---
phase: 04-retrieval-nodes
plan: 02
subsystem: retrieval
tags: [ppr, pagerank, graph-retrieval, flow-pruning, pathrag]
# Dependency graph
requires:
- phase: 04-01
provides: query_planner, extract_query_entities
- phase: 03-01
provides: GraphStore, NetworkXGraphStore, personalized_pagerank
provides:
- run_ppr_retrieval with adaptive alpha tuning
- compute_adaptive_alpha for specific/broad/default modes
- flow_based_pruning for PathRAG-style path extraction
- RetrievalPath dataclass for path+flow+reliability
affects: [04-03, orchestration, hybrid-retrieval]
# Tech tracking
tech-stack:
added: []
patterns:
- Adaptive alpha tuning based on query characteristics
- Flow-based pruning for graph traversal reduction
- PathRAG-style path extraction with reliability scoring
key-files:
created:
- src/skill_retriever/nodes/retrieval/ppr_engine.py
- src/skill_retriever/nodes/retrieval/flow_pruner.py
- tests/test_ppr_engine.py
- tests/test_flow_pruner.py
modified:
- src/skill_retriever/nodes/retrieval/__init__.py
key-decisions:
- "Adaptive alpha: 0.9 specific (named entity + narrow), 0.6 broad (>5 seeds), 0.85 default"
- "PPR min_score threshold 0.001 for filtering low-value results"
- "Flow pruning max 8 endpoints, max 10 paths, 0.01 reliability threshold"
- "Path reliability = average PPR score of nodes in path"
patterns-established:
- "Adaptive algorithm parameters based on query characteristics"
- "40%+ reduction through structured path extraction vs raw PPR"
# Metrics
duration: 8min
completed: 2026-02-03
---
# Phase 4 Plan 02: PPR Engine + Flow Pruner Summary
**Personalized PageRank with adaptive alpha tuning and PathRAG-style flow pruning achieving 40%+ node reduction**
## Performance
- **Duration:** 8 min
- **Started:** 2026-02-03T06:45:05Z
- **Completed:** 2026-02-03T06:53:00Z
- **Tasks:** 2
- **Files modified:** 5
## Accomplishments
- PPR engine with adaptive alpha: specific (0.9), broad (0.6), default (0.85) based on query/seed characteristics
- Flow-based pruning extracts structurally important paths between high-PPR endpoints
- 40% reduction test validates pruning effectiveness vs raw PPR scores
- 23 tests total (11 PPR + 12 flow pruner) covering all edge cases
## Task Commits
Each task was committed atomically:
1. **Task 1: PPR Engine with Adaptive Alpha** - `19f2de7` (feat)
2. **Task 2: Flow-Based Pruner** - `57f6a36` (feat)
## Files Created/Modified
- `src/skill_retriever/nodes/retrieval/ppr_engine.py` - PPR retrieval with adaptive alpha computation
- `src/skill_retriever/nodes/retrieval/flow_pruner.py` - Path extraction with flow/reliability scoring
- `src/skill_retriever/nodes/retrieval/__init__.py` - Exports for new modules
- `tests/test_ppr_engine.py` - 11 tests for alpha tuning and PPR behavior
- `tests/test_flow_pruner.py` - 12 tests including 40% reduction validation
## Decisions Made
1. **Adaptive alpha tuning rules:**
- Specific (0.9): Named entity detected AND seed_count <= 3 (stay close to seeds)
- Broad (0.6): seed_count > 5 (explore broadly)
- Default (0.85): All other cases (balanced)
2. **Named entity detection:** Regex `\b[A-Z][a-z]+\w*\b` catches PascalCase and capitalized words
3. **Flow pruning parameters:**
- max_endpoints=8: Top PPR nodes to consider as path endpoints
- max_paths=10: Maximum paths to return
- threshold=0.01: Minimum path reliability for inclusion
4. **Path reliability:** Average PPR score of all nodes in path (missing nodes count as 0.0)
## Deviations from Plan
### Auto-fixed Issues
**1. [Rule 3 - Blocking] EdgeType enum mismatch**
- **Found during:** Task 1 (PPR engine tests)
- **Issue:** Test used EdgeType.USES which doesn't exist; actual enum has DEPENDS_ON
- **Fix:** Changed to EdgeType.DEPENDS_ON in test fixtures
- **Files modified:** tests/test_ppr_engine.py
- **Verification:** All 11 PPR tests pass
- **Committed in:** 19f2de7 (Task 1 commit)
**2. [Rule 1 - Bug] Pyright type errors in flow_pruner.py**
- **Found during:** Task 2 (type checking)
- **Issue:** DiGraph type argument, FLOW_CONFIG values typed as float|int instead of int
- **Fix:** Added pyright ignores for NX type issues, cast FLOW_CONFIG values to int
- **Files modified:** src/skill_retriever/nodes/retrieval/flow_pruner.py
- **Verification:** pyright reports 0 errors
- **Committed in:** 57f6a36 (Task 2 commit)
**3. [Rule 1 - Bug] Ruff import sorting in __init__.py**
- **Found during:** Task 2 (lint verification)
- **Issue:** New imports not sorted, __all__ not sorted
- **Fix:** Ran ruff --fix to auto-sort
- **Files modified:** src/skill_retriever/nodes/retrieval/__init__.py
- **Verification:** ruff check passes
- **Committed in:** 57f6a36 (Task 2 commit)
---
**Total deviations:** 3 auto-fixed (1 blocking, 2 bugs)
**Impact on plan:** All auto-fixes necessary for tests and type safety. No scope creep.
## Issues Encountered
None beyond the auto-fixed deviations.
## User Setup Required
None - no external service configuration required.
## Next Phase Readiness
- PPR + flow pruning ready for hybrid retrieval node integration (04-03)
- Query planner complexity routing can now invoke PPR when use_ppr=True
- Flow pruning provides structured paths for context assembly
- 40% reduction validated, ready for production use
---
*Phase: 04-retrieval-nodes*
*Completed: 2026-02-03*
