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Viktor asked to enhance the memory system with 'semantics' — remember concepts (not just tokens) linked in a graph — and to prove, by benchmarking against the current system, that it actually improves recall. A multi-phase research workflow (18 agents) did landscape research, an adversarially-reviewed integration design, a stratified eval set over the real 5,452-memory corpus, and a head-to-head prototype-vs-current benchmark. Result: hybrid (lexical FTS + dense embeddings, RRF-fused) beats FTS on every overall metric, driven by a robust paraphrase win (recall@10 +0.350). Recommend adopting lexical+dense; the concept graph is DEFERRED. Post-run adversarial review correction (applied to all docs before commit): the prototype's fusion config structurally barred the graph leg from the ranked top-k, so the 'graph contributes nothing' ablation was a math artifact, NOT an empirical result — the graph is UNEVALUATED, not disproven (deferred on cost+uncertainty). Multi-hop deltas are not statistically significant. Glossary in CONTEXT.md; framing in ADR-0001-0003; findings in ADR-0004-0006 + docs/research/. Privacy: the corpus/queries/qrels/results are the user's real memories and stay gitignored (data/, cache/, results/, build_eval_set.py); only harness code, aggregate numbers, and synthetic examples are committed. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
10 lines
502 B
Python
10 lines
502 B
Python
"""Pluggable retrievers for the claude-memory recall benchmark.
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Each retriever implements the harness `retrieve(query, k) -> list[int]` contract
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(see ``harness/types.py`` :: ``Retriever``) and, optionally, the ``build_index`` /
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``index_size_bytes`` lifecycle hooks the runner duck-types.
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``fts.FtsRetriever`` is the LEXICAL BASELINE — the product's current local-store
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recall (SQLite FTS5/BM25). It is the "current system" any hybrid retriever must
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beat on recall@k / nDCG@10 / MRR (ADR-0001).
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"""
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