Retrieval Ratchet

Retrieval Ratchet makes retrieval quality a merge gate for RAG applications. A seeded generator derives QA sets from the corpus itself with no LLM calls, a pluggable retriever protocol feeds a metric engine computing recall@k, MRR, precision@k, and lexically-grounded faithfulness, and `check` exits non-zero when results fall below a checked-in baseline. It runs offline in seconds inside GitHub Actions, with byte-identical QA output for a given seed.

What's interesting about it

• Seeded synthetic-QA generator derives questions from the corpus itself — no LLM calls, byte-identical output per seed.
• Metric engine computes recall@k, MRR, precision@k, and lexically-grounded faithfulness, stable run-to-run.
• Regression gate compares against a checked-in baseline with float-noise tolerance; the exit code is the merge gate.
• Retriever protocol needs only retrieve(query, k); TF-IDF and HTTP adapters pass a shared contract suite covering determinism and unicode queries.