Repository-level code completion automatically predicts the unfinished code based on the broader information from the repository.Recent strides in Code Large Language Models (code LLMs) have spurred the development of repository-level code completion methods, yielding promising results.Nevertheless, they suffer from issues such as inappropriate query construction, single-path code retrieval, and misalignment between code retriever and code LLM.To address these problems, we introduce CodeRAG, a framework tailored to identify relevant and necessary knowledge for retrieval-augmented repository-level code completion.Its core components include log probability guided query construction, multi-path code retrieval, and preference-aligned BESTFIT reranking.Extensive experiments on benchmarks ReccEval and CCEval demonstrate that CodeRAG significantly and consistently outperforms state-of-the-art methods.The implementation of CodeRAG is available at https: //github.com/KDEGroup/CodeRAG.