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Phase 0: shared search core + reproducible corpus
Scaffold the StreamSearch repo and build the reusable heart with no infra: - embed.py: singleton MiniLM (all-MiniLM-L6-v2, 384-d), batched embed_texts - search.py: hybrid BM25 + dense (Qdrant) fused with RRF (k=60) - schema.py: Document contract + Cassandra/Qdrant definitions - data/corpus_sample.jsonl: 3000 arXiv abstracts (HF mirror, reproducible) - tests: RRF, embedding determinism, single model-load, hybrid ranking (10 pass) - pinned requirements.txt (Python 3.11), Makefile, MIT LICENSE, README Deployment: unified live site over managed free tiers (Qdrant Cloud, Astra Cassandra, Confluent/Redpanda Kafka); Spark shown via local compose + video.61d5e4e