The advent of large language models (LLMs) has significantly advanced artificial intelligence in software engineering, with source code embeddings playing a crucial role in tasks such as source code clone detection and source code clustering. However, existing methods for source code embedding, including those based on LLMs, often rely on costly supervised training or fine-tuning for domain adaptation. This paper proposes LSem2Vec (LLM-extracted code Semantics to Vector embedding), a simple yet effective two-stage approach to embedding source code by combining large language and sentence embedding models. Specifically, LSem2Vec leverages an LLM to extract the semantics of source code, and then uses a sentence embedding model to generate representation vectors. Compared with previous approaches, LSem2Vec eliminates the need for task-specific training or fine-tuning and effectively addresses erroneous information commonly found in LLM-generated outputs. To evaluate the performance of LSem2Vec, we conducted a series of experiments across three datasets spanning different programming languages, using various LLMs and sentence embedding models. The experimental results demonstrate the effectiveness and superiority of LSem2Vec over five state-of-the-art unsupervised methods. Our findings highlight the potential of LSem2Vec to advance the field of software engineering by providing robust and efficient solutions for source code embedding tasks.

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