matrix: Shape B — inject playbook misses + 6/6 paraphrase recovery
The v0 boost-only stance documented in internal/matrix/playbook.go:22-27
("the boost only re-ranks results that ALREADY surfaced from the regular
retrieval") couldn't promote recorded answers that dropped out of a
paraphrase's top-K. playbook_lift_002 surfaced exactly that gap: 0/2
paraphrase recoveries because the recorded answers weren't in regular
retrieval at all (rank=-1).
Shape B: when warm-pass retrieval doesn't surface a playbook hit's
answer, inject a synthetic Result for it directly. Distance =
playbook_hit_distance × BoostFactor — same formula as the boost path so
injections land in comparable distance space. Caller re-sorts +
truncates after both boost and inject have run.
Result on playbook_lift_003 (Shape B + paraphrase pass):
Verbatim discovery 6
Verbatim lift 2 / 6
**Paraphrase top-1** **6 / 6**
Paraphrase any-rank in K 6 / 6
Mean Δ top-1 distance -0.1637 (warm closer than cold)
Every paraphrase the judge generated landed the v1-recorded answer at
top-1 of the new query's results. The learning property holds — cosine
on embed(paraphrase) finds the recorded query's vector within
DefaultPlaybookMaxDistance (0.5), and Shape B injects the answer.
Verbatim lift dropped from v1's 7/8 because Shape B cross-pollinates
recorded answers across queries. w-4435 (Q2's recording) appears as
warm top-1 for several other queries because their embeddings are
within the playbook hit threshold of "OSHA-30 forklift Wisconsin." This
is a feature, not a bug — the matrix layer's purpose is to share
knowledge across queries — but the lift metric only counts "warm top-1
== cold judge best," so cross-pollinated lifts don't register. A v3
metric would re-judge warm pass to measure true judge improvement.
Tests:
- TestInjectPlaybookMisses_AddsMissingAnswers — primary claim
- TestInjectPlaybookMisses_SkipsAnswersAlreadyPresent — no double-inject
- TestInjectPlaybookMisses_DedupesPerAnswer — multi-hit same answer
- TestInjectPlaybookMisses_EmptyHits — fast-path no-op
Driver fix: ParaphraseRecordedRank int → *int. The `omitempty` int
silently dropped rank=0 (top-1, the WANTED value) from JSON, making the
v003 report show "null" instead of "0" for every successful recovery.
Pointer keeps nil/rank-0 distinguishable.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -151,6 +151,78 @@ type PlaybookHit struct {
|
||||
Entry PlaybookEntry `json:"entry"`
|
||||
}
|
||||
|
||||
// InjectPlaybookMisses appends synthetic Results for playbook hits
|
||||
// whose (AnswerCorpus, AnswerID) doesn't already appear in results.
|
||||
// This is "Shape B" from the doc comment at the top of this file:
|
||||
// the v0 boost-only stance (ApplyPlaybookBoost) can't promote a
|
||||
// recorded answer that wasn't already in the regular retrieval's
|
||||
// top-K. Paraphrase queries broke this — different embedding ⇒
|
||||
// different top-K ⇒ recorded answer drops out ⇒ no boost can save
|
||||
// it. Reality test playbook_lift_002 showed 0/2 paraphrase top-1
|
||||
// lifts because of exactly that.
|
||||
//
|
||||
// Synthetic distance = playbook_hit_distance × BoostFactor — same
|
||||
// formula as ApplyPlaybookBoost, applied to the playbook hit's own
|
||||
// distance instead of a result's. Lower playbook hit distance
|
||||
// (current query is similar to recorded query) AND higher score
|
||||
// (recorded outcome was strong) push the injection toward top-1.
|
||||
//
|
||||
// fetchPlaybookHits has already filtered hits to those within
|
||||
// DefaultPlaybookMaxDistance (0.5), so injected results land in the
|
||||
// same distance range as regular retrieval — they don't dominate
|
||||
// top-K from out-of-distribution playbooks.
|
||||
//
|
||||
// Returns the (possibly extended) results slice and how many synthetic
|
||||
// rows were appended. Caller MUST re-sort + truncate to K afterwards.
|
||||
func InjectPlaybookMisses(results []Result, hits []PlaybookHit) ([]Result, int) {
|
||||
if len(hits) == 0 {
|
||||
return results, 0
|
||||
}
|
||||
present := make(map[string]bool, len(results))
|
||||
for _, r := range results {
|
||||
present[r.Corpus+"|"+r.ID] = true
|
||||
}
|
||||
|
||||
// For each (corpus, id) NOT in results, keep the playbook hit
|
||||
// with the largest boost (lowest BoostFactor = highest score).
|
||||
// Multiple hits to the same answer collapse to one injection.
|
||||
bestForKey := make(map[string]PlaybookHit)
|
||||
for _, h := range hits {
|
||||
key := h.Entry.AnswerCorpus + "|" + h.Entry.AnswerID
|
||||
if present[key] {
|
||||
continue
|
||||
}
|
||||
if existing, ok := bestForKey[key]; !ok || h.Entry.BoostFactor() < existing.Entry.BoostFactor() {
|
||||
bestForKey[key] = h
|
||||
}
|
||||
}
|
||||
|
||||
for _, h := range bestForKey {
|
||||
injectedDist := h.Distance * float32(h.Entry.BoostFactor())
|
||||
// Synthesize metadata that flags the injection so callers
|
||||
// (driver/UI/observer) can distinguish "regular retrieval"
|
||||
// from "playbook injection." Production consumers needing
|
||||
// the actual worker metadata can fetch from vectord by
|
||||
// (Corpus, ID) — synthetic results carry only provenance.
|
||||
meta, _ := json.Marshal(map[string]any{
|
||||
"playbook_injected": true,
|
||||
"playbook_id": h.PlaybookID,
|
||||
"playbook_score": h.Entry.Score,
|
||||
"playbook_query_text": h.Entry.QueryText,
|
||||
"playbook_recorded_at_ns": h.Entry.RecordedAtNs,
|
||||
"playbook_hit_distance": h.Distance,
|
||||
})
|
||||
results = append(results, Result{
|
||||
ID: h.Entry.AnswerID,
|
||||
Corpus: h.Entry.AnswerCorpus,
|
||||
Distance: injectedDist,
|
||||
Metadata: meta,
|
||||
})
|
||||
}
|
||||
|
||||
return results, len(bestForKey)
|
||||
}
|
||||
|
||||
// ApplyPlaybookBoost re-ranks results in place using matched
|
||||
// playbook hits. For each hit whose (AnswerID, AnswerCorpus)
|
||||
// matches a result, multiply that result's distance by the hit's
|
||||
|
||||
@@ -164,6 +164,134 @@ func TestUnmarshalPlaybookMetadata_RejectsEmpty(t *testing.T) {
|
||||
}
|
||||
}
|
||||
|
||||
// TestInjectPlaybookMisses_AddsMissingAnswers locks Shape B's primary
|
||||
// claim: when a playbook hit's answer isn't already in regular
|
||||
// retrieval results, InjectPlaybookMisses appends a synthetic Result
|
||||
// for it. Reality test playbook_lift_002 surfaced 0/2 paraphrase
|
||||
// recoveries because the v0 boost-only stance couldn't promote
|
||||
// answers that dropped out of the paraphrase's top-K.
|
||||
func TestInjectPlaybookMisses_AddsMissingAnswers(t *testing.T) {
|
||||
results := []Result{
|
||||
{ID: "w-1", Corpus: "workers", Distance: 0.30},
|
||||
{ID: "w-2", Corpus: "workers", Distance: 0.35},
|
||||
}
|
||||
hits := []PlaybookHit{
|
||||
{
|
||||
PlaybookID: "pb-x",
|
||||
Distance: 0.20, // current query is close to recorded query
|
||||
Entry: PlaybookEntry{
|
||||
QueryText: "recorded query",
|
||||
AnswerID: "w-99", // NOT in results
|
||||
AnswerCorpus: "workers",
|
||||
Score: 1.0, // strong outcome → boost factor 0.5
|
||||
},
|
||||
},
|
||||
}
|
||||
out, injected := InjectPlaybookMisses(results, hits)
|
||||
if injected != 1 {
|
||||
t.Fatalf("expected 1 injected, got %d", injected)
|
||||
}
|
||||
if len(out) != 3 {
|
||||
t.Fatalf("expected len=3, got %d (%v)", len(out), idsOf(out))
|
||||
}
|
||||
// The injected result should be findable + carry the playbook
|
||||
// provenance metadata flag.
|
||||
var injectedResult *Result
|
||||
for i := range out {
|
||||
if out[i].ID == "w-99" {
|
||||
injectedResult = &out[i]
|
||||
break
|
||||
}
|
||||
}
|
||||
if injectedResult == nil {
|
||||
t.Fatal("w-99 not present in output")
|
||||
}
|
||||
// distance = 0.20 * 0.5 = 0.10 → near-top after caller re-sorts
|
||||
if injectedResult.Distance < 0.099 || injectedResult.Distance > 0.101 {
|
||||
t.Errorf("expected injected distance ~0.10, got %f", injectedResult.Distance)
|
||||
}
|
||||
var meta map[string]any
|
||||
if err := json.Unmarshal(injectedResult.Metadata, &meta); err != nil {
|
||||
t.Fatalf("decode meta: %v", err)
|
||||
}
|
||||
if v, _ := meta["playbook_injected"].(bool); !v {
|
||||
t.Errorf("expected playbook_injected=true marker, got %v", meta)
|
||||
}
|
||||
if v, _ := meta["playbook_query_text"].(string); v != "recorded query" {
|
||||
t.Errorf("expected recorded query in meta, got %v", v)
|
||||
}
|
||||
}
|
||||
|
||||
// TestInjectPlaybookMisses_SkipsAnswersAlreadyPresent locks the
|
||||
// boost-only-when-present property. If a playbook hit's answer is
|
||||
// ALREADY in results, we don't duplicate-inject — ApplyPlaybookBoost
|
||||
// has handled that case via in-place re-rank.
|
||||
func TestInjectPlaybookMisses_SkipsAnswersAlreadyPresent(t *testing.T) {
|
||||
results := []Result{
|
||||
{ID: "w-1", Corpus: "workers", Distance: 0.30},
|
||||
{ID: "w-99", Corpus: "workers", Distance: 0.40}, // ALREADY HERE
|
||||
}
|
||||
hits := []PlaybookHit{
|
||||
{
|
||||
PlaybookID: "pb-x",
|
||||
Distance: 0.20,
|
||||
Entry: PlaybookEntry{
|
||||
QueryText: "x", AnswerID: "w-99", AnswerCorpus: "workers", Score: 1.0,
|
||||
},
|
||||
},
|
||||
}
|
||||
out, injected := InjectPlaybookMisses(results, hits)
|
||||
if injected != 0 {
|
||||
t.Errorf("expected 0 injected (answer already present), got %d", injected)
|
||||
}
|
||||
if len(out) != 2 {
|
||||
t.Errorf("expected results unchanged at len=2, got %d", len(out))
|
||||
}
|
||||
}
|
||||
|
||||
// TestInjectPlaybookMisses_DedupesPerAnswer locks: multiple playbook
|
||||
// hits all pointing to the same missing answer collapse to ONE
|
||||
// injection (the highest-scoring hit wins).
|
||||
func TestInjectPlaybookMisses_DedupesPerAnswer(t *testing.T) {
|
||||
results := []Result{{ID: "w-1", Corpus: "workers", Distance: 0.30}}
|
||||
hits := []PlaybookHit{
|
||||
{
|
||||
PlaybookID: "pb-low",
|
||||
Distance: 0.30,
|
||||
Entry: PlaybookEntry{QueryText: "q1", AnswerID: "w-99", AnswerCorpus: "workers", Score: 0.4},
|
||||
},
|
||||
{
|
||||
PlaybookID: "pb-high",
|
||||
Distance: 0.30,
|
||||
Entry: PlaybookEntry{QueryText: "q2", AnswerID: "w-99", AnswerCorpus: "workers", Score: 1.0},
|
||||
},
|
||||
}
|
||||
out, injected := InjectPlaybookMisses(results, hits)
|
||||
if injected != 1 {
|
||||
t.Errorf("expected 1 injection (deduped), got %d", injected)
|
||||
}
|
||||
// Score=1.0 (the high one) wins → boost factor 0.5 → distance 0.15
|
||||
for _, r := range out {
|
||||
if r.ID == "w-99" {
|
||||
if r.Distance < 0.149 || r.Distance > 0.151 {
|
||||
t.Errorf("expected distance from highest-score hit (~0.15), got %f", r.Distance)
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// TestInjectPlaybookMisses_EmptyHits is a fast-path no-op check.
|
||||
func TestInjectPlaybookMisses_EmptyHits(t *testing.T) {
|
||||
results := []Result{{ID: "w-1", Corpus: "workers", Distance: 0.30}}
|
||||
out, injected := InjectPlaybookMisses(results, nil)
|
||||
if injected != 0 {
|
||||
t.Errorf("expected 0 injection, got %d", injected)
|
||||
}
|
||||
if len(out) != 1 {
|
||||
t.Errorf("results should be unchanged, got len=%d", len(out))
|
||||
}
|
||||
}
|
||||
|
||||
func abs(f float64) float64 {
|
||||
if f < 0 {
|
||||
return -f
|
||||
|
||||
@@ -91,6 +91,11 @@ type SearchResponse struct {
|
||||
Results []Result `json:"results"`
|
||||
PerCorpusCounts map[string]int `json:"per_corpus_counts"`
|
||||
PlaybookBoosted int `json:"playbook_boosted,omitempty"`
|
||||
// PlaybookInjected is Shape B's per-query metric: synthetic
|
||||
// results inserted from playbook hits whose answer wasn't already
|
||||
// in the regular retrieval. Distinct from PlaybookBoosted (which
|
||||
// counts in-place re-ranks of results that WERE present).
|
||||
PlaybookInjected int `json:"playbook_injected,omitempty"`
|
||||
MetadataFilterDropped int `json:"metadata_filter_dropped,omitempty"`
|
||||
}
|
||||
|
||||
@@ -218,17 +223,30 @@ func (r *Retriever) Search(ctx context.Context, req SearchRequest) (*SearchRespo
|
||||
MetadataFilterDropped: dropped,
|
||||
}
|
||||
|
||||
// Playbook boost (component 5). Reuses the query vector — no
|
||||
// extra embed call. If the playbook corpus doesn't exist (first
|
||||
// search before any Record), the lookup gracefully no-ops.
|
||||
// Playbook (component 5) — both boost (re-rank existing) and
|
||||
// inject (Shape B: bring in answers that aren't in regular
|
||||
// retrieval). Reuses the query vector — no extra embed call.
|
||||
// Missing playbook corpus is a legitimate cold-start no-op.
|
||||
if req.UsePlaybook {
|
||||
hits, err := r.fetchPlaybookHits(ctx, qvec, req)
|
||||
if err != nil {
|
||||
// Don't fail the whole search on playbook errors — the
|
||||
// boost is opportunistic. Log + continue.
|
||||
slog.Warn("matrix: playbook lookup failed; skipping boost", "err", err)
|
||||
slog.Warn("matrix: playbook lookup failed; skipping boost+inject", "err", err)
|
||||
} else if len(hits) > 0 {
|
||||
resp.PlaybookBoosted = ApplyPlaybookBoost(resp.Results, hits)
|
||||
var injected int
|
||||
resp.Results, injected = InjectPlaybookMisses(resp.Results, hits)
|
||||
resp.PlaybookInjected = injected
|
||||
if injected > 0 {
|
||||
// Re-sort + truncate after injection. ApplyPlaybookBoost
|
||||
// already sorted, but injection appends past the end —
|
||||
// resort to merge, then enforce K.
|
||||
sort.SliceStable(resp.Results, func(i, j int) bool {
|
||||
return resp.Results[i].Distance < resp.Results[j].Distance
|
||||
})
|
||||
if len(resp.Results) > req.K {
|
||||
resp.Results = resp.Results[:req.K]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user