** is Supported by the Fonds National de la Recherche, Luxembourg, project **17994255**.
References:

1. Zhao, W. X. et al. A Survey of Large Language Models. Preprint at [https://doi.org/10.48550/arXiv.2303.18223](https://doi.org/10.48550/arXiv.2303.18223) (2024).

2. Borger, J. G. et al. Artificial intelligence takes center stage: exploring the capabilities and implications of ChatGPT and other AI-assisted technologies in scientific research and education. *Immunol. Cell Biol.* **101**, 923–935. [https://doi.org/10.1111/imcb.12689](https://doi.org/10.1111/imcb.12689) (2023).

3. Fan, W. et al. A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models. Preprint at [https://doi.org/10.48550/arXiv.2405.06211](https://doi.org/10.48550/arXiv.2405.06211) (2024).

4. Douze, M. et al. The Faiss library. Preprint at [https://doi.org/10.48550/arXiv.2401.08281](https://doi.org/10.48550/arXiv.2401.08281) (2024).