  Code Large Language Models (CodeLLMs) have demonstrated impressive
proficiency in code completion tasks. However, they often fall short of fully
understanding the extensive context of a project repository, such as the
intricacies of relevant files and class hierarchies, which can result in less
precise completions. To overcome these limitations, we present \tool, a
multifaceted framework designed to address the complex challenges associated
with repository-level code completion. Central to \tool is the {\em Repo-level
Semantic Graph} (RSG), a novel semantic graph structure that encapsulates the
vast context of code repositories. Furthermore, RepoHyper leverages
\textit{Expand and Refine} retrieval method, including a graph expansion and a
link prediction algorithm applied to the RSG, enabling the effective retrieval
and prioritization of relevant code snippets. Our evaluations show that \tool
markedly outperforms existing techniques in repository-level code completion,
showcasing enhanced accuracy across various datasets when compared to several
strong baselines. Our implementation of RepoHyper can be found
at~\url{https://github.com/FSoft-AI4Code/RepoHyper}.
