test: Add query router tests (#663)

docs/holographic-vector-hybrid.md

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+# Holographic + Vector Hybrid Memory Architecture
+
+**Issue:** #663 — Research: Combining HRR Compositional Queries with Semantic Search
+**Date:** 2026-04-14
+
+## Executive Summary
+
+The optimal memory architecture is a **hybrid** combining three methods:
+- **HRR (Holographic Reduced Representations)** — Compositional reasoning
+- **Vector Search (Qdrant)** — Semantic similarity
+- **FTS5 (SQLite Full-Text Search)** — Exact keyword matching
+
+No single method covers all use cases. Each excels at different query types.
+
+## HRR Capabilities (What Makes It Unique)
+
+HRR provides capabilities no vector DB offers:
+
+### 1. Concept Binding
+Associate two concepts into a composite representation:
+```python
+# Bind "Python" + "programming language"
+bound = hrr_bind("Python", "programming language")
+```
+
+### 2. Concept Unbinding
+Retrieve a bound value:
+```python
+# Given "Python", retrieve what it's bound to
+result = hrr_unbind(bound, "Python")  # -> "programming language"
+```
+
+### 3. Contradiction Detection
+Identify conflicting information:
+```python
+# "Python is interpreted" vs "Python is compiled"
+# HRR detects phase opposition -> contradiction
+conflict = hrr_detect_contradiction(stmt1, stmt2)
+```
+
+### 4. Compositional Reasoning
+Combine concepts hierarchically:
+```python
+# "The cat sat on the mat"
+# HRR encodes: BIND(cat, BIND(sat, BIND(on, mat)))
+composition = hrr_compose(["cat", "sat", "on", "mat"])
+```
+
+## When to Use Each Method
+
+| Query Type | Best Method | Why |
+|------------|-------------|-----|
+| "What is Python?" | Vector | Semantic similarity |
+| "Python + database binding" | HRR | Compositional query |
+| "Find documents about FastAPI" | FTS5 | Exact keyword match |
+| "What contradicts X?" | HRR | Contradiction detection |
+| "Similar to this paragraph" | Vector | Semantic embedding |
+| "Exact phrase match" | FTS5 | Keyword precision |
+| "A related to B related to C" | HRR | Multi-hop binding |
+| "Recent documents" | FTS5 | Metadata filtering |
+
+## Query Routing Rules
+
+```python
+def route_query(query: str, context: dict) -> str:
+    """Route query to the best search method."""
+    
+    # HRR: Compositional/conceptual queries
+    if is_compositional(query):
+        return "hrr"
+    
+    # HRR: Contradiction detection
+    if is_contradiction_check(query):
+        return "hrr"
+    
+    # FTS5: Exact keywords, quotes, specific terms
+    if has_exact_keywords(query):
+        return "fts5"
+    
+    # FTS5: Time-based queries
+    if has_temporal_filter(query):
+        return "fts5"
+    
+    # Vector: Default for semantic similarity
+    return "vector"
+
+def is_compositional(query: str) -> bool:
+    """Check if query involves concept composition."""
+    patterns = [
+        r"related to",
+        r"combined with",
+        r"bound to",
+        r"associated with",
+        r"what connects",
+    ]
+    return any(re.search(p, query.lower()) for p in patterns)
+
+def is_contradiction_check(query: str) -> bool:
+    """Check if query is about contradictions."""
+    patterns = [
+        r"contradicts?",
+        r"conflicts? with",
+        r"inconsistent",
+        r"opposite of",
+    ]
+    return any(re.search(p, query.lower()) for p in patterns)
+
+def has_exact_keywords(query: str) -> bool:
+    """Check if query has exact keywords or quotes."""
+    return '"' in query or "'" in query or len(query.split()) <= 3
+```
+
+## Hybrid Result Merging
+
+### Reciprocal Rank Fusion (RRF)
+
+Combine ranked results from multiple methods:
+
+```python
+def reciprocal_rank_fusion(
+    results: Dict[str, List[Tuple[str, float]]],
+    k: int = 60
+) -> List[Tuple[str, float]]:
+    """
+    Merge results using RRF.
+    
+    Args:
+        results: {"hrr": [(id, score), ...], "vector": [...], "fts5": [...]}
+        k: RRF constant (default 60)
+    
+    Returns:
+        Merged and re-ranked results
+    """
+    scores = defaultdict(float)
+    
+    for method, ranked_items in results.items():
+        for rank, (item_id, _) in enumerate(ranked_items, 1):
+            scores[item_id] += 1.0 / (k + rank)
+    
+    return sorted(scores.items(), key=lambda x: x[1], reverse=True)
+```
+
+### HRR Priority Override
+
+For compositional queries, HRR results take priority:
+
+```python
+def merge_with_hrr_priority(
+    hrr_results: List,
+    vector_results: List,
+    fts5_results: List,
+    query_type: str
+) -> List:
+    """Merge with HRR priority for compositional queries."""
+    
+    if query_type == "compositional":
+        # HRR first, then vector as supplement
+        merged = hrr_results[:5]
+        seen = {r[0] for r in merged}
+        for r in vector_results[:5]:
+            if r[0] not in seen:
+                merged.append(r)
+        return merged
+    
+    # Default: RRF merge
+    return reciprocal_rank_fusion({
+        "hrr": hrr_results,
+        "vector": vector_results,
+        "fts5": fts5_results
+    })
+```
+
+## Integration Architecture
+
+```
+┌─────────────────────────────────────────────────────┐
+│                  Query Router                        │
+│  (classifies query → routes to best method)         │
+└───────────┬──────────────┬──────────────┬───────────┘
+            │              │              │
+     ┌──────▼──────┐ ┌────▼────┐ ┌───────▼───────┐
+     │     HRR     │ │ Qdrant  │ │     FTS5      │
+     │ Holographic │ │ Vector  │ │  SQLite Full  │
+     │  Compose    │ │ Search  │ │ Text Search   │
+     └──────┬──────┘ └────┬────┘ └───────┬───────┘
+            │              │              │
+     ┌──────▼──────────────▼──────────────▼───────┐
+     │           Result Merger (RRF)               │
+     │  - Deduplication                            │
+     │  - Score normalization                      │
+     │  - HRR priority for compositional queries   │
+     └───────────────────┬─────────────────────────┘
+                         │
+                    ┌────▼────┐
+                    │ Results │
+                    └─────────┘
+```
+
+### Storage Layout
+
+```
+~/.hermes/memory/
+├── holographic/
+│   ├── hrr_store.pkl       # HRR vectors (numpy arrays)
+│   ├── bindings.pkl        # Concept bindings
+│   └── contradictions.pkl  # Detected contradictions
+├── vector/
+│   └── qdrant/             # Qdrant collection
+├── fts5/
+│   └── memory.db           # SQLite with FTS5
+└── index.json              # Unified index
+```
+
+## Preserving HRR Unique Capabilities
+
+### Rules
+
+1. **Never replace HRR with vector for compositional queries**
+   - Vector can't do binding/unbinding
+   - Vector can't detect contradictions
+   - Vector can't compose concepts
+
+2. **HRR is primary for relational queries**
+   - "What relates X to Y?"
+   - "What contradicts this?"
+   - "Combine concept A with concept B"
+
+3. **Vector supplements HRR**
+   - Vector finds similar items
+   - HRR finds related items
+   - Together they cover more ground
+
+4. **FTS5 handles exact matches**
+   - Keyword search
+   - Time-based filtering
+   - Metadata queries
+
+## Implementation Plan
+
+### Phase 1: HRR Plugin (Existing)
+- Implement holographic.py with binding/unbinding
+- Phase encoding for compositional queries
+- Contradiction detection via phase opposition
+
+### Phase 2: Vector Integration
+- Add Qdrant as vector backend
+- Embed memories for semantic search
+- Maintain HRR alongside vector
+
+### Phase 3: Hybrid Router
+- Query classification
+- Method selection
+- Result merging with RRF
+
+### Phase 4: Testing
+- Benchmark each method
+- Test hybrid routing
+- Verify HRR preservation
+
+## Success Metrics
+
+- HRR compositional queries: 90%+ accuracy
+- Vector semantic search: 85%+ relevance
+- Hybrid routing: Correct method 95%+ of the time
+- Contradiction detection: 80%+ precision

tests/test_memory_query_router.py

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+"""
+Tests for hybrid memory query router
+
+Issue: #663
+"""
+
+import unittest
+from tools.memory_query_router import (
+    SearchMethod,
+    QueryRouter,
+    route_query,
+    reciprocal_rank_fusion,
+    merge_with_hrr_priority,
+)
+
+
+class TestQueryClassification(unittest.TestCase):
+    
+    def setUp(self):
+        self.router = QueryRouter()
+    
+    def test_contradiction_routes_hrr(self):
+        c = self.router.classify("What contradicts this statement?")
+        self.assertEqual(c.method, SearchMethod.HRR)
+        self.assertGreater(c.confidence, 0.9)
+    
+    def test_compositional_routes_hrr(self):
+        c = self.router.classify("How does Python relate to machine learning?")
+        self.assertEqual(c.method, SearchMethod.HRR)
+        
+        c = self.router.classify("What is associated with quantum computing?")
+        self.assertEqual(c.method, SearchMethod.HRR)
+    
+    def test_exact_keywords_routes_fts5(self):
+        c = self.router.classify('Find documents containing "FastAPI tutorial"')
+        self.assertEqual(c.method, SearchMethod.FTS5)
+    
+    def test_short_query_routes_fts5(self):
+        c = self.router.classify("Python syntax")
+        self.assertEqual(c.method, SearchMethod.FTS5)
+    
+    def test_temporal_routes_fts5(self):
+        c = self.router.classify("Recent changes to the config")
+        self.assertEqual(c.method, SearchMethod.FTS5)
+    
+    def test_semantic_routes_vector(self):
+        c = self.router.classify("Explain how transformers work in natural language processing")
+        self.assertEqual(c.method, SearchMethod.VECTOR)
+
+
+class TestReciprocalRankFusion(unittest.TestCase):
+    
+    def test_basic_fusion(self):
+        results = {
+            "hrr": [("a", 0.9), ("b", 0.8)],
+            "vector": [("b", 0.85), ("c", 0.7)],
+        }
+        merged = reciprocal_rank_fusion(results)
+        
+        # 'b' appears in both, should rank high
+        ids = [r[0] for r in merged]
+        self.assertIn("b", ids[:2])
+    
+    def test_empty_results(self):
+        merged = reciprocal_rank_fusion({})
+        self.assertEqual(len(merged), 0)
+
+
+class TestHRRPriority(unittest.TestCase):
+    
+    def test_compositional_hrr_first(self):
+        hrr = [("a", 0.9), ("b", 0.8)]
+        vector = [("c", 0.85), ("d", 0.7)]
+        fts5 = [("e", 0.6)]
+        
+        merged = merge_with_hrr_priority(hrr, vector, fts5, "compositional")
+        
+        # HRR results should come first
+        self.assertEqual(merged[0][0], "a")
+        self.assertEqual(merged[1][0], "b")
+
+
+class TestHybridDecision(unittest.TestCase):
+    
+    def test_low_confidence_uses_hybrid(self):
+        from tools.memory_query_router import should_use_hybrid
+        # Ambiguous query
+        self.assertTrue(should_use_hybrid("Tell me about things"))
+    
+    def test_clear_query_no_hybrid(self):
+        from tools.memory_query_router import should_use_hybrid
+        # Clear contradiction query
+        self.assertFalse(should_use_hybrid("What contradicts X?"))
+
+
+if __name__ == "__main__":
+    unittest.main()

tools/memory_query_router.py

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+"""
+Hybrid Memory Query Router
+
+Routes queries to the best search method:
+- HRR: Compositional/conceptual queries
+- Vector: Semantic similarity
+- FTS5: Exact keyword matching
+
+Issue: #663
+"""
+
+import re
+from collections import defaultdict
+from dataclasses import dataclass
+from enum import Enum
+from typing import Any, Dict, List, Optional, Tuple
+
+
+class SearchMethod(Enum):
+    """Available search methods."""
+    HRR = "hrr"          # Holographic Reduced Representations
+    VECTOR = "vector"    # Semantic vector search
+    FTS5 = "fts5"        # Full-text search (SQLite)
+    HYBRID = "hybrid"    # Combine multiple methods
+
+
+@dataclass
+class QueryClassification:
+    """Result of query classification."""
+    method: SearchMethod
+    confidence: float
+    reason: str
+    sub_queries: Optional[List[str]] = None
+
+
+# Query patterns for routing
+COMPOSITIONAL_PATTERNS = [
+    r"(?i)\brelated\s+to\b",
+    r"(?i)\bcombined\s+with\b",
+    r"(?i)\bbound\s+to\b",
+    r"(?i)\bassociated\s+with\b",
+    r"(?i)\bwhat\s+connects?\b",
+    r"(?i)\bhow\s+.*\s+relate\b",
+    r"(?i)\brelationship\s+between\b",
+]
+
+CONTRADICTION_PATTERNS = [
+    r"(?i)\bcontradicts?\b",
+    r"(?i)\bconflicts?\s+with\b",
+    r"(?i)\binconsistent\b",
+    r"(?i)\bopposite\s+of\b",
+    r"(?i)\bopposes?\b",
+    r"(?i)\bdisagrees?\s+with\b",
+]
+
+EXACT_KEYWORD_PATTERNS = [
+    r'"[^"]+"',  # Quoted phrases
+    r"'[^']+'",  # Single-quoted phrases
+    r"(?i)\bexact\b",
+    r"(?i)\bprecisely\b",
+    r"(?i)\bspecifically\b",
+]
+
+TEMPORAL_PATTERNS = [
+    r"(?i)\brecent\b",
+    r"(?i)\btoday\b",
+    r"(?i)\byesterday\b",
+    r"(?i)\blast\s+(week|month|hour)\b",
+    r"(?i)\bsince\b",
+    r"(?i)\bbefore\b",
+    r"(?i)\bafter\b",
+]
+
+
+class QueryRouter:
+    """Route queries to the best search method."""
+    
+    def classify(self, query: str) -> QueryClassification:
+        """Classify a query and route to best method."""
+        
+        # Check for contradiction queries (HRR)
+        for pattern in CONTRADICTION_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.HRR,
+                    confidence=0.95,
+                    reason="Contradiction detection query"
+                )
+        
+        # Check for compositional queries (HRR)
+        for pattern in COMPOSITIONAL_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.HRR,
+                    confidence=0.90,
+                    reason="Compositional/conceptual query"
+                )
+        
+        # Check for exact keyword queries (FTS5)
+        for pattern in EXACT_KEYWORD_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.FTS5,
+                    confidence=0.85,
+                    reason="Exact keyword query"
+                )
+        
+        # Check for temporal queries (FTS5)
+        for pattern in TEMPORAL_PATTERNS:
+            if re.search(pattern, query):
+                return QueryClassification(
+                    method=SearchMethod.FTS5,
+                    confidence=0.80,
+                    reason="Temporal query"
+                )
+        
+        # Short queries tend to be keyword searches
+        if len(query.split()) <= 3:
+            return QueryClassification(
+                method=SearchMethod.FTS5,
+                confidence=0.70,
+                reason="Short query (likely keyword)"
+            )
+        
+        # Default: vector search for semantic queries
+        return QueryClassification(
+            method=SearchMethod.VECTOR,
+            confidence=0.60,
+            reason="Semantic similarity query"
+        )
+    
+    def should_use_hybrid(self, query: str) -> bool:
+        """Check if query should use hybrid search."""
+        classification = self.classify(query)
+        
+        # Low confidence -> use hybrid
+        if classification.confidence < 0.70:
+            return True
+        
+        # Mixed signals -> use hybrid
+        has_compositional = any(re.search(p, query) for p in COMPOSITIONAL_PATTERNS)
+        has_keywords = any(re.search(p, query) for p in EXACT_KEYWORD_PATTERNS)
+        
+        return has_compositional and has_keywords
+
+
+def reciprocal_rank_fusion(
+    results: Dict[str, List[Tuple[str, float]]],
+    k: int = 60
+) -> List[Tuple[str, float]]:
+    """
+    Merge results using Reciprocal Rank Fusion.
+    
+    Args:
+        results: Dict of method -> [(item_id, score), ...]
+        k: RRF constant (default 60)
+    
+    Returns:
+        Merged and re-ranked results
+    """
+    scores = defaultdict(float)
+    
+    for method, ranked_items in results.items():
+        for rank, (item_id, _) in enumerate(ranked_items, 1):
+            scores[item_id] += 1.0 / (k + rank)
+    
+    return sorted(scores.items(), key=lambda x: x[1], reverse=True)
+
+
+def merge_with_hrr_priority(
+    hrr_results: List[Tuple[str, float]],
+    vector_results: List[Tuple[str, float]],
+    fts5_results: List[Tuple[str, float]],
+    query_type: str = "default"
+) -> List[Tuple[str, float]]:
+    """
+    Merge results with HRR priority for compositional queries.
+    """
+    if query_type == "compositional":
+        # HRR first, vector as supplement
+        merged = hrr_results[:5]
+        seen = {r[0] for r in merged}
+        
+        for r in vector_results[:5]:
+            if r[0] not in seen:
+                merged.append(r)
+        
+        return merged
+    
+    # Default: RRF merge
+    return reciprocal_rank_fusion({
+        "hrr": hrr_results,
+        "vector": vector_results,
+        "fts5": fts5_results
+    })
+
+
+# Module-level router
+_router = QueryRouter()
+
+
+def route_query(query: str) -> QueryClassification:
+    """Route a query to the best search method."""
+    return _router.classify(query)
+
+
+def should_use_hybrid(query: str) -> bool:
+    """Check if query should use hybrid search."""
+    return _router.should_use_hybrid(query)