Open‐source large language models in action: A bioinformatics chatbot for PRIDE database

ABSTRACT We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (m...

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Published in:Proteomics (Weinheim) Vol. 24; no. 21-22; pp. e2400005 - n/a
Main Authors: Bai, Jingwen, Kamatchinathan, Selvakumar, Kundu, Deepti J., Bandla, Chakradhar, Vizcaíno, Juan Antonio, Perez‐Riverol, Yasset
Format: Journal Article
Language:English
Published: Germany Wiley Subscription Services, Inc 2024-11-00
2024-Nov
20241101
Subjects:
Application programming interface
Bioinformatics
Chatbots
Computational Biology - methods
Data base management systems
Databases, Protein
dataset discoverability
Datasets
Documentation
Humans
Infrastructure
Internet
Large language models
Programming Languages
Proteomics
Proteomics - methods
public data
Recommender systems
Software
software architectures
training
User experience
User interfaces
User-Computer Interface
Web services
public data
proteomics
software architectures
bioinformatics
documentation
dataset discoverability
large language models
training
ISSN:1615-9853, 1615-9861, 1615-9861
Online Access:Get full text
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Summary:ABSTRACT We here present a chatbot assistant infrastructure (https://www.ebi.ac.uk/pride/chatbot/) that simplifies user interactions with the PRIDE database's documentation and dataset search functionality. The framework utilizes multiple Large Language Models (LLM): llama2, chatglm, mixtral (mistral), and openhermes. It also includes a web service API (Application Programming Interface), web interface, and components for indexing and managing vector databases. An Elo‐ranking system‐based benchmark component is included in the framework as well, which allows for evaluating the performance of each LLM and for improving PRIDE documentation. The chatbot not only allows users to interact with PRIDE documentation but can also be used to search and find PRIDE datasets using an LLM‐based recommendation system, enabling dataset discoverability. Importantly, while our infrastructure is exemplified through its application in the PRIDE database context, the modular and adaptable nature of our approach positions it as a valuable tool for improving user experiences across a spectrum of bioinformatics and proteomics tools and resources, among other domains. The integration of advanced LLMs, innovative vector‐based construction, the benchmarking framework, and optimized documentation collectively form a robust and transferable chatbot assistant infrastructure. The framework is open‐source (https://github.com/PRIDE‐Archive/pride‐chatbot).
Bibliography:Jingwen Bai and Selvakumar Kamatchinathan contributed equally to this work.
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ISSN:1615-9853
1615-9861
1615-9861
DOI:10.1002/pmic.202400005