Repository-level code generation aims to generate target code conditioned on the context of the specified repository. Existing approaches generally adopt the Retrieval-Augmented Generation (RAG) framework to retrieve relevant contextual information from the repository, thereby mitigating the challenge of long context windows. However, these methods typically struggle to identify truly relevant information for the current generation requirement, and they generally overlook the imperative need for understanding the repository-level code structure during the retrieval process. To address these issues, we propose RepoMind, a novel framework that advances repository-level code generation by leveraging a repository documentation library and LLM reasoning capabilities for API retrieval. Specifically, RepoMind first introduces the RepoDocs Agent. This component constructs multi-granularity, hierarchical structural documentation in a bottom-up manner, providing LLMs with a high-quality knowledge base for understanding repository functionality and structure. Built upon this hierarchical knowledge base, RepoMind further integrates the Reasoning-Retrieval Agent. This agent mimics human developer patterns by utilizing the repository documentation for layer-by-layer exploration, and ultimately precisely localizes the relevant API sets. Finally, the framework combines the functionally relevant API set retrieved via LLM reasoning with the semantically similar API set retrieved by the vector retriever, utilizing this hybrid context to boost code generation performance. Evaluation on widely-used repository-level code generation benchmarks, CoderEval and DevEval, demonstrates that RepoMind surpasses state-of-the-art methods, achieving up to a 13.50% relative improvement in Pass@1 scores.