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## Overview
This guide shows how to integrate Phase 2's MemoryWeighting into the actual AdapterRouter to enable adaptive adapter selection based on historical performance.
**Current State**: MemoryWeighting is built and wired into ForgeEngine, but not yet connected to AdapterRouter. This document bridges that gap.
---
## Architecture: Where MemoryWeighting Fits
```
Query
↓
AdapterRouter.route()
├─ [Current] Keyword matching → base_result = RouteResult(primary, secondary, confidence)
└─ [Phase 2] Memory-weighted boost → boosted_confidence = base_confidence * (1 + weight_modifier)
↓
ForgeEngine.forge_with_debate(primary=primary_adapter, secondary=secondary_adapters)
↓
Agents generate analyses → Conflicts detected → Stored in memory
↓
Next Query: Adapters with high historical coherence get +50% confidence boost
```
---
## Integration Steps
### Step 1: Wire MemoryWeighting into AdapterRouter.__init__()
**File**: `inference/adapter_router.py` (lines ~50-80)
**Current Code**:
```python
class AdapterRouter:
def __init__(self, adapter_registry):
self.adapter_registry = adapter_registry
self.keyword_index = {}
# ... initialize other components ...
```
**Phase 2 Enhancement**:
```python
from reasoning_forge.memory_weighting import MemoryWeighting
class AdapterRouter:
def __init__(self, adapter_registry, memory_weighting=None):
self.adapter_registry = adapter_registry
self.keyword_index = {}
self.memory_weighting = memory_weighting # NEW: optional memory weighting
# ... initialize other components ...
```
**Usage**:
```python
# In codette_session.py or app initialization:
from reasoning_forge.living_memory import LivingMemoryKernel
from reasoning_forge.memory_weighting import MemoryWeighting
from inference.adapter_router import AdapterRouter
memory = LivingMemoryKernel(max_memories=100)
weighting = MemoryWeighting(memory)
router = AdapterRouter(adapter_registry, memory_weighting=weighting)
```
---
### Step 2: Modify AdapterRouter.route() for Memory-Weighted Boost
**File**: `inference/adapter_router.py` (lines ~200-250)
**Current Code**:
```python
def route(self, query: str) -> RouteResult:
"""Route query to appropriate adapters."""
# Keyword matching
scores = self._route_keyword(query)
return RouteResult(
primary=best_adapter,
secondary=top_secondary,
confidence=max_score
)
```
**Phase 2 Enhancement - SOFT BOOST**:
```python
def route(self, query: str, use_memory_boost: bool = True) -> RouteResult:
"""Route query to appropriate adapters with optional memory weighting.
Args:
query: User query text
use_memory_boost: If True, boost confidence based on historical performance
Returns:
RouteResult with primary, secondary adapters and confidence
"""
# Step 1: Keyword-based routing (existing logic)
base_result = self._route_keyword(query)
# Step 2: Apply memory-weighted boost (Phase 2)
if use_memory_boost and self.memory_weighting:
boosted_conf = self.memory_weighting.get_boosted_confidence(
base_result.primary,
base_result.confidence
)
base_result.confidence = boosted_conf
# Optional: Explain the boost for debugging
if os.environ.get("DEBUG_ADAPTER_ROUTING"):
explanation = self.memory_weighting.explain_weight(base_result.primary)
print(f"[ROUTING] {base_result.primary}: "
f"base={base_result.confidence:.2f}, "
f"boosted={boosted_conf:.2f}, "
f"weight={explanation['final_weight']:.2f}")
return base_result
```
**Advanced Option - STRICT MEMORY-ONLY** (optional, higher risk):
```python
def route(self, query: str, strategy: str = "keyword") -> RouteResult:
"""Route query with pluggable strategy.
Args:
query: User query text
strategy: "keyword" (default), "memory_weighted", or "memory_only"
Returns:
RouteResult with primary, secondary adapters and confidence
"""
if strategy == "memory_only" and self.memory_weighting:
# Pure learning approach: ignore keywords
weights = self.memory_weighting.compute_weights()
if weights:
primary = max(weights.keys(), key=lambda a: weights[a])
return RouteResult(
primary=primary,
secondary=[], # No secondary adapters in memory-only mode
confidence=weights[primary] / 2.0 # Normalize [0, 1]
)
else:
# Fallback to keyword if no memory yet
return self._route_keyword(query)
elif strategy == "memory_weighted":
# Soft boost approach: keyword routing + memory confidence boost
base_result = self._route_keyword(query)
if self.memory_weighting:
boosted_conf = self.memory_weighting.get_boosted_confidence(
base_result.primary,
base_result.confidence
)
base_result.confidence = boosted_conf
return base_result
else: # strategy == "keyword"
# Pure keyword routing (existing behavior)
return self._route_keyword(query)
```
---
### Step 3: Pass MemoryWeighting Through Session/App
**File**: `inference/codette_session.py` (lines ~50-100)
**Current Code**:
```python
class CodetteSession:
def __init__(self):
self.memory_kernel = LivingMemoryKernel(max_memories=100)
self.router = AdapterRouter(adapter_registry)
self.forge = ForgeEngine()
```
**Phase 2 Enhancement**:
```python
from reasoning_forge.memory_weighting import MemoryWeighting
class CodetteSession:
def __init__(self):
self.memory_kernel = LivingMemoryKernel(max_memories=100)
# NEW: Initialize memory weighting
self.memory_weighting = MemoryWeighting(self.memory_kernel)
# Wire into router
self.router = AdapterRouter(
adapter_registry,
memory_weighting=self.memory_weighting
)
# Wire into forge (Phase 2)
self.forge = ForgeEngine(
living_memory=self.memory_kernel,
enable_memory_weighting=True
)
def on_submit(self, query: str):
"""Process user query with memory-weighted routing."""
# Route using memory weights
route_result = self.router.route(query, use_memory_boost=True)
# Run forge with memory enabled
result = self.forge.forge_with_debate(query)
# Conflicts automatically stored in memory
response = result["metadata"]["synthesized"]
return response
```
---
## Testing the Integration
### Unit Test: Memory Weighting + Router
```python
def test_memory_weighted_routing():
"""Test that memory weights modulate router confidence."""
from reasoning_forge.living_memory import LivingMemoryKernel, MemoryCocoon
from reasoning_forge.memory_weighting import MemoryWeighting
from inference.adapter_router import AdapterRouter
# Setup
memory = LivingMemoryKernel()
# Seed memory with Newton performance (high coherence)
newton_cocoon = MemoryCocoon(
title="Newton analysis",
content="Analytical approach",
adapter_used="newton",
coherence=0.9,
emotional_tag="neutral",
)
memory.store(newton_cocoon)
# Create weighting + router
weighting = MemoryWeighting(memory)
router = AdapterRouter(adapter_registry, memory_weighting=weighting)
# Test
query = "Analyze this algorithm"
result = router.route(query, use_memory_boost=True)
# If Newton scored high before, its confidence should be boosted
assert result.confidence > 0.5 # Baseline
print(f"✓ Routing test passed: {result.primary} @ {result.confidence:.2f}")
```
### E2E Test: Full Loop
```python
def test_memory_learning_loop():
"""Test that conflicts → memory → weights → better future routing."""
from reasoning_forge.forge_engine import ForgeEngine
from reasoning_forge.living_memory import LivingMemoryKernel
from reasoning_forge.memory_weighting import MemoryWeighting
from inference.adapter_router import AdapterRouter
# Run 1: Initial debate (no memory history)
memory = LivingMemoryKernel()
forge = ForgeEngine(living_memory=memory, enable_memory_weighting=True)
result1 = forge.forge_with_debate("Compare speed vs clarity", debate_rounds=1)
conflicts1 = result1["metadata"]["conflicts_round_0_count"]
print(f"Run 1: {conflicts1} conflicts detected, stored in memory")
# Run 2: Same query with memory history
# Adapters that resolved conflicts should get boosted
weighting = MemoryWeighting(memory) # Now has history
weights = weighting.get_all_weights()
print(f"\nAdapter weights after learning:")
for adapter, w_dict in weights.items():
print(f" {adapter}: weight={w_dict['weight']:.3f}, coherence={w_dict['coherence']:.3f}")
# Router should now boost high-performing adapters
router = AdapterRouter(adapter_registry, memory_weighting=weighting)
route_result = router.route("Compare speed vs clarity", use_memory_boost=True)
print(f"\nRouting decision: {route_result.primary} @ {route_result.confidence:.2f}")
# Run debate again (should use boosted adapters)
result2 = forge.forge_with_debate("Compare speed vs clarity", debate_rounds=1)
conflicts2 = result2["metadata"]["conflicts_round_0_count"]
# Measure improvement
improvement = (conflicts1 - conflicts2) / max(conflicts1, 1)
print(f"Run 2: {conflicts2} conflicts (improvement: {improvement:.1%})")
```
---
## Configuration: Tuning Parameters
**Memory Weighting Parameters** (in `MemoryWeighting`):
```python
# Update frequency (hours)
update_interval_hours = 1.0 # Recompute weights every hour
# Weight formula contributions
base_coherence_weight = 0.5 # Contribution from mean coherence
conflict_success_weight = 0.3 # Contribution from conflict resolution
recency_weight = 0.2 # Contribution from recency decay
# Recency decay half-life (hours)
recency_half_life_hours = 168 # 7 days
# Boost modulation
max_boost = 0.5 # ±50% confidence modification
```
**Router Integration Options**:
```python
# Memory boost enabled/disabled
router.route(query, use_memory_boost=True) # Default: enabled
router.route(query, use_memory_boost=False) # Keyword-only
# Strategy selection (advanced)
router.route(query, strategy="keyword") # Pure keyword
router.route(query, strategy="memory_weighted") # Soft boost (recommended)
router.route(query, strategy="memory_only") # Pure learning (risky)
```
---
## Production Deployment Checklist
- [ ] Wire MemoryWeighting into AdapterRouter.__init__()
- [ ] Modify route() method with use_memory_boost parameter
- [ ] Update CodetteSession to initialize memory_weighting
- [ ] Pass memory_weighting through all routing calls
- [ ] Update app.py/Gradio interface to pass memory context
- [ ] Add unit test for memory-weighted routing
- [ ] Add E2E test for full learning loop
- [ ] Monitor: Log adapter weights after each debate cycle
- [ ] Tune: Adjust weight formula coefficients based on results
- [ ] Document: User-facing explanation of why adapters were selected
---
## Monitoring & Debugging
### Enable Debug Logging
```python
import os
import logging
# In app initialization:
if os.environ.get("DEBUG_ADAPTER_ROUTING"):
logging.basicConfig(level=logging.DEBUG)
# This will print weight explanations on each route call
```
### Query Adapter Weight History
```python
from reasoning_forge.memory_weighting import MemoryWeighting
# Get snapshot of adapter weights
weights = memory_weighting.get_all_weights()
for adapter, w_dict in weights.items():
print(f"{adapter}: weight={w_dict['weight']:.3f}")
# Explain a specific adapter's weight
explanation = memory_weighting.explain_weight("newton")
print(explanation["explanation"])
# Output: "Adapter 'newton' has used 15 times with 0.8 avg coherence,
# 73% conflict resolution rate, and 0.95 recency score.
# Final weight: 1.45 (range [0, 2.0])"
```
### Memory State
```python
# Check memory cocoon counts per adapter
for cocoon in memory.memories:
if cocoon.emotional_tag == "tension":
print(f"Conflict: {cocoon.adapter_used}, coherence={cocoon.coherence}")
# Get emotional profile
profile = memory.emotional_profile()
print(f"Memory profile: {profile}") # {'tension': 25, 'neutral': 10, ...}
```
---
## Known Limitations & Future Work
1. **Adapter Naming**: Currently stores agent pairs (e.g., "Newton,Quantum"). For pure adapter routing, need to map to actual adapter names.
2. **Cold Start**: New adapters have neutral weights (1.0) until they accumulate history (~10-15 uses).
3. **Strict Mode Risk**: Memory-only routing (no keywords) can ignore important query context. Test thoroughly before production.
4. **Memory Pruning**: Automatic pruning at 100 memories may lose old patterns. Consider keeping high-importance conflicts longer.
5. **Next Phase**: Multi-round conflict resolution tracking would enable learning across multiple debate cycles, not just single-round.
---
## Summary
**To Enable Memory-Weighted Routing**:
1. Add `memory_weighting` parameter to AdapterRouter.__init__()
2. Modify route() to apply `get_boosted_confidence()` soft boost
3. Wire through CodetteSession / app initialization
4. Test with unit + E2E test suite
5. Monitor weights and tune formula if needed
**Recommended Approach**: Soft boost (preserve keyword intelligence) → can migrate to memory-only if results justify it.
**Expected Outcome**: Better adapter selection over time, converging to adapters that historically resolved more conflicts.
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