Repository-level code completion requires understanding complex cross-file dependencies, yet existing approaches struggle to balance context richness with the sub-200ms latency requirements of real-time IDE integration. We present RepoFuse, a dual-context framework deployed at Ant Group1 serving 10,000 developers across 50+ million lines of code. In a production deployment at Ant Group, RepoFuse maintained an average latency of 156ms and improved completion acceptance rate by 18%. This was accomplished through systematic categorization of cross-file context into semantic context (structural dependencies via lightweight static analysis) and similar context (functionally analogous patterns via similarity search), combined with a relevance-guided selection algorithm that optimizes the efficiency-accuracy trade-off. Benchmark evaluation on CrossCodeEval demonstrates 4.26 and 3.01 improvements in exact match scores for Python and Java respectively, while using only 25% of baseline token requirements. More significantly, our one-week A/B test with 80,000 completion requests revealed that developers particularly valued the dual-context approach when working across microservice boundaries, with 73% reporting reduced context switching between files. The system successfully operates under production constraints with distributed caching, real-time graph updates, and p95 latency under 190ms, demonstrating that principled context categorization enables practical repository-level completion at industrial scale.We have open-sourced RepoFuse at GitHub.