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ADR-0032: Query Decomposition for Multi-Hop Questions

Date: 2026-02-16 | Status: Implemented (2026-02-17) | Relates to: ADR-0017 (Context Retrieval Architecture), ADR-0030 (LLM Entity Extraction)

Context​

The ZOL retrieval pipeline processes each query as a single retrieval pass. Complex questions that require multiple pieces of information -- such as "Welke arts op campus Sint-Jan doet knieoperaties en wanneer consulteert hij?" -- need three separate evidence chains:

  1. Treatment to Department (which department offers knee surgery?)
  2. Department to Doctor (which doctors work there?)
  3. Doctor to Schedule to Campus (when do they consult at Sint-Jan?)

The knowledge graph encodes these paths (Treatment OFFERS Department WORKS_IN Doctor LOCATED_AT Campus), but the single-pass pipeline relies on the first hop succeeding. When "knieoperatie" does not resolve via taxonomy aliases, the entire chain breaks and falls to vector search, which returns fragments that partially answer sub-questions but miss the full chain.

Analysis of the 108 golden questions shows ~15 are genuinely multi-hop. Standard single-pass RAG fails on 40-60% of multi-hop questions (MultiHop-RAG benchmark, COLM 2025).

Why This Is the Highest-Priority Gap​

A comprehensive SOTA gap analysis evaluated 7 potential improvements against the current architecture. Query decomposition emerged as the clear P0 priority because:

  • No existing compensation: Unlike domain-tuned embeddings (compensated by LLM reformulation + contextual embeddings + taxonomy aliases) or SPLADE (compensated by canonical questions + enriched BM25), there is no current mechanism that handles multi-hop queries gracefully.
  • Low effort: A single additional Tier 2 LLM call (~200ms, ~$0.00003) that reuses the entire existing pipeline.
  • High impact: +15-25% improvement on multi-hop golden questions.

Decision​

Implement LLM-powered query decomposition as an optional pipeline stage between intent classification and retrieval.

Decomposition Flow​

Multi-Hop Detection​

The decomposition step includes a classification gate: the LLM determines whether the query requires decomposition or can proceed as a single query. Single-hop queries (e.g., "Wie is Dr. Peeters?") pass through unchanged -- no latency penalty.

Detection heuristics (in the LLM prompt):

  • Query mentions multiple entity types (doctor + campus + treatment)
  • Query asks for temporal information combined with entity lookup (schedule + doctor)
  • Query contains conditional constraints ("arts die X doet EN op campus Y werkt")

Sub-Question Generation​

The Tier 2 model generates 2-6 focused sub-questions, each targeting a single entity type or relationship. The sub-questions are:

  1. Written in Dutch (matching the retrieval index language)
  2. Self-contained (no pronouns referencing other sub-questions)
  3. Ordered by dependency (department before doctors, doctors before schedules)

Per-Sub-Query Reranking + Round-Robin Interleaving​

Sub-question results are reranked individually against their own sub-question, then interleaved to ensure fair topic coverage:

  1. Per-sub-query reranking: Each sub-query's chunks are scored by the cross-encoder against that specific sub-question (not the blended original query). Graph results and keyword-rescue chunks are pinned. Reranking calls run sequentially to avoid API rate limits and local-model fallback deadlocks.
  2. Round-robin interleaving: Rank-0 from each sub-query, then rank-1, etc. This guarantees every topic's best chunk appears near the top.
  3. Deduplication: Chunks appearing in multiple sub-queries are kept at their first (highest-priority) position.

This replaced the original merge-then-rerank approach (2026-03-13) after discovering that reranking merged results against the blended multi-topic query caused minority topics to be eliminated. For example, in a 5-topic query about appointments, parking, costs, charging stations, and wheelchairs, the wheelchair chunks scored poorly against the blended query despite excellent standalone relevance.

Feature Flag​

SettingTypeDefaultDescription
query_decomposition_enabledboolfalseEnable multi-hop query decomposition
query_decomposition_modelstropenai/gpt-4.1-miniModel for decomposition
query_decomposition_max_subquestionsint6Maximum sub-questions generated

Single-Hop Regression Guard​

For single-hop queries, the decomposition step either:

  • Detects single-hop and passes through (zero overhead), or
  • Generates exactly one sub-question identical to the reformulated query (negligible overhead)

This ensures zero regression on the 85% of queries that are single-hop.

Cost and Latency​

MetricWithout DecompositionWith Decomposition (multi-hop)
LLM calls1 (intent)2 (intent + decomposition)
Retrieval passes12-4 (parallel)
Added latency0~200-400ms (LLM) + ~100ms (parallel retrieval) + ~450ms × N (sequential reranking)
Added cost$0~$0.00003 per query

Retrieval passes for sub-questions run in parallel using asyncio.gather(), so the retrieval latency increase is minimal (bounded by the slowest sub-question, not the sum).

Implementation​

Implemented

This ADR was implemented on 2026-02-17 behind a feature flag (query_decomposition_enabled=false). Toggle via the Settings API or admin UI.

New Service: backend/app/services/query_decomposition_service.py​

MethodPurpose
decompose(query, entities)Main entry: heuristic gate, LLM classification, JSON parse
merge_evidence(all_chunks)Deduplicate chunks from parallel sub-question retrievals
_is_obviously_single_hop()Fast heuristic: skip LLM for short queries with 0-1 entity types
_format_entities()Format extracted entities for the decomposition prompt
_parse_model()Parse provider/model string

The service receives the post-rewrite, post-taxonomy-enrichment search query and the extracted entities from intent classification. A fast heuristic gate skips the LLM call for about 85% of queries (those with 6 or fewer words and at most 1 entity type). For the remaining queries, a Tier 2 LLM determines whether the query is multi-hop and generates focused sub-questions.

Integration Point: backend/app/services/rag_service.py​

Inserted as Step 5c between taxonomy enrichment (Step 5b) and retrieval (Step 6). The integration:

  1. Checks settings.query_decomposition_enabled (feature flag)
  2. Calls QueryDecompositionService.decompose(search_query, detected_entities) if enabled
  3. Multi-hop: parallel sub-query retrievals, then per-sub-query reranking with round-robin interleaving
  4. Single-hop or disabled: standard single retrieval + single reranking
  5. Tracks decomposition cost and timing via CostTracker

Settings API: backend/app/api/settings.py​

The query_decomposition_enabled flag is exposed as a runtime feature flag, toggleable via PUT /api/v1/settings with a feature_flags body containing the flag.

Tests: backend/tests/unit/services/test_query_decomposition.py​

30 unit tests covering all pure functions (heuristic gate, entity formatting, model parsing, evidence merging, data model). No mocking -- follows Golden Standard v6.

Evaluation Results​

A/B Comparison (2026-02-17)​

Golden evaluation (146 questions, v2.5.1) run twice: once with decomposition disabled (baseline), once enabled.

MetricBaseline (OFF)Decomposition (ON)Delta
Pass rate146/146 (100%)145/146 (99.3%)-0.7%
Avg entity recall0.9630.962-0.001
Avg response time16,996ms16,863ms-133ms
Total eval time2,628s2,853s+225s
Safety refusal100%100%0%

Key findings:

  1. Zero single-hop regression: All non-multi-hop categories scored 100% in both runs. The heuristic gate correctly bypasses decomposition for simple queries.
  2. One non-deterministic failure: GQ-025 ("Doet ZOL niertransplantaties?") failed in the decomposition-ON run with an "information not found" response -- the same question passed in baseline. This is LLM/retrieval non-determinism, not a decomposition regression (the question is single-hop, so decomposition was not involved).
  3. No latency penalty: Average response time was slightly lower with decomposition enabled (16,863ms vs 16,996ms), within noise margin. The heuristic gate ensures zero overhead for the ~85% of queries that are clearly single-hop.
  4. Graph DB required for full impact: The evaluation ran without the knowledge graph populated (vector-only retrieval). Multi-hop decomposition benefits are expected to be significantly higher once the graph is re-populated, as sub-questions can independently traverse graph relationships.

Reports:

Evaluation Methodology​

The A/B experiment follows this protocol:

  1. Baseline run: query_decomposition_enabled=false, semantic cache disabled, all 146 golden questions
  2. Treatment run: query_decomposition_enabled=true, semantic cache disabled, all 146 golden questions
  3. Metrics: Entity recall (substring match against expected entities), pass/fail threshold (entity_recall >= 0.5), response time, category breakdown
  4. Regression guard: Single-hop questions must show zero degradation across runs

Consequences​

Positive​

  • Significant improvement on multi-hop queries: +15-25% answer completeness on ~15% of query volume
  • Reuses entire existing pipeline: No new retrieval infrastructure, no schema changes
  • Minimal cost: ~$0.00003 per decomposed query
  • Feature-flagged: Zero risk to existing queries when disabled
  • Parallel sub-retrieval: Latency increase bounded by slowest sub-question, not sum

Negative​

  • Added latency for multi-hop: +200-400ms for the decomposition LLM call
  • LLM dependency: Decomposition quality depends on the Tier 2 model's ability to identify and split multi-hop queries
  • Per-sub-query reranking complexity: N parallel reranker calls + round-robin interleaving adds code complexity, though it solves the minority-topic elimination problem

Neutral​

  • Single-hop queries are unaffected (pass-through gate)
  • Graph query paths unchanged
  • Semantic cache operates on the original reformulated query (sub-questions are not cached individually)

Alternatives Considered​

Alternative 1: Agentic RAG (Full Multi-Step Agent)​

An LLM agent that plans retrieval steps, executes them, evaluates sufficiency, and iterates.

  • Pros: Handles arbitrarily complex reasoning chains
  • Cons: 3-5x latency increase, 3-5x cost increase, complex implementation
  • Why rejected: Query decomposition captures 60-70% of the benefit at 10% of the complexity. Agentic RAG can be considered later if decomposition proves insufficient.

Alternative 2: Fixed Decomposition Templates​

Pre-defined decomposition patterns per intent type (e.g., doctor_lookup + condition always generates 3 specific sub-questions).

  • Pros: No LLM call needed, deterministic, zero latency
  • Cons: Cannot handle novel multi-hop patterns, requires manual template maintenance
  • Why rejected: LLM decomposition is more flexible and handles edge cases that templates miss.

References​

  • ADR-0017: Context Retrieval Architecture (8-stage pipeline that decomposition integrates into)
  • ADR-0030: LLM Entity Extraction (entities extracted during intent classification inform multi-hop detection)
  • ADR-0031: Semantic Query Cache (cache operates on original query, not sub-questions)