Retrieval-augmented code completion for local projects using large language models

Hostnik, Marko (author), Robnik Šikonja, Marko (author)

The use of large language models (LLMs) is becoming increasingly widespread among software developers. However, privacy and computational requirements are problematic with commercial solutions and the use of LLMs. In this work, we focus on using relatively small and efficient LLMs with 160M parameters that are suitable for local execution and augmentation with retrieval from local projects. We train two open transformer-based models, the generative GPT-2 and the retrieval-adapted RETRO, on open-source Python files, and empirically compare them, confirming the benefits of embedding-based retrieval. Furthermore, we improve our models’ performance with In-context retrieval-augmented generation (RAG), which retrieves code snippets using the Jaccard similarity of tokens. We evaluate In-context RAG on larger models and determine that, despite its simplicity, the approach is more suitable than using the RETRO architecture. Experimental results indicate that In-context RAG improves the code completion baseline by over 26 %, while RETRO improves over the similarly sized GPT-2 baseline by 12 %. We highlight the key role of proper tokenization in achieving the full potential of LLMs in code completion.

• Language: English
• Keywords: large language models, code completion, retrieval-augmented generation, in-context retrieval
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science ; FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2025
• Number of pages: 15 str.
• Numbering: Vol. 292, art. 128596
• PID: 20.500.12556/RUL-174280
• UDC: 004.85:81'322
• ISSN on article: 0957-4174
• DOI: 10.1016/j.eswa.2025.128596
• COBISS.SI-ID: 242180867
• Publication date in RUL: 30.09.2025

Record is a part of a journal

• Title: Expert systems with applications
• Shortened title: Expert syst. appl.
• Publisher: Elsevier
• ISSN: 0957-4174
• COBISS.SI-ID: 171291

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: obdelava naravnega jezika, veliki jezikovni modeli, programsko inženirstvo, generiranje kode