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+
+
+
+ 20250124145808-a39ec87349a8d0e0b7bfdf327e886b8fcaa8520f
+ 20250124145808
+
+ JOSS Admin
+ admin@theoj.org
+
+ The Open Journal
+
+
+
+
+ Journal of Open Source Software
+ JOSS
+ 2475-9066
+
+ 10.21105/joss
+ https://joss.theoj.org
+
+
+
+
+ 01
+ 2025
+
+
+ 10
+
+ 105
+
+
+
+ ollamar: An R package for running large language models
+
+
+
+ Hause
+ Lin
+
+ Massachusetts Institute of Technology, USA
+
+ https://orcid.org/0000-0003-4590-7039
+
+
+ Tawab
+ Safi
+
+ Massachusetts Institute of Technology, USA
+
+ https://orcid.org/0009-0000-5659-9890
+
+
+
+ 01
+ 24
+ 2025
+
+
+ 7211
+
+
+ 10.21105/joss.07211
+
+
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+ http://creativecommons.org/licenses/by/4.0/
+
+
+
+ Software archive
+ 10.5281/zenodo.14728444
+
+
+ GitHub review issue
+ https://github.com/openjournals/joss-reviews/issues/7211
+
+
+
+ 10.21105/joss.07211
+ https://joss.theoj.org/papers/10.21105/joss.07211
+
+
+ https://joss.theoj.org/papers/10.21105/joss.07211.pdf
+
+
+
+
+
+ biorecap: an R package for summarizing bioRxiv preprints with a local LLM
+ Turner
+ arXiv
+ 10.48550/arXiv.2408.11707
+ 2024
+ Turner, S. D. (2024). biorecap: an R package for summarizing bioRxiv preprints with a local LLM. arXiv. https://doi.org/10.48550/arXiv.2408.11707
+
+
+ Comparing programming languages for data analytics: Accuracy of estimation in Python and R
+ Hill
+ Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
+ 3
+ 14
+ 10.1002/widm.1531
+ 1942-4787
+ 2024
+ Hill, C., Du, L., Johnson, M., & McCullough, B. D. (2024). Comparing programming languages for data analytics: Accuracy of estimation in Python and R. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 14(3), e1531. https://doi.org/10.1002/widm.1531
+
+
+ rollama: An R package for using generative large language models through Ollama
+ Gruber
+ arXiv
+ 10.48550/arXiv.2404.07654
+ 2024
+ Gruber, J. B., & Weber, M. (2024). rollama: An R package for using generative large language models through Ollama. arXiv. https://doi.org/10.48550/arXiv.2404.07654
+
+
+ Optimizing RAG techniques for automotive industry PDF chatbots: A case study with locally deployed Ollama models
+ Liu
+ arXiv
+ 10.48550/arXiv.2408.05933
+ 2024
+ Liu, F., Kang, Z., & Han, X. (2024). Optimizing RAG techniques for automotive industry PDF chatbots: A case study with locally deployed Ollama models. arXiv. https://doi.org/10.48550/arXiv.2408.05933
+
+
+ The boy who survived: Removing Harry Potter from an LLM is harder than reported
+ Shostack
+ arXiv
+ 10.48550/arXiv.2403.12082
+ 2024
+ Shostack, A. (2024). The boy who survived: Removing Harry Potter from an LLM is harder than reported. arXiv. https://doi.org/10.48550/arXiv.2403.12082
+
+
+ Enhancing propaganda detection with open source language models: A comparative study
+ Lytvyn
+ Proceedings of the MEi:CogSci Conference
+ 1
+ 18
+ 2960-5911
+ 2024
+ Lytvyn, O. (2024). Enhancing propaganda detection with open source language models: A comparative study. Proceedings of the MEi:CogSci Conference, 18(1). https://journals.phl.univie.ac.at/meicogsci/article/view/822
+
+
+ Prompto: An open source library for asynchronous querying of LLM endpoints
+ Chan
+ arXiv
+ 10.48550/arXiv.2408.11847
+ 2024
+ Chan, R. S.-Y., Nanni, F., Brown, E., Chapman, E., Williams, A. R., Bright, J., & Gabasova, E. (2024). Prompto: An open source library for asynchronous querying of LLM endpoints. arXiv. https://doi.org/10.48550/arXiv.2408.11847
+
+
+ Tidychatmodels: Chat with all kinds of AI models through a common interface
+ Albert
+ 2024
+ Albert, R. (2024). Tidychatmodels: Chat with all kinds of AI models through a common interface. https://github.com/AlbertRapp/tidychatmodels
+
+
+ Tidyllm: Tidy integration of large language models
+ Brüll
+ CRAN: Contributed Packages
+ 10.32614/cran.package.tidyllm
+ 2024
+ Brüll, E. (2024). Tidyllm: Tidy integration of large language models. CRAN: Contributed Packages. https://doi.org/10.32614/cran.package.tidyllm
+
+
+
+
+
+
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+
+
+
+
+
+
+
+Journal of Open Source Software
+JOSS
+
+2475-9066
+
+Open Journals
+
+
+
+7211
+10.21105/joss.07211
+
+ollamar: An R package for running large language
+models
+
+
+
+https://orcid.org/0000-0003-4590-7039
+
+Lin
+Hause
+
+
+
+
+https://orcid.org/0009-0000-5659-9890
+
+Safi
+Tawab
+
+
+
+
+
+Massachusetts Institute of Technology, USA
+
+
+
+
+21
+11
+2024
+
+10
+105
+7211
+
+Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)
+2025
+The article authors
+
+Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)
+
+
+
+R
+large language models
+Ollama
+natural language processing
+artificial intelligence
+
+
+
+
+
+ Summary
+
Large language models (LLMs) have transformed natural language
+ processing and AI applications across numerous domains. While
+ cloud-based LLMs are common, locally deployed models offer distinct
+ advantages in reproducibility, data privacy, security, and
+ customization. ollamar is an R package that
+ provides an interface to Ollama, enabling researchers and data
+ scientists to integrate locally-hosted LLMs into their R workflows
+ seamlessly. It implements a consistent API design that aligns with
+ other programming languages and follows established LLM usage
+ conventions. It further distinguishes itself by offering flexible
+ output formats and easy management of conversation history.
+ ollamar is maintained on GitHub and available
+ through the Comprehensive R Archive Network (CRAN), where it regularly
+ undergoes comprehensive continuous integration testing across multiple
+ platforms.
+
+
+ State of the Field
+
The increasing importance of LLMs in various fields has created a
+ demand for accessible tools that allow researchers and practitioners
+ to leverage LLMs within their preferred programming environments.
+ Locally deployed LLMs offer advantages in terms of data privacy,
+ security, reproducibility, and customization, making them an
+ attractive option for many users
+ (Chan
+ et al., 2024;
+ Liu
+ et al., 2024;
+ Lytvyn,
+ 2024;
+ Shostack,
+ 2024). Currently, Ollama (https://ollama.com/) is one of the
+ most popular tools for running locally hosted LLMs, offering access to
+ a range of models with different sizes and capabilities. Several R
+ packages currently facilitate interaction with locally deployed LLMs
+ through Ollama, each with distinct approaches, capabilities, and
+ limitations.
+
The rollama
+ (Gruber
+ & Weber, 2024) and tidyllm
+ (Brüll,
+ 2024) libraries focus on text generation, conversations, and
+ text embedding, but their core functions do not always or necessarily
+ mirror the official Ollama API endpoints, which lead to
+ inconsistencies and confusion for users familiar with the official
+ API. Additionally, these libraries may not support all Ollama
+ endpoints and features. Another popular R library is
+ tidychatmodels
+ (Albert,
+ 2024), which allows users to chat with different LLMs, but it
+ is not available on CRAN, and therefore is not subject to the same
+ level of testing and quality assurance as CRAN packages.
+
All these libraries also adopt the tidyverse workflow, which some
+ may find restrictive, opinionated, or unfamiliar. While the tidyverse
+ is popular in the R community, it may not align with the programming
+ style or workflow of all users, especially those coming from other
+ programming languages or domains. This limitation can hinder the
+ accessibility and usability of these libraries for a broader audience
+ of R users. Thus, the R ecosystem lacks a simple and reliable library
+ to interface with Ollama, even though R is a popular and crucial tool
+ in statistics, data science, and various research domains
+ (Hill
+ et al., 2024).
+
+
+ Statement of Need
+
ollamar addresses the limitations of
+ existing R libraries by providing a consistent API design that mirrors
+ official Ollama endpoints, enabling seamless integration across
+ programming environments. It also offers flexible output formats
+ supporting dataframes, JSON lists, raw strings, and text vectors,
+ allowing users to choose the format that best suits their needs. The
+ package also includes independent conversation management tools that
+ align with industry-standard chat formats, streamlining the
+ integration of locally deployed LLMs into R workflows. These features
+ make ollamar a versatile and user-friendly tool
+ for researchers and data scientists working with LLMs in R. It fills a
+ critical gap in the R ecosystem by providing a native interface to run
+ locally deployed LLMs, and is already being used by researchers and
+ practitioners
+ (Turner,
+ 2024).
+
+
+ Design
+
ollamar implements a modular,
+ non-opinionated, and consistent approach that aligns with established
+ software engineering principles, where breaking down complex systems
+ into manageable components enhances maintainability, reusability, and
+ overall performance. It also avoids feature bloat, ensuring the
+ library remains focused on its core functionality. The key design
+ philosophy of ollamar are described below.
+
Consistency and maintainability: It provides an
+ interface to the Ollama server and all API endpoints, closely
+ following the official API design, where each function corresponds to
+ a specific endpoint. This implementation ensures the library is
+ consistent with the official Ollama API and easy to maintain and
+ extend as new features are added to Ollama. It also makes it easy for
+ R users to understand how similar libraries (such as in Python and
+ JavaScript) work while allowing users familiar with other programming
+ languages to adapt to and use this library quickly. The consistent API
+ structure across languages facilitates seamless transitions and
+ knowledge transfer for developers working in multi-language
+ environments.
+
Consistent and flexible output formats: All functions
+ that call API endpoints return
+ httr2::httr2_response objects by default, which
+ provides a consistent interface for flexible downstream processing. If
+ preferred, users have the option to specify different output formats
+ when calling the endpoints, such as dataframes
+ ("df"), lists (of JSON objects)
+ ("jsonlist"), raw strings
+ ("raw"), text vectors
+ ("text"), or request objects
+ ("req"). Alternatively, use the
+ resp_process() function to convert and process
+ the response object as needed. This flexibility allows users to choose
+ the format that best suits their needs, such as when working with
+ different data structures, integrating the output with other R
+ packages, or allowing parallelization via the popular
+ httr2 library. It also allows users to easily
+ build applications or pipelines on top of the library, without being
+ constrained by a specific output format.
+
Easy management of LLM conversation history: LLM APIs
+ often expect conversation/chat history data as input, often nested
+ lists or JSON objects. Note that this data format is standard for
+ chat-based applications and APIs (not limited to Ollama), such as
+ those provided by OpenAI and Anthropic. ollamar
+ simplifies preparing and processing conversational data for input to
+ different LLMs, focusing on streamlining the workflow for the most
+ popular chat-based applications.
+
Automated regular testing:
+ ollamar is hosted on GitHub and available
+ through CRAN, where it undergoes comprehensive continuous integration
+ testing across multiple platforms to ensure reliability. Daily
+ automated quality checks maintain long-term stability, and scheduled
+ tests verify version compatibility.
+
+
+ Conclusion
+
ollamar bridges a crucial gap in the R
+ ecosystem by providing seamless access to large language models
+ through Ollama. Its focus on consistency, flexibility, and
+ maintainability makes it a versatile and user-friendly tool for
+ researchers and data scientists working with LLMs in R. These design
+ choices ensure ollamar is a user-friendly and
+ reliable tool for integrating locally deployed LLMs into R workflows,
+ accelerating research and development in fields relying on R for data
+ analysis and machine learning.
+
+
+ Acknowledgements
+
This project was partially supported by the Canadian Social
+ Sciences & Humanities Research Council Tri-Agency Funding (funding
+ reference: 192324).
+
+
+
+
+
+
+
+
+ TurnerStephen D.
+
+ biorecap: an R package for summarizing bioRxiv preprints with a local LLM
+ arXiv
+ 202408
+ 20240824
+ 10.48550/arXiv.2408.11707
+
+
+
+
+
+ HillChelsey
+ DuLanqing
+ JohnsonMarina
+ McCulloughB. D.
+
+ Comparing programming languages for data analytics: Accuracy of estimation in Python and R
+ Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
+ John Wiley & Sons, Ltd
+ 202405
+ 20240824
+ 14
+ 3
+ 1942-4787
+ 10.1002/widm.1531
+ e1531
+
+
+
+
+
+
+ GruberJohannes B.
+ WeberMaximilian
+
+ rollama: An R package for using generative large language models through Ollama
+ arXiv
+ 202404
+ 20240824
+ 10.48550/arXiv.2404.07654
+
+
+
+
+
+ LiuFei
+ KangZejun
+ HanXing
+
+ Optimizing RAG techniques for automotive industry PDF chatbots: A case study with locally deployed Ollama models
+ arXiv
+ 202408
+ 20240824
+ 10.48550/arXiv.2408.05933
+
+
+
+
+
+ ShostackAdam
+
+ The boy who survived: Removing Harry Potter from an LLM is harder than reported
+ arXiv
+ 202403
+ 20240824
+ 10.48550/arXiv.2403.12082
+
+
+
+
+
+ LytvynOleksandr
+
+ Enhancing propaganda detection with open source language models: A comparative study
+ Proceedings of the MEi:CogSci Conference
+ 202406
+ 20240824
+ 18
+ 1
+ 2960-5911
+ https://journals.phl.univie.ac.at/meicogsci/article/view/822
+
+
+
+
+
+ ChanRyan Sze-Yin
+ NanniFederico
+ BrownEdwin
+ ChapmanEd
+ WilliamsAngus R.
+ BrightJonathan
+ GabasovaEvelina
+
+ Prompto: An open source library for asynchronous querying of LLM endpoints
+ arXiv
+ 202408
+ 20240824
+ 10.48550/arXiv.2408.11847
+
+
+
+
+
+ AlbertRapp
+
+ Tidychatmodels: Chat with all kinds of AI models through a common interface
+
+ 2024
+
+
+ https://github.com/AlbertRapp/tidychatmodels
+
+
+
+
+
+
+
+ BrüllEduard
+
+ Tidyllm: Tidy integration of large language models
+ CRAN: Contributed Packages
+ 2024
+ https://edubruell.github.io/tidyllm/
+ 10.32614/cran.package.tidyllm
+
+
+
+
+