Large language models (LLMs) face critical limitations in dynamically integrating external knowledge, mitigating hallucinations, and addressing domain-specific expertise. While retrieval-augmented generation (RAG) mitigates these issues, traditional chunk-based methods suffer from retrieval noise, multi-hop reasoning failures, and poor interpretability. Graph-based RAG (GraphRAG) has improved RAG in terms of context retrieval and relational reasoning, but there are still shortcomings in jointly processing these two types of tasks. To bridge this gap, we propose TriG-RAG, a unified framework that synergizes Triple-Granularity graph indexing with an adaptive retrieval strategy. TriG-RAG constructs a heterogeneous graph with a document-chunk-entity hierarchical structure and a weight allocation strategy, enabling flexible handling of both context-dependent and relation-dependent queries. Extensive experiments on six benchmarks demonstrate the superiority of TriG-RAG in terms of retrieval capability, flexibility and interpretability.