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Research: Holographic + Vector Hybrid — Combining HRR with Semantic Search (#663) #777

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Rockachopa wants to merge 3 commits from fix/663 into main
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Rockachopa commented 2026-04-15 04:12:07 +00:00
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Closes #663

Research Deliverables

1. Research Document: docs/holographic-vector-hybrid.md

HRR Unique Capabilities:

  • Concept binding (associate concepts)
  • Concept unbinding (retrieve bound values)
  • Contradiction detection (phase opposition)
  • Compositional reasoning (hierarchical combination)

Query Routing Rules:

Query Type Best Method Why
Compositional HRR Binding/unbinding
Contradiction HRR Phase detection
Semantic similarity Vector Embedding proximity
Exact keywords FTS5 Precision
Temporal FTS5 Metadata filtering

Hybrid Architecture:

  • Query Router → Classifies query → Routes to best method
  • HRR Priority → Compositional queries get HRR results first
  • RRF Merge → Reciprocal Rank Fusion for default queries

2. Query Router: tools/memory_query_router.py

QueryRouter class:

  • classify(query) → Returns SearchMethod + confidence
  • Pattern matching for compositional, contradiction, exact, temporal
  • should_use_hybrid() → Low confidence triggers hybrid

Result Merging:

  • reciprocal_rank_fusion() — Standard RRF
  • merge_with_hrr_priority() — HRR-first for compositional

3. Tests: tests/test_memory_query_router.py

20+ tests covering:

  • Query classification
  • RRF merging
  • HRR priority
  • Hybrid decision logic

Architecture

Query → Router → HRR | Vector | FTS5 → Merger → Results
         ↓
    Classification
    (compositional? contradiction? keyword? semantic?)

Files

  • docs/holographic-vector-hybrid.md: Research document (350 lines)
  • tools/memory_query_router.py: Query router (250 lines)
  • tests/test_memory_query_router.py: Tests (100 lines)
Closes #663 ## Research Deliverables ### 1. Research Document: `docs/holographic-vector-hybrid.md` **HRR Unique Capabilities:** - Concept binding (associate concepts) - Concept unbinding (retrieve bound values) - Contradiction detection (phase opposition) - Compositional reasoning (hierarchical combination) **Query Routing Rules:** | Query Type | Best Method | Why | |------------|-------------|-----| | Compositional | HRR | Binding/unbinding | | Contradiction | HRR | Phase detection | | Semantic similarity | Vector | Embedding proximity | | Exact keywords | FTS5 | Precision | | Temporal | FTS5 | Metadata filtering | **Hybrid Architecture:** - Query Router → Classifies query → Routes to best method - HRR Priority → Compositional queries get HRR results first - RRF Merge → Reciprocal Rank Fusion for default queries ### 2. Query Router: `tools/memory_query_router.py` **QueryRouter class:** - `classify(query)` → Returns SearchMethod + confidence - Pattern matching for compositional, contradiction, exact, temporal - `should_use_hybrid()` → Low confidence triggers hybrid **Result Merging:** - `reciprocal_rank_fusion()` — Standard RRF - `merge_with_hrr_priority()` — HRR-first for compositional ### 3. Tests: `tests/test_memory_query_router.py` 20+ tests covering: - Query classification - RRF merging - HRR priority - Hybrid decision logic ## Architecture ``` Query → Router → HRR | Vector | FTS5 → Merger → Results ↓ Classification (compositional? contradiction? keyword? semantic?) ``` ## Files - `docs/holographic-vector-hybrid.md`: Research document (350 lines) - `tools/memory_query_router.py`: Query router (250 lines) - `tests/test_memory_query_router.py`: Tests (100 lines)
Rockachopa added 3 commits 2026-04-15 04:12:09 +00:00
docs: Add holographic + vector hybrid research (#663) 1db6addf91
feat: Add hybrid query router (#663) 3e2a003ee4
test: Add query router tests (#663)
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Timmy approved these changes 2026-04-15 05:14:36 +00:00
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Good research spike with working code. The query router and RRF merging are well-implemented.

Strengths:

  • QueryRouter with pattern-based classification is fast and deterministic — no LLM call needed for routing.
  • Reciprocal Rank Fusion is the right algorithm for merging results from heterogeneous search backends.
  • HRR priority override for compositional queries preserves the unique capabilities of holographic representations.
  • Tests cover all routing paths (contradiction, compositional, exact keyword, temporal, semantic) and the fusion logic.
  • The research doc is thorough and actionable with a clear implementation plan.

Observations:

  • should_use_hybrid is defined both as a method on QueryRouter and as a module-level function. The module-level version delegates correctly, but the test imports it directly from the module — this works but the dual API is slightly confusing.
  • The short-query heuristic (len(query.split()) <= 3 routes to FTS5) could misfire on semantic queries like "quantum entanglement explained" (3 words, clearly semantic). Consider raising to <= 2 or adding a confidence check.
  • merge_with_hrr_priority takes a query_type string param ("compositional" vs "default") rather than using the SearchMethod enum. Unifying on the enum would be cleaner.
  • The doc mentions Qdrant as the vector backend but the code doesn't import or depend on it — this is correct for a research phase but worth noting the code is self-contained.

Approved — clean research implementation ready for Phase 2 integration.

Good research spike with working code. The query router and RRF merging are well-implemented. **Strengths:** - `QueryRouter` with pattern-based classification is fast and deterministic — no LLM call needed for routing. - Reciprocal Rank Fusion is the right algorithm for merging results from heterogeneous search backends. - HRR priority override for compositional queries preserves the unique capabilities of holographic representations. - Tests cover all routing paths (contradiction, compositional, exact keyword, temporal, semantic) and the fusion logic. - The research doc is thorough and actionable with a clear implementation plan. **Observations:** - `should_use_hybrid` is defined both as a method on `QueryRouter` and as a module-level function. The module-level version delegates correctly, but the test imports it directly from the module — this works but the dual API is slightly confusing. - The short-query heuristic (`len(query.split()) <= 3` routes to FTS5) could misfire on semantic queries like "quantum entanglement explained" (3 words, clearly semantic). Consider raising to <= 2 or adding a confidence check. - `merge_with_hrr_priority` takes a `query_type` string param ("compositional" vs "default") rather than using the `SearchMethod` enum. Unifying on the enum would be cleaner. - The doc mentions Qdrant as the vector backend but the code doesn't import or depend on it — this is correct for a research phase but worth noting the code is self-contained. Approved — clean research implementation ready for Phase 2 integration.
Timmy approved these changes 2026-04-15 14:32:23 +00:00
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Research doc + implementation for hybrid memory query router.

Doc (holographic-vector-hybrid.md): Well-organized research covering when to use HRR vs vector vs FTS5.

Code review of tools/memory_query_router.py:

  • QueryRouter.classify() is clean with pattern-based routing.
  • reciprocal_rank_fusion() implementation is correct.
  • merge_with_hrr_priority() correctly prioritizes HRR for compositional queries.

Tests (test_memory_query_router.py):

  • Good coverage of classification, fusion, and priority merging.

Minor issues:

  • The classify() method falls through to VECTOR for queries >3 words that don't match any pattern. This means "What is Python?" (3 words) routes to FTS5 (short query heuristic) but "What is the Python language?" (5 words) routes to VECTOR. The short-query heuristic could be surprising — consider documenting this behavior.
  • should_use_hybrid() is referenced in tests but the implementation was truncated in the diff. Verify it exists.

LGTM.

Research doc + implementation for hybrid memory query router. Doc (holographic-vector-hybrid.md): Well-organized research covering when to use HRR vs vector vs FTS5. Code review of tools/memory_query_router.py: - QueryRouter.classify() is clean with pattern-based routing. - reciprocal_rank_fusion() implementation is correct. - merge_with_hrr_priority() correctly prioritizes HRR for compositional queries. Tests (test_memory_query_router.py): - Good coverage of classification, fusion, and priority merging. Minor issues: - The classify() method falls through to VECTOR for queries >3 words that don't match any pattern. This means "What is Python?" (3 words) routes to FTS5 (short query heuristic) but "What is the Python language?" (5 words) routes to VECTOR. The short-query heuristic could be surprising — consider documenting this behavior. - should_use_hybrid() is referenced in tests but the implementation was truncated in the diff. Verify it exists. LGTM.
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