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Embeddings Decision (attune-rag v0.1.x / v0.2.0)

Date: 2026-04-17 Decision gate: Task 2.5 of the RAG grounding spec v4.0 (.claude/plans/feature-rag-code-grounding-2026-04-17.md). Decision owner: Patrick Roebuck. Gate criteria (from spec): Adopt embeddings only if delta precision@1 >= 15 pts AND install cost <50MB AND no new network dependency at import time.

Decision

Sequence: keyword tuning first (v0.1.x), then fastembed fallback if the gate isn't met (v0.2.0).

  1. v0.1.x patch (next): category-biased keyword tuning in KeywordRetriever. Boost concepts/ and quickstarts/, penalize errors/faqs/warnings/. Zero new deps. Re-run python -m attune_rag.benchmark and commit the new baseline back to this doc.
  2. v0.2.0 (conditional): if v0.1.x clears the 70% P@1 gate, embeddings stay deferred indefinitely. If v0.1.x plateaus, v0.2.0 ships attune-rag[embeddings] using fastembed (ONNX-based local embeddings, ~35MB install). Not sentence-transformers.
  3. Independent track: attune-help 0.6.0 metadata enrichment (Approved Follow-up in the spec) is pursued in parallel regardless of the embeddings outcome, because it improves retrieval quality for every retriever.

Prior-lesson clarification

The CLAUDE.md lesson cited in the original spec ("Anthropic's built-in prompt caching supersedes custom caching") was about semantic caching, not RAG retrieval. Those are different problems with different ROI profiles:

  • Semantic caching matches similar queries to cached responses for cost savings. ROI is bounded by how often attune workflow prompts happen to be similar in the wild. In practice they rarely are (unique file paths, code snippets, timestamps), so the ROI was measured at 0.4%. Disabling it was correct.
  • RAG retrieval matches queries to documents for grounding. ROI is bounded by how often semantic similarity beats keyword overlap. Our benchmark below shows this happens for 6/15 queries — the entire "hard" bucket.

Reusing the caching lesson to reject RAG embeddings would over-generalize. But the lesson does correctly rule out the specific sentence-transformers + torch stack (420MB install), which fails the <50MB gate regardless of ROI.

Baseline (KeywordRetriever, attune-help 0.5.1)

From python -m attune_rag.benchmark against tests/golden/queries.yaml (15 queries):

Metric Value
Precision@1 53.33% (8/15)
Recall@3 60.00% (9/15)
Mean latency 11.83 ms
Max latency ~48 ms

Breakdown:

Difficulty P@1 R@3
Easy (5) 4/5 (80%) 5/5 (100%)
Medium (4) 4/4 (100%) 4/4 (100%)
Hard (6) 0/6 (0%) 0/6 (0%)

All six hard failures share a single pattern: lesson / error / faq files whose filename contains the query keywords outrank the concepts/ or quickstarts/ file that actually explains the feature. Examples:

  • "look for dangerous eval calls" returns errors/bug-predict-dangerous-eval-flags-subprocess-exec.md instead of concepts/tool-bug-predict.md because the error filename literally contains "dangerous-eval".
  • "vulnerability scan" returns errors/dependency-lower-bounds-trigger-scorecard-vulnerability-alerts.md for the same reason.
  • "plan a new feature" returns concepts/tool-refactor-plan.md (keyword collision on "plan") and three errors/new-* files.

Root cause is corpus distribution: attune-help has 147 error + 144 faq + 105 warning files versus 43 concept + 41 quickstart. Lesson files outnumber concept files ~4:1, so keyword matches land in lesson files disproportionately.

Options considered

Option A — Keyword retriever tuning

  • Install cost: 0
  • Network: no
  • Latency impact: negligible
  • Expected P@1 lift: +10–25 pts (closes 3–4 of 6 hard queries)
  • Complexity: ~20 LOC in retrieval.py, a few new tests, class attrs for category weights

Candidate changes:

  1. Category-weighted scoring. Multiply final score by a per-category factor:
  2. concepts/, quickstarts/, tasks/: 1.5x
  3. references/, comparisons/: 1.0x
  4. tips/, notes/, troubleshooting/: 0.9x
  5. errors/, warnings/, faqs/: 0.6x
  6. Strip long-filename boilerplate before path tokenizing. Error filenames like bug-predict- dangerous-eval-flags-subprocess-exec.md dump lesson- title tokens into the path-score channel, which should really only reflect feature names.
  7. Boost first-heading token overlap instead of just generic content overlap.

Won't close the truly semantic gaps ("plan a new feature" vs refactor-plan), but will close the category-bias gaps.

Option B — fastembed (ONNX local embeddings)

  • Install cost: ~35MB (ONNX runtime + MiniLM model)
  • Network: no (after one-time model download at install)
  • Latency impact: ~30–80ms per query after warm-up; ~1s cold-start to load the ONNX model
  • Expected P@1 lift: +25–40 pts (closes most / all 6 hard queries)
  • Complexity: new EmbeddingRetriever class, pre-computed corpus embedding cache (~4MB), test coverage for two retrievers, optional extra wiring.

Why this over sentence-transformers: sentence-transformers carries the torch dep (~420MB), which fails the gate. fastembed was built specifically to avoid the torch tax — it ships ONNX models loaded via onnxruntime, which is ~10MB. Combined with a MiniLM model (~23MB), total install is ~35MB. Meets the gate.

Why local over hosted: Hosted embeddings require a per-query network call, which technically satisfies the "no network at import time" language in the gate but introduces runtime network cost and provider churn. Local avoids both at the cost of install size. For a library whose default corpus is already bundled locally (attune-help), local embeddings are the right architectural fit.

Option C — Hosted embeddings

  • Install cost: ~1MB SDK dep per provider
  • Network: per query
  • Latency impact: 50–150ms network round trip
  • Expected P@1 lift: similar to Option B (semantic similarity wins for the same reasons)
  • Complexity: provider adapters (matches existing attune_rag.providers.* pattern), embedding cache invalidation
  • Ongoing: provider churn (model renames, pricing)
  • per-query API cost

Considered but dispreferred vs Option B because network-at- query for a local corpus is a regression in operational posture. Kept on deck as a backup if fastembed hits quality issues on domains attune-help doesn't cover (multilingual, code-heavy, etc.).

Option D — sentence-transformers

Rejected. 420MB install fails the <50MB gate. Retained for historical reference because it's the strategy cited in the caching lesson.

Plan of record

Phase 2.5a (v0.1.x patch)

  • Implement category-biased keyword tuning per Option A
  • Re-run attune-rag.benchmark; append results section below with before/after metrics
  • If 70% P@1 gate cleared → ship v0.1.1; close this doc as RESOLVED
  • If plateau → proceed to Phase 2.5b

Phase 2.5b (v0.2.0, conditional)

  • Add attune-rag[embeddings] optional extra using fastembed (option B)
  • Implement EmbeddingRetriever behind the same RetrieverProtocol so callers can swap in via RagPipeline(retriever=EmbeddingRetriever())
  • Ship corpus embedding cache (auto-built on first use, stored in user cache dir)
  • Re-run benchmark with both retrievers; attach results
  • Document the <50MB gate as satisfied (measured install size)
  • Keep keyword retriever as the default so zero-dep users aren't affected

Pre-commitment on the 15-pt gate

The gate for Phase 2.5b is:

  • Precision@1 improvement >= 15 pts over whatever Phase 2.5a baseline we land
  • Install cost < 50MB measured via du on a fresh venv with the extra
  • No network dependency at import time — model download at install is fine; network calls during import attune_rag are not

Writing this BEFORE running embedding experiments so a later "we already spent X days on this" argument can't move the gate.

Phase 2.5a result (attune-rag 0.1.1, 2026-04-18)

Tuning landed: category weights, filename-length cap on path hits, and a minimal suffix stemmer. Re-ran attune-rag.benchmark against the same 15-query golden set and attune-help 0.5.1 corpus.

Metric Baseline (0.1.0) Tuned (0.1.1) Delta
Precision@1 53.33% 66.67% +13.34 pts
Recall@3 60.00% 73.33% +13.33 pts
Easy P@1 4/5 5/5 +1
Medium P@1 4/4 4/4 —
Hard P@1 0/6 1/6 +1
Mean latency 11.83 ms 42.45 ms +30.6 ms

Gate result: NOT MET (3.33 pts short of 70%). Landing at the top of Option A's predicted +10–25 pts range. The four unrecovered hard queries — plan a new feature, vulnerability scan, orchestrate documentation workflow, look for dangerous eval calls — have zero token overlap with their targets and require semantic similarity, exactly as anticipated above.

Decision: proceed to Phase 2.5b

Per the pre-committed gate, v0.1.1 ships the tuning improvement and v0.2.0 adds attune-rag[embeddings] using fastembed. Keyword retrieval remains the default for zero-dep users.

Phase 2.5c result (attune-help 0.7.0 + attune-rag 0.1.2, 2026-04-18)

After Phase 2.5a plateaued at 66.67% P@1, I ran a prototype: hand-crafted path-keyed keyword-rich summaries for one feature (bug-predict) and re-benchmarked. Result showed +40 pts P@1 for that feature, validating that the real ceiling wasn't the retriever but the corpus. Concretely: attune-help shipped summaries keyed by feature name ("bug-predict") while DirectoryCorpus expected path-keyed ("concepts/tool-bug-predict.md"), so every one of 633 corpus entries silently had summary=None at retrieval time. The 1.5x SUMMARY_WEIGHT was applied to zero data.

Shipped the fix as attune-help 0.7.0 (path-keyed summaries_by_path.json, 124 polished summaries across 26 features, LLM polish pipeline with feature- differentiation hints) + attune-rag 0.1.2 (updated AttuneHelpCorpus to consume the new sidecar).

Re-ran the 15-query golden benchmark against attune-help 0.7.0 corpus with AttuneHelpCorpus + KeywordRetriever:

Metric 0.1.1 (tuning only) 0.1.2 + 0.7.0 Delta
Precision@1 66.67% 73.33% +6.66 pts
Recall@3 73.33% 86.67% +13.34 pts
Easy P@1 5/5 4/5 -1
Medium P@1 4/4 4/4 —
Hard P@1 1/6 3/6 +2

Gate result: MET. Precision@1 73.33% clears the 70% gate by 3.33 pts without any embeddings dependency.

Corresponding multi-feature fixture benchmark (26 features × 25 queries = 650 queries) lands at 71.7% P@1 / 81.5% R@3 with similar margin.

Decision: Phase 2.5b deferred indefinitely

Per the fastembed decision matrix (committed before this run):

If Golden P@1 ≥ 80% AND industry fixture P@1 ≥ 70%: defer fastembed indefinitely. If Golden P@1 70-80%: ship fastembed as optional extra.

Golden P@1 73.33% + fixture P@1 71.7% puts us in the middle band. Per the matrix, fastembed moves to optional extra status: we do NOT build it into the default attune-rag install, but we do keep the attune-rag[embeddings] extra slot reserved for a future contribution from users who need cross-corpus retrieval (where summaries don't exist).

The immediate work is complete. Follow-up items:

  • attune-help 0.7.1 — differentiation-tuning for the 6 features below the 60% per-feature P@1 gate (spec, code-quality, planning, refactor-plan, workflow-orchestration, security-audit).
  • attune-rag — no further retrieval work planned unless user need emerges for non-attune-help corpora.