### BLOCK 0: INPUT STATUS Mode: ⟪FULL INPUT⟫ All required fields are present. The supplied traces do not include retrieved chunk IDs, scores by rank, corpus contents, or ranked relevance judgments. Exact candidate-set membership and retrieval metrics are therefore **NOT_MEASURED**. ### BLOCK 1: FAILURE CLASSIFICATION Primary pattern: ⟪Mixed⟫ | Example | Coverage? | Pool or Ordering? | Basis | |---|---|---|---| | Opened blender return | Plausibly absent from the initial candidate pool because the result shifts to warranty content | Pool failure or coverage gap | ESTIMATED | | XR-410 battery | Yes; the expected table appears when more results are requested | Ordering failure | COMPUTED from the supplied trace | | TrailWatch water exposure | Unclear; the wording may not bridge “splash resistant” and “waterproof” | Pool failure or coverage gap | ESTIMATED | Severity: ⟪High⟫ — **ESTIMATED**. The failure affects both policy conditions and product-specific facts, and the supplied cases point to more than one retrieval stage. ### BLOCK 2: ROOT CAUSES 1. Weak exact-identifier handling — explains **1/3 supplied examples — COMPUTED** — add sparse retrieval or an identifier-preserving field boost for model codes. 2. Vocabulary mismatch between user language and indexed policy/specification language — explains **2/3 supplied examples — ESTIMATED** — add query expansion and verify that the relevant paragraphs are independently retrievable. 3. Broad pages competing with narrow answer-bearing chunks — explains **2/3 supplied examples — ESTIMATED** — re-chunk around policy clauses and specification rows, then rerank only after candidate coverage is confirmed. ### BLOCK 3: EMBEDDING DIAGNOSIS Model: text-embedding-3-small (dimension configuration **NOT_MEASURED** from the supplied setup) Strengths: a general-purpose semantic embedding model suited to similarity search and capable of bridging many paraphrases. Weaknesses relevant here: dense similarity alone is not designed to guarantee exact SKU matching, and embeddings cannot retrieve content that was omitted or split into a non-answer-bearing chunk. Recommendation: ⟪PROJECTED⟫ Retain the current dense model during the first repair cycle and add exact-term retrieval plus chunk inspection. This isolates whether the failures come from candidate coverage before a model migration introduces another variable. ### BLOCK 4: POOL vs. ORDERING FIX MAP The return-policy and water-exposure cases require a coverage-side test: confirm that a self-contained answer chunk exists, then add synonym-aware query expansion or sparse retrieval. A reranker cannot rescue a missing candidate. The XR-410 case requires a ranking-side fix because the expected table is already reachable at a deeper result depth. Identifier-aware sparse scoring and fusion should precede an optional reranker. ### BLOCK 5: RETRIEVAL ARCHITECTURE Recommended: ⟪Hybrid+Reranker⟫ Components, staged: - Clause- and table-aware chunks with product code and document-type metadata. - Dense retrieval for paraphrased intent. - Sparse retrieval for SKUs, policy terms, and exact attributes. - Reciprocal-rank fusion to create a shared candidate pool. - Lightweight reranking for the fused pool, with product-code mismatch penalties. Performance Expectations: - ⟪PROJECTED⟫ Higher candidate coverage for exact product codes because sparse matching preserves tokens that dense similarity may blur. - ⟪PROJECTED⟫ Better ordering of narrow policy/specification chunks because reranking evaluates the query against chunk text after coverage is established. - ⟪PROJECTED⟫ No guaranteed improvement for missing or badly split source content; those cases require corpus repair. ### BLOCK 6: PRODUCTION CODE The following runnable standard-library harness demonstrates the recommended fusion and identifier guard. Replace `dense_rank` with the existing retriever adapter in production. ```python import math import re from collections import Counter DOCS = { "returns_opened": "Opened appliances may be returned when the store return conditions are met.", "xr410_battery": "XR-410 replacement battery compatibility table and approved accessories.", "xr400_page": "XR-400 blender product overview and accessories.", "trailwatch_limits": "TrailWatch is splash resistant; it is not rated for submersion or swimming.", } def tokens(text): if not isinstance(text, str) or not text.strip(): raise ValueError("query must be a non-empty string") return re.findall(r"[a-z0-9-]+", text.lower()) def sparse_rank(query): q = Counter(tokens(query)) scored = [] for doc_id, text in DOCS.items(): d = Counter(tokens(text)) overlap = sum(q[t] * d[t] for t in q) norm = math.sqrt(sum(v * v for v in d.values())) or 1.0 scored.append((doc_id, overlap / norm)) return [doc_id for doc_id, _ in sorted(scored, key=lambda x: x[1], reverse=True)] def dense_rank(query): # POOL adapter: replace with the buyer's current vector search results. return sparse_rank(query) def rrf(*rankings, offset=60): scores = Counter() for ranking in rankings: for rank, doc_id in enumerate(ranking, start=1): scores[doc_id] += 1.0 / (offset + rank) return [doc_id for doc_id, _ in scores.most_common()] def identifiers(text): return set(re.findall(r"\b[A-Z]{2,}-\d+\b", text.upper())) def rerank(query, candidates): required = identifiers(query) def score(doc_id): present = identifiers(DOCS[doc_id]) exact_bonus = 1 if required and required <= present else 0 mismatch = -1 if required and present and not required <= present else 0 return exact_bonus + mismatch return sorted(candidates, key=score, reverse=True) def retrieve(query): fused = rrf(dense_rank(query), sparse_rank(query)) return rerank(query, fused) if __name__ == "__main__": try: for q in [ "Can I return an opened blender after three weeks?", "XR-410 replacement battery compatibility", "Is splash resistant the same as waterproof for the TrailWatch?", ]: print(q, "=>", retrieve(q)[:3]) except (TypeError, ValueError) as exc: raise SystemExit(f"Invalid retrieval input: {exc}") from exc ``` ### BLOCK 7: REPAIR DEMONSTRATION | Buyer query | Before | Repair path | Expected change | |---|---|---|---| | Opened blender after three weeks | Warranty overview | Retrieve return-policy clauses through sparse+dense fusion | ⟪PROJECTED⟫ The relevant policy chunk is more likely to enter the candidate pool because return conditions are indexed separately from warranties. | | XR-410 battery compatibility | XR-400 page | Preserve the exact identifier and penalize mismatched identifiers | ⟪PROJECTED⟫ The XR-410 table should outrank the nearby XR-400 page when both are candidates. | | Splash resistant vs waterproof | Outdoor lifestyle article | Expand the relation between resistance terms and retrieve the limitations chunk | ⟪PROJECTED⟫ The answer-bearing limitations text is more likely to replace marketing content. | Ranked relevance judgments were not supplied; NDCG and related ranked metrics are omitted.