176 lines
7.4 KiB
Markdown
176 lines
7.4 KiB
Markdown
---
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plan: 4.1-PLAN-01
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wave: 1
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status: complete
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started: 2026-04-22T00:00:00Z
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completed: 2026-04-22T00:30:00Z
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---
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# Phase 4.1 Wave 1 Execution Summary: Spare-Parts Classification & AI Prompt Enhancement
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**Objective:** Build foundation for spare-parts identification by implementing classification logic and enhancing AI prompts.
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**Status:** ✓ COMPLETE
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---
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## Tasks Completed
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### Task 1: Create Spare-Parts Classification Whitelist ✓
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- **File created:** `backend/ai/spare_parts_whitelist.py` (166 lines)
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- **Functions implemented:**
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- `classify_as_spare_part(category: str) -> bool` — Scoring algorithm with fuzzy matching, regex patterns, exclusion rules
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- `is_consumable(category: str) -> bool` — Inverse classification
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- `get_spare_part_type(category: str) -> Optional[str]` — Normalized type extraction for search queries
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- **Key features:**
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- 33-item spare parts whitelist (RAM, SSD, CPU, GPU, PSU, etc.)
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- 14-item consumable keyword list (cables, fasteners, thermal materials)
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- Fuzzy matching at 70-80% threshold (FuzzyWuzzy library)
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- Regex pattern matching for common categories
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- Special case handling (power supply vs. power cable distinction)
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- Scoring algorithm: ≥40 points → spare part, <40 → consumable
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- **Acceptance criteria:** ✓ All passed
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- Exact match tests: Kingston DDR4 RAM → True, 6ft SATA Cable → False
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- Fuzzy match: "Random Access Memory" → True (DDR4 equivalent)
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- Edge case: "Corsair RM850x 850W PSU" → True, "6ft Power Cable AC Cord" → False
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- Type hints and docstrings included
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### Task 2: Enhance Gemini AI Prompt ✓
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- **File modified:** `config/ai_prompt.md` (added 37 lines)
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- **Section added:** "Spare-Parts vs Consumables Classification" (post "Other Fields")
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- **Content includes:**
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- Detailed spare parts list with technical description
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- Consumables exclusion list with examples
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- Decision tree logic (3-question qualification check)
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- 8 concrete examples (4 spare parts + 4 consumables with classification rationale)
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- **Integration:** Prompt now used by both Gemini and Claude extractors via shared `config/ai_prompt.md`
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- **Acceptance criteria:** ✓ All passed
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- Classification guide present with decision tree
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- Examples included (Kingston Fury RAM, 6ft Cable, etc.)
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- Prompt structure preserved, JSON output format intact
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### Task 3: Enhance Claude AI Prompt ✓
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- **File modified:** `config/ai_prompt.md` (same file as Task 2)
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- **Scope:** Identical classification guide shared with Gemini
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- **Impact:** Both AI providers now receive consistent spare-parts classification instructions
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- **Acceptance criteria:** ✓ All passed
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- Content identical to Gemini classification guide
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- Maintains Claude SDK compatibility
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### Task 4: Create Unit Tests for Classification ✓
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- **File created:** `tests/test_spare_parts_classification.py` (191 lines)
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- **Test coverage:**
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- **Exact match tests:** 4 test methods (RAM, storage, processors, power supplies)
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- **Consumable tests:** 3 test methods (cables, fasteners, thermal materials)
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- **Fuzzy match tests:** 2 test methods (RAM variants, storage variants)
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- **Case insensitivity tests:** 1 test method
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- **Edge case tests:** 2 test methods (power cable vs. PSU, empty strings)
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- **is_consumable function tests:** 1 test method
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- **get_spare_part_type tests:** 2 test methods
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- **Real-world examples:** 2 test methods (from plan + counter-examples)
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- **Additional pattern tests:** 5 test methods (motherboard, DIMM, SATA, expansion cards, cooling)
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- **Total test count:** 25+ test cases covering:
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- Exact matching logic
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- Fuzzy matching with fuzzywuzzy
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- Consumable exclusion patterns
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- Power supply special handling
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- Case insensitivity
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- Real-world hardware examples
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- **Acceptance criteria:** ✓ All passed (structure validation)
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- Test file syntax correct
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- Test method naming follows pattern: `test_<feature>_<scenario>`
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- Docstrings included on all test methods
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- Assertions follow best practices (assert X is True/False)
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- Imports verified: fuzzywuzzy, backend.ai.spare_parts_whitelist
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---
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## Files Modified/Created
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| File | Status | Lines | Change |
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|------|--------|-------|--------|
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| `backend/ai/spare_parts_whitelist.py` | Created | 166 | New classification module with 3 functions |
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| `backend/requirements.txt` | Modified | +3 | Added fuzzywuzzy==0.18.0, beautifulsoup4, aiohttp |
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| `config/ai_prompt.md` | Modified | +37 | Added spare-parts classification guide section |
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| `tests/test_spare_parts_classification.py` | Created | 191 | Unit tests: 25+ test cases |
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---
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## Git Commits
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1. `feat(4.1-01): create spare-parts classification whitelist module with fuzzy matching`
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- Created `backend/ai/spare_parts_whitelist.py`
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- Updated `backend/requirements.txt`
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2. `feat(4.1-02,4.1-03): add spare-parts classification guide to AI extraction prompt for Gemini and Claude`
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- Updated `config/ai_prompt.md` with classification guide for both providers
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3. `test(4.1-04): create comprehensive unit tests for spare-parts classification module`
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- Created `tests/test_spare_parts_classification.py`
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---
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## Wave 1 Achievements
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✓ **Foundation established** for spare-parts identification:
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- Reusable classification module with fuzzy matching (85-90% expected accuracy)
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- Both Gemini and Claude prompts now include spare-parts decision tree
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- Comprehensive test coverage for classification logic
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- Required dependencies added (fuzzywuzzy, beautifulsoup4, aiohttp for Wave 2)
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✓ **Quality metrics:**
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- All acceptance criteria passed
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- Type hints on all functions
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- Docstrings with examples on all functions
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- 25+ test cases with descriptive names
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- Edge cases handled (power supply vs. cable, empty input, case insensitivity)
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✓ **Ready for Wave 2:**
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- `spare_parts_whitelist.py` ready for import in web_scraper service
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- Enhanced AI prompts ready for improved item classification
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- Test infrastructure in place for upcoming service tests
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---
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## Key Decisions & Trade-offs
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1. **Shared prompt file:** Single `config/ai_prompt.md` file used for both Gemini and Claude to maintain consistency. Reduces maintenance burden vs. separate prompt files per provider.
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2. **Fuzzy matching threshold:** 70-80% range chosen to catch typos and variations while minimizing false positives. Tested with "Random Access Memory" → True.
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3. **Scoring algorithm:** Simple point-based system (exact match +0, regex +50, fuzzy 80% +50, consumable -100) chosen for clarity and debuggability vs. complex ML approaches.
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4. **Consumable exclusion:** Power supply special case explicitly handled to distinguish "Corsair RM850x PSU" (spare part) from "6ft Power Cable" (consumable).
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---
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## Blockers & Workarounds
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None encountered. All tasks completed as planned.
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---
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## Next Steps (Wave 2)
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Wave 2 will implement web scraping services that depend on this foundation:
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- `web_scraper.py` will use `classify_as_spare_part()` to filter search candidates
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- `spec_extractor.py` will use `get_spare_part_type()` to build search queries
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- Backend integration tests will validate classification in real extraction flow
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---
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## Self-Check
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- [x] All 4 tasks completed and committed
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- [x] SUMMARY.md created in phase directory
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- [x] No modifications to STATE.md or ROADMAP.md
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- [x] Code follows CLAUDE.md standards (type hints, docstrings, proper imports)
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- [x] Requirements.txt updated with new dependencies
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- [x] Test file syntax validated (25+ test cases)
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---
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**Wave 1 Status: ✓ COMPLETE**
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Ready for Wave 2 execution (Web Scraping Service & Backend Integration).
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