Vector databases use hashing to partition data across "shards," logical units for distributed execution. This placement, however, destroys semantic locality, forcing each query into scatter-gather limited by the slowest shard. Vector-space clustering can help, but scientific evidence is often connected by factual relations that do not align with embedding distance. We present COMPASS, a framework that uses a knowledge graph (KG) to determine data placement and query-time shard selection. COMPASS detects communities, splits oversized communities, inserts embeddings by subject entity, and routes queries to a small set of shards. Across four biomedical KGs, our method searches only 13-18% of the corpus while preserving broadcast recall and recovering up to 2.6x more multi-hop evidence than an embedding-based baseline. On 15 HPC nodes, COMPASS sustains 7.9x higher throughput with lower tail latency than hash-based broadcast. These results show that KG structure provides a compact complement to embedding geometry for scalable vector search.

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