Legal case retrieval (LCR) defines relevance beyond text similarity, judgments may describe near-identical events yet be decided under different charges. Existing synthesis pipelines mine training pairs from text similarity and shared charges, which separates positives from negatives but leaves the difficulty of each pair implicit. We present Deka Retrieval, an LCR benchmark on Thai Supreme Court decisions with an LLM relevance cases mining pipeline for a civil-law jurisdiction. We decompose each judgment into case components (Charge, Fact, Reasoning, Decision) and split easy/hard level on exist binary signals and introduce an anomaly variant a conflict judgment that shares the anchor’s charge and facts but reaching a different reasoning and decision. Retriever performance follows query length: Fact > Lawyer expert > Lay people. Fine-tuning gains most on the weakest type, lifting Lay R@10 by 2.6x (7.6 -> 19.5) versus 1.5x on Lawyer and 1.3x on Fact. Training on the shortest query also transfers to the longer ones, but not the reverse. Adding anomaly cases as positives raises the chance of retrieving them at test time and degrades performance relative to training without them. Our dataset and models are available at https://hf.co/collections/VISAI-AI/deka-retrieval.