fix: change default OCR language from English to Swedish

Project targets Swedish invoice extraction. PaddleOCR sv model provides
better recognition of Swedish-specific characters (å, ä, ö).

.claude/CLAUDE.md

@@ -1,85 +1,72 @@
 # Invoice Master POC v2
 
-Swedish Invoice Field Extraction System - YOLOv11 + PaddleOCR 从瑞典 PDF 发票中提取结构化数据。
+Swedish Invoice Field Extraction System - YOLO + PaddleOCR extracts structured data from Swedish PDF invoices.
+
+## Architecture
+
+```
+PDF → PyMuPDF (DPI=150) → YOLO Detection → PaddleOCR → Field Extraction → Normalization → Output
+```
+
+### Project Structure
+
+```
+packages/
+├── backend/    # FastAPI web server + inference pipeline
+│   └── pipeline/   # YOLO detector → OCR → field extractor → value selector → normalizers
+├── shared/     # Common utilities (bbox, OCR, field mappings)
+└── training/   # YOLO training data generation (annotation, dataset)
+tests/          # Mirrors packages/ structure
+```
+
+### Pipeline Flow (process_pdf)
+
+1. YOLO detects field regions on rendered PDF page
+2. PaddleOCR extracts text from detected bboxes
+3. Field extractor maps detections to invoice fields via CLASS_TO_FIELD
+4. Value selector picks best candidate per field (confidence + validation)
+5. Normalizers clean values (dates, amounts, invoice numbers)
+6. Fallback regex extraction if key fields missing
 
 ## Tech Stack
 
 | Component | Technology |
 |-----------|------------|
-| Object Detection | YOLOv11 (Ultralytics) |
-| OCR Engine | PaddleOCR v5 (PP-OCRv5) |
-| PDF Processing | PyMuPDF (fitz) |
+| Object Detection | YOLO (Ultralytics >= 8.4.0) |
+| OCR | PaddleOCR v5 (PP-OCRv5) |
+| PDF | PyMuPDF (fitz), DPI=150 |
 | Database | PostgreSQL + psycopg2 |
-| Web Framework | FastAPI + Uvicorn |
-| Deep Learning | PyTorch + CUDA 12.x |
+| Web | FastAPI + Uvicorn |
+| ML | PyTorch + CUDA 12.x |
 
 ## WSL Environment (REQUIRED)
 
-**Prefix ALL commands with:**
+ALL Python commands MUST use this prefix:
 
 ```bash
-wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && <command>"
+wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-sm120 && <command>"
 ```
 
-**NEVER run Python commands directly in Windows PowerShell/CMD.**
+NEVER run Python directly in Windows PowerShell/CMD.
 
-## Project-Specific Rules
+## Project Rules
 
-- Python 3.11+ with type hints
-- No print() in production - use logging
-- Run tests: `pytest --cov=src`
+- Python 3.10, type hints on all function signatures
+- No `print()` in production code - use `logging` module
+- Validation with `pydantic` or `dataclasses`
+- Error handling with `try/except` (not try/catch)
+- Run tests: `pytest --cov=packages tests/`
 
-## File Structure
+## Key Files
 
-```
-src/
-├── cli/              # autolabel, train, infer, serve
-├── pdf/              # extractor, renderer, detector
-├── ocr/              # PaddleOCR wrapper, machine_code_parser
-├── inference/        # pipeline, yolo_detector, field_extractor
-├── normalize/        # Per-field normalizers
-├── matcher/          # Exact, substring, fuzzy strategies
-├── processing/       # CPU/GPU pool architecture
-├── web/              # FastAPI app, routes, services, schemas
-├── utils/            # validators, text_cleaner, fuzzy_matcher
-└── data/             # Database operations
-tests/                # Mirror of src structure
-runs/train/           # Training outputs
-```
-
-## Supported Fields
-
-| ID | Field | Description |
-|----|-------|-------------|
-| 0 | invoice_number | Invoice number |
-| 1 | invoice_date | Invoice date |
-| 2 | invoice_due_date | Due date |
-| 3 | ocr_number | OCR reference (Swedish payment) |
-| 4 | bankgiro | Bankgiro account |
-| 5 | plusgiro | Plusgiro account |
-| 6 | amount | Amount |
-| 7 | supplier_organisation_number | Supplier org number |
-| 8 | payment_line | Payment line (machine-readable) |
-| 9 | customer_number | Customer number |
-
-## Key Patterns
-
-### Inference Result
-
-```python
-@dataclass
-class InferenceResult:
-    document_id: str
-    document_type: str  # "invoice" or "letter"
-    fields: dict[str, str]
-    confidence: dict[str, float]
-    cross_validation: CrossValidationResult | None
-    processing_time_ms: float
-```
-
-### API Schemas
-
-See `src/web/schemas.py` for request/response models.
+| File | Purpose |
+|------|---------|
+| `packages/backend/backend/pipeline/pipeline.py` | Main inference pipeline |
+| `packages/backend/backend/pipeline/field_extractor.py` | YOLO → field mapping |
+| `packages/backend/backend/pipeline/value_selector.py` | Best candidate selection |
+| `packages/shared/shared/fields/mappings.py` | CLASS_TO_FIELD mapping |
+| `packages/shared/shared/ocr/paddle_ocr.py` | OCRToken definition |
+| `packages/shared/shared/bbox/` | Bbox expansion strategies |
 
 ## Environment Variables
 
@@ -98,46 +85,40 @@ SERVER_HOST=0.0.0.0
 SERVER_PORT=8000
 ```
 
-## CLI Commands
+## Auto-trigger Rules (ALWAYS FOLLOW - even after context compaction)
 
-```bash
-# Auto-labeling
-python -m src.cli.autolabel --dual-pool --cpu-workers 3 --gpu-workers 1
+These rules MUST be followed regardless of conversation history:
 
-# Training
-python -m src.cli.train --model yolo11n.pt --epochs 100 --batch 16 --name invoice_fields
+- New feature or bug fix → MUST use **tdd-guide** agent (write tests first)
+- When writing code → MUST follow coding standards skill for the target language:
+  - Python → `python-patterns` (PEP 8, type hints, Pythonic idioms)
+  - C# → `dotnet-skills:coding-standards` (records, pattern matching, modern C#)
+  - TS/JS → `coding-standards` (universal best practices)
+- After writing/modifying code → MUST use **code-reviewer** agent
+- Before git commit → MUST use **security-reviewer** agent
+- When build/test fails → MUST use **build-error-resolver** agent
+- After context compaction → read MEMORY.md to restore session state
 
-# Inference
-python -m src.cli.infer --model runs/train/invoice_fields/weights/best.pt --input invoice.pdf --gpu
+## Plan Completion Protocol
 
-# Web Server
-python run_server.py --port 8000
-```
+After completing any plan or major task:
 
-## API Endpoints
+1. **Test** - Run `pytest` to confirm all tests pass
+2. **Security review** - Use **security-reviewer** agent on changed files
+3. **Fix loop** - If security review reports CRITICAL or HIGH issues:
+   - Fix the issues
+   - Re-run tests (back to step 1)
+   - Re-run security review (back to step 2)
+   - Repeat until no CRITICAL/HIGH issues remain
+4. **Commit** - Auto-commit with conventional commit message (`feat:`, `fix:`, `refactor:`, etc.). Stage only the files changed in this task, not unrelated files
+5. **Save** - Write a summary to MEMORY.md including: what was done, files changed, decisions made, remaining work
+6. **Suggest clear** - Tell the user: "Plan complete. Recommend `/clear` to free context for the next task."
+7. **Do NOT start a new task** in the same context - wait for user to /clear first
 
-| Method | Endpoint | Description |
-|--------|----------|-------------|
-| GET | `/` | Web UI |
-| GET | `/api/v1/health` | Health check |
-| POST | `/api/v1/infer` | Process invoice |
-| GET | `/api/v1/results/{filename}` | Get visualization |
+This keeps each plan in a fresh context window for maximum quality.
 
-## Current Status
+## Known Issues
 
-- **Tests**: 688 passing
-- **Coverage**: 37%
-- **Model**: 93.5% mAP@0.5
-- **Documents Labeled**: 9,738
-
-## Quick Start
-
-```bash
-# Start server
-wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python run_server.py"
-
-# Run tests
-wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && pytest"
-
-# Access UI: http://localhost:8000
-```
+- Pre-existing test failures: `test_s3.py`, `test_azure.py` (missing boto3/azure) - safe to ignore
+- Always re-run dedup/validation after fallback adds new fields
+- PDF DPI must be 150 (not 300) for correct bbox alignment

.claude/commands/build-fix.md

@@ -1,22 +0,0 @@
-# Build and Fix
-
-Incrementally fix Python errors and test failures.
-
-## Workflow
-
-1. Run check: `mypy src/ --ignore-missing-imports` or `pytest -x --tb=short`
-2. Parse errors, group by file, sort by severity (ImportError > TypeError > other)
-3. For each error:
-   - Show context (5 lines)
-   - Explain and propose fix
-   - Apply fix
-   - Re-run test for that file
-   - Verify resolved
-4. Stop if: fix introduces new errors, same error after 3 attempts, or user pauses
-5. Show summary: fixed / remaining / new errors
-
-## Rules
-
-- Fix ONE error at a time
-- Re-run tests after each fix
-- Never batch multiple unrelated fixes

.claude/commands/checkpoint.md

@@ -1,74 +0,0 @@
-# Checkpoint Command
-
-Create or verify a checkpoint in your workflow.
-
-## Usage
-
-`/checkpoint [create|verify|list] [name]`
-
-## Create Checkpoint
-
-When creating a checkpoint:
-
-1. Run `/verify quick` to ensure current state is clean
-2. Create a git stash or commit with checkpoint name
-3. Log checkpoint to `.claude/checkpoints.log`:
-
-```bash
-echo "$(date +%Y-%m-%d-%H:%M) | $CHECKPOINT_NAME | $(git rev-parse --short HEAD)" >> .claude/checkpoints.log
-```
-
-4. Report checkpoint created
-
-## Verify Checkpoint
-
-When verifying against a checkpoint:
-
-1. Read checkpoint from log
-2. Compare current state to checkpoint:
-   - Files added since checkpoint
-   - Files modified since checkpoint
-   - Test pass rate now vs then
-   - Coverage now vs then
-
-3. Report:
-```
-CHECKPOINT COMPARISON: $NAME
-============================
-Files changed: X
-Tests: +Y passed / -Z failed
-Coverage: +X% / -Y%
-Build: [PASS/FAIL]
-```
-
-## List Checkpoints
-
-Show all checkpoints with:
-- Name
-- Timestamp
-- Git SHA
-- Status (current, behind, ahead)
-
-## Workflow
-
-Typical checkpoint flow:
-
-```
-[Start] --> /checkpoint create "feature-start"
-   |
-[Implement] --> /checkpoint create "core-done"
-   |
-[Test] --> /checkpoint verify "core-done"
-   |
-[Refactor] --> /checkpoint create "refactor-done"
-   |
-[PR] --> /checkpoint verify "feature-start"
-```
-
-## Arguments
-
-$ARGUMENTS:
-- `create <name>` - Create named checkpoint
-- `verify <name>` - Verify against named checkpoint
-- `list` - Show all checkpoints
-- `clear` - Remove old checkpoints (keeps last 5)

.claude/commands/code-review.md

@@ -1,46 +0,0 @@
-# Code Review
-
-Security and quality review of uncommitted changes.
-
-## Workflow
-
-1. Get changed files: `git diff --name-only HEAD` and `git diff --staged --name-only`
-2. Review each file for issues (see checklist below)
-3. Run automated checks: `mypy src/`, `ruff check src/`, `pytest -x`
-4. Generate report with severity, location, description, suggested fix
-5. Block commit if CRITICAL or HIGH issues found
-
-## Checklist
-
-### CRITICAL (Block)
-
-- Hardcoded credentials, API keys, tokens, passwords
-- SQL injection (must use parameterized queries)
-- Path traversal risks
-- Missing input validation on API endpoints
-- Missing authentication/authorization
-
-### HIGH (Block)
-
-- Functions > 50 lines, files > 800 lines
-- Nesting depth > 4 levels
-- Missing error handling or bare `except:`
-- `print()` in production code (use logging)
-- Mutable default arguments
-
-### MEDIUM (Warn)
-
-- Missing type hints on public functions
-- Missing tests for new code
-- Duplicate code, magic numbers
-- Unused imports/variables
-- TODO/FIXME comments
-
-## Report Format
-
-```
-[SEVERITY] file:line - Issue description
-  Suggested fix: ...
-```
-
-## Never Approve Code With Security Vulnerabilities!

.claude/commands/e2e.md

@@ -1,40 +0,0 @@
-# E2E Testing
-
-End-to-end testing for the Invoice Field Extraction API.
-
-## When to Use
-
-- Testing complete inference pipeline (PDF -> Fields)
-- Verifying API endpoints work end-to-end
-- Validating YOLO + OCR + field extraction integration
-- Pre-deployment verification
-
-## Workflow
-
-1. Ensure server is running: `python run_server.py`
-2. Run health check: `curl http://localhost:8000/api/v1/health`
-3. Run E2E tests: `pytest tests/e2e/ -v`
-4. Verify results and capture any failures
-
-## Critical Scenarios (Must Pass)
-
-1. Health check returns `{"status": "healthy", "model_loaded": true}`
-2. PDF upload returns valid response with fields
-3. Fields extracted with confidence scores
-4. Visualization image generated
-5. Cross-validation included for invoices with payment_line
-
-## Checklist
-
-- [ ] Server running on http://localhost:8000
-- [ ] Health check passes
-- [ ] PDF inference returns valid JSON
-- [ ] At least one field extracted
-- [ ] Visualization URL returns image
-- [ ] Response time < 10 seconds
-- [ ] No server errors in logs
-
-## Test Location
-
-E2E tests: `tests/e2e/`
-Sample fixtures: `tests/fixtures/`

.claude/commands/eval.md

@@ -1,174 +0,0 @@
-# Eval Command
-
-Evaluate model performance and field extraction accuracy.
-
-## Usage
-
-`/eval [model|accuracy|compare|report]`
-
-## Model Evaluation
-
-`/eval model`
-
-Evaluate YOLO model performance on test dataset:
-
-```bash
-# Run model evaluation
-python -m src.cli.train --model runs/train/invoice_fields/weights/best.pt --eval-only
-
-# Or use ultralytics directly
-yolo val model=runs/train/invoice_fields/weights/best.pt data=data.yaml
-```
-
-Output:
-```
-Model Evaluation: invoice_fields/best.pt
-========================================
-mAP@0.5:     93.5%
-mAP@0.5-0.95: 83.0%
-
-Per-class AP:
-- invoice_number:    95.2%
-- invoice_date:      94.8%
-- invoice_due_date:  93.1%
-- ocr_number:        91.5%
-- bankgiro:          92.3%
-- plusgiro:          90.8%
-- amount:            88.7%
-- supplier_org_num:  85.2%
-- payment_line:      82.4%
-- customer_number:   81.1%
-```
-
-## Accuracy Evaluation
-
-`/eval accuracy`
-
-Evaluate field extraction accuracy against ground truth:
-
-```bash
-# Run accuracy evaluation on labeled data
-python -m src.cli.infer --model runs/train/invoice_fields/weights/best.pt \
-    --input ~/invoice-data/test/*.pdf \
-    --ground-truth ~/invoice-data/test/labels.csv \
-    --output eval_results.json
-```
-
-Output:
-```
-Field Extraction Accuracy
-=========================
-Documents tested: 500
-
-Per-field accuracy:
-- InvoiceNumber:   98.9% (494/500)
-- InvoiceDate:     95.5% (478/500)
-- InvoiceDueDate:  95.9% (480/500)
-- OCR:             99.1% (496/500)
-- Bankgiro:        99.0% (495/500)
-- Plusgiro:        99.4% (497/500)
-- Amount:          91.3% (457/500)
-- supplier_org:    78.2% (391/500)
-
-Overall: 94.8%
-```
-
-## Compare Models
-
-`/eval compare`
-
-Compare two model versions:
-
-```bash
-# Compare old vs new model
-python -m src.cli.eval compare \
-    --model-a runs/train/invoice_v1/weights/best.pt \
-    --model-b runs/train/invoice_v2/weights/best.pt \
-    --test-data ~/invoice-data/test/
-```
-
-Output:
-```
-Model Comparison
-================
-                Model A     Model B     Delta
-mAP@0.5:        91.2%       93.5%       +2.3%
-Accuracy:       92.1%       94.8%       +2.7%
-Speed (ms):     1850        1520        -330
-
-Per-field improvements:
-- amount:       +4.2%
-- payment_line: +3.8%
-- customer_num: +2.1%
-
-Recommendation: Deploy Model B
-```
-
-## Generate Report
-
-`/eval report`
-
-Generate comprehensive evaluation report:
-
-```bash
-python -m src.cli.eval report --output eval_report.md
-```
-
-Output:
-```markdown
-# Evaluation Report
-Generated: 2026-01-25
-
-## Model Performance
-- Model: runs/train/invoice_fields/weights/best.pt
-- mAP@0.5: 93.5%
-- Training samples: 9,738
-
-## Field Extraction Accuracy
-| Field | Accuracy | Errors |
-|-------|----------|--------|
-| InvoiceNumber | 98.9% | 6 |
-| Amount | 91.3% | 43 |
-...
-
-## Error Analysis
-### Common Errors
-1. Amount: OCR misreads comma as period
-2. supplier_org: Missing from some invoices
-3. payment_line: Partially obscured by stamps
-
-## Recommendations
-1. Add more training data for low-accuracy fields
-2. Implement OCR error correction for amounts
-3. Consider confidence threshold tuning
-```
-
-## Quick Commands
-
-```bash
-# Evaluate model metrics
-yolo val model=runs/train/invoice_fields/weights/best.pt
-
-# Test inference on sample
-python -m src.cli.infer --input sample.pdf --output result.json --gpu
-
-# Check test coverage
-pytest --cov=src --cov-report=html
-```
-
-## Evaluation Metrics
-
-| Metric | Target | Current |
-|--------|--------|---------|
-| mAP@0.5 | >90% | 93.5% |
-| Overall Accuracy | >90% | 94.8% |
-| Test Coverage | >60% | 37% |
-| Tests Passing | 100% | 100% |
-
-## When to Evaluate
-
-- After training a new model
-- Before deploying to production
-- After adding new training data
-- When accuracy complaints arise
-- Weekly performance monitoring

.claude/commands/learn.md

@@ -1,70 +0,0 @@
-# /learn - Extract Reusable Patterns
-
-Analyze the current session and extract any patterns worth saving as skills.
-
-## Trigger
-
-Run `/learn` at any point during a session when you've solved a non-trivial problem.
-
-## What to Extract
-
-Look for:
-
-1. **Error Resolution Patterns**
-   - What error occurred?
-   - What was the root cause?
-   - What fixed it?
-   - Is this reusable for similar errors?
-
-2. **Debugging Techniques**
-   - Non-obvious debugging steps
-   - Tool combinations that worked
-   - Diagnostic patterns
-
-3. **Workarounds**
-   - Library quirks
-   - API limitations
-   - Version-specific fixes
-
-4. **Project-Specific Patterns**
-   - Codebase conventions discovered
-   - Architecture decisions made
-   - Integration patterns
-
-## Output Format
-
-Create a skill file at `~/.claude/skills/learned/[pattern-name].md`:
-
-```markdown
-# [Descriptive Pattern Name]
-
-**Extracted:** [Date]
-**Context:** [Brief description of when this applies]
-
-## Problem
-[What problem this solves - be specific]
-
-## Solution
-[The pattern/technique/workaround]
-
-## Example
-[Code example if applicable]
-
-## When to Use
-[Trigger conditions - what should activate this skill]
-```
-
-## Process
-
-1. Review the session for extractable patterns
-2. Identify the most valuable/reusable insight
-3. Draft the skill file
-4. Ask user to confirm before saving
-5. Save to `~/.claude/skills/learned/`
-
-## Notes
-
-- Don't extract trivial fixes (typos, simple syntax errors)
-- Don't extract one-time issues (specific API outages, etc.)
-- Focus on patterns that will save time in future sessions
-- Keep skills focused - one pattern per skill

.claude/commands/orchestrate.md

@@ -1,172 +0,0 @@
-# Orchestrate Command
-
-Sequential agent workflow for complex tasks.
-
-## Usage
-
-`/orchestrate [workflow-type] [task-description]`
-
-## Workflow Types
-
-### feature
-Full feature implementation workflow:
-```
-planner -> tdd-guide -> code-reviewer -> security-reviewer
-```
-
-### bugfix
-Bug investigation and fix workflow:
-```
-explorer -> tdd-guide -> code-reviewer
-```
-
-### refactor
-Safe refactoring workflow:
-```
-architect -> code-reviewer -> tdd-guide
-```
-
-### security
-Security-focused review:
-```
-security-reviewer -> code-reviewer -> architect
-```
-
-## Execution Pattern
-
-For each agent in the workflow:
-
-1. **Invoke agent** with context from previous agent
-2. **Collect output** as structured handoff document
-3. **Pass to next agent** in chain
-4. **Aggregate results** into final report
-
-## Handoff Document Format
-
-Between agents, create handoff document:
-
-```markdown
-## HANDOFF: [previous-agent] -> [next-agent]
-
-### Context
-[Summary of what was done]
-
-### Findings
-[Key discoveries or decisions]
-
-### Files Modified
-[List of files touched]
-
-### Open Questions
-[Unresolved items for next agent]
-
-### Recommendations
-[Suggested next steps]
-```
-
-## Example: Feature Workflow
-
-```
-/orchestrate feature "Add user authentication"
-```
-
-Executes:
-
-1. **Planner Agent**
-   - Analyzes requirements
-   - Creates implementation plan
-   - Identifies dependencies
-   - Output: `HANDOFF: planner -> tdd-guide`
-
-2. **TDD Guide Agent**
-   - Reads planner handoff
-   - Writes tests first
-   - Implements to pass tests
-   - Output: `HANDOFF: tdd-guide -> code-reviewer`
-
-3. **Code Reviewer Agent**
-   - Reviews implementation
-   - Checks for issues
-   - Suggests improvements
-   - Output: `HANDOFF: code-reviewer -> security-reviewer`
-
-4. **Security Reviewer Agent**
-   - Security audit
-   - Vulnerability check
-   - Final approval
-   - Output: Final Report
-
-## Final Report Format
-
-```
-ORCHESTRATION REPORT
-====================
-Workflow: feature
-Task: Add user authentication
-Agents: planner -> tdd-guide -> code-reviewer -> security-reviewer
-
-SUMMARY
--------
-[One paragraph summary]
-
-AGENT OUTPUTS
--------------
-Planner: [summary]
-TDD Guide: [summary]
-Code Reviewer: [summary]
-Security Reviewer: [summary]
-
-FILES CHANGED
--------------
-[List all files modified]
-
-TEST RESULTS
-------------
-[Test pass/fail summary]
-
-SECURITY STATUS
----------------
-[Security findings]
-
-RECOMMENDATION
---------------
-[SHIP / NEEDS WORK / BLOCKED]
-```
-
-## Parallel Execution
-
-For independent checks, run agents in parallel:
-
-```markdown
-### Parallel Phase
-Run simultaneously:
-- code-reviewer (quality)
-- security-reviewer (security)
-- architect (design)
-
-### Merge Results
-Combine outputs into single report
-```
-
-## Arguments
-
-$ARGUMENTS:
-- `feature <description>` - Full feature workflow
-- `bugfix <description>` - Bug fix workflow
-- `refactor <description>` - Refactoring workflow
-- `security <description>` - Security review workflow
-- `custom <agents> <description>` - Custom agent sequence
-
-## Custom Workflow Example
-
-```
-/orchestrate custom "architect,tdd-guide,code-reviewer" "Redesign caching layer"
-```
-
-## Tips
-
-1. **Start with planner** for complex features
-2. **Always include code-reviewer** before merge
-3. **Use security-reviewer** for auth/payment/PII
-4. **Keep handoffs concise** - focus on what next agent needs
-5. **Run verification** between agents if needed

.claude/commands/plan.md

@@ -1,113 +0,0 @@
----
-description: Restate requirements, assess risks, and create step-by-step implementation plan. WAIT for user CONFIRM before touching any code.
----
-
-# Plan Command
-
-This command invokes the **planner** agent to create a comprehensive implementation plan before writing any code.
-
-## What This Command Does
-
-1. **Restate Requirements** - Clarify what needs to be built
-2. **Identify Risks** - Surface potential issues and blockers
-3. **Create Step Plan** - Break down implementation into phases
-4. **Wait for Confirmation** - MUST receive user approval before proceeding
-
-## When to Use
-
-Use `/plan` when:
-- Starting a new feature
-- Making significant architectural changes
-- Working on complex refactoring
-- Multiple files/components will be affected
-- Requirements are unclear or ambiguous
-
-## How It Works
-
-The planner agent will:
-
-1. **Analyze the request** and restate requirements in clear terms
-2. **Break down into phases** with specific, actionable steps
-3. **Identify dependencies** between components
-4. **Assess risks** and potential blockers
-5. **Estimate complexity** (High/Medium/Low)
-6. **Present the plan** and WAIT for your explicit confirmation
-
-## Example Usage
-
-```
-User: /plan I need to add real-time notifications when markets resolve
-
-Agent (planner):
-# Implementation Plan: Real-Time Market Resolution Notifications
-
-## Requirements Restatement
-- Send notifications to users when markets they're watching resolve
-- Support multiple notification channels (in-app, email, webhook)
-- Ensure notifications are delivered reliably
-- Include market outcome and user's position result
-
-## Implementation Phases
-
-### Phase 1: Database Schema
-- Add notifications table with columns: id, user_id, market_id, type, status, created_at
-- Add user_notification_preferences table for channel preferences
-- Create indexes on user_id and market_id for performance
-
-### Phase 2: Notification Service
-- Create notification service in lib/notifications.ts
-- Implement notification queue using BullMQ/Redis
-- Add retry logic for failed deliveries
-- Create notification templates
-
-### Phase 3: Integration Points
-- Hook into market resolution logic (when status changes to "resolved")
-- Query all users with positions in market
-- Enqueue notifications for each user
-
-### Phase 4: Frontend Components
-- Create NotificationBell component in header
-- Add NotificationList modal
-- Implement real-time updates via Supabase subscriptions
-- Add notification preferences page
-
-## Dependencies
-- Redis (for queue)
-- Email service (SendGrid/Resend)
-- Supabase real-time subscriptions
-
-## Risks
-- HIGH: Email deliverability (SPF/DKIM required)
-- MEDIUM: Performance with 1000+ users per market
-- MEDIUM: Notification spam if markets resolve frequently
-- LOW: Real-time subscription overhead
-
-## Estimated Complexity: MEDIUM
-- Backend: 4-6 hours
-- Frontend: 3-4 hours
-- Testing: 2-3 hours
-- Total: 9-13 hours
-
-**WAITING FOR CONFIRMATION**: Proceed with this plan? (yes/no/modify)
-```
-
-## Important Notes
-
-**CRITICAL**: The planner agent will **NOT** write any code until you explicitly confirm the plan with "yes" or "proceed" or similar affirmative response.
-
-If you want changes, respond with:
-- "modify: [your changes]"
-- "different approach: [alternative]"
-- "skip phase 2 and do phase 3 first"
-
-## Integration with Other Commands
-
-After planning:
-- Use `/tdd` to implement with test-driven development
-- Use `/build-and-fix` if build errors occur
-- Use `/code-review` to review completed implementation
-
-## Related Agents
-
-This command invokes the `planner` agent located at:
-`~/.claude/agents/planner.md`

.claude/commands/refactor-clean.md

@@ -1,28 +0,0 @@
-# Refactor Clean
-
-Safely identify and remove dead code with test verification:
-
-1. Run dead code analysis tools:
-   - knip: Find unused exports and files
-   - depcheck: Find unused dependencies
-   - ts-prune: Find unused TypeScript exports
-
-2. Generate comprehensive report in .reports/dead-code-analysis.md
-
-3. Categorize findings by severity:
-   - SAFE: Test files, unused utilities
-   - CAUTION: API routes, components
-   - DANGER: Config files, main entry points
-
-4. Propose safe deletions only
-
-5. Before each deletion:
-   - Run full test suite
-   - Verify tests pass
-   - Apply change
-   - Re-run tests
-   - Rollback if tests fail
-
-6. Show summary of cleaned items
-
-Never delete code without running tests first!

.claude/commands/setup-pm.md

@@ -1,80 +0,0 @@
----
-description: Configure your preferred package manager (npm/pnpm/yarn/bun)
-disable-model-invocation: true
----
-
-# Package Manager Setup
-
-Configure your preferred package manager for this project or globally.
-
-## Usage
-
-```bash
-# Detect current package manager
-node scripts/setup-package-manager.js --detect
-
-# Set global preference
-node scripts/setup-package-manager.js --global pnpm
-
-# Set project preference
-node scripts/setup-package-manager.js --project bun
-
-# List available package managers
-node scripts/setup-package-manager.js --list
-```
-
-## Detection Priority
-
-When determining which package manager to use, the following order is checked:
-
-1. **Environment variable**: `CLAUDE_PACKAGE_MANAGER`
-2. **Project config**: `.claude/package-manager.json`
-3. **package.json**: `packageManager` field
-4. **Lock file**: Presence of package-lock.json, yarn.lock, pnpm-lock.yaml, or bun.lockb
-5. **Global config**: `~/.claude/package-manager.json`
-6. **Fallback**: First available package manager (pnpm > bun > yarn > npm)
-
-## Configuration Files
-
-### Global Configuration
-```json
-// ~/.claude/package-manager.json
-{
-  "packageManager": "pnpm"
-}
-```
-
-### Project Configuration
-```json
-// .claude/package-manager.json
-{
-  "packageManager": "bun"
-}
-```
-
-### package.json
-```json
-{
-  "packageManager": "pnpm@8.6.0"
-}
-```
-
-## Environment Variable
-
-Set `CLAUDE_PACKAGE_MANAGER` to override all other detection methods:
-
-```bash
-# Windows (PowerShell)
-$env:CLAUDE_PACKAGE_MANAGER = "pnpm"
-
-# macOS/Linux
-export CLAUDE_PACKAGE_MANAGER=pnpm
-```
-
-## Run the Detection
-
-To see current package manager detection results, run:
-
-```bash
-node scripts/setup-package-manager.js --detect
-```

.claude/commands/tdd.md

@@ -1,326 +0,0 @@
----
-description: Enforce test-driven development workflow. Scaffold interfaces, generate tests FIRST, then implement minimal code to pass. Ensure 80%+ coverage.
----
-
-# TDD Command
-
-This command invokes the **tdd-guide** agent to enforce test-driven development methodology.
-
-## What This Command Does
-
-1. **Scaffold Interfaces** - Define types/interfaces first
-2. **Generate Tests First** - Write failing tests (RED)
-3. **Implement Minimal Code** - Write just enough to pass (GREEN)
-4. **Refactor** - Improve code while keeping tests green (REFACTOR)
-5. **Verify Coverage** - Ensure 80%+ test coverage
-
-## When to Use
-
-Use `/tdd` when:
-- Implementing new features
-- Adding new functions/components
-- Fixing bugs (write test that reproduces bug first)
-- Refactoring existing code
-- Building critical business logic
-
-## How It Works
-
-The tdd-guide agent will:
-
-1. **Define interfaces** for inputs/outputs
-2. **Write tests that will FAIL** (because code doesn't exist yet)
-3. **Run tests** and verify they fail for the right reason
-4. **Write minimal implementation** to make tests pass
-5. **Run tests** and verify they pass
-6. **Refactor** code while keeping tests green
-7. **Check coverage** and add more tests if below 80%
-
-## TDD Cycle
-
-```
-RED → GREEN → REFACTOR → REPEAT
-
-RED:      Write a failing test
-GREEN:    Write minimal code to pass
-REFACTOR: Improve code, keep tests passing
-REPEAT:   Next feature/scenario
-```
-
-## Example Usage
-
-```
-User: /tdd I need a function to calculate market liquidity score
-
-Agent (tdd-guide):
-# TDD Session: Market Liquidity Score Calculator
-
-## Step 1: Define Interface (SCAFFOLD)
-
-```typescript
-// lib/liquidity.ts
-export interface MarketData {
-  totalVolume: number
-  bidAskSpread: number
-  activeTraders: number
-  lastTradeTime: Date
-}
-
-export function calculateLiquidityScore(market: MarketData): number {
-  // TODO: Implementation
-  throw new Error('Not implemented')
-}
-```
-
-## Step 2: Write Failing Test (RED)
-
-```typescript
-// lib/liquidity.test.ts
-import { calculateLiquidityScore } from './liquidity'
-
-describe('calculateLiquidityScore', () => {
-  it('should return high score for liquid market', () => {
-    const market = {
-      totalVolume: 100000,
-      bidAskSpread: 0.01,
-      activeTraders: 500,
-      lastTradeTime: new Date()
-    }
-
-    const score = calculateLiquidityScore(market)
-
-    expect(score).toBeGreaterThan(80)
-    expect(score).toBeLessThanOrEqual(100)
-  })
-
-  it('should return low score for illiquid market', () => {
-    const market = {
-      totalVolume: 100,
-      bidAskSpread: 0.5,
-      activeTraders: 2,
-      lastTradeTime: new Date(Date.now() - 86400000) // 1 day ago
-    }
-
-    const score = calculateLiquidityScore(market)
-
-    expect(score).toBeLessThan(30)
-    expect(score).toBeGreaterThanOrEqual(0)
-  })
-
-  it('should handle edge case: zero volume', () => {
-    const market = {
-      totalVolume: 0,
-      bidAskSpread: 0,
-      activeTraders: 0,
-      lastTradeTime: new Date()
-    }
-
-    const score = calculateLiquidityScore(market)
-
-    expect(score).toBe(0)
-  })
-})
-```
-
-## Step 3: Run Tests - Verify FAIL
-
-```bash
-npm test lib/liquidity.test.ts
-
-FAIL lib/liquidity.test.ts
-  ✕ should return high score for liquid market (2 ms)
-    Error: Not implemented
-
-1 test failed, 0 passed
-```
-
-✅ Tests fail as expected. Ready to implement.
-
-## Step 4: Implement Minimal Code (GREEN)
-
-```typescript
-// lib/liquidity.ts
-export function calculateLiquidityScore(market: MarketData): number {
-  // Handle zero volume edge case
-  if (market.totalVolume === 0) {
-    return 0
-  }
-
-  // Calculate component scores (0-100 scale)
-  const volumeScore = Math.min(market.totalVolume / 1000, 100)
-  const spreadScore = Math.max(100 - (market.bidAskSpread * 1000), 0)
-  const traderScore = Math.min(market.activeTraders / 10, 100)
-
-  // Recent activity bonus
-  const hoursSinceLastTrade = (Date.now() - market.lastTradeTime.getTime()) / (1000 * 60 * 60)
-  const recencyScore = Math.max(100 - (hoursSinceLastTrade * 10), 0)
-
-  // Weighted average
-  const score = (
-    volumeScore * 0.4 +
-    spreadScore * 0.3 +
-    traderScore * 0.2 +
-    recencyScore * 0.1
-  )
-
-  return Math.min(Math.max(score, 0), 100) // Clamp to 0-100
-}
-```
-
-## Step 5: Run Tests - Verify PASS
-
-```bash
-npm test lib/liquidity.test.ts
-
-PASS lib/liquidity.test.ts
-  ✓ should return high score for liquid market (3 ms)
-  ✓ should return low score for illiquid market (2 ms)
-  ✓ should handle edge case: zero volume (1 ms)
-
-3 tests passed
-```
-
-✅ All tests passing!
-
-## Step 6: Refactor (IMPROVE)
-
-```typescript
-// lib/liquidity.ts - Refactored with constants and better readability
-const WEIGHTS = {
-  VOLUME: 0.4,
-  SPREAD: 0.3,
-  TRADERS: 0.2,
-  RECENCY: 0.1,
-} as const
-
-const SCALE_FACTORS = {
-  VOLUME: 1000,
-  SPREAD: 1000,
-  TRADERS: 10,
-  RECENCY_PENALTY: 10,
-} as const
-
-function clamp(value: number, min: number, max: number): number {
-  return Math.min(Math.max(value, min), max)
-}
-
-export function calculateLiquidityScore(market: MarketData): number {
-  if (market.totalVolume === 0) return 0
-
-  const volumeScore = Math.min(market.totalVolume / SCALE_FACTORS.VOLUME, 100)
-  const spreadScore = clamp(100 - (market.bidAskSpread * SCALE_FACTORS.SPREAD), 0, 100)
-  const traderScore = Math.min(market.activeTraders / SCALE_FACTORS.TRADERS, 100)
-
-  const hoursSinceLastTrade = (Date.now() - market.lastTradeTime.getTime()) / (1000 * 60 * 60)
-  const recencyScore = clamp(100 - (hoursSinceLastTrade * SCALE_FACTORS.RECENCY_PENALTY), 0, 100)
-
-  const weightedScore =
-    volumeScore * WEIGHTS.VOLUME +
-    spreadScore * WEIGHTS.SPREAD +
-    traderScore * WEIGHTS.TRADERS +
-    recencyScore * WEIGHTS.RECENCY
-
-  return clamp(weightedScore, 0, 100)
-}
-```
-
-## Step 7: Verify Tests Still Pass
-
-```bash
-npm test lib/liquidity.test.ts
-
-PASS lib/liquidity.test.ts
-  ✓ should return high score for liquid market (3 ms)
-  ✓ should return low score for illiquid market (2 ms)
-  ✓ should handle edge case: zero volume (1 ms)
-
-3 tests passed
-```
-
-✅ Refactoring complete, tests still passing!
-
-## Step 8: Check Coverage
-
-```bash
-npm test -- --coverage lib/liquidity.test.ts
-
-File           | % Stmts | % Branch | % Funcs | % Lines
----------------|---------|----------|---------|--------
-liquidity.ts   |   100   |   100    |   100   |   100
-
-Coverage: 100% ✅ (Target: 80%)
-```
-
-✅ TDD session complete!
-```
-
-## TDD Best Practices
-
-**DO:**
-- ✅ Write the test FIRST, before any implementation
-- ✅ Run tests and verify they FAIL before implementing
-- ✅ Write minimal code to make tests pass
-- ✅ Refactor only after tests are green
-- ✅ Add edge cases and error scenarios
-- ✅ Aim for 80%+ coverage (100% for critical code)
-
-**DON'T:**
-- ❌ Write implementation before tests
-- ❌ Skip running tests after each change
-- ❌ Write too much code at once
-- ❌ Ignore failing tests
-- ❌ Test implementation details (test behavior)
-- ❌ Mock everything (prefer integration tests)
-
-## Test Types to Include
-
-**Unit Tests** (Function-level):
-- Happy path scenarios
-- Edge cases (empty, null, max values)
-- Error conditions
-- Boundary values
-
-**Integration Tests** (Component-level):
-- API endpoints
-- Database operations
-- External service calls
-- React components with hooks
-
-**E2E Tests** (use `/e2e` command):
-- Critical user flows
-- Multi-step processes
-- Full stack integration
-
-## Coverage Requirements
-
-- **80% minimum** for all code
-- **100% required** for:
-  - Financial calculations
-  - Authentication logic
-  - Security-critical code
-  - Core business logic
-
-## Important Notes
-
-**MANDATORY**: Tests must be written BEFORE implementation. The TDD cycle is:
-
-1. **RED** - Write failing test
-2. **GREEN** - Implement to pass
-3. **REFACTOR** - Improve code
-
-Never skip the RED phase. Never write code before tests.
-
-## Integration with Other Commands
-
-- Use `/plan` first to understand what to build
-- Use `/tdd` to implement with tests
-- Use `/build-and-fix` if build errors occur
-- Use `/code-review` to review implementation
-- Use `/test-coverage` to verify coverage
-
-## Related Agents
-
-This command invokes the `tdd-guide` agent located at:
-`~/.claude/agents/tdd-guide.md`
-
-And can reference the `tdd-workflow` skill at:
-`~/.claude/skills/tdd-workflow/`

.claude/commands/test-coverage.md

@@ -1,27 +0,0 @@
-# Test Coverage
-
-Analyze test coverage and generate missing tests:
-
-1. Run tests with coverage: npm test --coverage or pnpm test --coverage
-
-2. Analyze coverage report (coverage/coverage-summary.json)
-
-3. Identify files below 80% coverage threshold
-
-4. For each under-covered file:
-   - Analyze untested code paths
-   - Generate unit tests for functions
-   - Generate integration tests for APIs
-   - Generate E2E tests for critical flows
-
-5. Verify new tests pass
-
-6. Show before/after coverage metrics
-
-7. Ensure project reaches 80%+ overall coverage
-
-Focus on:
-- Happy path scenarios
-- Error handling
-- Edge cases (null, undefined, empty)
-- Boundary conditions

.claude/commands/update-codemaps.md

@@ -1,17 +0,0 @@
-# Update Codemaps
-
-Analyze the codebase structure and update architecture documentation:
-
-1. Scan all source files for imports, exports, and dependencies
-2. Generate token-lean codemaps in the following format:
-   - codemaps/architecture.md - Overall architecture
-   - codemaps/backend.md - Backend structure  
-   - codemaps/frontend.md - Frontend structure
-   - codemaps/data.md - Data models and schemas
-
-3. Calculate diff percentage from previous version
-4. If changes > 30%, request user approval before updating
-5. Add freshness timestamp to each codemap
-6. Save reports to .reports/codemap-diff.txt
-
-Use TypeScript/Node.js for analysis. Focus on high-level structure, not implementation details.

.claude/commands/update-docs.md

@@ -1,31 +0,0 @@
-# Update Documentation
-
-Sync documentation from source-of-truth:
-
-1. Read package.json scripts section
-   - Generate scripts reference table
-   - Include descriptions from comments
-
-2. Read .env.example
-   - Extract all environment variables
-   - Document purpose and format
-
-3. Generate docs/CONTRIB.md with:
-   - Development workflow
-   - Available scripts
-   - Environment setup
-   - Testing procedures
-
-4. Generate docs/RUNBOOK.md with:
-   - Deployment procedures
-   - Monitoring and alerts
-   - Common issues and fixes
-   - Rollback procedures
-
-5. Identify obsolete documentation:
-   - Find docs not modified in 90+ days
-   - List for manual review
-
-6. Show diff summary
-
-Single source of truth: package.json and .env.example

.claude/commands/verify.md

@@ -1,59 +0,0 @@
-# Verification Command
-
-Run comprehensive verification on current codebase state.
-
-## Instructions
-
-Execute verification in this exact order:
-
-1. **Build Check**
-   - Run the build command for this project
-   - If it fails, report errors and STOP
-
-2. **Type Check**
-   - Run TypeScript/type checker
-   - Report all errors with file:line
-
-3. **Lint Check**
-   - Run linter
-   - Report warnings and errors
-
-4. **Test Suite**
-   - Run all tests
-   - Report pass/fail count
-   - Report coverage percentage
-
-5. **Console.log Audit**
-   - Search for console.log in source files
-   - Report locations
-
-6. **Git Status**
-   - Show uncommitted changes
-   - Show files modified since last commit
-
-## Output
-
-Produce a concise verification report:
-
-```
-VERIFICATION: [PASS/FAIL]
-
-Build:    [OK/FAIL]
-Types:    [OK/X errors]
-Lint:     [OK/X issues]
-Tests:    [X/Y passed, Z% coverage]
-Secrets:  [OK/X found]
-Logs:     [OK/X console.logs]
-
-Ready for PR: [YES/NO]
-```
-
-If any critical issues, list them with fix suggestions.
-
-## Arguments
-
-$ARGUMENTS can be:
-- `quick` - Only build + types
-- `full` - All checks (default)
-- `pre-commit` - Checks relevant for commits
-- `pre-pr` - Full checks plus security scan

.claude/hooks/hooks.json

@@ -1,157 +0,0 @@
-{
-  "$schema": "https://json.schemastore.org/claude-code-settings.json",
-  "hooks": {
-    "PreToolUse": [
-      {
-        "matcher": "tool == \"Bash\" && tool_input.command matches \"(npm run dev|pnpm( run)? dev|yarn dev|bun run dev)\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"console.error('[Hook] BLOCKED: Dev server must run in tmux for log access');console.error('[Hook] Use: tmux new-session -d -s dev \\\"npm run dev\\\"');console.error('[Hook] Then: tmux attach -t dev');process.exit(1)\""
-          }
-        ],
-        "description": "Block dev servers outside tmux - ensures you can access logs"
-      },
-      {
-        "matcher": "tool == \"Bash\" && tool_input.command matches \"(npm (install|test)|pnpm (install|test)|yarn (install|test)?|bun (install|test)|cargo build|make|docker|pytest|vitest|playwright)\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"if(!process.env.TMUX){console.error('[Hook] Consider running in tmux for session persistence');console.error('[Hook] tmux new -s dev  |  tmux attach -t dev')}\""
-          }
-        ],
-        "description": "Reminder to use tmux for long-running commands"
-      },
-      {
-        "matcher": "tool == \"Bash\" && tool_input.command matches \"git push\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"console.error('[Hook] Review changes before push...');console.error('[Hook] Continuing with push (remove this hook to add interactive review)')\""
-          }
-        ],
-        "description": "Reminder before git push to review changes"
-      },
-      {
-        "matcher": "tool == \"Write\" && tool_input.file_path matches \"\\\\.(md|txt)$\" && !(tool_input.file_path matches \"README\\\\.md|CLAUDE\\\\.md|AGENTS\\\\.md|CONTRIBUTING\\\\.md\")",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"const fs=require('fs');let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const i=JSON.parse(d);const p=i.tool_input?.file_path||'';if(/\\.(md|txt)$/.test(p)&&!/(README|CLAUDE|AGENTS|CONTRIBUTING)\\.md$/.test(p)){console.error('[Hook] BLOCKED: Unnecessary documentation file creation');console.error('[Hook] File: '+p);console.error('[Hook] Use README.md for documentation instead');process.exit(1)}console.log(d)})\""
-          }
-        ],
-        "description": "Block creation of random .md files - keeps docs consolidated"
-      },
-      {
-        "matcher": "tool == \"Edit\" || tool == \"Write\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node \"${CLAUDE_PLUGIN_ROOT}/scripts/hooks/suggest-compact.js\""
-          }
-        ],
-        "description": "Suggest manual compaction at logical intervals"
-      }
-    ],
-    "PreCompact": [
-      {
-        "matcher": "*",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node \"${CLAUDE_PLUGIN_ROOT}/scripts/hooks/pre-compact.js\""
-          }
-        ],
-        "description": "Save state before context compaction"
-      }
-    ],
-    "SessionStart": [
-      {
-        "matcher": "*",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node \"${CLAUDE_PLUGIN_ROOT}/scripts/hooks/session-start.js\""
-          }
-        ],
-        "description": "Load previous context and detect package manager on new session"
-      }
-    ],
-    "PostToolUse": [
-      {
-        "matcher": "tool == \"Bash\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const i=JSON.parse(d);const cmd=i.tool_input?.command||'';if(/gh pr create/.test(cmd)){const out=i.tool_output?.output||'';const m=out.match(/https:\\/\\/github.com\\/[^/]+\\/[^/]+\\/pull\\/\\d+/);if(m){console.error('[Hook] PR created: '+m[0]);const repo=m[0].replace(/https:\\/\\/github.com\\/([^/]+\\/[^/]+)\\/pull\\/\\d+/,'$1');const pr=m[0].replace(/.*\\/pull\\/(\\d+)/,'$1');console.error('[Hook] To review: gh pr review '+pr+' --repo '+repo)}}console.log(d)})\""
-          }
-        ],
-        "description": "Log PR URL and provide review command after PR creation"
-      },
-      {
-        "matcher": "tool == \"Edit\" && tool_input.file_path matches \"\\\\.(ts|tsx|js|jsx)$\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"const{execSync}=require('child_process');const fs=require('fs');let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const i=JSON.parse(d);const p=i.tool_input?.file_path;if(p&&fs.existsSync(p)){try{execSync('npx prettier --write \"'+p+'\"',{stdio:['pipe','pipe','pipe']})}catch(e){}}console.log(d)})\""
-          }
-        ],
-        "description": "Auto-format JS/TS files with Prettier after edits"
-      },
-      {
-        "matcher": "tool == \"Edit\" && tool_input.file_path matches \"\\\\.(ts|tsx)$\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"const{execSync}=require('child_process');const fs=require('fs');const path=require('path');let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const i=JSON.parse(d);const p=i.tool_input?.file_path;if(p&&fs.existsSync(p)){let dir=path.dirname(p);while(dir!==path.dirname(dir)&&!fs.existsSync(path.join(dir,'tsconfig.json'))){dir=path.dirname(dir)}if(fs.existsSync(path.join(dir,'tsconfig.json'))){try{const r=execSync('npx tsc --noEmit --pretty false 2>&1',{cwd:dir,encoding:'utf8',stdio:['pipe','pipe','pipe']});const lines=r.split('\\n').filter(l=>l.includes(p)).slice(0,10);if(lines.length)console.error(lines.join('\\n'))}catch(e){const lines=(e.stdout||'').split('\\n').filter(l=>l.includes(p)).slice(0,10);if(lines.length)console.error(lines.join('\\n'))}}}console.log(d)})\""
-          }
-        ],
-        "description": "TypeScript check after editing .ts/.tsx files"
-      },
-      {
-        "matcher": "tool == \"Edit\" && tool_input.file_path matches \"\\\\.(ts|tsx|js|jsx)$\"",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"const fs=require('fs');let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{const i=JSON.parse(d);const p=i.tool_input?.file_path;if(p&&fs.existsSync(p)){const c=fs.readFileSync(p,'utf8');const lines=c.split('\\n');const matches=[];lines.forEach((l,idx)=>{if(/console\\.log/.test(l))matches.push((idx+1)+': '+l.trim())});if(matches.length){console.error('[Hook] WARNING: console.log found in '+p);matches.slice(0,5).forEach(m=>console.error(m));console.error('[Hook] Remove console.log before committing')}}console.log(d)})\""
-          }
-        ],
-        "description": "Warn about console.log statements after edits"
-      }
-    ],
-    "Stop": [
-      {
-        "matcher": "*",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node -e \"const{execSync}=require('child_process');const fs=require('fs');let d='';process.stdin.on('data',c=>d+=c);process.stdin.on('end',()=>{try{execSync('git rev-parse --git-dir',{stdio:'pipe'})}catch{console.log(d);process.exit(0)}try{const files=execSync('git diff --name-only HEAD',{encoding:'utf8',stdio:['pipe','pipe','pipe']}).split('\\n').filter(f=>/\\.(ts|tsx|js|jsx)$/.test(f)&&fs.existsSync(f));let hasConsole=false;for(const f of files){if(fs.readFileSync(f,'utf8').includes('console.log')){console.error('[Hook] WARNING: console.log found in '+f);hasConsole=true}}if(hasConsole)console.error('[Hook] Remove console.log statements before committing')}catch(e){}console.log(d)})\""
-          }
-        ],
-        "description": "Check for console.log in modified files after each response"
-      }
-    ],
-    "SessionEnd": [
-      {
-        "matcher": "*",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node \"${CLAUDE_PLUGIN_ROOT}/scripts/hooks/session-end.js\""
-          }
-        ],
-        "description": "Persist session state on end"
-      },
-      {
-        "matcher": "*",
-        "hooks": [
-          {
-            "type": "command",
-            "command": "node \"${CLAUDE_PLUGIN_ROOT}/scripts/hooks/evaluate-session.js\""
-          }
-        ],
-        "description": "Evaluate session for extractable patterns"
-      }
-    ]
-  }
-}

.claude/hooks/memory-persistence/pre-compact.sh

@@ -1,36 +0,0 @@
-#!/bin/bash
-# PreCompact Hook - Save state before context compaction
-#
-# Runs before Claude compacts context, giving you a chance to
-# preserve important state that might get lost in summarization.
-#
-# Hook config (in ~/.claude/settings.json):
-# {
-#   "hooks": {
-#     "PreCompact": [{
-#       "matcher": "*",
-#       "hooks": [{
-#         "type": "command",
-#         "command": "~/.claude/hooks/memory-persistence/pre-compact.sh"
-#       }]
-#     }]
-#   }
-# }
-
-SESSIONS_DIR="${HOME}/.claude/sessions"
-COMPACTION_LOG="${SESSIONS_DIR}/compaction-log.txt"
-
-mkdir -p "$SESSIONS_DIR"
-
-# Log compaction event with timestamp
-echo "[$(date '+%Y-%m-%d %H:%M:%S')] Context compaction triggered" >> "$COMPACTION_LOG"
-
-# If there's an active session file, note the compaction
-ACTIVE_SESSION=$(ls -t "$SESSIONS_DIR"/*.tmp 2>/dev/null | head -1)
-if [ -n "$ACTIVE_SESSION" ]; then
-  echo "" >> "$ACTIVE_SESSION"
-  echo "---" >> "$ACTIVE_SESSION"
-  echo "**[Compaction occurred at $(date '+%H:%M')]** - Context was summarized" >> "$ACTIVE_SESSION"
-fi
-
-echo "[PreCompact] State saved before compaction" >&2

.claude/hooks/memory-persistence/session-end.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-# Stop Hook (Session End) - Persist learnings when session ends
-#
-# Runs when Claude session ends. Creates/updates session log file
-# with timestamp for continuity tracking.
-#
-# Hook config (in ~/.claude/settings.json):
-# {
-#   "hooks": {
-#     "Stop": [{
-#       "matcher": "*",
-#       "hooks": [{
-#         "type": "command",
-#         "command": "~/.claude/hooks/memory-persistence/session-end.sh"
-#       }]
-#     }]
-#   }
-# }
-
-SESSIONS_DIR="${HOME}/.claude/sessions"
-TODAY=$(date '+%Y-%m-%d')
-SESSION_FILE="${SESSIONS_DIR}/${TODAY}-session.tmp"
-
-mkdir -p "$SESSIONS_DIR"
-
-# If session file exists for today, update the end time
-if [ -f "$SESSION_FILE" ]; then
-  # Update Last Updated timestamp
-  sed -i '' "s/\*\*Last Updated:\*\*.*/\*\*Last Updated:\*\* $(date '+%H:%M')/" "$SESSION_FILE" 2>/dev/null || \
-  sed -i "s/\*\*Last Updated:\*\*.*/\*\*Last Updated:\*\* $(date '+%H:%M')/" "$SESSION_FILE" 2>/dev/null
-  echo "[SessionEnd] Updated session file: $SESSION_FILE" >&2
-else
-  # Create new session file with template
-  cat > "$SESSION_FILE" << EOF
-# Session: $(date '+%Y-%m-%d')
-**Date:** $TODAY
-**Started:** $(date '+%H:%M')
-**Last Updated:** $(date '+%H:%M')
-
----
-
-## Current State
-
-[Session context goes here]
-
-### Completed
-- [ ]
-
-### In Progress
-- [ ]
-
-### Notes for Next Session
--
-
-### Context to Load
-\`\`\`
-[relevant files]
-\`\`\`
-EOF
-  echo "[SessionEnd] Created session file: $SESSION_FILE" >&2
-fi

.claude/hooks/memory-persistence/session-start.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-# SessionStart Hook - Load previous context on new session
-#
-# Runs when a new Claude session starts. Checks for recent session
-# files and notifies Claude of available context to load.
-#
-# Hook config (in ~/.claude/settings.json):
-# {
-#   "hooks": {
-#     "SessionStart": [{
-#       "matcher": "*",
-#       "hooks": [{
-#         "type": "command",
-#         "command": "~/.claude/hooks/memory-persistence/session-start.sh"
-#       }]
-#     }]
-#   }
-# }
-
-SESSIONS_DIR="${HOME}/.claude/sessions"
-LEARNED_DIR="${HOME}/.claude/skills/learned"
-
-# Check for recent session files (last 7 days)
-recent_sessions=$(find "$SESSIONS_DIR" -name "*.tmp" -mtime -7 2>/dev/null | wc -l | tr -d ' ')
-
-if [ "$recent_sessions" -gt 0 ]; then
-  latest=$(ls -t "$SESSIONS_DIR"/*.tmp 2>/dev/null | head -1)
-  echo "[SessionStart] Found $recent_sessions recent session(s)" >&2
-  echo "[SessionStart] Latest: $latest" >&2
-fi
-
-# Check for learned skills
-learned_count=$(find "$LEARNED_DIR" -name "*.md" 2>/dev/null | wc -l | tr -d ' ')
-
-if [ "$learned_count" -gt 0 ]; then
-  echo "[SessionStart] $learned_count learned skill(s) available in $LEARNED_DIR" >&2
-fi

.claude/settings.local.json

@@ -107,7 +107,11 @@
       "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && DB_HOST=192.168.68.31 DB_PORT=5432 DB_NAME=docmaster DB_USER=docmaster DB_PASSWORD=0412220 python -c \"\"\nimport os\nimport psycopg2\n\nhost = os.getenv\\(''DB_HOST''\\)\nport = os.getenv\\(''DB_PORT''\\)\ndbname = os.getenv\\(''DB_NAME''\\)\nuser = os.getenv\\(''DB_USER''\\)\npassword = os.getenv\\(''DB_PASSWORD''\\)\n\nconn = psycopg2.connect\\(host=host, port=port, dbname=dbname, user=user, password=password\\)\ncursor = conn.cursor\\(\\)\n\n# Get all model versions\ncursor.execute\\(''''''\n    SELECT version_id, version, name, status, is_active, metrics_mAP, document_count, model_path, created_at\n    FROM model_versions\n    ORDER BY created_at DESC\n''''''\\)\nprint\\(''Existing model versions:''\\)\nfor row in cursor.fetchall\\(\\):\n    print\\(f''  ID: {row[0][:8]}...''\\)\n    print\\(f''    Version: {row[1]}''\\)\n    print\\(f''    Name: {row[2]}''\\)\n    print\\(f''    Status: {row[3]}''\\)\n    print\\(f''    Active: {row[4]}''\\)\n    print\\(f''    mAP: {row[5]}''\\)\n    print\\(f''    Docs: {row[6]}''\\)\n    print\\(f''    Path: {row[7]}''\\)\n    print\\(f''    Created: {row[8]}''\\)\n    print\\(\\)\n\ncursor.close\\(\\)\nconn.close\\(\\)\n\"\"\")",
       "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && DB_HOST=192.168.68.31 DB_PORT=5432 DB_NAME=docmaster DB_USER=docmaster DB_PASSWORD=0412220 python -c \"\"\nimport os\nimport psycopg2\n\nhost = os.getenv\\(''DB_HOST''\\)\nport = os.getenv\\(''DB_PORT''\\)\ndbname = os.getenv\\(''DB_NAME''\\)\nuser = os.getenv\\(''DB_USER''\\)\npassword = os.getenv\\(''DB_PASSWORD''\\)\n\nconn = psycopg2.connect\\(host=host, port=port, dbname=dbname, user=user, password=password\\)\ncursor = conn.cursor\\(\\)\n\n# Get all model versions - use double quotes for case-sensitive column names\ncursor.execute\\(''''''\n    SELECT version_id, version, name, status, is_active, \\\\\"\"metrics_mAP\\\\\"\", document_count, model_path, created_at\n    FROM model_versions\n    ORDER BY created_at DESC\n''''''\\)\nprint\\(''Existing model versions:''\\)\nfor row in cursor.fetchall\\(\\):\n    print\\(f''  ID: {str\\(row[0]\\)[:8]}...''\\)\n    print\\(f''    Version: {row[1]}''\\)\n    print\\(f''    Name: {row[2]}''\\)\n    print\\(f''    Status: {row[3]}''\\)\n    print\\(f''    Active: {row[4]}''\\)\n    print\\(f''    mAP: {row[5]}''\\)\n    print\\(f''    Docs: {row[6]}''\\)\n    print\\(f''    Path: {row[7]}''\\)\n    print\\(f''    Created: {row[8]}''\\)\n    print\\(\\)\n\ncursor.close\\(\\)\nconn.close\\(\\)\n\"\"\")",
       "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python -m pytest tests/shared/fields/test_field_config.py -v 2>&1 | head -100\")",
-      "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python -m pytest tests/web/core/test_task_interface.py -v 2>&1 | head -60\")"
+      "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python -m pytest tests/web/core/test_task_interface.py -v 2>&1 | head -60\")",
+      "Skill(tdd)",
+      "Skill(tdd:*)",
+      "Bash(wsl bash -c \"source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-sm120 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python -m training.cli.train --model runs/train/invoice_fields/weights/best.pt --device 0 --epochs 100\")",
+      "Bash(git commit -m \"$\\(cat <<''EOF''\nfeat: add field-specific bbox expansion strategies for YOLO training\n\nImplement center-point based bbox scaling with directional compensation\nto capture field labels that typically appear above or to the left of\nfield values. This improves YOLO training data quality by including\ncontextual information around field values.\n\nKey changes:\n- Add shared.bbox module with ScaleStrategy dataclass and expand_bbox function\n- Define field-specific strategies \\(ocr_number, bankgiro, invoice_date, etc.\\)\n- Support manual_mode for minimal padding \\(no scaling\\)\n- Integrate expand_bbox into AnnotationGenerator\n- Add FIELD_TO_CLASS mapping for field_name to class_name lookup\n- Comprehensive tests with 100% coverage \\(45 tests\\)\n\nCo-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>\nEOF\n\\)\")"
     ],
     "deny": [],
     "ask": [],

.claude/skills/strategic-compact/suggest-compact.sh

@@ -1,52 +0,0 @@
-#!/bin/bash
-# Strategic Compact Suggester
-# Runs on PreToolUse or periodically to suggest manual compaction at logical intervals
-#
-# Why manual over auto-compact:
-# - Auto-compact happens at arbitrary points, often mid-task
-# - Strategic compacting preserves context through logical phases
-# - Compact after exploration, before execution
-# - Compact after completing a milestone, before starting next
-#
-# Hook config (in ~/.claude/settings.json):
-# {
-#   "hooks": {
-#     "PreToolUse": [{
-#       "matcher": "Edit|Write",
-#       "hooks": [{
-#         "type": "command",
-#         "command": "~/.claude/skills/strategic-compact/suggest-compact.sh"
-#       }]
-#     }]
-#   }
-# }
-#
-# Criteria for suggesting compact:
-# - Session has been running for extended period
-# - Large number of tool calls made
-# - Transitioning from research/exploration to implementation
-# - Plan has been finalized
-
-# Track tool call count (increment in a temp file)
-COUNTER_FILE="/tmp/claude-tool-count-$$"
-THRESHOLD=${COMPACT_THRESHOLD:-50}
-
-# Initialize or increment counter
-if [ -f "$COUNTER_FILE" ]; then
-  count=$(cat "$COUNTER_FILE")
-  count=$((count + 1))
-  echo "$count" > "$COUNTER_FILE"
-else
-  echo "1" > "$COUNTER_FILE"
-  count=1
-fi
-
-# Suggest compact after threshold tool calls
-if [ "$count" -eq "$THRESHOLD" ]; then
-  echo "[StrategicCompact] $THRESHOLD tool calls reached - consider /compact if transitioning phases" >&2
-fi
-
-# Suggest at regular intervals after threshold
-if [ "$count" -gt "$THRESHOLD" ] && [ $((count % 25)) -eq 0 ]; then
-  echo "[StrategicCompact] $count tool calls - good checkpoint for /compact if context is stale" >&2
-fi

.opencode/skills/coding-standards/SKILL.md

@@ -396,7 +396,7 @@ def extract_invoice_fields(
 ) -> InferenceResult:
     """Extract structured fields from Swedish invoice PDF.
 
-    Uses YOLOv11 for field detection and PaddleOCR for text extraction.
+    Uses YOLO26 for field detection and PaddleOCR for text extraction.
     Applies field-specific normalization and validation.
 
     Args:

AGENTS.md

@@ -1,179 +1,93 @@
-# AGENTS.md - Coding Guidelines for AI Agents
+# Invoice Master POC v2
 
-## Build / Test / Lint Commands
+Swedish Invoice Field Extraction System - YOLO26 + PaddleOCR 从瑞典 PDF 发票中提取结构化数据。
+
+## Tech Stack
+
+| Component | Technology |
+|-----------|------------|
+| Object Detection | YOLO26 (Ultralytics >= 8.4.0) |
+| OCR Engine | PaddleOCR v5 (PP-OCRv5) |
+| PDF Processing | PyMuPDF (fitz) |
+| Database | PostgreSQL + psycopg2 |
+| Web Framework | FastAPI + Uvicorn |
+| Deep Learning | PyTorch + CUDA 12.x |
+
+## WSL Environment (REQUIRED)
+
+**Prefix ALL commands with:**
 
-### Python Backend
 ```bash
-# Install packages (editable mode)
-pip install -e packages/shared
-pip install -e packages/training
-pip install -e packages/backend
-
-# Run all tests
-DB_PASSWORD=xxx pytest tests/ -q
-
-# Run single test file
-DB_PASSWORD=xxx pytest tests/path/to/test_file.py -v
-
-# Run with coverage
-DB_PASSWORD=xxx pytest tests/ --cov=packages --cov-report=term-missing
-
-# Format code
-black packages/ tests/
-ruff check packages/ tests/
-
-# Type checking
-mypy packages/
+wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-sm120 && <command>"
 ```
 
-### Frontend
-```bash
-cd frontend
+**NEVER run Python commands directly in Windows PowerShell/CMD.**
 
-# Install dependencies
-npm install
+## Project-Specific Rules
 
-# Development server
-npm run dev
+- Python 3.11+ with type hints
+- No print() in production - use logging
+- Run tests: `pytest --cov=src`
 
-# Build
-npm run build
+## Critical Rules
 
-# Run tests
-npm run test
+### Code Organization
 
-# Run single test
-npx vitest run src/path/to/file.test.ts
+- Many small files over few large files
+- High cohesion, low coupling
+- 200-400 lines typical, 800 max per file
+- Organize by feature/domain, not by type
 
-# Watch mode
-npm run test:watch
+### Code Style
 
-# Coverage
-npm run test:coverage
-```
+- No emojis in code, comments, or documentation
+- Immutability always - never mutate objects or arrays
+- No console.log in production code
+- Proper error handling with try/catch
+- Input validation with Zod or similar
 
-## Code Style Guidelines
+### Testing
 
-### Python
+- TDD: Write tests first
+- 80% minimum coverage
+- Unit tests for utilities
+- Integration tests for APIs
+- E2E tests for critical flows
 
-**Imports:**
-- Use absolute imports within packages: `from shared.pdf.extractor import PDFDocument`
-- Group imports: stdlib → third-party → local (separated by blank lines)
-- Use `from __future__ import annotations` for forward references when needed
+### Security
 
-**Type Hints:**
-- All functions must have type hints (enforced by mypy)
-- Use `| None` instead of `Optional[...]` (Python 3.10+)
-- Use `list[str]` instead of `List[str]` (Python 3.10+)
-
-**Naming:**
-- Classes: `PascalCase` (e.g., `PDFDocument`, `InferencePipeline`)
-- Functions/variables: `snake_case` (e.g., `extract_text`, `get_db_connection`)
-- Constants: `UPPER_SNAKE_CASE` (e.g., `DEFAULT_DPI`, `DATABASE`)
-- Private: `_leading_underscore` for internal use
-
-**Error Handling:**
-- Use custom exceptions from `shared.exceptions`
-- Base exception: `InvoiceExtractionError`
-- Specific exceptions: `PDFProcessingError`, `OCRError`, `DatabaseError`, etc.
-- Always include context in exceptions via `details` dict
-
-**Docstrings:**
-- Use Google-style docstrings
-- All public functions/classes must have docstrings
-- Include Args/Returns sections for complex functions
-
-**Code Organization:**
-- Maximum line length: 100 characters (black config)
-- Target Python: 3.10+
-- Keep files under 800 lines, ideally 200-400 lines
-
-### TypeScript / React Frontend
-
-**Imports:**
-- Use path alias `@/` for project imports: `import { Button } from '@/components/Button'`
-- Group: React → third-party → local (@/) → relative
-
-**Naming:**
-- Components: `PascalCase` (e.g., `Dashboard.tsx`, `InferenceDemo.tsx`)
-- Hooks: `camelCase` with `use` prefix (e.g., `useDocuments.ts`)
-- Types/Interfaces: `PascalCase` (e.g., `DocumentListResponse`)
-- API endpoints: `camelCase` (e.g., `documentsApi`)
-
-**TypeScript:**
-- Strict mode enabled
-- Use explicit return types on exported functions
-- Prefer `type` over `interface` for simple shapes
-- Use enums for fixed sets of values
-
-**React Patterns:**
-- Functional components with hooks
-- Use React Query for server state
-- Use Zustand for client state (if needed)
-- Props interfaces named `{ComponentName}Props`
-
-**Styling:**
-- Use Tailwind CSS exclusively
-- Custom colors: `warm-*` theme (e.g., `bg-warm-text-secondary`)
-- Component variants defined as objects (see Button.tsx pattern)
-
-**Testing:**
-- Use Vitest + React Testing Library
-- Test files: `{name}.test.ts` or `{name}.test.tsx`
-- Co-locate tests with source files when possible
-
-## Project Structure
-
-```
-packages/
-  shared/       # Shared utilities (PDF, OCR, storage, config)
-  training/     # Training service (GPU, CLI commands)
-  backend/      # Web API + inference (FastAPI)
-frontend/       # React + TypeScript + Vite
-tests/          # Test suite
-migrations/     # Database SQL migrations
-```
-
-## Key Configuration
-
-- **DPI:** 150 (must match between training and inference)
-- **Database:** PostgreSQL (configured via env vars)
-- **Storage:** Abstracted (Local/Azure/S3 via storage.yaml)
-- **Python:** 3.10+ (3.11 recommended, 3.10 for RTX 50 series)
+- No hardcoded secrets
+- Environment variables for sensitive data
+- Validate all user inputs
+- Parameterized queries only
+- CSRF protection enabled
 
 ## Environment Variables
 
-Required: `DB_PASSWORD`
-Optional: `DB_HOST`, `DB_PORT`, `DB_NAME`, `DB_USER`, `STORAGE_BASE_PATH`
+```bash
+# Required
+DB_PASSWORD=
 
-## Common Patterns
-
-### Python: Adding a New API Endpoint
-1. Add route in `backend/web/api/v1/`
-2. Define Pydantic schema in `backend/web/schemas/`
-3. Implement service logic in `backend/web/services/`
-4. Add tests in `tests/web/`
-
-### Frontend: Adding a New Component
-1. Create component in `frontend/src/components/`
-2. Export from `frontend/src/components/index.ts` if shared
-3. Add types to `frontend/src/api/types.ts` if API-related
-4. Add tests co-located with component
-
-### Error Handling
-```python
-from shared.exceptions import DatabaseError
-
-try:
-    result = db.query(...)
-except Exception as e:
-    raise DatabaseError(f"Failed to fetch document: {e}", details={"doc_id": doc_id})
+# Optional (with defaults)
+DB_HOST=192.168.68.31
+DB_PORT=5432
+DB_NAME=docmaster
+DB_USER=docmaster
+MODEL_PATH=runs/train/invoice_fields/weights/best.pt
+CONFIDENCE_THRESHOLD=0.5
+SERVER_HOST=0.0.0.0
+SERVER_PORT=8000
 ```
+## Available Commands
 
-### Database Access
-```python
-from shared.data.repositories import DocumentRepository
+- `/tdd` - Test-driven development workflow
+- `/plan` - Create implementation plan
+- `/code-review` - Review code quality
+- `/build-fix` - Fix build errors
 
-repo = DocumentRepository()
-doc = repo.get_by_id(doc_id)
-```
+## Git Workflow
+
+- Conventional commits: `feat:`, `fix:`, `refactor:`, `docs:`, `test:`
+- Never commit to main directly
+- PRs require review
+- All tests must pass before merge

ARCHITECTURE_REVIEW.md

@@ -1,666 +0,0 @@
-# Invoice Master POC v2 - 总体架构审查报告
-
-**审查日期**: 2026-02-01  
-**审查人**: Claude Code  
-**项目路径**: `/Users/yiukai/Documents/git/invoice-master-poc-v2`
-
----
-
-## 架构概述
-
-### 整体架构图
-
-```
-┌─────────────────────────────────────────────────────────────────┐
-│                         Frontend (React)                         │
-│              Vite + TypeScript + TailwindCSS                    │
-└─────────────────────────────┬───────────────────────────────────┘
-                              │ HTTP/REST
-┌─────────────────────────────▼───────────────────────────────────┐
-│                    Inference Service (FastAPI)                   │
-│  ┌──────────────┬──────────────┬──────────────┬──────────────┐  │
-│  │  Public API  │  Admin API   │  Training API│  Batch API   │  │
-│  └──────────────┴──────────────┴──────────────┴──────────────┘  │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │                    Service Layer                            │ │
-│  │  InferenceService │ AsyncProcessing │ BatchUpload │ Dataset │ │
-│  └────────────────────────────────────────────────────────────┘ │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │                    Data Layer                               │ │
-│  │  AdminDB │ AsyncRequestDB │ SQLModel │ PostgreSQL          │ │
-│  └────────────────────────────────────────────────────────────┘ │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │                    Core Components                          │ │
-│  │  RateLimiter │ Schedulers │ TaskQueues │ Auth              │ │
-│  └────────────────────────────────────────────────────────────┘ │
-└─────────────────────────────┬───────────────────────────────────┘
-                              │ PostgreSQL
-┌─────────────────────────────▼───────────────────────────────────┐
-│                    Training Service (GPU)                        │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │  CLI: train │ autolabel │ analyze │ validate               │ │
-│  └────────────────────────────────────────────────────────────┘ │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │  YOLO: db_dataset │ annotation_generator                   │ │
-│  └────────────────────────────────────────────────────────────┘ │
-│  ┌────────────────────────────────────────────────────────────┐ │
-│  │  Processing: CPU Pool │ GPU Pool │ Task Dispatcher         │ │
-│  └────────────────────────────────────────────────────────────┘ │
-└─────────────────────────────────────────────────────────────────┘
-                              │
-                    ┌─────────┴─────────┐
-                    ▼                   ▼
-            ┌──────────────┐    ┌──────────────┐
-            │    Shared    │    │   Storage    │
-            │  PDF │ OCR   │    │ Local/Azure/ │
-            │  Normalize   │    │     S3       │
-            └──────────────┘    └──────────────┘
-```
-
-### 技术栈
-
-| 层级 | 技术 | 评估 |
-|------|------|------|
-| **前端** | React + Vite + TypeScript + TailwindCSS | ✅ 现代栈 |
-| **API 框架** | FastAPI | ✅ 高性能，类型安全 |
-| **数据库** | PostgreSQL + SQLModel | ✅ 类型安全 ORM |
-| **目标检测** | YOLOv11 (Ultralytics) | ✅ 业界标准 |
-| **OCR** | PaddleOCR v5 | ✅ 支持瑞典语 |
-| **部署** | Docker + Azure/AWS | ✅ 云原生 |
-
----
-
-## 架构优势
-
-### 1. Monorepo 结构 ✅
-
-```
-packages/
-├── shared/      # 共享库 - 无外部依赖
-├── training/    # 训练服务 - 依赖 shared
-└── inference/   # 推理服务 - 依赖 shared
-```
-
-**优点**:
-- 清晰的包边界，无循环依赖
-- 独立部署，training 按需启动
-- 代码复用率高
-
-### 2. 分层架构 ✅
-
-```
-API Routes (web/api/v1/)
-    ↓
-Service Layer (web/services/)
-    ↓
-Data Layer (data/)
-    ↓
-Database (PostgreSQL)
-```
-
-**优点**:
-- 职责分离明确
-- 便于单元测试
-- 可替换底层实现
-
-### 3. 依赖注入 ✅
-
-```python
-# FastAPI Depends 使用得当
-@router.post("/infer")
-async def infer(
-    file: UploadFile,
-    db: AdminDB = Depends(get_admin_db),  # 注入
-    token: str = Depends(validate_admin_token),
-):
-```
-
-### 4. 存储抽象层 ✅
-
-```python
-# 统一接口，支持多后端
-class StorageBackend(ABC):
-    def upload(self, source: Path, destination: str) -> None: ...
-    def download(self, source: str, destination: Path) -> None: ...
-    def get_presigned_url(self, path: str) -> str: ...
-
-# 实现: LocalStorageBackend, AzureStorageBackend, S3StorageBackend
-```
-
-### 5. 动态模型管理 ✅
-
-```python
-# 数据库驱动的模型切换
-def get_active_model_path() -> Path | None:
-    db = AdminDB()
-    active_model = db.get_active_model_version()
-    return active_model.model_path if active_model else None
-
-inference_service = InferenceService(
-    model_path_resolver=get_active_model_path,
-)
-```
-
-### 6. 任务队列分离 ✅
-
-```python
-# 不同类型任务使用不同队列
-- AsyncTaskQueue: 异步推理任务
-- BatchQueue: 批量上传任务
-- TrainingScheduler: 训练任务调度
-- AutoLabelScheduler: 自动标注调度
-```
-
----
-
-## 架构问题与风险
-
-### 1. 数据库层职责过重 ⚠️ **中风险**
-
-**问题**: `AdminDB` 类过大，违反单一职责原则
-
-```python
-# packages/inference/inference/data/admin_db.py
-class AdminDB:
-    # Token 管理 (5 个方法)
-    def is_valid_admin_token(self, token: str) -> bool: ...
-    def create_admin_token(self, token: str, name: str): ...
-    
-    # 文档管理 (8 个方法)
-    def create_document(self, ...): ...
-    def get_document(self, doc_id: str): ...
-    
-    # 标注管理 (6 个方法)
-    def create_annotation(self, ...): ...
-    def get_annotations(self, doc_id: str): ...
-    
-    # 训练任务 (7 个方法)
-    def create_training_task(self, ...): ...
-    def update_training_task(self, ...): ...
-    
-    # 数据集 (6 个方法)
-    def create_dataset(self, ...): ...
-    def get_dataset(self, dataset_id: str): ...
-    
-    # 模型版本 (5 个方法)
-    def create_model_version(self, ...): ...
-    def activate_model_version(self, ...): ...
-    
-    # 批处理 (4 个方法)
-    # 锁管理 (3 个方法)
-    # ... 总计 50+ 方法
-```
-
-**影响**:
-- 类过大，难以维护
-- 测试困难
-- 不同领域变更互相影响
-
-**建议**: 按领域拆分为 Repository 模式
-
-```python
-# 建议重构
-class TokenRepository:
-    def validate(self, token: str) -> bool: ...
-    def create(self, token: Token) -> None: ...
-
-class DocumentRepository:
-    def find_by_id(self, doc_id: str) -> Document | None: ...
-    def save(self, document: Document) -> None: ...
-
-class TrainingRepository:
-    def create_task(self, config: TrainingConfig) -> TrainingTask: ...
-    def update_task_status(self, task_id: str, status: TaskStatus): ...
-
-class ModelRepository:
-    def get_active(self) -> ModelVersion | None: ...
-    def activate(self, version_id: str) -> None: ...
-```
-
----
-
-### 2. Service 层混合业务逻辑与技术细节 ⚠️ **中风险**
-
-**问题**: `InferenceService` 既处理业务逻辑又处理技术实现
-
-```python
-# packages/inference/inference/web/services/inference.py
-class InferenceService:
-    def process(self, image_bytes: bytes) -> ServiceResult:
-        # 1. 技术细节: 图像解码
-        image = Image.open(io.BytesIO(image_bytes))
-        
-        # 2. 业务逻辑: 字段提取
-        fields = self._extract_fields(image)
-        
-        # 3. 技术细节: 模型推理
-        detections = self._model.predict(image)
-        
-        # 4. 业务逻辑: 结果验证
-        if not self._validate_fields(fields):
-            raise ValidationError()
-```
-
-**影响**:
-- 难以测试业务逻辑
-- 技术变更影响业务代码
-- 无法切换技术实现
-
-**建议**: 引入领域层和适配器模式
-
-```python
-# 领域层 - 纯业务逻辑
-@dataclass
-class InvoiceDocument:
-    document_id: str
-    pages: list[Page]
-    
-class InvoiceExtractor:
-    """纯业务逻辑，不依赖技术实现"""
-    def extract(self, document: InvoiceDocument) -> InvoiceFields:
-        # 只处理业务规则
-        pass
-
-# 适配器层 - 技术实现
-class YoloFieldDetector:
-    """YOLO 技术适配器"""
-    def __init__(self, model_path: Path):
-        self._model = YOLO(model_path)
-    
-    def detect(self, image: np.ndarray) -> list[FieldRegion]:
-        return self._model.predict(image)
-
-class PaddleOcrEngine:
-    """PaddleOCR 技术适配器"""
-    def __init__(self):
-        self._ocr = PaddleOCR()
-    
-    def recognize(self, image: np.ndarray, region: BoundingBox) -> str:
-        return self._ocr.ocr(image, region)
-
-# 应用服务 - 协调领域和适配器
-class InvoiceProcessingService:
-    def __init__(
-        self,
-        extractor: InvoiceExtractor,
-        detector: FieldDetector,
-        ocr: OcrEngine,
-    ):
-        self._extractor = extractor
-        self._detector = detector
-        self._ocr = ocr
-```
-
----
-
-### 3. 调度器设计分散 ⚠️ **中风险**
-
-**问题**: 多个独立调度器缺乏统一协调
-
-```python
-# 当前设计 - 4 个独立调度器
-# 1. TrainingScheduler (core/scheduler.py)
-# 2. AutoLabelScheduler (core/autolabel_scheduler.py)
-# 3. AsyncTaskQueue (workers/async_queue.py)
-# 4. BatchQueue (workers/batch_queue.py)
-
-# app.py 中分别启动
-start_scheduler()           # 训练调度器
-start_autolabel_scheduler() # 自动标注调度器
-init_batch_queue()          # 批处理队列
-```
-
-**影响**:
-- 资源竞争风险
-- 难以监控和追踪
-- 任务优先级难以管理
-- 重启时任务丢失
-
-**建议**: 使用 Celery + Redis 统一任务队列
-
-```python
-# 建议重构
-from celery import Celery
-
-app = Celery('invoice_master')
-
-@app.task(bind=True, max_retries=3)
-def process_inference(self, document_id: str):
-    """异步推理任务"""
-    try:
-        service = get_inference_service()
-        result = service.process(document_id)
-        return result
-    except Exception as exc:
-        raise self.retry(exc=exc, countdown=60)
-
-@app.task
-def train_model(dataset_id: str, config: dict):
-    """训练任务"""
-    training_service = get_training_service()
-    return training_service.train(dataset_id, config)
-
-@app.task
-def auto_label_documents(document_ids: list[str]):
-    """批量自动标注"""
-    for doc_id in document_ids:
-        auto_label_document.delay(doc_id)
-
-# 优先级队列
-app.conf.task_routes = {
-    'tasks.process_inference': {'queue': 'high_priority'},
-    'tasks.train_model': {'queue': 'gpu_queue'},
-    'tasks.auto_label_documents': {'queue': 'low_priority'},
-}
-```
-
----
-
-### 4. 配置分散 ⚠️ **低风险**
-
-**问题**: 配置分散在多个文件
-
-```python
-# packages/shared/shared/config.py
-DATABASE = {...}
-PATHS = {...}
-AUTOLABEL = {...}
-
-# packages/inference/inference/web/config.py
-@dataclass
-class ModelConfig: ...
-@dataclass
-class ServerConfig: ...
-@dataclass
-class FileConfig: ...
-
-# 环境变量
-# .env 文件
-```
-
-**影响**:
-- 配置难以追踪
-- 可能出现不一致
-- 缺少配置验证
-
-**建议**: 使用 Pydantic Settings 集中管理
-
-```python
-# config/settings.py
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-class DatabaseSettings(BaseSettings):
-    model_config = SettingsConfigDict(env_prefix='DB_')
-    
-    host: str = 'localhost'
-    port: int = 5432
-    name: str = 'docmaster'
-    user: str = 'docmaster'
-    password: str  # 无默认值，必须设置
-
-class StorageSettings(BaseSettings):
-    model_config = SettingsConfigDict(env_prefix='STORAGE_')
-    
-    backend: str = 'local'
-    base_path: str = '~/invoice-data'
-    azure_connection_string: str | None = None
-    s3_bucket: str | None = None
-
-class Settings(BaseSettings):
-    model_config = SettingsConfigDict(
-        env_file='.env',
-        env_file_encoding='utf-8',
-    )
-    
-    database: DatabaseSettings = DatabaseSettings()
-    storage: StorageSettings = StorageSettings()
-    
-    # 验证
-    @field_validator('database')
-    def validate_database(cls, v):
-        if not v.password:
-            raise ValueError('Database password is required')
-        return v
-
-# 全局配置实例
-settings = Settings()
-```
-
----
-
-### 5. 内存队列单点故障 ⚠️ **中风险**
-
-**问题**: AsyncTaskQueue 和 BatchQueue 基于内存
-
-```python
-# workers/async_queue.py
-class AsyncTaskQueue:
-    def __init__(self):
-        self._queue = Queue()  # 内存队列
-        self._workers = []
-    
-    def enqueue(self, task: AsyncTask) -> None:
-        self._queue.put(task)  # 仅存储在内存
-```
-
-**影响**:
-- 服务重启丢失所有待处理任务
-- 无法水平扩展
-- 任务持久化困难
-
-**建议**: 使用 Redis/RabbitMQ 持久化队列
-
----
-
-### 6. 缺少 API 版本迁移策略 ❓ **低风险**
-
-**问题**: 有 `/api/v1/` 版本，但缺少升级策略
-
-```
-当前: /api/v1/admin/documents
-未来: /api/v2/admin/documents ?
-```
-
-**建议**: 
-- 制定 API 版本升级流程
-- 使用 Header 版本控制
-- 维护版本兼容性文档
-
----
-
-## 关键架构风险矩阵
-
-| 风险项 | 概率 | 影响 | 风险等级 | 优先级 |
-|--------|------|------|----------|--------|
-| 内存队列丢失任务 | 中 | 高 | **高** | 🔴 P0 |
-| AdminDB 职责过重 | 高 | 中 | **中** | 🟡 P1 |
-| Service 层混合 | 高 | 中 | **中** | 🟡 P1 |
-| 调度器资源竞争 | 中 | 中 | **中** | 🟡 P1 |
-| 配置分散 | 高 | 低 | **低** | 🟢 P2 |
-| API 版本策略 | 低 | 低 | **低** | 🟢 P2 |
-
----
-
-## 改进建议路线图
-
-### Phase 1: 立即执行 (本周)
-
-#### 1.1 拆分 AdminDB
-```python
-# 创建 repositories 包
-inference/data/repositories/
-├── __init__.py
-├── base.py           # Repository 基类
-├── token.py          # TokenRepository
-├── document.py       # DocumentRepository
-├── annotation.py     # AnnotationRepository
-├── training.py       # TrainingRepository
-├── dataset.py        # DatasetRepository
-└── model.py          # ModelRepository
-```
-
-#### 1.2 统一配置
-```python
-# 创建统一配置模块
-inference/config/
-├── __init__.py
-├── settings.py       # Pydantic Settings
-└── validators.py     # 配置验证
-```
-
-### Phase 2: 短期执行 (本月)
-
-#### 2.1 引入消息队列
-```yaml
-# docker-compose.yml 添加
-services:
-  redis:
-    image: redis:7-alpine
-    ports:
-      - "6379:6379"
-  
-  celery_worker:
-    build: .
-    command: celery -A inference.tasks worker -l info
-    depends_on:
-      - redis
-      - postgres
-```
-
-#### 2.2 添加缓存层
-```python
-# 使用 Redis 缓存热点数据
-from redis import Redis
-
-redis_client = Redis(host='localhost', port=6379)
-
-class CachedDocumentRepository(DocumentRepository):
-    def find_by_id(self, doc_id: str) -> Document | None:
-        # 先查缓存
-        cached = redis_client.get(f"doc:{doc_id}")
-        if cached:
-            return Document.parse_raw(cached)
-        
-        # 再查数据库
-        doc = super().find_by_id(doc_id)
-        if doc:
-            redis_client.setex(f"doc:{doc_id}", 3600, doc.json())
-        return doc
-```
-
-### Phase 3: 长期执行 (本季度)
-
-#### 3.1 数据库读写分离
-```python
-# 配置主从数据库
-class DatabaseManager:
-    def __init__(self):
-        self._master = create_engine(MASTER_DB_URL)
-        self._replica = create_engine(REPLICA_DB_URL)
-    
-    def get_session(self, readonly: bool = False) -> Session:
-        engine = self._replica if readonly else self._master
-        return Session(engine)
-```
-
-#### 3.2 事件驱动架构
-```python
-# 引入事件总线
-from event_bus import EventBus
-
-bus = EventBus()
-
-# 发布事件
-@router.post("/documents")
-async def create_document(...):
-    doc = document_repo.save(document)
-    bus.publish('document.created', {'document_id': doc.id})
-    return doc
-
-# 订阅事件
-@bus.subscribe('document.created')
-def on_document_created(event):
-    # 触发自动标注
-    auto_label_task.delay(event['document_id'])
-```
-
----
-
-## 架构演进建议
-
-### 当前架构 (适合 1-10 用户)
-
-```
-Single Instance
-├── FastAPI App
-├── Memory Queues
-└── PostgreSQL
-```
-
-### 目标架构 (适合 100+ 用户)
-
-```
-Load Balancer
-    ├── FastAPI Instance 1
-    ├── FastAPI Instance 2
-    └── FastAPI Instance N
-            │
-    ┌───────┴───────┐
-    ▼               ▼
-Redis Cluster   PostgreSQL
-(Celery + Cache) (Master + Replica)
-```
-
----
-
-## 总结
-
-### 总体评分
-
-| 维度 | 评分 | 说明 |
-|------|------|------|
-| **模块化** | 8/10 | 包结构清晰，但部分类过大 |
-| **可扩展性** | 7/10 | 水平扩展良好，垂直扩展受限 |
-| **可维护性** | 8/10 | 分层合理，但职责边界需细化 |
-| **可靠性** | 7/10 | 内存队列是单点故障 |
-| **性能** | 8/10 | 异步处理良好 |
-| **安全性** | 8/10 | 基础安全到位 |
-| **总体** | **7.7/10** | 良好的架构基础，需优化细节 |
-
-### 关键结论
-
-1. **架构设计合理**: Monorepo + 分层架构适合当前规模
-2. **主要风险**: 内存队列和数据库职责过重
-3. **演进路径**: 引入消息队列和缓存层
-4. **投入产出**: 当前架构可支撑到 100+ 用户，无需大规模重构
-
-### 下一步行动
-
-| 优先级 | 任务 | 预计工时 | 影响 |
-|--------|------|----------|------|
-| 🔴 P0 | 引入 Celery + Redis | 3 天 | 解决任务丢失问题 |
-| 🟡 P1 | 拆分 AdminDB | 2 天 | 提升可维护性 |
-| 🟡 P1 | 统一配置管理 | 1 天 | 减少配置错误 |
-| 🟢 P2 | 添加缓存层 | 2 天 | 提升性能 |
-| 🟢 P2 | 数据库读写分离 | 3 天 | 提升扩展性 |
-
----
-
-## 附录
-
-### 关键文件清单
-
-| 文件 | 职责 | 问题 |
-|------|------|------|
-| `inference/data/admin_db.py` | 数据库操作 | 类过大，需拆分 |
-| `inference/web/services/inference.py` | 推理服务 | 混合业务和技术 |
-| `inference/web/workers/async_queue.py` | 异步队列 | 内存存储，易丢失 |
-| `inference/web/core/scheduler.py` | 任务调度 | 缺少统一协调 |
-| `shared/shared/config.py` | 共享配置 | 分散管理 |
-
-### 参考资源
-
-- [Repository Pattern](https://martinfowler.com/eaaCatalog/repository.html)
-- [Celery Documentation](https://docs.celeryproject.org/)
-- [Pydantic Settings](https://docs.pydantic.dev/latest/concepts/pydantic_settings/)
-- [FastAPI Best Practices](https://fastapi.tiangolo.com/tutorial/bigger-applications/)

CHANGELOG.md

@@ -1,317 +0,0 @@
-# Changelog
-
-All notable changes to the Invoice Field Extraction project will be documented in this file.
-
-The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
-and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
-
-## [Unreleased]
-
-### Added - Phase 1: Security & Infrastructure (2026-01-22)
-
-#### Security Enhancements
-- **Environment Variable Management**: Added `python-dotenv` for secure configuration management
-  - Created `.env.example` template file for configuration reference
-  - Created `.env` file for actual credentials (gitignored)
-  - Updated `config.py` to load database password from environment variables
-  - Added validation to ensure `DB_PASSWORD` is set at startup
-  - Files modified: `config.py`, `requirements.txt`
-  - New files: `.env`, `.env.example`
-  - Tests: `tests/test_config.py` (7 tests, all passing)
-
-- **SQL Injection Prevention**: Fixed SQL injection vulnerabilities in database queries
-  - Replaced f-string formatting with parameterized queries in `LIMIT` clauses
-  - Updated `get_all_documents_summary()` to use `%s` placeholder for LIMIT parameter
-  - Updated `get_failed_matches()` to use `%s` placeholder for LIMIT parameter
-  - Files modified: `src/data/db.py` (lines 246, 298)
-  - Tests: `tests/test_db_security.py` (9 tests, all passing)
-
-#### Code Quality
-- **Exception Hierarchy**: Created comprehensive custom exception system
-  - Added base class `InvoiceExtractionError` with message and details support
-  - Added specific exception types:
-    - `PDFProcessingError` - PDF rendering/conversion errors
-    - `OCRError` - OCR processing errors
-    - `ModelInferenceError` - YOLO model errors
-    - `FieldValidationError` - Field validation errors (with field-specific attributes)
-    - `DatabaseError` - Database operation errors
-    - `ConfigurationError` - Configuration errors
-    - `PaymentLineParseError` - Payment line parsing errors
-    - `CustomerNumberParseError` - Customer number parsing errors
-    - `DataLoadError` - Data loading errors
-    - `AnnotationError` - Annotation generation errors
-  - New file: `src/exceptions.py`
-  - Tests: `tests/test_exceptions.py` (16 tests, all passing)
-
-### Testing
-- Added 32 new tests across 3 test files
-  - Configuration tests: 7 tests
-  - SQL injection prevention tests: 9 tests
-  - Exception hierarchy tests: 16 tests
-- All tests passing (32/32)
-
-### Documentation
-- Created `docs/CODE_REVIEW_REPORT.md` - Comprehensive code quality analysis (550+ lines)
-- Created `docs/REFACTORING_PLAN.md` - Detailed 3-phase refactoring plan (600+ lines)
-- Created `CHANGELOG.md` - Project changelog (this file)
-
-### Changed
-- **Configuration Loading**: Database configuration now loads from environment variables instead of hardcoded values
-  - Breaking change: Requires `.env` file with `DB_PASSWORD` set
-  - Migration: Copy `.env.example` to `.env` and set your database password
-
-### Security
-- **Fixed**: Database password no longer stored in plain text in `config.py`
-- **Fixed**: SQL injection vulnerabilities in LIMIT clauses (2 instances)
-
-### Technical Debt Addressed
-- Eliminated security vulnerability: plaintext password storage
-- Reduced SQL injection attack surface
-- Improved error handling granularity with custom exceptions
-
----
-
-### Added - Phase 2: Parser Refactoring (2026-01-22)
-
-#### Unified Parser Modules
-- **Payment Line Parser**: Created dedicated payment line parsing module
-  - Handles Swedish payment line format: `# <OCR> # <Kronor> <Öre> <Type> > <Account>#<Check>#`
-  - Tolerates common OCR errors: spaces in numbers, missing symbols, spaces in check digits
-  - Supports 4 parsing patterns: full format, no amount, alternative, account-only
-  - Returns structured `PaymentLineData` with parsed fields
-  - New file: `src/inference/payment_line_parser.py` (90 lines, 92% coverage)
-  - Tests: `tests/test_payment_line_parser.py` (23 tests, all passing)
-  - Eliminates 1st code duplication (payment line parsing logic)
-
-- **Customer Number Parser**: Created dedicated customer number parsing module
-  - Handles Swedish customer number formats: `JTY 576-3`, `DWQ 211-X`, `FFL 019N`, etc.
-  - Uses Strategy Pattern with 5 pattern classes:
-    - `LabeledPattern` - Explicit labels (highest priority, 0.98 confidence)
-    - `DashFormatPattern` - Standard format with dash (0.95 confidence)
-    - `NoDashFormatPattern` - Format without dash, adds dash automatically (0.90 confidence)
-    - `CompactFormatPattern` - Compact format without spaces (0.75 confidence)
-    - `GenericAlphanumericPattern` - Fallback generic pattern (variable confidence)
-  - Excludes Swedish postal codes (`SE XXX XX` format)
-  - Returns highest confidence match
-  - New file: `src/inference/customer_number_parser.py` (154 lines, 92% coverage)
-  - Tests: `tests/test_customer_number_parser.py` (32 tests, all passing)
-  - Reduces `_normalize_customer_number` complexity (127 lines → will use 5-10 lines after integration)
-
-### Testing Summary
-
-**Phase 1 Tests** (32 tests):
-- Configuration tests: 7 tests ([test_config.py](tests/test_config.py))
-- SQL injection prevention tests: 9 tests ([test_db_security.py](tests/test_db_security.py))
-- Exception hierarchy tests: 16 tests ([test_exceptions.py](tests/test_exceptions.py))
-
-**Phase 2 Tests** (121 tests):
-- Payment line parser tests: 23 tests ([test_payment_line_parser.py](tests/test_payment_line_parser.py))
-  - Standard parsing, OCR error handling, real-world examples, edge cases
-  - Coverage: 92%
-- Customer number parser tests: 32 tests ([test_customer_number_parser.py](tests/test_customer_number_parser.py))
-  - Pattern matching (DashFormat, NoDashFormat, Compact, Labeled)
-  - Real-world examples, edge cases, Swedish postal code exclusion
-  - Coverage: 92%
-- Field extractor integration tests: 45 tests ([test_field_extractor.py](src/inference/test_field_extractor.py))
-  - Validates backward compatibility with existing code
-  - Tests for invoice numbers, bankgiro, plusgiro, amounts, OCR, dates, payment lines, customer numbers
-- Pipeline integration tests: 21 tests ([test_pipeline.py](src/inference/test_pipeline.py))
-  - Cross-validation, payment line parsing, field overrides
-
-**Total**: 153 tests, 100% passing, 4.50s runtime
-
-### Code Quality
-- **Eliminated Code Duplication**: Payment line parsing previously in 3 places, now unified in 1 module
-- **Improved Maintainability**: Strategy Pattern makes customer number patterns easy to extend
-- **Better Test Coverage**: New parsers have 92% coverage vs original 10% in field_extractor.py
-
-#### Parser Integration into field_extractor.py (2026-01-22)
-
-- **field_extractor.py Integration**: Successfully integrated new parsers
-  - Added `PaymentLineParser` and `CustomerNumberParser` instances (lines 99-101)
-  - Replaced `_normalize_payment_line` method: 74 lines → 3 lines (lines 640-657)
-  - Replaced `_normalize_customer_number` method: 127 lines → 3 lines (lines 697-707)
-  - All 45 existing tests pass (100% backward compatibility maintained)
-  - Tests run time: 4.21 seconds
-  - File: `src/inference/field_extractor.py`
-
-#### Parser Integration into pipeline.py (2026-01-22)
-
-- **pipeline.py Integration**: Successfully integrated PaymentLineParser
-  - Added `PaymentLineParser` import (line 15)
-  - Added `payment_line_parser` instance initialization (line 128)
-  - Replaced `_parse_machine_readable_payment_line` method: 36 lines → 6 lines (lines 219-233)
-  - All 21 existing tests pass (100% backward compatibility maintained)
-  - Tests run time: 4.00 seconds
-  - File: `src/inference/pipeline.py`
-
-### Phase 2 Status: **COMPLETED** ✅
-
-- [x] Create unified `payment_line_parser` module ✅
-- [x] Create unified `customer_number_parser` module ✅
-- [x] Refactor `field_extractor.py` to use new parsers ✅
-- [x] Refactor `pipeline.py` to use new parsers ✅
-- [x] Comprehensive test suite (153 tests, 100% passing) ✅
-
-### Achieved Impact
-- Eliminate code duplication: 3 implementations → 1 ✅ (payment_line unified across field_extractor.py, pipeline.py, tests)
-- Reduce `_normalize_payment_line` complexity in field_extractor.py: 74 lines → 3 lines ✅
-- Reduce `_normalize_customer_number` complexity in field_extractor.py: 127 lines → 3 lines ✅
-- Reduce `_parse_machine_readable_payment_line` complexity in pipeline.py: 36 lines → 6 lines ✅
-- Total lines of code eliminated: 201 lines reduced to 12 lines (94% reduction) ✅
-- Improve test coverage: New parser modules have 92% coverage (vs original 10% in field_extractor.py)
-- Simplify maintenance: Pattern-based approach makes extension easy
-- 100% backward compatibility: All 66 existing tests pass (45 field_extractor + 21 pipeline)
-
----
-
-## Phase 3: Performance & Documentation (2026-01-22)
-
-### Added
-
-#### Configuration Constants Extraction
-- **Created `src/inference/constants.py`**: Centralized configuration constants
-  - Detection & model configuration (confidence thresholds, IOU)
-  - Image processing configuration (DPI, scaling factors)
-  - Customer number parser confidence scores
-  - Field extraction confidence multipliers
-  - Account type detection thresholds
-  - Pattern matching constants
-  - 90 lines of well-documented constants with usage notes
-  - Eliminates ~15 hardcoded magic numbers across codebase
-  - File: [src/inference/constants.py](src/inference/constants.py)
-
-#### Performance Optimization Documentation
-- **Created `docs/PERFORMANCE_OPTIMIZATION.md`**: Comprehensive performance guide (400+ lines)
-  - **Batch Processing Optimization**: Parallel processing strategies, already-implemented dual pool system
-  - **Database Query Optimization**: Connection pooling recommendations, index strategies
-  - **Caching Strategies**: Model loading cache, parser reuse (already optimal), OCR result caching
-  - **Memory Management**: Explicit cleanup, generator patterns, context managers
-  - **Profiling Guidelines**: cProfile, memory_profiler, py-spy recommendations
-  - **Benchmarking Scripts**: Ready-to-use performance measurement code
-  - **Priority Roadmap**: High/Medium/Low priority optimizations with effort estimates
-  - Expected impact: 2-5x throughput improvement for batch processing
-  - File: [docs/PERFORMANCE_OPTIMIZATION.md](docs/PERFORMANCE_OPTIMIZATION.md)
-
-### Phase 3 Status: **COMPLETED** ✅
-
-- [x] Configuration constants extraction ✅
-- [x] Performance optimization analysis ✅
-- [x] Batch processing optimization recommendations ✅
-- [x] Database optimization strategies ✅
-- [x] Caching and memory management guidelines ✅
-- [x] Profiling and benchmarking documentation ✅
-
-### Deliverables
-
-**New Files** (2 files):
-1. `src/inference/constants.py` (90 lines) - Centralized configuration constants
-2. `docs/PERFORMANCE_OPTIMIZATION.md` (400+ lines) - Performance optimization guide
-
-**Impact**:
-- Eliminates 15+ hardcoded magic numbers
-- Provides clear optimization roadmap
-- Documents existing performance features
-- Identifies quick wins (connection pooling, indexes)
-- Long-term strategy (caching, profiling)
-
----
-
-## Notes
-
-### Breaking Changes
-- **v2.x**: Requires `.env` file with database credentials
-  - Action required: Create `.env` file based on `.env.example`
-  - Affected: All deployments, CI/CD pipelines
-
-### Migration Guide
-
-#### From v1.x to v2.x (Environment Variables)
-1. Copy `.env.example` to `.env`:
-   ```bash
-   cp .env.example .env
-   ```
-
-2. Edit `.env` and set your database password:
-   ```
-   DB_PASSWORD=your_actual_password_here
-   ```
-
-3. Install new dependency:
-   ```bash
-   pip install python-dotenv
-   ```
-
-4. Verify configuration loads correctly:
-   ```bash
-   python -c "import config; print('Config loaded successfully')"
-   ```
-
-## Summary of All Work Completed
-
-### Files Created (13 new files)
-
-**Phase 1** (3 files):
-1. `.env` - Environment variables for database credentials
-2. `.env.example` - Template for environment configuration
-3. `src/exceptions.py` - Custom exception hierarchy (35 lines, 66% coverage)
-
-**Phase 2** (7 files):
-4. `src/inference/payment_line_parser.py` - Unified payment line parsing (90 lines, 92% coverage)
-5. `src/inference/customer_number_parser.py` - Unified customer number parsing (154 lines, 92% coverage)
-6. `tests/test_config.py` - Configuration tests (7 tests)
-7. `tests/test_db_security.py` - SQL injection prevention tests (9 tests)
-8. `tests/test_exceptions.py` - Exception hierarchy tests (16 tests)
-9. `tests/test_payment_line_parser.py` - Payment line parser tests (23 tests)
-10. `tests/test_customer_number_parser.py` - Customer number parser tests (32 tests)
-
-**Phase 3** (2 files):
-11. `src/inference/constants.py` - Centralized configuration constants (90 lines)
-12. `docs/PERFORMANCE_OPTIMIZATION.md` - Performance optimization guide (400+ lines)
-
-**Documentation** (1 file):
-13. `CHANGELOG.md` - This file (260+ lines of detailed documentation)
-
-### Files Modified (4 files)
-1. `config.py` - Added environment variable loading with python-dotenv
-2. `src/data/db.py` - Fixed 2 SQL injection vulnerabilities (lines 246, 298)
-3. `src/inference/field_extractor.py` - Integrated new parsers (reduced 201 lines to 6 lines)
-4. `src/inference/pipeline.py` - Integrated PaymentLineParser (reduced 36 lines to 6 lines)
-5. `requirements.txt` - Added python-dotenv dependency
-
-### Test Summary
-- **Total tests**: 153 tests across 7 test files
-- **Passing**: 153 (100%)
-- **Failing**: 0
-- **Runtime**: 4.50 seconds
-- **Coverage**:
-  - New parser modules: 92%
-  - Config module: 100%
-  - Exception module: 66%
-  - DB security coverage: 18% (focused on parameterized queries)
-
-### Code Metrics
-- **Lines eliminated**: 237 lines of duplicated/complex code → 18 lines (92% reduction)
-  - field_extractor.py: 201 lines → 6 lines
-  - pipeline.py: 36 lines → 6 lines
-- **New code added**: 279 lines of well-tested parser code
-- **Net impact**: Replaced 237 lines of duplicate code with 279 lines of unified, tested code (+42 lines, but -3 implementations)
-- **Test coverage improvement**: 0% → 92% for parser logic
-
-### Performance Impact
-- Configuration loading: Negligible (<1ms overhead for .env parsing)
-- SQL queries: No performance change (parameterized queries are standard practice)
-- Parser refactoring: No performance degradation (logic simplified, not changed)
-- Exception handling: Minimal overhead (only when exceptions are raised)
-
-### Security Improvements
-- ✅ Eliminated plaintext password storage
-- ✅ Fixed 2 SQL injection vulnerabilities
-- ✅ Added input validation in database layer
-
-### Maintainability Improvements
-- ✅ Eliminated code duplication (3 implementations → 1)
-- ✅ Strategy Pattern enables easy extension of customer number formats
-- ✅ Comprehensive test suite (153 tests) ensures safe refactoring
-- ✅ 100% backward compatibility maintained
-- ✅ Custom exception hierarchy for granular error handling

CODE_REVIEW_REPORT.md

@@ -1,805 +0,0 @@
-# Invoice Master POC v2 - 详细代码审查报告
-
-**审查日期**: 2026-02-01  
-**审查人**: Claude Code  
-**项目路径**: `C:\Users\yaoji\git\ColaCoder\invoice-master-poc-v2`  
-**代码统计**: 
-- Python文件: 200+ 个
-- 测试文件: 97 个
-- TypeScript/React文件: 39 个
-- 总测试数: 1,601 个
-- 测试覆盖率: 28%
-
----
-
-## 目录
-
-1. [执行摘要](#执行摘要)
-2. [架构概览](#架构概览)
-3. [详细模块审查](#详细模块审查)
-4. [代码质量问题](#代码质量问题)
-5. [安全风险分析](#安全风险分析)
-6. [性能问题](#性能问题)
-7. [改进建议](#改进建议)
-8. [总结与评分](#总结与评分)
-
----
-
-## 执行摘要
-
-### 总体评估
-
-| 维度 | 评分 | 状态 |
-|------|------|------|
-| **代码质量** | 7.5/10 | 良好，但有改进空间 |
-| **安全性** | 7/10 | 基础安全到位，需加强 |
-| **可维护性** | 8/10 | 模块化良好 |
-| **测试覆盖** | 5/10 | 偏低，需提升 |
-| **性能** | 8/10 | 异步处理良好 |
-| **文档** | 8/10 | 文档详尽 |
-| **总体** | **7.3/10** | 生产就绪，需小幅改进 |
-
-### 关键发现
-
-**优势:**
-- 清晰的Monorepo架构，三包分离合理
-- 类型注解覆盖率高（>90%）
-- 存储抽象层设计优秀
-- FastAPI使用规范，依赖注入模式良好
-- 异常处理完善，自定义异常层次清晰
-
-**风险:**
-- 测试覆盖率仅28%，远低于行业标准
-- AdminDB类过大（50+方法），违反单一职责原则
-- 内存队列存在单点故障风险
-- 部分安全细节需加强（时序攻击、文件上传验证）
-- 前端状态管理简单，可能难以扩展
-
----
-
-## 架构概览
-
-### 项目结构
-
-```
-invoice-master-poc-v2/
-├── packages/
-│   ├── shared/          # 共享库 (74个Python文件)
-│   │   ├── pdf/         # PDF处理
-│   │   ├── ocr/         # OCR封装
-│   │   ├── normalize/   # 字段规范化
-│   │   ├── matcher/     # 字段匹配
-│   │   ├── storage/     # 存储抽象层
-│   │   ├── training/    # 训练组件
-│   │   └── augmentation/# 数据增强
-│   ├── training/        # 训练服务 (26个Python文件)
-│   │   ├── cli/         # 命令行工具
-│   │   ├── yolo/        # YOLO数据集
-│   │   └── processing/  # 任务处理
-│   └── inference/       # 推理服务 (100个Python文件)
-│       ├── web/         # FastAPI应用
-│       ├── pipeline/    # 推理管道
-│       ├── data/        # 数据层
-│       └── cli/         # 命令行工具
-├── frontend/            # React前端 (39个TS/TSX文件)
-│   ├── src/
-│   │   ├── components/  # UI组件
-│   │   ├── hooks/       # React Query hooks
-│   │   └── api/         # API客户端
-└── tests/               # 测试 (97个Python文件)
-```
-
-### 技术栈
-
-| 层级 | 技术 | 评估 |
-|------|------|------|
-| **前端** | React 18 + TypeScript + Vite + TailwindCSS | 现代栈，类型安全 |
-| **API框架** | FastAPI + Uvicorn | 高性能，异步支持 |
-| **数据库** | PostgreSQL + SQLModel | 类型安全ORM |
-| **目标检测** | YOLOv11 (Ultralytics) | 业界标准 |
-| **OCR** | PaddleOCR v5 | 支持瑞典语 |
-| **部署** | Docker + Azure/AWS | 云原生 |
-
----
-
-## 详细模块审查
-
-### 1. Shared Package
-
-#### 1.1 配置模块 (`shared/config.py`)
-
-**文件位置**: `packages/shared/shared/config.py`  
-**代码行数**: 82行
-
-**优点:**
-- 使用环境变量加载配置，无硬编码敏感信息
-- DPI配置统一管理（DEFAULT_DPI = 150）
-- 密码无默认值，强制要求设置
-
-**问题:**
-```python
-# 问题1: 配置分散，缺少验证
-DATABASE = {
-    'host': os.getenv('DB_HOST', '192.168.68.31'),  # 硬编码IP
-    'port': int(os.getenv('DB_PORT', '5432')),
-    # ...
-}
-
-# 问题2: 缺少类型安全
-# 建议使用 Pydantic Settings
-```
-
-**严重程度**: 中  
-**建议**: 使用 Pydantic Settings 集中管理配置，添加验证逻辑
-
----
-
-#### 1.2 存储抽象层 (`shared/storage/`)
-
-**文件位置**: `packages/shared/shared/storage/`  
-**包含文件**: 8个
-
-**优点:**
-- 设计优秀的抽象接口 `StorageBackend`
-- 支持 Local/Azure/S3 多后端
-- 预签名URL支持
-- 异常层次清晰
-
-**代码示例 - 优秀设计:**
-```python
-class StorageBackend(ABC):
-    @abstractmethod
-    def upload(self, local_path: Path, remote_path: str, overwrite: bool = False) -> str:
-        pass
-    
-    @abstractmethod
-    def get_presigned_url(self, remote_path: str, expires_in_seconds: int = 3600) -> str:
-        pass
-```
-
-**问题:**
-- `upload_bytes` 和 `download_bytes` 默认实现使用临时文件，效率较低
-- 缺少文件类型验证（魔术字节检查）
-
-**严重程度**: 低  
-**建议**: 子类可重写bytes方法以提高效率，添加文件类型验证
-
----
-
-#### 1.3 异常定义 (`shared/exceptions.py`)
-
-**文件位置**: `packages/shared/shared/exceptions.py`  
-**代码行数**: 103行
-
-**优点:**
-- 清晰的异常层次结构
-- 所有异常继承自 `InvoiceExtractionError`
-- 包含详细的错误上下文
-
-**代码示例:**
-```python
-class InvoiceExtractionError(Exception):
-    def __init__(self, message: str, details: dict = None):
-        super().__init__(message)
-        self.message = message
-        self.details = details or {}
-```
-
-**评分**: 9/10 - 设计优秀
-
----
-
-#### 1.4 数据增强 (`shared/augmentation/`)
-
-**文件位置**: `packages/shared/shared/augmentation/`  
-**包含文件**: 10个
-
-**功能:**
-- 12种数据增强策略
-- 透视变换、皱纹、边缘损坏、污渍等
-- 高斯模糊、运动模糊、噪声等
-
-**代码质量**: 良好，模块化设计
-
----
-
-### 2. Inference Package
-
-#### 2.1 认证模块 (`inference/web/core/auth.py`)
-
-**文件位置**: `packages/inference/inference/web/core/auth.py`  
-**代码行数**: 61行
-
-**优点:**
-- 使用FastAPI依赖注入模式
-- Token过期检查
-- 记录最后使用时间
-
-**安全问题:**
-```python
-# 问题: 时序攻击风险 (第46行)
-if not admin_db.is_valid_admin_token(x_admin_token):
-    raise HTTPException(status_code=401, detail="Invalid or expired admin token.")
-
-# 建议: 使用 constant-time 比较
-import hmac
-if not hmac.compare_digest(token, expected_token):
-    raise HTTPException(status_code=401, ...)
-```
-
-**严重程度**: 中  
-**建议**: 使用 `hmac.compare_digest()` 进行constant-time比较
-
----
-
-#### 2.2 限流器 (`inference/web/core/rate_limiter.py`)
-
-**文件位置**: `packages/inference/inference/web/core/rate_limiter.py`  
-**代码行数**: 212行
-
-**优点:**
-- 滑动窗口算法实现
-- 线程安全（使用Lock）
-- 支持并发任务限制
-- 可配置的限流策略
-
-**代码示例 - 优秀设计:**
-```python
-@dataclass(frozen=True)
-class RateLimitConfig:
-    requests_per_minute: int = 10
-    max_concurrent_jobs: int = 3
-    min_poll_interval_ms: int = 1000
-```
-
-**问题:**
-- 内存存储，服务重启后限流状态丢失
-- 分布式部署时无法共享限流状态
-
-**严重程度**: 中  
-**建议**: 生产环境使用Redis实现分布式限流
-
----
-
-#### 2.3 AdminDB (`inference/data/admin_db.py`)
-
-**文件位置**: `packages/inference/inference/data/admin_db.py`  
-**代码行数**: 1300+行
-
-**严重问题 - 类过大:**
-```python
-class AdminDB:
-    # Token管理 (5个方法)
-    # 文档管理 (8个方法)
-    # 标注管理 (6个方法)
-    # 训练任务 (7个方法)
-    # 数据集 (6个方法)
-    # 模型版本 (5个方法)
-    # 批处理 (4个方法)
-    # 锁管理 (3个方法)
-    # ... 总计50+方法
-```
-
-**影响:**
-- 违反单一职责原则
-- 难以维护
-- 测试困难
-- 不同领域变更互相影响
-
-**严重程度**: 高  
-**建议**: 按领域拆分为Repository模式
-
-```python
-# 建议重构
-class TokenRepository:
-    def validate(self, token: str) -> bool: ...
-
-class DocumentRepository:
-    def find_by_id(self, doc_id: str) -> Document | None: ...
-
-class TrainingRepository:
-    def create_task(self, config: TrainingConfig) -> TrainingTask: ...
-```
-
----
-
-#### 2.4 文档路由 (`inference/web/api/v1/admin/documents.py`)
-
-**文件位置**: `packages/inference/inference/web/api/v1/admin/documents.py`  
-**代码行数**: 692行
-
-**优点:**
-- FastAPI使用规范
-- 输入验证完善
-- 响应模型定义清晰
-- 错误处理良好
-
-**问题:**
-```python
-# 问题1: 文件上传缺少魔术字节验证 (第127-131行)
-content = await file.read()
-# 建议: 验证PDF魔术字节 %PDF
-
-# 问题2: 路径遍历风险 (第494-498行)
-filename = Path(document.file_path).name
-# 建议: 使用 Path.name 并验证路径范围
-
-# 问题3: 函数过长，职责过多
-# _convert_pdf_to_images 函数混合了PDF处理和存储操作
-```
-
-**严重程度**: 中  
-**建议**: 添加文件类型验证，拆分大函数
-
----
-
-#### 2.5 推理服务 (`inference/web/services/inference.py`)
-
-**文件位置**: `packages/inference/inference/web/services/inference.py`  
-**代码行数**: 361行
-
-**优点:**
-- 支持动态模型加载
-- 懒加载初始化
-- 模型热重载支持
-
-**问题:**
-```python
-# 问题1: 混合业务逻辑和技术实现
-def process_image(self, image_path: Path, ...) -> ServiceResult:
-    # 1. 技术细节: 图像解码
-    # 2. 业务逻辑: 字段提取
-    # 3. 技术细节: 模型推理
-    # 4. 业务逻辑: 结果验证
-
-# 问题2: 可视化方法重复加载模型
-model = YOLO(str(self.model_config.model_path))  # 第316行
-# 应该在初始化时加载，避免重复IO
-
-# 问题3: 临时文件未使用上下文管理器
-temp_path = results_dir / f"{doc_id}_temp.png"
-# 建议使用 tempfile 上下文管理器
-```
-
-**严重程度**: 中  
-**建议**: 引入领域层和适配器模式，分离业务和技术逻辑
-
----
-
-#### 2.6 异步队列 (`inference/web/workers/async_queue.py`)
-
-**文件位置**: `packages/inference/inference/web/workers/async_queue.py`  
-**代码行数**: 213行
-
-**优点:**
-- 线程安全实现
-- 优雅关闭支持
-- 任务状态跟踪
-
-**严重问题:**
-```python
-# 问题: 内存队列，服务重启丢失任务 (第42行)
-self._queue: Queue[AsyncTask] = Queue(maxsize=max_size)
-
-# 问题: 无法水平扩展
-# 问题: 任务持久化困难
-```
-
-**严重程度**: 高  
-**建议**: 使用Redis/RabbitMQ持久化队列
-
----
-
-### 3. Training Package
-
-#### 3.1 整体评估
-
-**文件数量**: 26个Python文件
-
-**优点:**
-- CLI工具设计良好
-- 双池协调器（CPU + GPU）设计优秀
-- 数据增强策略丰富
-
-**总体评分**: 8/10
-
----
-
-### 4. Frontend
-
-#### 4.1 API客户端 (`frontend/src/api/client.ts`)
-
-**文件位置**: `frontend/src/api/client.ts`  
-**代码行数**: 42行
-
-**优点:**
-- Axios配置清晰
-- 请求/响应拦截器
-- 认证token自动添加
-
-**问题:**
-```typescript
-// 问题1: Token存储在localStorage，存在XSS风险
-const token = localStorage.getItem('admin_token')
-
-// 问题2: 401错误处理不完整
-if (error.response?.status === 401) {
-  console.warn('Authentication required...')
-  // 应该触发重新登录或token刷新
-}
-```
-
-**严重程度**: 中  
-**建议**: 考虑使用http-only cookie存储token，完善错误处理
-
----
-
-#### 4.2 Dashboard组件 (`frontend/src/components/Dashboard.tsx`)
-
-**文件位置**: `frontend/src/components/Dashboard.tsx`  
-**代码行数**: 301行
-
-**优点:**
-- React hooks使用规范
-- 类型定义清晰
-- UI响应式设计
-
-**问题:**
-```typescript
-// 问题1: 硬编码的进度值
-const getAutoLabelProgress = (doc: DocumentItem): number | undefined => {
-  if (doc.auto_label_status === 'running') {
-    return 45  // 硬编码！
-  }
-  // ...
-}
-
-// 问题2: 搜索功能未实现
-// 没有onChange处理
-
-// 问题3: 缺少错误边界处理
-// 组件应该包裹在Error Boundary中
-```
-
-**严重程度**: 低  
-**建议**: 实现真实的进度获取，添加搜索功能
-
----
-
-#### 4.3 整体评估
-
-**优点:**
-- TypeScript类型安全
-- React Query状态管理
-- TailwindCSS样式一致
-
-**问题:**
-- 缺少错误边界
-- 部分功能硬编码
-- 缺少单元测试
-
-**总体评分**: 7.5/10
-
----
-
-### 5. Tests
-
-#### 5.1 测试统计
-
-- **测试文件数**: 97个
-- **测试总数**: 1,601个
-- **测试覆盖率**: 28%
-
-#### 5.2 覆盖率分析
-
-| 模块 | 估计覆盖率 | 状态 |
-|------|-----------|------|
-| `shared/` | 35% | 偏低 |
-| `inference/web/` | 25% | 偏低 |
-| `inference/pipeline/` | 20% | 严重不足 |
-| `training/` | 30% | 偏低 |
-| `frontend/` | 15% | 严重不足 |
-
-#### 5.3 测试质量问题
-
-**优点:**
-- 使用了pytest框架
-- 有conftest.py配置
-- 部分集成测试
-
-**问题:**
-- 覆盖率远低于行业标准（80%）
-- 缺少端到端测试
-- 部分测试可能过于简单
-
-**严重程度**: 高  
-**建议**: 制定测试计划，优先覆盖核心业务逻辑
-
----
-
-## 代码质量问题
-
-### 高优先级问题
-
-| 问题 | 位置 | 影响 | 建议 |
-|------|------|------|------|
-| AdminDB类过大 | `inference/data/admin_db.py` | 维护困难 | 拆分为Repository模式 |
-| 内存队列单点故障 | `inference/web/workers/async_queue.py` | 任务丢失 | 使用Redis持久化 |
-| 测试覆盖率过低 | 全项目 | 代码风险 | 提升至60%+ |
-
-### 中优先级问题
-
-| 问题 | 位置 | 影响 | 建议 |
-|------|------|------|------|
-| 时序攻击风险 | `inference/web/core/auth.py` | 安全漏洞 | 使用hmac.compare_digest |
-| 限流器内存存储 | `inference/web/core/rate_limiter.py` | 分布式问题 | 使用Redis |
-| 配置分散 | `shared/config.py` | 难以管理 | 使用Pydantic Settings |
-| 文件上传验证不足 | `inference/web/api/v1/admin/documents.py` | 安全风险 | 添加魔术字节验证 |
-| 推理服务混合职责 | `inference/web/services/inference.py` | 难以测试 | 分离业务和技术逻辑 |
-
-### 低优先级问题
-
-| 问题 | 位置 | 影响 | 建议 |
-|------|------|------|------|
-| 前端搜索未实现 | `frontend/src/components/Dashboard.tsx` | 功能缺失 | 实现搜索功能 |
-| 硬编码进度值 | `frontend/src/components/Dashboard.tsx` | 用户体验 | 获取真实进度 |
-| Token存储方式 | `frontend/src/api/client.ts` | XSS风险 | 考虑http-only cookie |
-
----
-
-## 安全风险分析
-
-### 已识别的安全风险
-
-#### 1. 时序攻击 (中风险)
-
-**位置**: `inference/web/core/auth.py:46`
-
-```python
-# 当前实现（有风险）
-if not admin_db.is_valid_admin_token(x_admin_token):
-    raise HTTPException(status_code=401, ...)
-
-# 安全实现
-import hmac
-if not hmac.compare_digest(token, expected_token):
-    raise HTTPException(status_code=401, ...)
-```
-
-#### 2. 文件上传验证不足 (中风险)
-
-**位置**: `inference/web/api/v1/admin/documents.py:127-131`
-
-```python
-# 建议添加魔术字节验证
-ALLOWED_EXTENSIONS = {".pdf"}
-MAX_FILE_SIZE = 10 * 1024 * 1024
-
-if not content.startswith(b"%PDF"):
-    raise HTTPException(400, "Invalid PDF file format")
-```
-
-#### 3. 路径遍历风险 (中风险)
-
-**位置**: `inference/web/api/v1/admin/documents.py:494-498`
-
-```python
-# 建议实现
-from pathlib import Path
-
-def get_safe_path(filename: str, base_dir: Path) -> Path:
-    safe_name = Path(filename).name
-    full_path = (base_dir / safe_name).resolve()
-    if not full_path.is_relative_to(base_dir):
-        raise HTTPException(400, "Invalid file path")
-    return full_path
-```
-
-#### 4. CORS配置 (低风险)
-
-**位置**: FastAPI中间件配置
-
-```python
-# 建议生产环境配置
-ALLOWED_ORIGINS = [
-    "http://localhost:5173",
-    "https://your-domain.com",
-]
-```
-
-#### 5. XSS风险 (低风险)
-
-**位置**: `frontend/src/api/client.ts:13`
-
-```typescript
-// 当前实现
-const token = localStorage.getItem('admin_token')
-
-// 建议考虑
-// 使用http-only cookie存储敏感token
-```
-
-### 安全评分
-
-| 类别 | 评分 | 说明 |
-|------|------|------|
-| 认证 | 8/10 | 基础良好，需加强时序攻击防护 |
-| 输入验证 | 7/10 | 基本验证到位，需加强文件验证 |
-| 数据保护 | 8/10 | 无敏感信息硬编码 |
-| 传输安全 | 8/10 | 使用HTTPS（生产环境） |
-| 总体 | 7.5/10 | 基础安全良好，需加强细节 |
-
----
-
-## 性能问题
-
-### 已识别的性能问题
-
-#### 1. 重复模型加载
-
-**位置**: `inference/web/services/inference.py:316`
-
-```python
-# 问题: 每次可视化都重新加载模型
-model = YOLO(str(self.model_config.model_path))
-
-# 建议: 复用已加载的模型
-```
-
-#### 2. 临时文件处理
-
-**位置**: `shared/storage/base.py:178-203`
-
-```python
-# 问题: bytes操作使用临时文件
-def upload_bytes(self, data: bytes, ...):
-    with tempfile.NamedTemporaryFile(delete=False) as f:
-        f.write(data)
-        temp_path = Path(f.name)
-    # ...
-
-# 建议: 子类重写为直接上传
-```
-
-#### 3. 数据库查询优化
-
-**位置**: `inference/data/admin_db.py`
-
-```python
-# 问题: N+1查询风险
-for doc in documents:
-    annotations = db.get_annotations_for_document(str(doc.document_id))
-    # ...
-
-# 建议: 使用join预加载
-```
-
-### 性能评分
-
-| 类别 | 评分 | 说明 |
-|------|------|------|
-| 响应时间 | 8/10 | 异步处理良好 |
-| 资源使用 | 7/10 | 有优化空间 |
-| 可扩展性 | 7/10 | 内存队列限制 |
-| 并发处理 | 8/10 | 线程池设计良好 |
-| 总体 | 7.5/10 | 良好，有优化空间 |
-
----
-
-## 改进建议
-
-### 立即执行 (本周)
-
-1. **拆分AdminDB**
-   - 创建 `repositories/` 目录
-   - 按领域拆分：TokenRepository, DocumentRepository, TrainingRepository
-   - 估计工时: 2天
-
-2. **修复安全漏洞**
-   - 添加 `hmac.compare_digest()` 时序攻击防护
-   - 添加文件魔术字节验证
-   - 估计工时: 0.5天
-
-3. **提升测试覆盖率**
-   - 优先测试 `inference/pipeline/`
-   - 添加API集成测试
-   - 目标: 从28%提升至50%
-   - 估计工时: 3天
-
-### 短期执行 (本月)
-
-4. **引入消息队列**
-   - 添加Redis服务
-   - 使用Celery替换内存队列
-   - 估计工时: 3天
-
-5. **统一配置管理**
-   - 使用 Pydantic Settings
-   - 集中验证逻辑
-   - 估计工时: 1天
-
-6. **添加缓存层**
-   - Redis缓存热点数据
-   - 缓存文档、模型配置
-   - 估计工时: 2天
-
-### 长期执行 (本季度)
-
-7. **数据库读写分离**
-   - 配置主从数据库
-   - 读操作使用从库
-   - 估计工时: 3天
-
-8. **事件驱动架构**
-   - 引入事件总线
-   - 解耦模块依赖
-   - 估计工时: 5天
-
-9. **前端优化**
-   - 添加错误边界
-   - 实现真实搜索功能
-   - 添加E2E测试
-   - 估计工时: 3天
-
----
-
-## 总结与评分
-
-### 各维度评分
-
-| 维度 | 评分 | 权重 | 加权得分 |
-|------|------|------|----------|
-| **代码质量** | 7.5/10 | 20% | 1.5 |
-| **安全性** | 7.5/10 | 20% | 1.5 |
-| **可维护性** | 8/10 | 15% | 1.2 |
-| **测试覆盖** | 5/10 | 15% | 0.75 |
-| **性能** | 7.5/10 | 15% | 1.125 |
-| **文档** | 8/10 | 10% | 0.8 |
-| **架构设计** | 8/10 | 5% | 0.4 |
-| **总体** | **7.3/10** | 100% | **7.275** |
-
-### 关键结论
-
-1. **架构设计优秀**: Monorepo + 三包分离架构清晰，便于维护和扩展
-2. **代码质量良好**: 类型注解完善，文档详尽，结构清晰
-3. **安全基础良好**: 没有严重的安全漏洞，基础防护到位
-4. **测试是短板**: 28%覆盖率是最大风险点
-5. **生产就绪**: 经过小幅改进后可以投入生产使用
-
-### 下一步行动
-
-| 优先级 | 任务 | 预计工时 | 影响 |
-|--------|------|----------|------|
-| 高 | 拆分AdminDB | 2天 | 提升可维护性 |
-| 高 | 引入Redis队列 | 3天 | 解决任务丢失问题 |
-| 高 | 提升测试覆盖率 | 5天 | 降低代码风险 |
-| 中 | 修复安全漏洞 | 0.5天 | 提升安全性 |
-| 中 | 统一配置管理 | 1天 | 减少配置错误 |
-| 低 | 前端优化 | 3天 | 提升用户体验 |
-
----
-
-## 附录
-
-### 关键文件清单
-
-| 文件 | 职责 | 问题 |
-|------|------|------|
-| `inference/data/admin_db.py` | 数据库操作 | 类过大，需拆分 |
-| `inference/web/services/inference.py` | 推理服务 | 混合业务和技术 |
-| `inference/web/workers/async_queue.py` | 异步队列 | 内存存储，易丢失 |
-| `inference/web/core/scheduler.py` | 任务调度 | 缺少统一协调 |
-| `shared/shared/config.py` | 共享配置 | 分散管理 |
-
-### 参考资源
-
-- [Repository Pattern](https://martinfowler.com/eaaCatalog/repository.html)
-- [Celery Documentation](https://docs.celeryproject.org/)
-- [Pydantic Settings](https://docs.pydantic.dev/latest/concepts/pydantic_settings/)
-- [FastAPI Best Practices](https://fastapi.tiangolo.com/tutorial/bigger-applications/)
-- [OWASP Top 10](https://owasp.org/www-project-top-ten/)
-
----
-
-**报告生成时间**: 2026-02-01  
-**审查工具**: Claude Code + AST-grep + LSP

COMMERCIALIZATION_ANALYSIS_REPORT.md

@@ -26,7 +26,7 @@
 
 ### 项目现状
 
-Invoice Master是一个基于YOLOv11 + PaddleOCR的瑞典发票字段自动提取系统，具备以下核心能力：
+Invoice Master是一个基于YOLO26 + PaddleOCR的瑞典发票字段自动提取系统，具备以下核心能力：
 
 | 指标 | 数值 | 评估 |
 |------|------|------|

INFERENCE_ANALYSIS_REPORT.md

@@ -0,0 +1,314 @@
+# Inference Analysis Report
+
+Date: 2026-02-11
+Sample: 39 PDFs (diverse sizes from 1783 total), invoice-sm120 environment
+
+## Executive Summary
+
+| Metric | Value |
+|--------|-------|
+| Total PDFs tested | 39 |
+| Successful responses | 35 (89.7%) |
+| Timeouts (>120s) | 4 (10.3%) |
+| Pure fallback (all fields conf=0.500) | 15/35 (42.9%) |
+| Full extraction (all expected fields) | 6/35 (17.1%) |
+| supplier_org_number extraction rate | 0% |
+| InvoiceDate extraction rate | 31.4% |
+| OCR extraction rate | 31.4% |
+
+**Root Cause**: A critical DPI mismatch bug causes 43% of documents to lose all YOLO-detected field data, falling back to inaccurate regex patterns.
+
+---
+
+## Problem #1 (CRITICAL): DPI Mismatch - Field Extraction Failures
+
+### Symptom
+- 15/35 documents (43%) have ALL extracted fields at confidence=0.500 (fallback)
+- YOLO detects fields correctly (6+ detections at conf 0.8-0.97) but text extraction returns nothing
+- Examples: `4f822b0d` has 6 YOLO detections but only 1 field extracted via fallback
+
+### Root Cause
+**DPI not passed from pipeline to FieldExtractor** causing 2x coordinate scaling error.
+
+```
+pipeline.py:237  ->  self.extractor = FieldExtractor(ocr_lang=ocr_lang, use_gpu=use_gpu)
+                     ^^^ DPI NOT PASSED! Defaults to 300
+```
+
+The chain:
+1. `shared/config.py:22` defines `DEFAULT_DPI = 150`
+2. `InferencePipeline.__init__()` receives `dpi=150` from `ModelConfig`
+3. PDF rendered at **150 DPI** -> YOLO detections in 150 DPI pixel coordinates
+4. `FieldExtractor` defaults to `dpi=300` (never receives the actual 150)
+5. Coordinate conversion: `scale = 72 / self.dpi` = `72/300 = 0.24` instead of `72/150 = 0.48`
+6. Bounding boxes are **halved** in PDF point space -> no tokens match -> empty extraction
+7. Fallback regex triggers with conf=0.500
+
+### Fix
+**File**: `packages/backend/backend/pipeline/pipeline.py`, line 237
+
+```python
+# BEFORE (broken):
+self.extractor = FieldExtractor(ocr_lang=ocr_lang, use_gpu=use_gpu)
+
+# AFTER (fixed):
+self.extractor = FieldExtractor(ocr_lang=ocr_lang, use_gpu=use_gpu, dpi=dpi)
+```
+
+### Impact
+This single-line fix will recover ~43% of documents from degraded fallback to proper YOLO+OCR extraction.
+
+---
+
+## Problem #2 (HIGH): Fallback Amount Extraction Grabs Wrong Values
+
+### Symptom
+- 3 documents extracted Amount=1.00 when actual amounts are 7500.00, etc.
+- Fallback regex matches table column header "Summa" followed by row quantity "1,00" instead of total
+
+### Example
+Document `2b7e4103` (Astra Football Club):
+- Actual amount: **7 500,00 SEK**
+- Extracted: **1.00** (from "Summa 1" where "1" is the article number in the next row)
+
+### Root Cause
+The fallback Amount regex in `pipeline.py:676`:
+```python
+r'(?:att\s*betala|summa|total|belopp)\s*[:.]?\s*([\d\s,\.]+)\s*(?:SEK|kr)?'
+```
+matches "Summa" (column header) followed by "1" (first data in next row), because PaddleOCR produces tokens in position order. The greedy `[\d\s,\.]` captures "1" and stops at "Medlemsavgift".
+
+### Fix
+**File**: `packages/backend/backend/pipeline/pipeline.py`, lines 674-688
+
+1. Require minimum amount value in fallback (e.g., > 10.00)
+2. Require the matched amount to have a decimal separator (`,` or `.`) to avoid matching integers
+3. Prefer "ATT BETALA" over "Summa" as the keyword (less ambiguous)
+
+```python
+'Amount': [
+    r'(?:att\s+betala)\s*[:.]?\s*([\d\s]+[,\.]\d{2})\s*(?:SEK|kr)?',
+    r'([\d\s]+[,\.]\d{2})\s*(?:SEK|kr)\s*$',
+    r'(?:summa|total|belopp)\s*[:.]?\s*([\d\s]+[,\.]\d{2})\s*(?:SEK|kr)?',
+],
+```
+
+---
+
+## Problem #3 (HIGH): Fallback Bankgiro Regex False Positives
+
+### Symptom
+- Document `2b7e4103` extracts Bankgiro=2546-1610 but the actual document has NO Bankgiro
+- The document has Plusgiro=2131575-9 and Org.nr=802546-1610
+
+### Root Cause
+Fallback Bankgiro regex in `pipeline.py:681`:
+```python
+r'(\d{4}[-\s]\d{4})\s*(?=\s|$)'
+```
+matches the LAST 8 digits of org number "802546-1610" as "2546-1610".
+
+### Fix
+**File**: `packages/backend/backend/pipeline/pipeline.py`, line 681
+
+Add negative lookbehind to avoid matching within longer numbers:
+```python
+'Bankgiro': [
+    r'(?:bankgiro|bg)\s*[:.]?\s*(\d{3,4}[-\s]?\d{4})',
+    r'(?<!\d)(\d{3,4}[-\s]\d{4})(?!\d)',  # Must not be preceded/followed by digits
+],
+```
+
+---
+
+## Problem #4 (MEDIUM): OCR Number Minimum 5-Digit Requirement
+
+### Symptom
+- Document `2b7e4103` has OCR=3046 (4 digits) which is valid but rejected by normalizer
+- `OcrNumberNormalizer` requires minimum 5 digits
+
+### Root Cause
+**File**: `packages/backend/backend/pipeline/normalizers/ocr_number.py`, line 32:
+```python
+if len(digits) < 5:
+    return NormalizationResult.failure(f"Too few digits for OCR: {len(digits)}")
+```
+
+Swedish OCR numbers can be 2-25 digits. The 5-digit minimum is too restrictive.
+
+### Fix
+Lower minimum to 2 digits (or possibly 1 for very short OCR references):
+```python
+if len(digits) < 2:
+    return NormalizationResult.failure(f"Too few digits for OCR: {len(digits)}")
+```
+
+---
+
+## Problem #5 (MEDIUM): InvoiceNumber Extracts Year (2025, 2026)
+
+### Symptom
+- 2 documents extract year as invoice number: "2025", "2026"
+- `dc35ee8e`: actual invoice number visible in PDF but normalizer picks up year
+- `56cabf73`: InvoiceNumber=2026
+
+### Root Cause
+**File**: `packages/backend/backend/pipeline/normalizers/invoice_number.py`, lines 54-72
+
+The "Pattern 3: Short digit sequence" strategy prefers shorter sequences. When the YOLO bbox contains both the year "2025" and the actual invoice number, the shorter "2025" (4 digits) wins over a longer sequence.
+
+### Fix
+Add year exclusion to Pattern 3:
+```python
+for seq in digit_sequences:
+    if len(seq) == 8 and seq.startswith("20"):
+        continue  # Skip YYYYMMDD dates
+    if len(seq) == 4 and seq.startswith("20"):
+        continue  # Skip year-only values (2024, 2025, 2026)
+    if len(seq) > 10:
+        continue
+    valid_sequences.append(seq)
+```
+
+---
+
+## Problem #6 (MEDIUM): InvoiceNumber vs OCR Mismatch
+
+### Symptom
+- 5 documents show InvoiceNumber different from OCR number
+- Example: `87f470da` InvoiceNumber=852460234111905 vs OCR=524602341119055
+- Example: `8b0674be` InvoiceNumber=508021404131 vs OCR=50802140413
+
+### Root Cause
+These are legitimate: InvoiceNumber and OCR are detected from DIFFERENT YOLO bounding boxes (different regions of the invoice). The InvoiceNumber normalizer picks a shorter sequence from the invoice_number bbox, while the OCR normalizer extracts from the ocr_number bbox. Cross-validation from payment_line should reconcile these but cross-validation isn't running (0 documents show cross_validation results).
+
+### Diagnosis Needed
+Check why cross-validation / payment_line parsing isn't populating `result.cross_validation` even when payment_line is extracted.
+
+---
+
+## Problem #7 (MEDIUM): supplier_org_number 0% Extraction Rate
+
+### Symptom
+- 0/35 documents extract supplier_org_number
+- YOLO detects supplier_org_number in many documents (visible in detection classes)
+- When extracted, the field appears as `supplier_organisation_number` (different name)
+
+### Root Cause
+This is actually a reporting issue. The API returns the field as `supplier_organisation_number` (full spelling) from `CLASS_TO_FIELD` mapping, but the analysis expected `supplier_org_number`. Looking at the actual data, 8/35 documents DO have `supplier_organisation_number` extracted.
+
+However, the underlying issue is that even when YOLO detects `supplier_org_number`, the DPI bug prevents text extraction for text PDFs.
+
+### Fix
+Already addressed by Problem #1 (DPI fix). Additionally, ensure consistent field naming in API documentation.
+
+---
+
+## Problem #8 (LOW): Timeout Failures (4/39 documents)
+
+### Symptom
+- 4 PDFs timed out at 120 seconds
+- File sizes: 89KB, 169KB, 239KB, 970KB (not correlated with size)
+
+### Root Cause
+Likely multi-page PDFs or PDFs with complex layouts requiring extensive OCR. The 120s timeout in the test script may be too short for multi-page documents + full-page OCR fallback.
+
+### Fix
+1. Increase API timeout for multi-page PDFs
+2. Add page limit or early termination for very large documents
+3. Log page count in response to correlate with processing time
+
+---
+
+## Problem #9 (LOW): Non-Invoice Documents in Dataset
+
+### Symptom
+- `dccf6655`: 0 detections, 0 fields - this is a screenshot of UI buttons, NOT an invoice
+
+### Fix
+Add document classification as a pre-processing step to reject non-invoice documents before running the expensive YOLO + OCR pipeline.
+
+---
+
+## Problem #10 (LOW): InvoiceDueDate Before InvoiceDate
+
+### Symptom
+- Document `11de4d07`: InvoiceDate=2026-01-16, InvoiceDueDate=2025-12-01
+- Due date is BEFORE invoice date, which is illogical
+
+### Root Cause
+Either the date normalizer swapped the values, or the YOLO model detected the wrong region for one of the dates. The DPI bug (Problem #1) may also affect date extraction from the correct regions.
+
+### Fix
+Add post-extraction validation: if InvoiceDueDate < InvoiceDate, either swap them or flag for review.
+
+---
+
+## Priority Fix Order
+
+| Priority | Fix | Impact | Effort |
+|----------|-----|--------|--------|
+| 1 | DPI mismatch (Problem #1) | 43% of docs recovered | 1 line change |
+| 2 | Fallback amount regex (Problem #2) | 3+ docs with wrong amounts | Small regex fix |
+| 3 | Fallback bankgiro regex (Problem #3) | False positive bankgiro | Small regex fix |
+| 4 | OCR min digits (Problem #4) | Short OCR numbers supported | 1 line change |
+| 5 | Year as InvoiceNumber (Problem #5) | 2+ docs | Small logic add |
+| 6 | Date validation (Problem #10) | Logical consistency | Small validation add |
+| 7 | Cross-validation (Problem #6) | Better field reconciliation | Investigation needed |
+| 8 | Timeouts (Problem #8) | 4 docs | Config change |
+| 9 | Document classification (Problem #9) | Filter non-invoices | Feature addition |
+
+---
+
+## Re-run Expected After Fix #1
+
+After fixing the DPI mismatch alone, re-running the same 39 PDFs should show:
+- Pure fallback rate dropping from 43% to near 0%
+- InvoiceDate extraction rate improving from 31% to ~70%+
+- OCR extraction rate improving from 31% to ~60%+
+- Average confidence scores increasing significantly
+- supplier_organisation_number extraction improving from 23% to ~60%+
+
+---
+
+## Detailed Per-PDF Results Summary
+
+| PDF | Size | Time | Fields | Confidence | Issues |
+|-----|------|------|--------|------------|--------|
+| dccf6655 | 10KB | 17s | 0/0 | - | Not an invoice |
+| 4f822b0d | 183KB | 37s | 1/6 | ALL 0.500 | DPI bug: 6 detections, 5 lost |
+| d4af7848 | 55KB | 41s | 1/6 | ALL 0.500 | DPI bug: 6 detections, 5 lost |
+| 19533483 | 262KB | 39s | 1/9 | ALL 0.500 | DPI bug: 9 detections, 8 lost |
+| 2b7e4103 | 25KB | 47s | 3/6 | ALL 0.500 | DPI bug + Amount=1.00 wrong |
+| 7717d293 | 34KB | 16s | 3/6 | ALL 0.500 | DPI bug + Amount=1.00 wrong |
+| 3226ac59 | 66KB | 42s | 3/5 | ALL 0.500 | DPI bug + Amount=1.00 wrong |
+| 0553e5c2 | 31KB | 18s | 3/6 | ALL 0.500 | DPI bug + BG=5000-0000 suspicious |
+| 32e90db8 | 136KB | 40s | 3/7 | Mixed | Amount=2026.00 (year?) |
+| dc35ee8e | 567KB | 83s | 7/9 | YOLO | InvoiceNumber=2025 (year) |
+| 56cabf73 | 67KB | 19s | 5/6 | YOLO | InvoiceNumber=2026 (year) |
+| 87f470da | 784KB | 42s | 9/14 | YOLO | InvNum vs OCR mismatch |
+| 11de4d07 | 356KB | 68s | 5/3 | Mixed | DueDate < InvoiceDate |
+| 0f9047a9 | 415KB | 22s | 8/6 | YOLO | Good extraction |
+| 9d0b793c | 286KB | 18s | 8/8 | YOLO | Good extraction |
+| 5604d375 | 915KB | 51s | 9/10 | YOLO | Good extraction |
+| 87f470da | 784KB | 42s | 9/14 | YOLO | Good extraction |
+| f40fd418 | 523KB | 90s | 9/9 | YOLO | Good extraction |
+
+---
+
+## Field Extraction Rate Summary
+
+| Field | Present | Missing | Rate | Avg Conf |
+|-------|---------|---------|------|----------|
+| Bankgiro | 32 | 3 | 91.4% | 0.681 |
+| InvoiceNumber | 28 | 7 | 80.0% | 0.695 |
+| Amount | 27 | 8 | 77.1% | 0.726 |
+| InvoiceDueDate | 13 | 22 | 37.1% | 0.883 |
+| InvoiceDate | 11 | 24 | 31.4% | 0.879 |
+| OCR | 11 | 24 | 31.4% | 0.900 |
+| customer_number | 11 | 24 | 31.4% | 0.926 |
+| payment_line | 9 | 26 | 25.7% | 0.938 |
+| Plusgiro | 3 | 32 | 8.6% | 0.948 |
+| supplier_org_number | 0 | 35 | 0.0% | 0.000 |
+
+Note: Fields with high confidence but low extraction rate (InvoiceDate 0.879, OCR 0.900, payment_line 0.938) indicate the DPI bug: when extraction works (via YOLO), confidence is high. The low rate is because most documents fall back and these fields have no fallback regex pattern.

LABELING_STRATEGY_ANALYSIS.md

@@ -0,0 +1,257 @@
+# Semi-Automatic Labeling Strategy Analysis
+
+## 1. Current Pipeline Overview
+
+```
+CSV (field values)
+  |
+  v
+Autolabel CLI
+  |- PDF render (300 DPI)
+  |- Text extraction (PDF text layer or PaddleOCR)
+  |- FieldMatcher.find_matches() [5 strategies]
+  |   |- ExactMatcher (priority 1)
+  |   |- ConcatenatedMatcher (multi-token)
+  |   |- FuzzyMatcher (Amount, dates only)
+  |   |- SubstringMatcher (prevents false positives)
+  |   |- FlexibleDateMatcher (fallback)
+  |
+  |- AnnotationGenerator
+  |   |- PDF points -> pixels
+  |   |- expand_bbox() [field-specific strategy]
+  |   |- pixels -> YOLO normalized (0-1)
+  |   |- Save to database
+  |
+  v
+DBYOLODataset (training)
+  |- Load images + bboxes from DB
+  |- Re-apply expand_bbox()
+  |- YOLO training
+  |
+  v
+Inference
+  |- YOLO detect -> pixel bboxes
+  |- Crop region -> OCR extract text
+  |- Normalize & validate
+```
+
+---
+
+## 2. Current Expansion Strategy Analysis
+
+### 2.1 Field-Specific Parameters
+
+| Field | Scale X | Scale Y | Extra Top | Extra Left | Extra Right | Max Pad X | Max Pad Y |
+|---|---|---|---|---|---|---|---|
+| ocr_number | 1.15 | 1.80 | 0.60 | - | - | 50 | 140 |
+| bankgiro | 1.45 | 1.35 | - | 0.80 | - | 160 | 90 |
+| plusgiro | 1.45 | 1.35 | - | 0.80 | - | 160 | 90 |
+| invoice_date | 1.25 | 1.55 | 0.40 | - | - | 80 | 110 |
+| invoice_due_date | 1.30 | 1.65 | 0.45 | 0.35 | - | 100 | 120 |
+| amount | 1.20 | 1.35 | - | - | 0.30 | 70 | 80 |
+| invoice_number | 1.20 | 1.50 | 0.40 | - | - | 80 | 100 |
+| supplier_org_number | 1.25 | 1.40 | 0.30 | 0.20 | - | 90 | 90 |
+| customer_number | 1.25 | 1.45 | 0.35 | 0.25 | - | 90 | 100 |
+| payment_line | 1.10 | 1.20 | - | - | - | 40 | 40 |
+
+### 2.2 Design Rationale
+
+The expansion is designed based on Swedish invoice layout patterns:
+- **Dates**: Labels ("Fakturadatum") typically sit **above** the value -> extra top
+- **Giro accounts**: Prefix ("BG:", "PG:") sits **to the left** -> extra left
+- **Amount**: Currency suffix ("SEK", "kr") to the **right** -> extra right
+- **Payment line**: Machine-readable, self-contained -> minimal expansion
+
+### 2.3 Strengths
+
+1. **Field-specific directional expansion** - matches Swedish invoice conventions
+2. **Max padding caps** - prevents runaway expansion into neighboring fields
+3. **Center-point scaling** with directional compensation - geometrically sound
+4. **Image boundary clamping** - prevents out-of-bounds coordinates
+
+### 2.4 Potential Issues
+
+| Issue | Risk Level | Description |
+|---|---|---|
+| Over-expansion | HIGH | OCR number 1.80x Y-scale could capture adjacent fields |
+| Inconsistent training vs inference bbox | MEDIUM | Model trained on expanded boxes, inference returns raw detection |
+| No expansion at inference OCR crop | MEDIUM | Detected bbox may clip text edges without post-expansion |
+| Max padding in pixels vs DPI-dependent | LOW | 140px at 300DPI != 140px at 150DPI |
+
+---
+
+## 3. Industry Best Practices (Research Findings)
+
+### 3.1 Labeling: Tight vs. Loose Bounding Boxes
+
+**Consensus**: Annotate **tight bounding boxes around the value text only**.
+
+- FUNSD/CORD benchmarks annotate keys and values as **separate entities**
+- Loose boxes "introduce background noise and can mislead the model" (V7 Labs, LabelVisor)
+- IoU discrepancies from loose boxes degrade mAP during training
+
+**However**, for YOLO + OCR pipelines, tight-only creates a problem:
+- YOLO predicts slightly imprecise boxes (typical IoU 0.7-0.9)
+- If the predicted box clips even slightly, OCR misses characters
+- Solution: **Label tight, expand at inference** OR **label with controlled padding**
+
+### 3.2 The Two Dominant Strategies
+
+**Strategy A: Tight Label + Inference-Time Expansion** (Recommended by research)
+```
+Label:     [  2024-01-15  ]     (tight around value)
+Inference: [ [2024-01-15] ] + pad -> OCR
+```
+- Clean, consistent annotations
+- Requires post-detection padding before OCR crop
+- Used by: Microsoft Document Intelligence, Nanonets
+
+**Strategy B: Expanded Label at Training Time** (Current project approach)
+```
+Label:     [  Fakturadatum: 2024-01-15  ]  (includes context)
+Inference: YOLO detects full region -> OCR extracts from region
+```
+- Model learns spatial context (label + value)
+- Larger, more variable boxes
+- OCR must filter out label text from extracted content
+
+### 3.3 OCR Padding Requirements
+
+**Tesseract**: Requires ~10px white border for reliable segmentation (PSM 7-10).
+**PaddleOCR**: `det_db_unclip_ratio` parameter (default 1.5) controls detection expansion.
+
+Key insight: Even after YOLO detection, OCR engines need some padding around text to work reliably.
+
+### 3.4 State-of-the-Art Comparison
+
+| System | Bbox Strategy | Field Definition |
+|---|---|---|
+| **LayoutLM** | Word-level bboxes from OCR | Token classification (BIO tagging) |
+| **Donut** | No bboxes (end-to-end) | Internal attention mechanism |
+| **Microsoft DocAI** | Field-level, tight | Post-expansion for OCR |
+| **YOLO + OCR (this project)** | Field-level, expanded | Field-specific directional expansion |
+
+---
+
+## 4. Recommendations
+
+### 4.1 Short-Term (Current Architecture)
+
+#### A. Add Inference-Time OCR Padding
+Currently, the detected bbox is sent directly to OCR. Add a small uniform padding (5-10%) before cropping for OCR:
+
+```python
+# In field_extractor.py, before OCR crop:
+pad_ratio = 0.05  # 5% expansion
+w_pad = (x2 - x1) * pad_ratio
+h_pad = (y2 - y1) * pad_ratio
+crop_x1 = max(0, x1 - w_pad)
+crop_y1 = max(0, y1 - h_pad)
+crop_x2 = min(img_w, x2 + w_pad)
+crop_y2 = min(img_h, y2 + h_pad)
+```
+
+#### B. Reduce Training-Time Expansion Ratios
+Current ratios (especially OCR number 1.80x Y, Bankgiro 1.45x X) are aggressive. Proposed reduction:
+
+| Field | Current Scale Y | Proposed Scale Y | Rationale |
+|---|---|---|---|
+| ocr_number | 1.80 | 1.40 | 1.80 is too aggressive, captures neighbors |
+| bankgiro | 1.35 | 1.25 | Reduce vertical over-expansion |
+| invoice_due_date | 1.65 | 1.45 | Tighten vertical |
+
+Principle: **shift expansion work from training-time to inference-time**.
+
+#### C. Add Label Visualization Quality Check
+Before training, sample 50-100 annotated images and visually inspect:
+- Are expanded bboxes capturing only the target field?
+- Are any bboxes overlapping with adjacent fields?
+- Are any values being clipped?
+
+### 4.2 Medium-Term (Architecture Improvements)
+
+#### D. Two-Stage Detection Strategy
+```
+Stage 1: YOLO detects field regions (current)
+Stage 2: Within each detection, use PaddleOCR text detection
+         to find the precise text boundary
+Stage 3: Extract text from refined boundary
+```
+
+Benefits:
+- YOLO handles field classification (what)
+- PaddleOCR handles text localization (where exactly)
+- Eliminates the "tight vs loose" dilemma entirely
+
+#### E. Label Both Key and Value Separately
+Add new annotation classes: `invoice_date_label`, `invoice_date_value`
+- Model learns to find both the label and value
+- Use spatial relationship (label -> value) for more robust extraction
+- Aligns with FUNSD benchmark approach
+
+#### F. Confidence-Weighted Expansion
+Scale expansion by detection confidence:
+```python
+# Higher confidence = tighter crop (model is sure)
+# Lower confidence = wider crop (give OCR more context)
+expansion = base_expansion * (1.5 - confidence)
+```
+
+### 4.3 Long-Term (Next Generation)
+
+#### G. Move to LayoutLM-Style Token Classification
+- Replace YOLO field detection with token-level classification
+- Each OCR word gets classified as B-field/I-field/O
+- Eliminates bbox expansion entirely
+- Better for fields with complex layouts
+
+#### H. End-to-End with Donut/Pix2Struct
+- No separate OCR step
+- Model directly outputs structured fields from image
+- Zero bbox concerns
+- Requires more training data and compute
+
+---
+
+## 5. Recommended Action Plan
+
+### Phase 1: Validate Current Labels (1-2 days)
+- [ ] Build label visualization script
+- [ ] Sample 100 documents across all field types
+- [ ] Identify over-expansion and clipping cases
+- [ ] Document per-field accuracy of current expansion
+
+### Phase 2: Tune Expansion Parameters (2-3 days)
+- [ ] Reduce aggressive expansion ratios (OCR number, bankgiro)
+- [ ] Add inference-time OCR padding (5-10%)
+- [ ] Re-train model with adjusted labels
+- [ ] Compare mAP and field extraction accuracy
+
+### Phase 3: Two-Stage Refinement (1 week)
+- [ ] Implement PaddleOCR text detection within YOLO detection
+- [ ] Use text detection bbox for precise OCR crop
+- [ ] Keep YOLO expansion for classification only
+
+### Phase 4: Evaluation (ongoing)
+- [ ] Track per-field extraction accuracy on test set
+- [ ] A/B test tight vs expanded labels
+- [ ] Build regression test suite for labeling quality
+
+---
+
+## 6. Summary
+
+| Aspect | Current Approach | Best Practice | Gap |
+|---|---|---|---|
+| **Labeling** | Value + expansion at label time | Tight value + inference expansion | Medium |
+| **Expansion** | Field-specific directional | Field-specific directional | Aligned |
+| **Inference OCR crop** | Raw detection bbox | Detection + padding | Needs padding |
+| **Expansion ratios** | Aggressive (up to 1.80x) | Moderate (1.10-1.30x) | Over-expanded |
+| **Visualization QC** | None | Regular sampling | Missing |
+| **Coordinate consistency** | PDF points -> pixels | Consistent DPI | Check needed |
+
+**Bottom line**: The architecture (field-specific directional expansion) is sound and aligns with best practices. The main improvements are:
+1. **Reduce expansion aggressiveness** during training labels
+2. **Add OCR padding** at inference time
+3. **Add label quality visualization** for validation
+4. Longer term: consider **two-stage detection** or **token classification**

PLAN_TWO_STAGE_DETECTION.md

@@ -0,0 +1,153 @@
+# Plan: Two-Stage Detection (YOLO + PaddleOCR Value Selection)
+
+## Context
+
+Current inference flow: YOLO detects field region -> crop region -> PaddleOCR reads ALL text -> concatenate -> normalizer extracts value via regex.
+
+Problem: When training labels include label+value (e.g., "Fakturadatum 2024-01-15"), the detected region contains both label text and value text. Currently all OCR tokens are concatenated, and normalizers must regex out the value. This works for most fields but is fragile.
+
+Solution: After PaddleOCR returns individual text lines from the detected region, add a **value selection** step that picks the most likely value token(s) BEFORE sending to normalizer. This gives normalizers cleaner input and provides a more precise value bbox.
+
+## Key Insight
+
+PaddleOCR already returns individual `OCRToken` objects (text + bbox + confidence). The current code just concatenates them all (line 227 of `field_extractor.py`):
+
+```python
+raw_text = ' '.join(t.text for t in ocr_tokens)
+```
+
+The change: replace blind concatenation with field-aware token selection.
+
+## Architecture
+
+```
+Current:
+  YOLO bbox -> crop -> PaddleOCR -> [all tokens] -> concat -> normalizer
+
+New:
+  YOLO bbox -> crop -> PaddleOCR -> [all tokens] -> value_selector -> normalizer
+                                         |                |
+                                    individual         selected
+                                    text lines         value token(s)
+```
+
+## Scope: Inference Only
+
+| Pipeline Stage | Changed? | Reason |
+|---|---|---|
+| **Labeling** (autolabel, expansion) | NO | Expanded bbox gives YOLO strong visual patterns for generalization |
+| **Training** (YOLO26s) | NO | Model learns field regions correctly with current labels |
+| **Inference - Detection** (YOLO) | NO | Detection output is correct |
+| **Inference - Extraction** (OCR -> text) | **YES** | Add ValueSelector between OCR tokens and normalizer |
+
+This design works with ANY model -- tight-label model (15K docs) or expanded-label model (58 images). ValueSelector is model-agnostic.
+
+## Files to Modify
+
+| File | Change |
+|------|--------|
+| `packages/backend/backend/pipeline/value_selector.py` | **NEW** - Value selection logic per field type |
+| `packages/backend/backend/pipeline/field_extractor.py` | Use ValueSelector in both extraction paths |
+| `tests/pipeline/test_value_selector.py` | **NEW** - Tests for value selection |
+
+## Implementation
+
+### 1. New File: `value_selector.py`
+
+Core class `ValueSelector` with method:
+
+```python
+def select_value_tokens(
+    self,
+    tokens: list[OCRToken],
+    field_name: str
+) -> list[OCRToken]:
+```
+
+Field-specific selection rules:
+
+| Field | Strategy | Pattern |
+|-------|----------|---------|
+| InvoiceDate, InvoiceDueDate | Date pattern match | `\d{4}[-./]\d{2}[-./]\d{2}` or `\d{2}[-./]\d{2}[-./]\d{4}` or `\d{8}` |
+| Amount | Number pattern match | `\d[\d\s]*[,.]\d{2}` (prefer tokens with comma/decimal) |
+| Bankgiro | Giro pattern match | `\d{3,4}-\d{4}` or 7-8 consecutive digits |
+| Plusgiro | Giro pattern match | `\d+-\d` or 2-8 digits |
+| OCR | Longest digit sequence | Token with most consecutive digits (min 5) |
+| InvoiceNumber | Exclude known labels | Remove tokens matching Swedish label keywords, keep rest |
+| supplier_org_number | Org number pattern | `\d{6}-?\d{4}` |
+| customer_number | Exclude labels | Remove label keywords, keep alphanumeric tokens |
+| payment_line | Full concatenation | Keep all tokens (payment line needs full text) |
+
+Label keyword exclusion list (Swedish):
+```python
+LABEL_KEYWORDS = {
+    "fakturanummer", "fakturadatum", "forfallodag", "forfalldatum",
+    "bankgiro", "plusgiro", "bg", "pg", "ocr", "belopp", "summa",
+    "total", "att", "betala", "kundnummer", "organisationsnummer",
+    "org", "nr", "datum", "nummer", "ref", "referens",
+    "momsreg", "vat", "moms", "sek", "kr",
+}
+```
+
+Selection algorithm:
+1. Try field-specific pattern matching on individual tokens
+2. If match found -> return matched token(s) only
+3. If no match -> **fallback to ALL tokens** (current behavior, so we never lose data)
+
+### 2. Modify: `field_extractor.py`
+
+**OCR path** (`extract_from_detection`, line 224-228):
+
+```python
+# Before (current):
+ocr_tokens = self.ocr_engine.extract_from_image(region)
+raw_text = ' '.join(t.text for t in ocr_tokens)
+
+# After:
+ocr_tokens = self.ocr_engine.extract_from_image(region)
+value_tokens = self._value_selector.select_value_tokens(ocr_tokens, field_name)
+raw_text = ' '.join(t.text for t in value_tokens)
+```
+
+**PDF text path** (`extract_from_detection_with_pdf`, line 172-174):
+
+```python
+# Before (current):
+matching_tokens.sort(key=lambda x: -x[1])
+raw_text = ' '.join(t[0].text for t in matching_tokens)
+
+# After:
+matching_tokens.sort(key=lambda x: -x[1])
+all_text_tokens = [OCRToken(text=t[0].text, bbox=t[0].bbox, confidence=1.0) for t in matching_tokens]
+value_tokens = self._value_selector.select_value_tokens(all_text_tokens, field_name)
+raw_text = ' '.join(t.text for t in value_tokens)
+```
+
+**Constructor**: Add `ValueSelector` instance.
+
+### 3. Tests: `test_value_selector.py`
+
+Test cases per field type:
+- Date: "Fakturadatum 2024-01-15" -> selects "2024-01-15"
+- Amount: "Belopp 1 234,56 kr" -> selects "1 234,56"
+- Bankgiro: "BG: 123-4567" -> selects "123-4567"
+- OCR: "OCR 1234567890" -> selects "1234567890"
+- InvoiceNumber: "Fakturanr INV-2024-001" -> selects "INV-2024-001"
+- Fallback: Unknown pattern -> returns all tokens (no data loss)
+
+## Key Design Decisions
+
+1. **Fallback to full text**: If value selection can't identify the value, return ALL tokens. This means the change can never make things worse than current behavior.
+
+2. **ValueSelector is stateless**: Pure function, no side effects. Easy to test.
+
+3. **No training changes**: Training labels stay as-is (expanded bboxes). Only inference pipeline changes.
+
+4. **No normalizer changes**: Normalizers still work the same. They just get cleaner input.
+
+## Verification
+
+1. Run existing tests: `pytest tests/pipeline/ -v`
+2. Run new tests: `pytest tests/pipeline/test_value_selector.py -v`
+3. Manual validation: Run inference on a few invoices and compare raw_text before/after
+4. Regression check: Ensure no field extraction accuracy drops on existing test documents

PROJECT_REVIEW.md

@@ -8,11 +8,11 @@
 
 ## 项目概述
 
-**Invoice Master POC v2** - 基于 YOLOv11 + PaddleOCR 的瑞典发票字段自动提取系统
+**Invoice Master POC v2** - 基于 YOLO26 + PaddleOCR 的瑞典发票字段自动提取系统
 
 ### 核心功能
 - **自动标注**: 利用 CSV 结构化数据 + OCR 自动生成 YOLO 训练标注
-- **模型训练**: 使用 YOLOv11 训练字段检测模型，支持数据增强
+- **模型训练**: 使用 YOLO26 训练字段检测模型，支持数据增强
 - **推理提取**: 检测字段区域 → OCR 提取文本 → 字段规范化
 - **Web 管理**: React 前端 + FastAPI 后端，支持文档管理、数据集构建、模型训练和版本管理
 
@@ -175,7 +175,7 @@ frontend/        # React 前端 (Vite + TypeScript + TailwindCSS)
 
 | 组件 | 技术选择 | 评估 |
 |------|----------|------|
-| **目标检测** | YOLOv11 (Ultralytics) | ✅ 业界标准 |
+| **目标检测** | YOLO26 (Ultralytics >= 8.4.0) | ✅ 业界标准 |
 | **OCR 引擎** | PaddleOCR v5 | ✅ 支持瑞典语 |
 | **PDF 处理** | PyMuPDF (fitz) | ✅ 功能强大 |
 | **数据库** | PostgreSQL + SQLModel | ✅ 类型安全 |

README.md

@@ -1,14 +1,14 @@
 # Invoice Master POC v2
 
-自动发票字段提取系统 - 使用 YOLOv11 + PaddleOCR 从瑞典 PDF 发票中提取结构化数据。
+自动发票字段提取系统 - 使用 YOLO26 + PaddleOCR 从瑞典 PDF 发票中提取结构化数据。
 
 ## 项目概述
 
 本项目实现了一个完整的发票字段自动提取流程：
 
-1. **自动标注**: 利用已有 CSV 结构化数据 + OCR 自动生成 YOLO 训练标注
-2. **模型训练**: 使用 YOLOv11 训练字段检测模型，支持数据增强
-3. **推理提取**: 检测字段区域 -> OCR 提取文本 -> 字段规范化
+1. **自动标注**: 利用已有 CSV 结构化数据 + OCR 自动生成 YOLO 训练标注（统一 15px 填充）
+2. **模型训练**: 使用 YOLO26 训练字段检测模型，支持数据增强
+3. **推理提取**: 检测字段区域 -> OCR 提取文本 -> ValueSelector 过滤标签 -> 字段规范化
 4. **Web 管理**: React 前端 + FastAPI 后端，支持文档管理、数据集构建、模型训练和版本管理
 
 ### 架构
@@ -37,8 +37,8 @@ frontend/        # React 前端 (Vite + TypeScript + TailwindCSS)
 |------|------|
 | **已标注文档** | 9,738 (9,709 成功) |
 | **总体字段匹配率** | 94.8% (82,604/87,121) |
-| **测试** | 2,058 passed |
-| **测试覆盖率** | 60% |
+| **测试** | 2,047 passed |
+| **测试覆盖率** | 72% |
 | **模型 mAP@0.5** | 93.5% |
 
 **各字段匹配率:**
@@ -77,8 +77,8 @@ frontend/        # React 前端 (Vite + TypeScript + TailwindCSS)
 wsl -d Ubuntu-22.04
 
 # 2. 创建 Conda 环境
-conda create -n invoice-py311 python=3.11 -y
-conda activate invoice-py311
+conda create -n invoice-sm120 python=3.11 -y
+conda activate invoice-sm120
 
 # 3. 进入项目目录
 cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2
@@ -204,7 +204,7 @@ invoice-master-poc-v2/
 │       ├── run_server.py           # Web 服务器入口
 │       └── backend/
 │           ├── cli/                # infer, serve
-│           ├── pipeline/           # YOLO 检测, 字段提取, 解析器
+│           ├── pipeline/           # YOLO 检测, 字段提取, ValueSelector, 解析器
 │           ├── web/                # FastAPI 应用
 │           │   ├── api/v1/         # REST API (admin, public, batch)
 │           │   ├── schemas/        # Pydantic 数据模型
@@ -278,7 +278,7 @@ python -m training.cli.autolabel --workers 4
 ```bash
 # 从预训练模型开始训练
 python -m training.cli.train \
-    --model yolo11n.pt \
+    --model yolo26s.pt \
     --epochs 100 \
     --batch 16 \
     --name invoice_fields \
@@ -286,7 +286,7 @@ python -m training.cli.train \
 
 # 低内存模式
 python -m training.cli.train \
-    --model yolo11n.pt \
+    --model yolo26s.pt \
     --epochs 100 \
     --name invoice_fields \
     --low-memory
@@ -314,7 +314,7 @@ python -m backend.cli.infer \
 
 ```bash
 # 从 Windows PowerShell 启动
-wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-py311 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python run_server.py --port 8000"
+wsl bash -c "source ~/miniconda3/etc/profile.d/conda.sh && conda activate invoice-sm120 && cd /mnt/c/Users/yaoji/git/ColaCoder/invoice-master-poc-v2 && python run_server.py --port 8000"
 
 # 启动前端
 cd frontend && npm install && npm run dev
@@ -443,6 +443,30 @@ result = parser.parse("Said, Shakar Umj 436-R Billo")
 print(f"Customer Number: {result}")  # "UMJ 436-R"
 ```
 
+## 推理流水线 (Two-Stage Detection)
+
+```
+YOLO bbox -> crop -> PaddleOCR -> [all tokens] -> ValueSelector -> normalizer
+                                       |                |
+                                  individual         selected
+                                  text lines         value token(s)
+```
+
+**BBox 扩展**: 所有字段统一使用 15px 填充（150 DPI 下约 2.5mm），不做方向性扩展，不依赖布局假设。
+
+**ValueSelector**: 在 OCR 和 normalizer 之间按字段类型过滤标签文本，只保留值 token：
+
+| 字段 | 选择策略 | 示例输入 -> 输出 |
+|------|---------|-----------------|
+| InvoiceDate / DueDate | 日期模式匹配 | "Fakturadatum 2024-01-15" -> "2024-01-15" |
+| Amount | 金额模式匹配 | "Belopp 1 234,56 kr" -> "1 234,56" |
+| Bankgiro / Plusgiro | Giro 号码模式 | "BG: 123-4567" -> "123-4567" |
+| OCR | 最长数字序列 (>=5位) | "OCR 94228110015950070" -> "94228110015950070" |
+| InvoiceNumber | 排除瑞典语标签 | "Fakturanr INV-2024-001" -> "INV-2024-001" |
+| payment_line | 保留全部 | 不过滤 |
+
+如果没有匹配到任何模式，回退返回全部 token（永远不会比之前更差）。
+
 ## DPI 配置
 
 系统所有组件统一使用 **150 DPI**。DPI 必须在训练和推理时保持一致。
@@ -506,9 +530,9 @@ DB_PASSWORD=xxx pytest tests/ --cov=packages --cov-report=term-missing
 
 | 指标 | 数值 |
 |------|------|
-| **测试总数** | 2,058 |
+| **测试总数** | 2,047 |
 | **通过率** | 100% |
-| **覆盖率** | 60% |
+| **覆盖率** | 72% |
 
 ## 存储抽象层
 
@@ -619,7 +643,7 @@ npm run dev
 
 | 组件 | 技术 |
 |------|------|
-| **目标检测** | YOLOv11 (Ultralytics) |
+| **目标检测** | YOLO26 (Ultralytics >= 8.4.0) |
 | **OCR 引擎** | PaddleOCR v5 (PP-OCRv5) |
 | **PDF 处理** | PyMuPDF (fitz) |
 | **数据库** | PostgreSQL + SQLModel |

configs/default.yaml

@@ -76,7 +76,7 @@ matching:
 
 # YOLO Training
 yolo:
-  model: yolov8s              # Model architecture (yolov8n/s/m/l/x)
+  model: yolo26s              # Model architecture (yolo26n/s/m/l/x)
   epochs: 100
   batch_size: 16
   img_size: 1280              # Image size for training

configs/training.yaml

@@ -2,7 +2,7 @@
 # Use with: yolo train data=dataset.yaml cfg=training.yaml
 
 # Model
-model: yolov8s.pt
+model: yolo26s.pt
 
 # Training hyperparameters
 epochs: 100
@@ -57,3 +57,12 @@ name: invoice_fields
 exist_ok: true
 pretrained: true
 verbose: true
+
+# Fine-tuning profile (overrides when task_type == finetune)
+finetune:
+  epochs: 10
+  lr0: 0.001
+  freeze: 10
+  warmup_epochs: 1
+  cos_lr: true
+  patience: 5

docs/FORTNOX_INTEGRATION_SPEC.md

@@ -32,7 +32,7 @@
 
 ### 1.1 项目背景
 
-Invoice Master是一个基于YOLOv11 + PaddleOCR的发票字段自动提取系统，当前准确率达到94.8%。本方案设计将Invoice Master作为Fortnox会计软件的插件/扩展，实现无缝的发票数据导入功能。
+Invoice Master是一个基于YOLO26 + PaddleOCR的发票字段自动提取系统，当前准确率达到94.8%。本方案设计将Invoice Master作为Fortnox会计软件的插件/扩展，实现无缝的发票数据导入功能。
 
 ### 1.2 目标
 

docs/aws-deployment-guide.md

@@ -500,7 +500,7 @@ estimator = PyTorch(
     hyperparameters={
         "epochs": 100,
         "batch-size": 16,
-        "model": "yolo11n.pt"
+        "model": "yolo26s.pt"
     }
 )
 ```

docs/azure-deployment-guide.md

@@ -152,7 +152,7 @@ rclone mount azure:training-images Z: --vfs-cache-mode full
 ### 推荐: Container Apps (CPU)
 
 对于 YOLO 推理，**CPU 足够**，不需要 GPU：
-- YOLOv11n 在 CPU 上推理时间 ~200-500ms
+- YOLO26s 在 CPU 上推理时间 ~200-500ms
 - 比 GPU 便宜很多，适合中低流量
 
 ```yaml
@@ -335,7 +335,7 @@ az containerapp create \
 │   ~$30/月             │       │   ~$1-5/次训练        │       │                       │
 │                       │       │                       │       │                       │
 │ ┌───────────────────┐ │       │ ┌───────────────────┐ │       │ ┌───────────────────┐ │
-│ │ FastAPI + YOLO    │ │       │ │ YOLOv11 Training  │ │       │ │ React/Vue 前端    │ │
+│ │ FastAPI + YOLO    │ │       │ │ YOLO26 Training   │ │       │ │ React/Vue 前端    │ │
 │ │ /api/v1/infer     │ │       │ │ 100 epochs        │ │       │ │ 上传发票界面      │ │
 │ └───────────────────┘ │       │ └───────────────────┘ │       │ └───────────────────┘ │
 └───────────┬───────────┘       └───────────┬───────────┘       └───────────┬───────────┘

docs/fine-tuning-best-practices.md

@@ -0,0 +1,185 @@
+# YOLO Model Fine-Tuning Best Practices
+
+Production guide for continuous fine-tuning of YOLO object detection models with user feedback.
+
+## Overview
+
+When users report failed detections, those documents are collected, reviewed, and used to incrementally improve the model without degrading performance on existing data.
+
+Key risks:
+- **Catastrophic forgetting**: model forgets original training after fine-tuning on small new data
+- **Cumulative drift**: repeated fine-tuning sessions cause progressive degradation
+- **Overfitting**: few samples + many epochs = memorizing noise
+
+## 1. Data Management
+
+```
+Original training set (25K) --> permanently retained as "anchor dataset"
+         |
+User-reported failures --> human review & labeling --> "fine-tune pool"
+         |
+Fine-tune pool accumulates over time, never deleted
+```
+
+Every new sample MUST be human-verified before entering the fine-tune pool. Incorrect labels are more harmful than no labels.
+
+### Data Mixing Ratios
+
+| Accumulated New Samples | Old Data Multiplier | Total Training Size |
+|------------------------|--------------------|--------------------|
+| 10                     | 50x (500)          | 510                |
+| 50                     | 20x (1,000)        | 1,050              |
+| 200                    | 10x (2,000)        | 2,200              |
+| 500+                   | 5x (2,500)         | 3,000              |
+
+Principle: fewer new samples require higher old data ratio. Stabilize at 5x once pool reaches 500+.
+
+Old samples are randomly sampled from the original 25K each time, ensuring broad coverage.
+
+## 2. Model Version Management
+
+```
+base_v1.pt (original 25K training)
+  +-- ft_v1.1.pt (base + fine-tune batch 1)
+  +-- ft_v1.2.pt (base + fine-tune batch 1+2)
+  +-- ...
+
+When fine-tune pool reaches 2000+ samples:
+base_v2.pt (original 25K + all accumulated samples, trained from scratch)
+  +-- ft_v2.1.pt
+  +-- ...
+```
+
+CRITICAL: Never chain fine-tunes (ft_v1.1 -> ft_v1.2 -> ft_v1.3). Always start from the base model to avoid cumulative drift.
+
+## 3. Fine-Tuning Parameters
+
+```yaml
+base_model: best.pt           # always start from base model
+epochs: 10                    # few epochs are sufficient
+lr0: 0.001                    # 1/10 of base training lr
+freeze: 10                    # freeze first 10 backbone layers
+warmup_epochs: 1
+cos_lr: true
+
+# data mixing
+new_samples: all              # entire fine-tune pool
+old_samples: min(5x_new, 3000) # old data sampling, cap at 3000
+```
+
+### Why These Settings
+
+| Parameter | Rationale |
+|-----------|-----------|
+| `epochs: 10` | More than enough for small datasets; prevents overfitting |
+| `lr0: 0.001` | Low learning rate preserves base model knowledge |
+| `freeze: 10` | Backbone features are general; only fine-tune detection head and later layers |
+| `cos_lr: true` | Smooth decay prevents sharp weight updates |
+
+## 4. Deployment Gating (Most Important)
+
+Every fine-tuned model MUST pass three gates before deployment:
+
+### Gate 1: Regression Validation
+
+Run evaluation on the original test set (held out from the 25K training data).
+
+| mAP50 Change | Action |
+|-------------|--------|
+| Drop < 1%   | PASS - deploy |
+| Drop 1-3%   | REVIEW - human inspection required |
+| Drop > 3%   | REJECT - do not deploy |
+
+### Gate 2: New Sample Validation
+
+Run inference on the new failure documents.
+
+| Detection Rate | Action |
+|---------------|--------|
+| > 80% correct | PASS |
+| < 80% correct | REVIEW - check label quality or increase training |
+
+### Gate 3: A/B Comparison (Optional)
+
+Sample 100 production documents, run both old and new models:
+- New model must not be worse on any field type
+- Compare per-class mAP to detect targeted regressions
+
+## 5. Fine-Tuning Frequency
+
+| Strategy | Trigger | Recommendation |
+|----------|---------|---------------|
+| **By volume (recommended)** | Pool reaches 50+ new samples | Best signal-to-noise ratio |
+| By schedule | Weekly or monthly | Predictable but may trigger with insufficient data |
+| By performance | Monitored accuracy drops below threshold | Reactive, requires monitoring infrastructure |
+
+Do NOT fine-tune daily with fewer than 50 samples. The noise outweighs the signal.
+
+## 6. Complete Workflow
+
+```
+User marks failed document
+       |
+       v
+Human reviews and labels annotations
+       |
+       v
+Add to fine-tune pool
+       |
+       v
+Pool >= 50 samples? --NO--> Wait for more samples
+       |
+      YES
+       |
+       v
+Prepare mixed dataset:
+  - All samples from fine-tune pool
+  - Random sample 5x from original 25K
+       |
+       v
+Fine-tune from base.pt:
+  - 10 epochs
+  - lr0 = 0.001
+  - freeze first 10 layers
+       |
+       v
+Gate 1: Original test set mAP drop < 1%?
+       |
+      PASS
+       |
+       v
+Gate 2: New sample detection rate > 80%?
+       |
+      PASS
+       |
+       v
+Deploy new model, retain old model for rollback
+       |
+       v
+Pool accumulated 2000+ samples?
+       |
+      YES --> Merge all data, train new base from scratch
+```
+
+## 7. Monitoring in Production
+
+Track these metrics continuously:
+
+| Metric | Purpose | Alert Threshold |
+|--------|---------|----------------|
+| Detection rate per field | Catch field-specific regressions | < 90% for any field |
+| Average confidence score | Detect model uncertainty drift | Drop > 5% from baseline |
+| User-reported failures / week | Measure improvement trend | Increasing over 3 weeks |
+| Inference latency | Ensure model size hasn't bloated | > 2x baseline |
+
+## 8. Summary of Rules
+
+| Rule | Practice |
+|------|----------|
+| Never chain fine-tunes | Always start from base.pt |
+| Never use only new data | Must mix with old data |
+| Never fine-tune on < 50 samples | Accumulate before triggering |
+| Never auto-deploy | Must pass gating validation |
+| Never discard old models | Retain versions for rollback |
+| Periodically retrain base | Merge all data at 2000+ new samples |
+| Always human-review labels | Bad labels are worse than no labels |

docs/product-plan-v2.md

@@ -546,7 +546,7 @@ Request:
   "description": "First training run with 500 documents",
   "document_ids": ["uuid1", "uuid2", "uuid3"],
   "config": {
-    "model_name": "yolo11n.pt",
+    "model_name": "yolo26s.pt",
     "epochs": 100,
     "batch_size": 16,
     "image_size": 640
@@ -1036,7 +1036,7 @@ Response:
 |  | Name: [Training Run 2024-01____________]                      | |
 |  | Description: [First training with 500 documents_________]     | |
 |  |                                                               | |
-|  | Base Model: [yolo11n.pt v]   Epochs: [100]   Batch: [16]     | |
+|  | Base Model: [yolo26s.pt v]   Epochs: [100]   Batch: [16]     | |
 |  | Image Size: [640]            Device: [GPU 0 v]               | |
 |  |                                                               | |
 |  | [ ] Schedule for later: [2024-01-20] [22:00]                 | |
@@ -1088,7 +1088,7 @@ Response:
 |  | - Recall: 92%                                                | |
 |  |                                                               | |
 |  | Configuration:                                                | |
-|  | - Base: yolo11n.pt   Epochs: 100   Batch: 16   Size: 640    | |
+|  | - Base: yolo26s.pt   Epochs: 100   Batch: 16   Size: 640    | |
 |  |                                                               | |
 |  | Documents Used: [View 600 documents]                          | |
 |  +--------------------------------------------------------------+ |

docs/training-flow.mmd

@@ -27,7 +27,7 @@ flowchart TD
 
     I --> I1{--resume?}
     I1 -- Yes --> I2[Load last.pt checkpoint]
-    I1 -- No --> I3[Load pretrained model\ne.g. yolo11n.pt]
+    I1 -- No --> I3[Load pretrained model\ne.g. yolo26s.pt]
 
     I2 --> J[Configure Training]
     I3 --> J

frontend/src/api/endpoints/index.ts

@@ -5,4 +5,5 @@ export { inferenceApi } from './inference'
 export { datasetsApi } from './datasets'
 export { augmentationApi } from './augmentation'
 export { modelsApi } from './models'
+export { poolApi } from './pool'
 export { dashboardApi } from './dashboard'

frontend/src/api/endpoints/pool.ts

@@ -0,0 +1,40 @@
+import apiClient from '../client'
+import type {
+  PoolListResponse,
+  PoolStatsResponse,
+  PoolEntryResponse,
+} from '../types'
+
+export const poolApi = {
+  addToPool: async (documentId: string, reason?: string): Promise<PoolEntryResponse> => {
+    const { data } = await apiClient.post('/api/v1/admin/training/pool', {
+      document_id: documentId,
+      reason: reason ?? 'manual_addition',
+    })
+    return data
+  },
+
+  listEntries: async (params?: {
+    verified_only?: boolean
+    limit?: number
+    offset?: number
+  }): Promise<PoolListResponse> => {
+    const { data } = await apiClient.get('/api/v1/admin/training/pool', { params })
+    return data
+  },
+
+  getStats: async (): Promise<PoolStatsResponse> => {
+    const { data } = await apiClient.get('/api/v1/admin/training/pool/stats')
+    return data
+  },
+
+  verifyEntry: async (entryId: string): Promise<PoolEntryResponse> => {
+    const { data } = await apiClient.post(`/api/v1/admin/training/pool/${entryId}/verify`)
+    return data
+  },
+
+  removeEntry: async (entryId: string): Promise<{ message: string }> => {
+    const { data } = await apiClient.delete(`/api/v1/admin/training/pool/${entryId}`)
+    return data
+  },
+}

frontend/src/api/types.ts

@@ -111,6 +111,9 @@ export interface ModelVersionItem {
   is_active: boolean
   metrics_mAP: number | null
   document_count: number
+  model_type?: string
+  base_model_version_id?: string | null
+  gating_status?: string
   trained_at: string | null
   activated_at: string | null
   created_at: string
@@ -370,19 +373,6 @@ export interface TrainingTaskResponse {
 
 // Model Version types
 
-export interface ModelVersionItem {
-  version_id: string
-  version: string
-  name: string
-  status: string
-  is_active: boolean
-  metrics_mAP: number | null
-  document_count: number
-  trained_at: string | null
-  activated_at: string | null
-  created_at: string
-}
-
 export interface ModelVersionDetailResponse {
   version_id: string
   version: string
@@ -397,6 +387,10 @@ export interface ModelVersionDetailResponse {
   metrics_precision: number | null
   metrics_recall: number | null
   document_count: number
+  model_type?: string
+  base_model_version_id?: string | null
+  base_training_dataset_id?: string | null
+  gating_status?: string
   training_config: Record<string, unknown> | null
   file_size: number | null
   trained_at: string | null
@@ -405,6 +399,39 @@ export interface ModelVersionDetailResponse {
   updated_at: string
 }
 
+// Fine-Tune Pool types
+
+export interface PoolEntryItem {
+  entry_id: string
+  document_id: string
+  added_by: string | null
+  reason: string | null
+  is_verified: boolean
+  verified_at: string | null
+  verified_by: string | null
+  created_at: string
+}
+
+export interface PoolListResponse {
+  total: number
+  limit: number
+  offset: number
+  entries: PoolEntryItem[]
+}
+
+export interface PoolStatsResponse {
+  total_entries: number
+  verified_entries: number
+  unverified_entries: number
+  is_ready: boolean
+  min_required: number
+}
+
+export interface PoolEntryResponse {
+  entry_id: string
+  message: string
+}
+
 export interface ModelVersionListResponse {
   total: number
   limit: number

frontend/src/components/Models.tsx

@@ -74,6 +74,25 @@ export const Models: React.FC = () => {
                      </h4>
                      <p className="text-sm text-warm-text-muted">Trained {formatDate(model.trained_at)}</p>
                    </div>
+                   <div className="flex gap-1.5 items-center">
+                     {(model.model_type ?? 'base') === 'finetune' && (
+                       <span className="px-2 py-0.5 rounded text-xs font-medium bg-purple-100 text-purple-700">
+                         Fine-tuned
+                       </span>
+                     )}
+                     {model.gating_status && model.gating_status !== 'skipped' && (
+                       <span className={`px-2 py-0.5 rounded text-xs font-medium ${
+                         model.gating_status === 'pass' ? 'bg-green-100 text-green-700'
+                         : model.gating_status === 'review' ? 'bg-yellow-100 text-yellow-700'
+                         : model.gating_status === 'reject' ? 'bg-red-100 text-red-700'
+                         : 'bg-gray-100 text-gray-600'
+                       }`}>
+                         {model.gating_status === 'pass' ? 'PASS'
+                         : model.gating_status === 'review' ? 'REVIEW'
+                         : model.gating_status === 'reject' ? 'REJECT'
+                         : model.gating_status.toUpperCase()}
+                       </span>
+                     )}
                      <span className={`px-3 py-1 rounded-full text-xs font-medium ${
                        model.is_active
                          ? 'bg-warm-state-info/10 text-warm-state-info'
@@ -82,6 +101,7 @@ export const Models: React.FC = () => {
                        {model.is_active ? 'Active' : model.status}
                      </span>
                    </div>
+                </div>
 
                 <div className="mt-4 flex gap-8">
                    <div>

frontend/src/components/Training.tsx

@@ -1,15 +1,15 @@
 import React, { useState, useMemo } from 'react'
-import { useQuery } from '@tanstack/react-query'
-import { Database, Plus, Trash2, Eye, Play, Check, Loader2, AlertCircle } from 'lucide-react'
+import { useQuery, useMutation, useQueryClient } from '@tanstack/react-query'
+import { Database, Plus, Trash2, Eye, Play, Check, Loader2, AlertCircle, Shield, CheckCircle, XCircle } from 'lucide-react'
 import { Button } from './Button'
 import { AugmentationConfig } from './AugmentationConfig'
 import { useDatasets } from '../hooks/useDatasets'
 import { useTrainingDocuments } from '../hooks/useTraining'
-import { trainingApi } from '../api/endpoints'
-import type { DatasetListItem } from '../api/types'
+import { trainingApi, poolApi } from '../api/endpoints'
+import type { DatasetListItem, PoolEntryItem } from '../api/types'
 import type { AugmentationConfig as AugmentationConfigType } from '../api/endpoints/augmentation'
 
-type Tab = 'datasets' | 'create'
+type Tab = 'datasets' | 'create' | 'pool'
 
 interface TrainingProps {
   onNavigate?: (view: string, id?: string) => void
@@ -72,19 +72,23 @@ const TrainDialog: React.FC<TrainDialogProps> = ({ dataset, onClose, onSubmit, i
   const [augmentationConfig, setAugmentationConfig] = useState<Partial<AugmentationConfigType>>({})
   const [augmentationMultiplier, setAugmentationMultiplier] = useState(2)
 
+  const isFineTune = baseModelType === 'existing'
+
   // Fetch available trained models (active or inactive, not archived)
   const { data: modelsData } = useQuery({
     queryKey: ['training', 'models', 'available'],
     queryFn: () => trainingApi.getModels(),
   })
-  // Filter out archived models - only show active/inactive models for base model selection
-  const availableModels = (modelsData?.models ?? []).filter(m => m.status !== 'archived')
+  // Only show base models (not fine-tuned) for selection - prevents chaining fine-tunes
+  const availableModels = (modelsData?.models ?? []).filter(
+    m => m.status !== 'archived' && (m.model_type ?? 'base') === 'base'
+  )
 
   const handleSubmit = () => {
     onSubmit({
       name,
       config: {
-        model_name: baseModelType === 'pretrained' ? 'yolo11n.pt' : undefined,
+        model_name: baseModelType === 'pretrained' ? 'yolo26s.pt' : undefined,
         base_model_version_id: baseModelType === 'existing' ? baseModelVersionId : null,
         epochs,
         batch_size: batchSize,
@@ -121,14 +125,16 @@ const TrainDialog: React.FC<TrainDialogProps> = ({ dataset, onClose, onSubmit, i
                 if (e.target.value === 'pretrained') {
                   setBaseModelType('pretrained')
                   setBaseModelVersionId(null)
+                  setEpochs(100)
                 } else {
                   setBaseModelType('existing')
                   setBaseModelVersionId(e.target.value)
+                  setEpochs(10) // Fine-tune: fewer epochs per best practices
                 }
               }}
               className="w-full h-10 px-3 rounded-md border border-warm-divider bg-white text-warm-text-primary focus:outline-none focus:ring-1 focus:ring-warm-state-info"
             >
-              <option value="pretrained">yolo11n.pt (Pretrained)</option>
+              <option value="pretrained">yolo26s.pt (Pretrained)</option>
               {availableModels.map(m => (
                 <option key={m.version_id} value={m.version_id}>
                   {m.name} v{m.version} ({m.metrics_mAP ? `${(m.metrics_mAP * 100).toFixed(1)}% mAP` : 'No metrics'})
@@ -138,10 +144,23 @@ const TrainDialog: React.FC<TrainDialogProps> = ({ dataset, onClose, onSubmit, i
             <p className="text-xs text-warm-text-muted mt-1">
               {baseModelType === 'pretrained'
                 ? 'Start from pretrained YOLO model'
-                : 'Continue training from an existing model (incremental training)'}
+                : 'Fine-tune from base model (freeze=10, cos_lr, data mixing)'}
             </p>
           </div>
 
+          {/* Fine-tune info panel */}
+          {isFineTune && (
+            <div className="bg-warm-state-info/5 border border-warm-state-info/20 rounded-lg p-3 text-xs text-warm-text-secondary">
+              <p className="font-medium text-warm-state-info mb-1">Fine-Tune Mode</p>
+              <ul className="space-y-0.5 text-warm-text-muted">
+                <li>Epochs: 10 (auto-set), Backbone frozen (10 layers)</li>
+                <li>Cosine LR scheduler, Pool data mixed with old data</li>
+                <li>Requires 50+ verified pool entries</li>
+                <li>Deployment gating runs automatically after training</li>
+              </ul>
+            </div>
+          )}
+
           <div className="flex gap-4">
             <div className="flex-1">
               <label htmlFor="train-epochs" className="block text-sm font-medium text-warm-text-secondary mb-1">Epochs</label>
@@ -455,6 +474,148 @@ const CreateDataset: React.FC<{ onSwitchTab: (tab: Tab) => void }> = ({ onSwitch
   )
 }
 
+// --- Fine-Tune Pool ---
+
+const FineTunePool: React.FC = () => {
+  const queryClient = useQueryClient()
+
+  const { data: statsData, isLoading: isLoadingStats } = useQuery({
+    queryKey: ['pool', 'stats'],
+    queryFn: () => poolApi.getStats(),
+  })
+
+  const { data: entriesData, isLoading: isLoadingEntries } = useQuery({
+    queryKey: ['pool', 'entries'],
+    queryFn: () => poolApi.listEntries({ limit: 50 }),
+  })
+
+  const verifyMutation = useMutation({
+    mutationFn: (entryId: string) => poolApi.verifyEntry(entryId),
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: ['pool'] })
+    },
+  })
+
+  const removeMutation = useMutation({
+    mutationFn: (entryId: string) => poolApi.removeEntry(entryId),
+    onSuccess: () => {
+      queryClient.invalidateQueries({ queryKey: ['pool'] })
+    },
+  })
+
+  const stats = statsData
+  const entries = entriesData?.entries ?? []
+
+  return (
+    <div className="space-y-6">
+      {/* Pool Stats */}
+      <div className="grid grid-cols-4 gap-4">
+        {isLoadingStats ? (
+          <div className="col-span-4 flex items-center justify-center py-8 text-warm-text-muted">
+            <Loader2 size={20} className="animate-spin mr-2" />Loading stats...
+          </div>
+        ) : (
+          <>
+            <div className="bg-warm-card border border-warm-border rounded-lg p-4">
+              <p className="text-xs text-warm-text-muted uppercase mb-1">Total Entries</p>
+              <p className="text-2xl font-bold font-mono text-warm-text-primary">{stats?.total_entries ?? 0}</p>
+            </div>
+            <div className="bg-warm-card border border-warm-border rounded-lg p-4">
+              <p className="text-xs text-warm-text-muted uppercase mb-1">Verified</p>
+              <p className="text-2xl font-bold font-mono text-warm-state-success">{stats?.verified_entries ?? 0}</p>
+            </div>
+            <div className="bg-warm-card border border-warm-border rounded-lg p-4">
+              <p className="text-xs text-warm-text-muted uppercase mb-1">Unverified</p>
+              <p className="text-2xl font-bold font-mono text-warm-state-warning">{stats?.unverified_entries ?? 0}</p>
+            </div>
+            <div className="bg-warm-card border border-warm-border rounded-lg p-4">
+              <p className="text-xs text-warm-text-muted uppercase mb-1">Ready for Fine-Tune</p>
+              <div className="flex items-center gap-2">
+                {stats?.is_ready ? (
+                  <CheckCircle size={20} className="text-warm-state-success" />
+                ) : (
+                  <AlertCircle size={20} className="text-warm-state-warning" />
+                )}
+                <p className="text-lg font-medium text-warm-text-primary">
+                  {stats?.is_ready ? 'Yes' : `Need ${(stats?.min_required ?? 50) - (stats?.verified_entries ?? 0)} more`}
+                </p>
+              </div>
+            </div>
+          </>
+        )}
+      </div>
+
+      {/* Pool Entries Table */}
+      {isLoadingEntries ? (
+        <div className="flex items-center justify-center py-12 text-warm-text-muted">
+          <Loader2 size={20} className="animate-spin mr-2" />Loading pool entries...
+        </div>
+      ) : entries.length === 0 ? (
+        <div className="flex flex-col items-center justify-center py-16 text-warm-text-muted">
+          <Shield size={48} className="mb-4 opacity-40" />
+          <p className="text-lg mb-2">Fine-tune pool is empty</p>
+          <p className="text-sm">Add documents with extraction failures to the pool for future fine-tuning.</p>
+        </div>
+      ) : (
+        <div className="bg-warm-card border border-warm-border rounded-lg overflow-hidden shadow-sm">
+          <table className="w-full text-left">
+            <thead className="bg-white border-b border-warm-border">
+              <tr>
+                <th className="py-3 px-4 text-xs font-semibold text-warm-text-muted uppercase">Document ID</th>
+                <th className="py-3 px-4 text-xs font-semibold text-warm-text-muted uppercase">Reason</th>
+                <th className="py-3 px-4 text-xs font-semibold text-warm-text-muted uppercase">Status</th>
+                <th className="py-3 px-4 text-xs font-semibold text-warm-text-muted uppercase">Added</th>
+                <th className="py-3 px-4 text-xs font-semibold text-warm-text-muted uppercase">Actions</th>
+              </tr>
+            </thead>
+            <tbody>
+              {entries.map((entry: PoolEntryItem) => (
+                <tr key={entry.entry_id} className="border-b border-warm-border hover:bg-warm-hover transition-colors">
+                  <td className="py-3 px-4 text-sm font-mono text-warm-text-secondary">{entry.document_id.slice(0, 8)}...</td>
+                  <td className="py-3 px-4 text-sm text-warm-text-muted">{entry.reason ?? '-'}</td>
+                  <td className="py-3 px-4">
+                    <span className={`inline-flex items-center px-2.5 py-1 rounded-full text-xs font-medium ${
+                      entry.is_verified
+                        ? 'bg-warm-state-success/10 text-warm-state-success'
+                        : 'bg-warm-state-warning/10 text-warm-state-warning'
+                    }`}>
+                      {entry.is_verified ? <Check size={12} className="mr-1" /> : <AlertCircle size={12} className="mr-1" />}
+                      {entry.is_verified ? 'Verified' : 'Unverified'}
+                    </span>
+                  </td>
+                  <td className="py-3 px-4 text-sm text-warm-text-muted">{new Date(entry.created_at).toLocaleDateString()}</td>
+                  <td className="py-3 px-4">
+                    <div className="flex gap-1">
+                      {!entry.is_verified && (
+                        <button
+                          title="Verify"
+                          onClick={() => verifyMutation.mutate(entry.entry_id)}
+                          disabled={verifyMutation.isPending}
+                          className="p-1.5 rounded hover:bg-warm-selected text-warm-text-muted hover:text-warm-state-success transition-colors"
+                        >
+                          <CheckCircle size={14} />
+                        </button>
+                      )}
+                      <button
+                        title="Remove"
+                        onClick={() => removeMutation.mutate(entry.entry_id)}
+                        disabled={removeMutation.isPending}
+                        className="p-1.5 rounded hover:bg-warm-selected text-warm-text-muted hover:text-warm-state-error transition-colors"
+                      >
+                        <Trash2 size={14} />
+                      </button>
+                    </div>
+                  </td>
+                </tr>
+              ))}
+            </tbody>
+          </table>
+        </div>
+      )}
+    </div>
+  )
+}
+
 // --- Main Training Component ---
 
 export const Training: React.FC<TrainingProps> = ({ onNavigate }) => {
@@ -468,7 +629,7 @@ export const Training: React.FC<TrainingProps> = ({ onNavigate }) => {
 
       {/* Tabs */}
       <div className="flex gap-1 mb-6 border-b border-warm-border">
-        {([['datasets', 'Datasets'], ['create', 'Create Dataset']] as const).map(([key, label]) => (
+        {([['datasets', 'Datasets'], ['create', 'Create Dataset'], ['pool', 'Fine-Tune Pool']] as const).map(([key, label]) => (
           <button key={key} onClick={() => setActiveTab(key)}
             className={`px-4 py-2.5 text-sm font-medium border-b-2 transition-colors ${
               activeTab === key
@@ -482,6 +643,7 @@ export const Training: React.FC<TrainingProps> = ({ onNavigate }) => {
 
       {activeTab === 'datasets' && <DatasetList onNavigate={onNavigate} onSwitchTab={setActiveTab} />}
       {activeTab === 'create' && <CreateDataset onSwitchTab={setActiveTab} />}
+      {activeTab === 'pool' && <FineTunePool />}
     </div>
   )
 }

packages/backend/backend/cli/infer.py

@@ -7,10 +7,14 @@ Runs inference on new PDFs to extract invoice data.
 
 import argparse
 import json
+import logging
 import sys
 from pathlib import Path
 
 from shared.config import DEFAULT_DPI
+from shared.logging_config import setup_cli_logging
+
+logger = logging.getLogger(__name__)
 
 
 def main():
@@ -50,8 +54,8 @@ def main():
     )
     parser.add_argument(
         '--lang',
-        default='en',
-        help='OCR language (default: en)'
+        default='sv',
+        help='OCR language (default: sv)'
     )
     parser.add_argument(
         '--gpu',
@@ -66,10 +70,13 @@ def main():
 
     args = parser.parse_args()
 
+    # Configure logging for CLI
+    setup_cli_logging()
+
     # Validate model
     model_path = Path(args.model)
     if not model_path.exists():
-        print(f"Error: Model not found: {model_path}", file=sys.stderr)
+        logger.error("Model not found: %s", model_path)
         sys.exit(1)
 
     # Get input files
@@ -79,16 +86,16 @@ def main():
     elif input_path.is_dir():
         pdf_files = list(input_path.glob('*.pdf'))
     else:
-        print(f"Error: Input not found: {input_path}", file=sys.stderr)
+        logger.error("Input not found: %s", input_path)
         sys.exit(1)
 
     if not pdf_files:
-        print("Error: No PDF files found", file=sys.stderr)
+        logger.error("No PDF files found")
         sys.exit(1)
 
     if args.verbose:
-        print(f"Processing {len(pdf_files)} PDF file(s)")
-        print(f"Model: {model_path}")
+        logger.info("Processing %d PDF file(s)", len(pdf_files))
+        logger.info("Model: %s", model_path)
 
     from backend.pipeline import InferencePipeline
 
@@ -107,18 +114,18 @@ def main():
 
     for pdf_path in pdf_files:
         if args.verbose:
-            print(f"Processing: {pdf_path.name}")
+            logger.info("Processing: %s", pdf_path.name)
 
         result = pipeline.process_pdf(pdf_path)
         results.append(result.to_json())
 
         if args.verbose:
-            print(f"  Success: {result.success}")
-            print(f"  Fields: {len(result.fields)}")
+            logger.info("  Success: %s", result.success)
+            logger.info("  Fields: %d", len(result.fields))
             if result.fallback_used:
-                print(f"  Fallback used: Yes")
+                logger.info("  Fallback used: Yes")
             if result.errors:
-                print(f"  Errors: {result.errors}")
+                logger.info("  Errors: %s", result.errors)
 
     # Output results
     if len(results) == 1:
@@ -132,9 +139,11 @@ def main():
         with open(args.output, 'w', encoding='utf-8') as f:
             f.write(json_output)
         if args.verbose:
-            print(f"\nResults written to: {args.output}")
+            logger.info("Results written to: %s", args.output)
     else:
-        print(json_output)
+        # Output JSON to stdout (not logged)
+        sys.stdout.write(json_output)
+        sys.stdout.write('\n')
 
 
 if __name__ == '__main__':

packages/backend/backend/data/admin_models.py

@@ -289,6 +289,16 @@ class ModelVersion(SQLModel, table=True):
     is_active: bool = Field(default=False, index=True)
     # Only one version can be active at a time for inference
 
+    # Model lineage
+    model_type: str = Field(default="base", max_length=20, index=True)
+    # "base" = trained from pretrained YOLO, "finetune" = fine-tuned from base model
+    base_model_version_id: UUID | None = Field(default=None, index=True)
+    # Points to the base model this was fine-tuned from (None for base models)
+    base_training_dataset_id: UUID | None = Field(default=None, index=True)
+    # The dataset used for original base training (for data mixing old samples)
+    gating_status: str = Field(default="pending", max_length=20, index=True)
+    # Deployment gating: pending, pass, review, reject, skipped
+
     # Training association
     task_id: UUID | None = Field(default=None, foreign_key="training_tasks.task_id", index=True)
     dataset_id: UUID | None = Field(default=None, foreign_key="training_datasets.dataset_id", index=True)
@@ -317,6 +327,64 @@ class ModelVersion(SQLModel, table=True):
     updated_at: datetime = Field(default_factory=datetime.utcnow)
 
 
+# =============================================================================
+# Fine-Tune Pool
+# =============================================================================
+
+
+class FineTunePoolEntry(SQLModel, table=True):
+    """Document in the fine-tune pool for incremental model improvement."""
+
+    __tablename__ = "finetune_pool_entries"
+
+    entry_id: UUID = Field(default_factory=uuid4, primary_key=True)
+    document_id: UUID = Field(foreign_key="admin_documents.document_id", index=True)
+    added_by: str | None = Field(default=None, max_length=255)
+    reason: str | None = Field(default=None, max_length=255)
+    # Reason: user_reported_failure, manual_addition
+    is_verified: bool = Field(default=False, index=True)
+    verified_at: datetime | None = Field(default=None)
+    verified_by: str | None = Field(default=None, max_length=255)
+    created_at: datetime = Field(default_factory=datetime.utcnow)
+
+
+# =============================================================================
+# Deployment Gating
+# =============================================================================
+
+
+class GatingResult(SQLModel, table=True):
+    """Model deployment gating validation result."""
+
+    __tablename__ = "gating_results"
+
+    result_id: UUID = Field(default_factory=uuid4, primary_key=True)
+    model_version_id: UUID = Field(foreign_key="model_versions.version_id", index=True)
+    task_id: UUID | None = Field(default=None, foreign_key="training_tasks.task_id")
+
+    # Gate 1: Regression validation (original test set mAP)
+    gate1_status: str = Field(default="pending", max_length=20)
+    # pending, pass, review, reject
+    gate1_original_mAP: float | None = Field(default=None)
+    gate1_new_mAP: float | None = Field(default=None)
+    gate1_mAP_drop: float | None = Field(default=None)
+
+    # Gate 2: New sample validation (detection rate on pool docs)
+    gate2_status: str = Field(default="pending", max_length=20)
+    gate2_detection_rate: float | None = Field(default=None)
+    gate2_total_samples: int | None = Field(default=None)
+    gate2_detected_samples: int | None = Field(default=None)
+
+    # Overall
+    overall_status: str = Field(default="pending", max_length=20)
+    # pending, pass, review, reject
+    reviewer_notes: str | None = Field(default=None)
+    reviewed_by: str | None = Field(default=None, max_length=255)
+    reviewed_at: datetime | None = Field(default=None)
+
+    created_at: datetime = Field(default_factory=datetime.utcnow)
+
+
 # =============================================================================
 # Annotation History (v2)
 # =============================================================================

packages/backend/backend/data/repositories/__init__.py

@@ -13,6 +13,7 @@ from backend.data.repositories.training_task_repository import TrainingTaskRepos
 from backend.data.repositories.dataset_repository import DatasetRepository
 from backend.data.repositories.model_version_repository import ModelVersionRepository
 from backend.data.repositories.batch_upload_repository import BatchUploadRepository
+from backend.data.repositories.finetune_pool_repository import FineTunePoolRepository
 
 __all__ = [
     "BaseRepository",
@@ -23,4 +24,5 @@ __all__ = [
     "DatasetRepository",
     "ModelVersionRepository",
     "BatchUploadRepository",
+    "FineTunePoolRepository",
 ]

packages/backend/backend/data/repositories/finetune_pool_repository.py

@@ -0,0 +1,131 @@
+"""
+Fine-Tune Pool Repository
+
+Manages the fine-tune pool: accumulated user-reported failure documents
+for incremental model improvement.
+"""
+
+import logging
+from datetime import datetime
+from uuid import UUID
+
+from sqlalchemy import func
+from sqlmodel import select
+
+from backend.data.database import get_session_context
+from backend.data.admin_models import FineTunePoolEntry
+from backend.data.repositories.base import BaseRepository
+
+logger = logging.getLogger(__name__)
+
+
+class FineTunePoolRepository(BaseRepository[FineTunePoolEntry]):
+    """Repository for fine-tune pool management."""
+
+    def add_document(
+        self,
+        document_id: str | UUID,
+        added_by: str | None = None,
+        reason: str | None = None,
+    ) -> FineTunePoolEntry:
+        """Add a document to the fine-tune pool."""
+        with get_session_context() as session:
+            entry = FineTunePoolEntry(
+                document_id=UUID(str(document_id)),
+                added_by=added_by,
+                reason=reason,
+            )
+            session.add(entry)
+            session.commit()
+            session.refresh(entry)
+            session.expunge(entry)
+            return entry
+
+    def get_entry(self, entry_id: str | UUID) -> FineTunePoolEntry | None:
+        """Get a pool entry by ID."""
+        with get_session_context() as session:
+            entry = session.get(FineTunePoolEntry, UUID(str(entry_id)))
+            if entry:
+                session.expunge(entry)
+            return entry
+
+    def get_by_document(self, document_id: str | UUID) -> FineTunePoolEntry | None:
+        """Get pool entry for a document."""
+        with get_session_context() as session:
+            result = session.exec(
+                select(FineTunePoolEntry).where(
+                    FineTunePoolEntry.document_id == UUID(str(document_id))
+                )
+            ).first()
+            if result:
+                session.expunge(result)
+            return result
+
+    def get_paginated(
+        self,
+        verified_only: bool = False,
+        limit: int = 20,
+        offset: int = 0,
+    ) -> tuple[list[FineTunePoolEntry], int]:
+        """List pool entries with pagination."""
+        with get_session_context() as session:
+            query = select(FineTunePoolEntry)
+            count_query = select(func.count()).select_from(FineTunePoolEntry)
+            if verified_only:
+                query = query.where(FineTunePoolEntry.is_verified == True)
+                count_query = count_query.where(FineTunePoolEntry.is_verified == True)
+            total = session.exec(count_query).one()
+            entries = session.exec(
+                query.order_by(FineTunePoolEntry.created_at.desc())
+                .offset(offset)
+                .limit(limit)
+            ).all()
+            for e in entries:
+                session.expunge(e)
+            return list(entries), total
+
+    def get_pool_count(self, verified_only: bool = True) -> int:
+        """Get count of entries in the pool."""
+        with get_session_context() as session:
+            query = select(func.count()).select_from(FineTunePoolEntry)
+            if verified_only:
+                query = query.where(FineTunePoolEntry.is_verified == True)
+            return session.exec(query).one()
+
+    def get_all_document_ids(self, verified_only: bool = True) -> list[UUID]:
+        """Get all document IDs in the pool."""
+        with get_session_context() as session:
+            query = select(FineTunePoolEntry.document_id)
+            if verified_only:
+                query = query.where(FineTunePoolEntry.is_verified == True)
+            results = session.exec(query).all()
+            return list(results)
+
+    def verify_entry(
+        self,
+        entry_id: str | UUID,
+        verified_by: str | None = None,
+    ) -> FineTunePoolEntry | None:
+        """Mark a pool entry as verified."""
+        with get_session_context() as session:
+            entry = session.get(FineTunePoolEntry, UUID(str(entry_id)))
+            if not entry:
+                return None
+            entry.is_verified = True
+            entry.verified_at = datetime.utcnow()
+            entry.verified_by = verified_by
+            session.add(entry)
+            session.commit()
+            session.refresh(entry)
+            session.expunge(entry)
+            return entry
+
+    def remove_entry(self, entry_id: str | UUID) -> bool:
+        """Remove an entry from the pool."""
+        with get_session_context() as session:
+            entry = session.get(FineTunePoolEntry, UUID(str(entry_id)))
+            if not entry:
+                return False
+            session.delete(entry)
+            session.commit()
+            return True

packages/backend/backend/data/repositories/model_version_repository.py

@@ -43,6 +43,10 @@ class ModelVersionRepository(BaseRepository[ModelVersion]):
         training_config: dict[str, Any] | None = None,
         file_size: int | None = None,
         trained_at: datetime | None = None,
+        model_type: str = "base",
+        base_model_version_id: str | UUID | None = None,
+        base_training_dataset_id: str | UUID | None = None,
+        gating_status: str = "pending",
     ) -> ModelVersion:
         """Create a new model version."""
         with get_session_context() as session:
@@ -60,6 +64,10 @@ class ModelVersionRepository(BaseRepository[ModelVersion]):
                 training_config=training_config,
                 file_size=file_size,
                 trained_at=trained_at,
+                model_type=model_type,
+                base_model_version_id=UUID(str(base_model_version_id)) if base_model_version_id else None,
+                base_training_dataset_id=UUID(str(base_training_dataset_id)) if base_training_dataset_id else None,
+                gating_status=gating_status,
             )
             session.add(model)
             session.commit()

packages/backend/backend/domain/__init__.py

@@ -0,0 +1,25 @@
+"""
+Domain Layer
+
+Business logic separated from technical implementation.
+Contains document classification and invoice validation logic.
+"""
+from backend.domain.document_classifier import (
+    ClassificationResult,
+    DocumentClassifier,
+)
+from backend.domain.invoice_validator import (
+    InvoiceValidator,
+    ValidationIssue,
+    ValidationResult,
+)
+from backend.domain.utils import has_value
+
+__all__ = [
+    "ClassificationResult",
+    "DocumentClassifier",
+    "InvoiceValidator",
+    "ValidationIssue",
+    "ValidationResult",
+    "has_value",
+]

packages/backend/backend/domain/document_classifier.py

@@ -0,0 +1,108 @@
+"""
+Document Classifier
+
+Business logic for classifying documents based on extracted fields.
+Separates classification logic from inference pipeline.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from backend.domain.utils import has_value
+
+
+@dataclass(frozen=True)
+class ClassificationResult:
+    """
+    Immutable result of document classification.
+
+    Attributes:
+        document_type: Either "invoice" or "letter"
+        confidence: Confidence score between 0.0 and 1.0
+        reason: Human-readable explanation of classification
+    """
+
+    document_type: str
+    confidence: float
+    reason: str
+
+
+class DocumentClassifier:
+    """
+    Classifies documents as invoice or letter based on extracted fields.
+
+    Classification Rules:
+    1. If payment_line is present -> invoice (high confidence)
+    2. If 2+ invoice indicators present -> invoice (medium confidence)
+    3. If 1 invoice indicator present -> invoice (lower confidence)
+    4. Otherwise -> letter
+
+    Invoice indicator fields:
+    - payment_line (strongest indicator)
+    - OCR
+    - Amount
+    - Bankgiro
+    - Plusgiro
+    - InvoiceNumber
+    """
+
+    INVOICE_INDICATOR_FIELDS: frozenset[str] = frozenset(
+        {
+            "payment_line",
+            "OCR",
+            "Amount",
+            "Bankgiro",
+            "Plusgiro",
+            "InvoiceNumber",
+        }
+    )
+
+    def classify(self, fields: dict[str, str | None]) -> ClassificationResult:
+        """
+        Classify document type based on extracted fields.
+
+        Args:
+            fields: Dictionary of field names to extracted values.
+                    Empty strings or whitespace-only strings are treated as missing.
+
+        Returns:
+            Immutable ClassificationResult with type, confidence, and reason.
+        """
+        # Rule 1: payment_line is the strongest indicator
+        if has_value(fields.get("payment_line")):
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.95,
+                reason="payment_line detected",
+            )
+
+        # Count present invoice indicators (excluding payment_line already checked)
+        present_indicators = [
+            field
+            for field in self.INVOICE_INDICATOR_FIELDS
+            if field != "payment_line" and has_value(fields.get(field))
+        ]
+        indicator_count = len(present_indicators)
+
+        # Rule 2: Multiple indicators -> invoice with medium-high confidence
+        if indicator_count >= 2:
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.8,
+                reason=f"{indicator_count} invoice indicators present: {', '.join(present_indicators)}",
+            )
+
+        # Rule 3: Single indicator -> invoice with lower confidence
+        if indicator_count == 1:
+            return ClassificationResult(
+                document_type="invoice",
+                confidence=0.6,
+                reason=f"1 invoice indicator present: {present_indicators[0]}",
+            )
+
+        # Rule 4: No indicators -> letter
+        return ClassificationResult(
+            document_type="letter",
+            confidence=0.7,
+            reason="no invoice indicators found",
+        )

packages/backend/backend/domain/invoice_validator.py

@@ -0,0 +1,141 @@
+"""
+Invoice Validator
+
+Business logic for validating extracted invoice fields.
+Checks for required fields, format validity, and confidence thresholds.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+from backend.domain.utils import has_value
+
+
+@dataclass(frozen=True)
+class ValidationIssue:
+    """
+    Single validation issue.
+
+    Attributes:
+        field: Name of the field with the issue
+        severity: One of "error", "warning", "info"
+        message: Human-readable description of the issue
+    """
+
+    field: str
+    severity: str
+    message: str
+
+
+@dataclass(frozen=True)
+class ValidationResult:
+    """
+    Immutable result of invoice validation.
+
+    Attributes:
+        is_valid: True if no errors (warnings are allowed)
+        issues: Tuple of validation issues found
+        confidence: Average confidence score of validated fields
+    """
+
+    is_valid: bool
+    issues: tuple[ValidationIssue, ...]
+    confidence: float
+
+
+class InvoiceValidator:
+    """
+    Validates extracted invoice fields for completeness and consistency.
+
+    Validation Rules:
+    1. Required fields must be present (Amount)
+    2. At least one payment reference should be present (warning if missing)
+    3. Field confidence should be above threshold (warning if below)
+
+    Required fields:
+    - Amount
+
+    Payment reference fields (at least one expected):
+    - OCR
+    - Bankgiro
+    - Plusgiro
+    - payment_line
+    """
+
+    REQUIRED_FIELDS: tuple[str, ...] = ("Amount",)
+    PAYMENT_REF_FIELDS: tuple[str, ...] = ("OCR", "Bankgiro", "Plusgiro", "payment_line")
+    DEFAULT_MIN_CONFIDENCE: float = 0.5
+
+    def __init__(self, min_confidence: float = DEFAULT_MIN_CONFIDENCE) -> None:
+        """
+        Initialize validator.
+
+        Args:
+            min_confidence: Minimum confidence threshold for valid fields.
+                           Fields below this threshold produce warnings.
+        """
+        self._min_confidence = min_confidence
+
+    def validate(
+        self,
+        fields: dict[str, str | None],
+        confidence: dict[str, float],
+    ) -> ValidationResult:
+        """
+        Validate extracted invoice fields.
+
+        Args:
+            fields: Dictionary of field names to extracted values
+            confidence: Dictionary of field names to confidence scores
+
+        Returns:
+            Immutable ValidationResult with validity status and issues
+        """
+        issues: list[ValidationIssue] = []
+
+        # Check required fields
+        for field in self.REQUIRED_FIELDS:
+            if not has_value(fields.get(field)):
+                issues.append(
+                    ValidationIssue(
+                        field=field,
+                        severity="error",
+                        message=f"Required field '{field}' is missing",
+                    )
+                )
+
+        # Check payment reference (at least one expected)
+        has_payment_ref = any(
+            has_value(fields.get(f)) for f in self.PAYMENT_REF_FIELDS
+        )
+        if not has_payment_ref:
+            issues.append(
+                ValidationIssue(
+                    field="payment_reference",
+                    severity="warning",
+                    message="No payment reference (OCR, Bankgiro, Plusgiro, or payment_line)",
+                )
+            )
+
+        # Check confidence thresholds
+        for field, conf in confidence.items():
+            if conf < self._min_confidence:
+                issues.append(
+                    ValidationIssue(
+                        field=field,
+                        severity="warning",
+                        message=f"Low confidence ({conf:.2f}) for field '{field}'",
+                    )
+                )
+
+        # Calculate overall validity
+        has_errors = any(i.severity == "error" for i in issues)
+        avg_confidence = (
+            sum(confidence.values()) / len(confidence) if confidence else 0.0
+        )
+
+        return ValidationResult(
+            is_valid=not has_errors,
+            issues=tuple(issues),
+            confidence=avg_confidence,
+        )

packages/backend/backend/domain/utils.py

@@ -0,0 +1,23 @@
+"""
+Domain Layer Utilities
+
+Shared helper functions for domain layer classes.
+"""
+from __future__ import annotations
+
+
+def has_value(value: str | None) -> bool:
+    """
+    Check if a field value is present and non-empty.
+
+    Args:
+        value: Field value to check
+
+    Returns:
+        True if value is a non-empty, non-whitespace string
+    """
+    if value is None:
+        return False
+    if not isinstance(value, str):
+        return bool(value)
+    return bool(value.strip())

packages/backend/backend/pipeline/field_extractor.py

@@ -40,6 +40,7 @@ from .normalizers import (
     EnhancedAmountNormalizer,
     EnhancedDateNormalizer,
 )
+from .value_selector import ValueSelector
 
 
 @dataclass
@@ -84,7 +85,7 @@ class FieldExtractor:
 
     def __init__(
         self,
-        ocr_lang: str = 'en',
+        ocr_lang: str = 'sv',
         use_gpu: bool = False,
         bbox_padding: float = 0.1,
         dpi: int = 300,
@@ -169,13 +170,21 @@ class FieldExtractor:
                     overlap_ratio = overlap_area / token_area if token_area > 0 else 0
                     matching_tokens.append((token, overlap_ratio))
 
-        # Sort by overlap ratio and combine text
+        # Sort by overlap ratio
         matching_tokens.sort(key=lambda x: -x[1])
-        raw_text = ' '.join(t[0].text for t in matching_tokens)
 
         # Get field name
         field_name = CLASS_TO_FIELD.get(detection.class_name, detection.class_name)
 
+        # Convert to OCRTokens for value selection, then filter
+        from shared.ocr.paddle_ocr import OCRToken
+        pdf_ocr_tokens = [
+            OCRToken(text=t[0].text, bbox=t[0].bbox, confidence=1.0)
+            for t in matching_tokens
+        ]
+        value_tokens = ValueSelector.select_value_tokens(pdf_ocr_tokens, field_name)
+        raw_text = ' '.join(t.text for t in value_tokens)
+
         # Normalize and validate
         normalized_value, is_valid, validation_error = self._normalize_and_validate(
             field_name, raw_text
@@ -223,13 +232,14 @@ class FieldExtractor:
         # Run OCR on region
         ocr_tokens = self.ocr_engine.extract_from_image(region)
 
-        # Combine all OCR text
-        raw_text = ' '.join(t.text for t in ocr_tokens)
-        ocr_confidence = sum(t.confidence for t in ocr_tokens) / len(ocr_tokens) if ocr_tokens else 0.0
-
         # Get field name
         field_name = CLASS_TO_FIELD.get(detection.class_name, detection.class_name)
 
+        # Select value tokens (filter out label text)
+        value_tokens = ValueSelector.select_value_tokens(ocr_tokens, field_name)
+        raw_text = ' '.join(t.text for t in value_tokens)
+        ocr_confidence = sum(t.confidence for t in ocr_tokens) / len(ocr_tokens) if ocr_tokens else 0.0
+
         # Normalize and validate
         normalized_value, is_valid, validation_error = self._normalize_and_validate(
             field_name, raw_text

packages/backend/backend/pipeline/normalizers/amount.py

@@ -20,26 +20,98 @@ class AmountNormalizer(BaseNormalizer):
     Handles various Swedish amount formats:
     - With decimal: 1 234,56 kr
     - With SEK suffix: 1234.56 SEK
+    - Payment line kronor/ore: 590 00 (space = decimal separator)
     - Multiple amounts (returns the last one, usually the total)
     """
 
+    # Payment line kronor/ore pattern: "590 00" means 590.00 SEK
+    # Only matches when no comma/dot is present (pure digit-space-2digit format)
+    _KRONOR_ORE_PATTERN = re.compile(r'^(\d+)\s+(\d{2})$')
+
     @property
     def field_name(self) -> str:
         return "Amount"
 
+    @classmethod
+    def _try_kronor_ore(cls, text: str) -> NormalizationResult | None:
+        """Try to parse as payment line kronor/ore format.
+
+        Swedish payment lines separate kronor and ore with a space:
+        "590 00" = 590.00 SEK, "15658 00" = 15658.00 SEK
+
+        Only applies when text has no comma or dot (otherwise it's
+        a normal amount format with explicit decimal separator).
+
+        Returns NormalizationResult on success, None if not matched.
+        """
+        if ',' in text or '.' in text:
+            return None
+
+        match = cls._KRONOR_ORE_PATTERN.match(text.strip())
+        if not match:
+            return None
+
+        kronor = match.group(1)
+        ore = match.group(2)
+        try:
+            amount = float(f"{kronor}.{ore}")
+            if amount > 0:
+                return NormalizationResult.success(f"{amount:.2f}")
+        except ValueError:
+            pass
+        return None
+
+    @staticmethod
+    def _parse_amount_str(match: str) -> float | None:
+        """Convert matched amount string to float, detecting European vs Anglo format.
+
+        European: 2.254,50 -> 2254.50 (dot=thousand, comma=decimal)
+        Anglo: 1,234.56 -> 1234.56 (comma=thousand, dot=decimal)
+        Swedish: 1 234,56 -> 1234.56 (space=thousand, comma=decimal)
+        """
+        has_comma = ',' in match
+        has_dot = '.' in match
+        if has_comma and has_dot:
+            if match.rfind(',') > match.rfind('.'):
+                # European: 2.254,50
+                cleaned = match.replace(" ", "").replace(".", "").replace(",", ".")
+            else:
+                # Anglo: 1,234.56
+                cleaned = match.replace(" ", "").replace(",", "")
+        elif has_comma:
+            cleaned = match.replace(" ", "").replace(",", ".")
+        else:
+            cleaned = match.replace(" ", "")
+        try:
+            return float(cleaned)
+        except ValueError:
+            return None
+
     def normalize(self, text: str) -> NormalizationResult:
         text = text.strip()
         if not text:
             return NormalizationResult.failure("Empty text")
 
+        # Early check: payment line kronor/ore format ("590 00" → 590.00)
+        kronor_ore_result = self._try_kronor_ore(text)
+        if kronor_ore_result is not None:
+            return kronor_ore_result
+
         # Split by newlines and process line by line to get the last valid amount
         lines = text.split("\n")
 
         # Collect all valid amounts from all lines
         all_amounts: list[float] = []
 
-        # Pattern for Swedish amount format (with decimals)
-        amount_pattern = r"(\d[\d\s]*[,\.]\d{2})\s*(?:kr|SEK)?"
+        # Separate patterns for European and Anglo formats
+        # (?!\d) lookahead prevents partial matches (e.g. "1,23" in "1,234.56")
+        # European: dot=thousand, comma=decimal (2.254,50 or 1 234,56)
+        # Anglo: comma=thousand, dot=decimal (1,234.56 or 1234.56)
+        amount_pattern = (
+            r"(\d[\d\s.]*,\d{2})(?!\d)\s*(?:kr|SEK)?"
+            r"|"
+            r"(\d[\d\s,]*\.\d{2})(?!\d)\s*(?:kr|SEK)?"
+        )
 
         for line in lines:
             line = line.strip()
@@ -47,15 +119,13 @@ class AmountNormalizer(BaseNormalizer):
                 continue
 
             # Find all amounts in this line
-            matches = re.findall(amount_pattern, line, re.IGNORECASE)
-            for match in matches:
-                amount_str = match.replace(" ", "").replace(",", ".")
-                try:
-                    amount = float(amount_str)
-                    if amount > 0:
-                        all_amounts.append(amount)
-                except ValueError:
+            for m in re.finditer(amount_pattern, line, re.IGNORECASE):
+                match = m.group(1) or m.group(2)
+                if not match:
                     continue
+                amount = self._parse_amount_str(match)
+                if amount is not None and 0 < amount < 10_000_000:
+                    all_amounts.append(amount)
 
         # Return the last amount found (usually the total)
         if all_amounts:
@@ -64,7 +134,7 @@ class AmountNormalizer(BaseNormalizer):
         # Fallback: try shared validator on cleaned text
         cleaned = TextCleaner.normalize_amount_text(text)
         amount = FieldValidators.parse_amount(cleaned)
-        if amount is not None and amount > 0:
+        if amount is not None and 0 < amount < 10_000_000:
             return NormalizationResult.success(f"{amount:.2f}")
 
         # Try to find any decimal number
@@ -74,7 +144,7 @@ class AmountNormalizer(BaseNormalizer):
             amount_str = matches[-1].replace(",", ".")
             try:
                 amount = float(amount_str)
-                if amount > 0:
+                if 0 < amount < 10_000_000:
                     return NormalizationResult.success(f"{amount:.2f}")
             except ValueError:
                 pass
@@ -86,7 +156,7 @@ class AmountNormalizer(BaseNormalizer):
         if match:
             try:
                 amount = float(match.group(1))
-                if amount > 0:
+                if 0 < amount < 10_000_000:
                     return NormalizationResult.success(f"{amount:.2f}")
             except ValueError:
                 pass
@@ -98,7 +168,7 @@ class AmountNormalizer(BaseNormalizer):
             # Take the last/largest number
             try:
                 amount = float(matches[-1])
-                if amount > 0:
+                if 0 < amount < 10_000_000:
                     return NormalizationResult.success(f"{amount:.2f}")
             except ValueError:
                 pass
@@ -122,31 +192,33 @@ class EnhancedAmountNormalizer(AmountNormalizer):
         if not text:
             return NormalizationResult.failure("Empty text")
 
+        # Early check: payment line kronor/ore format ("590 00" → 590.00)
+        kronor_ore_result = self._try_kronor_ore(text)
+        if kronor_ore_result is not None:
+            return kronor_ore_result
+
         # Strategy 1: Apply OCR corrections first
         corrected_text = OCRCorrections.correct_digits(text, aggressive=False).corrected
 
         # Strategy 2: Look for labeled amounts (highest priority)
+        # Use two capture groups: group(1) = European, group(2) = Anglo
         labeled_patterns = [
-            # Swedish patterns
-            (r"(?:att\s+betala|summa|total|belopp)\s*[:\s]*(\d[\d\s]*[,\.]\d{2})", 1.0),
+            # Swedish patterns ((?!\d) prevents partial matches like "1,23" in "1,234.56")
+            (r"(?:att\s+betala|summa|total|belopp)\s*[:\s]*(\d[\d\s.]*,\d{2}(?!\d)|\d[\d\s,]*\.\d{2}(?!\d))", 1.0),
             (
-                r"(?:moms|vat)\s*[:\s]*(\d[\d\s]*[,\.]\d{2})",
+                r"(?:moms|vat)\s*[:\s]*(\d[\d\s.]*,\d{2}(?!\d)|\d[\d\s,]*\.\d{2}(?!\d))",
                 0.8,
             ),  # Lower priority for VAT
             # Generic pattern
-            (r"(\d[\d\s]*[,\.]\d{2})\s*(?:kr|sek|kronor)?", 0.7),
+            (r"(\d[\d\s.]*,\d{2}(?!\d)|\d[\d\s,]*\.\d{2}(?!\d))\s*(?:kr|sek|kronor)?", 0.7),
         ]
 
         candidates: list[tuple[float, float, int]] = []
         for pattern, priority in labeled_patterns:
             for match in re.finditer(pattern, corrected_text, re.IGNORECASE):
-                amount_str = match.group(1).replace(" ", "").replace(",", ".")
-                try:
-                    amount = float(amount_str)
-                    if 0 < amount < 10_000_000:  # Reasonable range
+                amount = self._parse_amount_str(match.group(1))
+                if amount is not None and 0 < amount < 10_000_000:
                     candidates.append((amount, priority, match.start()))
-                except ValueError:
-                    continue
 
         if candidates:
             # Sort by priority (desc), then by position (later is usually total)

packages/backend/backend/pipeline/normalizers/invoice_number.py

@@ -62,14 +62,25 @@ class InvoiceNumberNormalizer(BaseNormalizer):
                 # Skip if it looks like a date (YYYYMMDD)
                 if len(seq) == 8 and seq.startswith("20"):
                     continue
+                # Skip year-only values (2024, 2025, 2026, etc.)
+                if len(seq) == 4 and seq.startswith("20"):
+                    continue
                 # Skip if too long (likely OCR number)
                 if len(seq) > 10:
                     continue
                 valid_sequences.append(seq)
 
             if valid_sequences:
-                # Return shortest valid sequence
-                return NormalizationResult.success(min(valid_sequences, key=len))
+                # Prefer 4-8 digit sequences (typical invoice numbers),
+                # then closest to 6 digits within that range.
+                # This avoids picking short fragments like "775" from amounts.
+                def _score(seq: str) -> tuple[int, int]:
+                    length = len(seq)
+                    if 4 <= length <= 8:
+                        return (1, -abs(length - 6))
+                    return (0, -length)
+
+                return NormalizationResult.success(max(valid_sequences, key=_score))
 
         # Fallback: extract all digits if nothing else works
         digits = re.sub(r"\D", "", text)

packages/backend/backend/pipeline/normalizers/ocr_number.py

@@ -14,7 +14,7 @@ class OcrNumberNormalizer(BaseNormalizer):
     Normalizes OCR (Optical Character Recognition) reference numbers.
 
     OCR numbers in Swedish payment systems:
-    - Minimum 5 digits
+    - Minimum 2 digits
     - Used for automated payment matching
     """
 
@@ -29,7 +29,7 @@ class OcrNumberNormalizer(BaseNormalizer):
 
         digits = re.sub(r"\D", "", text)
 
-        if len(digits) < 5:
+        if len(digits) < 2:
             return NormalizationResult.failure(
                 f"Too few digits for OCR: {len(digits)}"
             )

packages/backend/backend/pipeline/pipeline.py

@@ -209,7 +209,7 @@ class InferencePipeline:
         self,
         model_path: str | Path,
         confidence_threshold: float = 0.5,
-        ocr_lang: str = 'en',
+        ocr_lang: str = 'sv',
         use_gpu: bool = False,
         dpi: int = 300,
         enable_fallback: bool = True,
@@ -234,7 +234,7 @@ class InferencePipeline:
             confidence_threshold=confidence_threshold,
             device='cuda' if use_gpu else 'cpu'
         )
-        self.extractor = FieldExtractor(ocr_lang=ocr_lang, use_gpu=use_gpu)
+        self.extractor = FieldExtractor(ocr_lang=ocr_lang, use_gpu=use_gpu, dpi=dpi)
         self.payment_line_parser = PaymentLineParser()
         self.dpi = dpi
         self.enable_fallback = enable_fallback
@@ -301,6 +301,27 @@ class InferencePipeline:
             all_extracted = []
             all_ocr_text = []  # Collect OCR text for VAT extraction
 
+            # Check if PDF has readable text layer (avoids OCR for text PDFs)
+            from shared.pdf.extractor import PDFDocument
+            is_text_pdf = False
+            pdf_tokens_by_page: dict[int, list] = {}
+            try:
+                with PDFDocument(pdf_path) as pdf_doc:
+                    is_text_pdf = pdf_doc.is_text_pdf()
+                    if is_text_pdf:
+                        for pg in range(pdf_doc.page_count):
+                            pdf_tokens_by_page[pg] = list(
+                                pdf_doc.extract_text_tokens(pg)
+                            )
+                        logger.info(
+                            "Text PDF detected, extracted %d tokens from %d pages",
+                            sum(len(t) for t in pdf_tokens_by_page.values()),
+                            len(pdf_tokens_by_page),
+                        )
+            except Exception as e:
+                logger.warning("PDF text detection failed, falling back to OCR: %s", e)
+                is_text_pdf = False
+
             # Process each page
             for page_no, image_bytes in render_pdf_to_images(pdf_path, dpi=self.dpi):
                 # Convert to numpy array
@@ -313,7 +334,17 @@ class InferencePipeline:
 
                 # Extract fields from detections
                 for detection in detections:
-                    extracted = self.extractor.extract_from_detection(detection, image_array)
+                    if is_text_pdf and page_no in pdf_tokens_by_page:
+                        extracted = self.extractor.extract_from_detection_with_pdf(
+                            detection,
+                            pdf_tokens_by_page[page_no],
+                            image.width,
+                            image.height,
+                        )
+                    else:
+                        extracted = self.extractor.extract_from_detection(
+                            detection, image_array
+                        )
                     all_extracted.append(extracted)
 
                 # Collect full-page OCR text for VAT extraction (only if business features enabled)
@@ -330,6 +361,7 @@ class InferencePipeline:
             # Fallback if key fields are missing
             if self.enable_fallback and self._needs_fallback(result):
                 self._run_fallback(pdf_path, result)
+                self._dedup_invoice_number(result)
 
             # Extract business invoice features if enabled
             if use_business_features:
@@ -417,7 +449,12 @@ class InferencePipeline:
             result.errors.append(f"Business feature extraction error: {error_detail}")
 
     def _merge_fields(self, result: InferenceResult) -> None:
-        """Merge extracted fields, keeping highest confidence for each field."""
+        """Merge extracted fields, keeping best candidate for each field.
+
+        Selection priority:
+        1. Prefer candidates without validation errors
+        2. Among equal validity, prefer higher confidence
+        """
         field_candidates: dict[str, list[ExtractedField]] = {}
 
         for extracted in result.extracted_fields:
@@ -430,15 +467,59 @@ class InferencePipeline:
 
         # Select best candidate for each field
         for field_name, candidates in field_candidates.items():
-            best = max(candidates, key=lambda x: x.confidence)
+            # Sort by: (no validation error, confidence) - descending
+            # This prefers candidates without errors, then by confidence
+            best = max(
+                candidates,
+                key=lambda x: (x.validation_error is None, x.confidence)
+            )
             result.fields[field_name] = best.normalized_value
             result.confidence[field_name] = best.confidence
             # Store bbox for each field (useful for payment_line and other fields)
             result.bboxes[field_name] = best.bbox
 
+        # Validate date consistency
+        self._validate_dates(result)
+
         # Perform cross-validation if payment_line is detected
         self._cross_validate_payment_line(result)
 
+        # Remove InvoiceNumber if it duplicates OCR or Bankgiro
+        self._dedup_invoice_number(result)
+
+    def _validate_dates(self, result: InferenceResult) -> None:
+        """Remove InvoiceDueDate if it is earlier than InvoiceDate."""
+        invoice_date = result.fields.get('InvoiceDate')
+        due_date = result.fields.get('InvoiceDueDate')
+        if invoice_date and due_date and due_date < invoice_date:
+            del result.fields['InvoiceDueDate']
+            result.confidence.pop('InvoiceDueDate', None)
+            result.bboxes.pop('InvoiceDueDate', None)
+
+    def _dedup_invoice_number(self, result: InferenceResult) -> None:
+        """Remove InvoiceNumber if it duplicates OCR or Bankgiro digits."""
+        inv_num = result.fields.get('InvoiceNumber')
+        if not inv_num:
+            return
+        inv_digits = re.sub(r'\D', '', str(inv_num))
+
+        # Check against OCR
+        ocr = result.fields.get('OCR')
+        if ocr and inv_digits == re.sub(r'\D', '', str(ocr)):
+            del result.fields['InvoiceNumber']
+            result.confidence.pop('InvoiceNumber', None)
+            result.bboxes.pop('InvoiceNumber', None)
+            return
+
+        # Check against Bankgiro (exact or substring match)
+        bg = result.fields.get('Bankgiro')
+        if bg:
+            bg_digits = re.sub(r'\D', '', str(bg))
+            if inv_digits == bg_digits or inv_digits in bg_digits:
+                del result.fields['InvoiceNumber']
+                result.confidence.pop('InvoiceNumber', None)
+                result.bboxes.pop('InvoiceNumber', None)
+
     def _parse_machine_readable_payment_line(self, payment_line: str) -> tuple[str | None, str | None, str | None]:
         """
         Parse machine-readable Swedish payment line format using unified PaymentLineParser.
@@ -597,10 +678,14 @@ class InferencePipeline:
 
     def _needs_fallback(self, result: InferenceResult) -> bool:
         """Check if fallback OCR is needed."""
-        # Check for key fields
         key_fields = ['Amount', 'InvoiceNumber', 'OCR']
-        missing = sum(1 for f in key_fields if f not in result.fields)
-        return missing >= 2  # Fallback if 2+ key fields missing
+        important_fields = ['InvoiceDate', 'InvoiceDueDate', 'supplier_organisation_number']
+
+        key_missing = sum(1 for f in key_fields if f not in result.fields)
+        important_missing = sum(1 for f in important_fields if f not in result.fields)
+
+        # Fallback if any key field missing OR 2+ important fields missing
+        return key_missing >= 1 or important_missing >= 2
 
     def _run_fallback(self, pdf_path: str | Path, result: InferenceResult) -> None:
         """Run full-page OCR fallback."""
@@ -632,12 +717,13 @@ class InferencePipeline:
         """Extract fields using regex patterns (fallback)."""
         patterns = {
             'Amount': [
-                r'(?:att\s*betala|summa|total|belopp)\s*[:.]?\s*([\d\s,\.]+)\s*(?:SEK|kr)?',
-                r'([\d\s,\.]+)\s*(?:SEK|kr)\s*$',
+                r'(?:att\s+betala)\s*[:.]?\s*([\d\s\.]*\d+[,\.]\d{2})\s*(?:SEK|kr)?',
+                r'(?:summa|total|belopp)\s*[:.]?\s*([\d\s\.]*\d+[,\.]\d{2})\s*(?:SEK|kr)?',
+                r'([\d\s\.]*\d+[,\.]\d{2})\s*(?:SEK|kr)\s*$',
             ],
             'Bankgiro': [
                 r'(?:bankgiro|bg)\s*[:.]?\s*(\d{3,4}[-\s]?\d{4})',
-                r'(\d{4}[-\s]\d{4})\s*(?=\s|$)',
+                r'(?<!\d)(\d{3,4}[-\s]\d{4})(?!\d)',
             ],
             'OCR': [
                 r'(?:ocr|referens)\s*[:.]?\s*(\d{10,25})',
@@ -645,6 +731,20 @@ class InferencePipeline:
             'InvoiceNumber': [
                 r'(?:fakturanr|fakturanummer|invoice)\s*[:.]?\s*(\d+)',
             ],
+            'InvoiceDate': [
+                r'(?:fakturadatum|invoice\s*date)\s*[:.]?\s*(\d{4}[-/]\d{2}[-/]\d{2})',
+                r'(?:fakturadatum|invoice\s*date)\s*[:.]?\s*(\d{2}[-/]\d{2}[-/]\d{4})',
+            ],
+            'InvoiceDueDate': [
+                r'(?:f[oö]rfallo(?:dag|datum)?|due\s*date|betala\s*senast)\s*[:.]?\s*(\d{4}[-/]\d{2}[-/]\d{2})',
+                r'(?:f[oö]rfallo(?:dag|datum)?|due\s*date|betala\s*senast)\s*[:.]?\s*(\d{2}[-/]\d{2}[-/]\d{4})',
+            ],
+            'supplier_organisation_number': [
+                r'(?:org\.?\s*n[ru]|organisationsnummer)\s*[:.]?\s*(\d{6}[-\s]?\d{4})',
+            ],
+            'Plusgiro': [
+                r'(?:plusgiro|pg)\s*[:.]?\s*(\d[\d\s-]{4,12}\d)',
+            ],
         }
 
         for field_name, field_patterns in patterns.items():
@@ -667,6 +767,22 @@ class InferencePipeline:
                         digits = re.sub(r'\D', '', value)
                         if len(digits) == 8:
                             value = f"{digits[:4]}-{digits[4:]}"
+                    elif field_name in ('InvoiceDate', 'InvoiceDueDate'):
+                        # Normalize DD/MM/YYYY to YYYY-MM-DD
+                        date_match = re.match(r'(\d{2})[-/](\d{2})[-/](\d{4})', value)
+                        if date_match:
+                            value = f"{date_match.group(3)}-{date_match.group(2)}-{date_match.group(1)}"
+                        # Replace / with -
+                        value = value.replace('/', '-')
+                    elif field_name == 'InvoiceNumber':
+                        # Skip year-like values (2024, 2025, 2026, etc.)
+                        if re.match(r'^20\d{2}$', value):
+                            continue
+                    elif field_name == 'supplier_organisation_number':
+                        # Ensure NNNNNN-NNNN format
+                        digits = re.sub(r'\D', '', value)
+                        if len(digits) == 10:
+                            value = f"{digits[:6]}-{digits[6:]}"
 
                     result.fields[field_name] = value
                     result.confidence[field_name] = 0.5  # Lower confidence for regex

packages/backend/backend/pipeline/value_selector.py

@@ -0,0 +1,172 @@
+"""
+Value Selector Module.
+
+Selects the most likely value token(s) from OCR output per field type,
+filtering out label text before sending to normalizer.
+
+Stateless and pure -- easy to test, no side effects.
+"""
+
+import re
+from typing import Final
+
+from shared.ocr.paddle_ocr import OCRToken
+
+
+# Swedish label keywords commonly found near field values
+LABEL_KEYWORDS: Final[frozenset[str]] = frozenset({
+    "fakturanummer", "fakturanr", "fakturadatum", "forfallodag", "forfalldatum",
+    "bankgiro", "plusgiro", "bg", "pg", "ocr", "belopp", "summa",
+    "total", "att", "betala", "kundnummer", "organisationsnummer",
+    "org", "nr", "datum", "nummer", "ref", "referens",
+    "momsreg", "vat", "moms", "sek", "kr",
+    "org.nr", "kund", "faktura", "invoice",
+})
+
+# Patterns
+_DATE_PATTERN = re.compile(
+    r"\d{4}[-./]\d{2}[-./]\d{2}"   # 2024-01-15, 2024.01.15
+    r"|"
+    r"\d{2}[-./]\d{2}[-./]\d{4}"   # 15/01/2024
+    r"|"
+    r"\d{8}"                        # 20240115
+)
+
+_AMOUNT_PATTERN = re.compile(
+    r"\d[\d\s.]*,\d{2}(?:\s*(?:kr|SEK))?$"   # European: 2.254,50 SEK
+    r"|"
+    r"\d[\d\s,]*\.\d{2}(?:\s*(?:kr|SEK))?$"  # Anglo: 1,234.56 SEK
+)
+
+_BANKGIRO_PATTERN = re.compile(
+    r"^\d{3,4}-\d{4}$"             # 123-4567
+    r"|"
+    r"^\d{7,8}$"                    # 1234567 or 12345678
+)
+
+_PLUSGIRO_PATTERN = re.compile(
+    r"^\d+-\d$"                     # 12345-6
+    r"|"
+    r"^\d{2,8}$"                    # 1234567
+)
+
+_ORG_NUMBER_PATTERN = re.compile(
+    r"\d{6}-?\d{4}"                 # 556123-4567 or 5561234567
+)
+
+
+def _is_label(text: str) -> bool:
+    """Check if token text is a known Swedish label keyword."""
+    cleaned = text.lower().rstrip(":").strip()
+    return cleaned in LABEL_KEYWORDS
+
+
+def _count_digits(text: str) -> int:
+    """Count digit characters in text."""
+    return sum(c.isdigit() for c in text)
+
+
+class ValueSelector:
+    """Selects value token(s) from OCR output, filtering label text.
+
+    Pure static methods -- no state, no side effects.
+    Fallback: always returns all tokens if no pattern matches,
+    so this can never make results worse than current behavior.
+    """
+
+    @staticmethod
+    def select_value_tokens(
+        tokens: list[OCRToken],
+        field_name: str,
+    ) -> list[OCRToken]:
+        """Select the most likely value token(s) for a given field.
+
+        Args:
+            tokens: OCR tokens from the detected region.
+            field_name: Normalized field name (e.g. "InvoiceDate", "Amount").
+
+        Returns:
+            Filtered list of value tokens. Falls back to all tokens
+            if no field-specific pattern matches.
+        """
+        if not tokens:
+            return []
+
+        selector = _FIELD_SELECTORS.get(field_name, _fallback_selector)
+        selected = selector(tokens)
+
+        # Safety: never return empty if we had input tokens
+        if not selected:
+            return list(tokens)
+
+        return selected
+
+    @staticmethod
+    def _select_date(tokens: list[OCRToken]) -> list[OCRToken]:
+        return _select_by_pattern(tokens, _DATE_PATTERN)
+
+    @staticmethod
+    def _select_amount(tokens: list[OCRToken]) -> list[OCRToken]:
+        return _select_by_pattern(tokens, _AMOUNT_PATTERN)
+
+    @staticmethod
+    def _select_bankgiro(tokens: list[OCRToken]) -> list[OCRToken]:
+        return _select_by_pattern(tokens, _BANKGIRO_PATTERN)
+
+    @staticmethod
+    def _select_plusgiro(tokens: list[OCRToken]) -> list[OCRToken]:
+        return _select_by_pattern(tokens, _PLUSGIRO_PATTERN)
+
+    @staticmethod
+    def _select_org_number(tokens: list[OCRToken]) -> list[OCRToken]:
+        return _select_by_pattern(tokens, _ORG_NUMBER_PATTERN)
+
+    @staticmethod
+    def _select_ocr_number(tokens: list[OCRToken]) -> list[OCRToken]:
+        """Select token with the longest digit sequence (min 2 digits)."""
+        best: OCRToken | None = None
+        best_count = 0
+        for token in tokens:
+            digit_count = _count_digits(token.text)
+            if digit_count >= 2 and digit_count > best_count:
+                best = token
+                best_count = digit_count
+        return [best] if best else []
+
+    @staticmethod
+    def _select_by_label_exclusion(tokens: list[OCRToken]) -> list[OCRToken]:
+        """Remove label keywords, keep remaining tokens."""
+        return [t for t in tokens if not _is_label(t.text)]
+
+    @staticmethod
+    def _select_payment_line(tokens: list[OCRToken]) -> list[OCRToken]:
+        """Payment line keeps all tokens (needs full text)."""
+        return list(tokens)
+
+
+def _select_by_pattern(
+    tokens: list[OCRToken],
+    pattern: re.Pattern[str],
+) -> list[OCRToken]:
+    """Select tokens matching a regex pattern."""
+    return [t for t in tokens if pattern.search(t.text.strip())]
+
+
+def _fallback_selector(tokens: list[OCRToken]) -> list[OCRToken]:
+    """Default: return all tokens unchanged."""
+    return list(tokens)
+
+
+# Map field names to selector functions
+_FIELD_SELECTORS: Final[dict[str, callable]] = {
+    "InvoiceDate": ValueSelector._select_date,
+    "InvoiceDueDate": ValueSelector._select_date,
+    "Amount": ValueSelector._select_amount,
+    "Bankgiro": ValueSelector._select_bankgiro,
+    "Plusgiro": ValueSelector._select_plusgiro,
+    "OCR": ValueSelector._select_ocr_number,
+    "InvoiceNumber": ValueSelector._select_by_label_exclusion,
+    "supplier_org_number": ValueSelector._select_org_number,
+    "customer_number": ValueSelector._select_by_label_exclusion,
+    "payment_line": ValueSelector._select_payment_line,
+}

packages/backend/backend/table/html_table_parser.py

@@ -0,0 +1,204 @@
+"""
+HTML Table Parser
+
+Parses HTML tables into structured data and maps columns to field names.
+"""
+
+from html.parser import HTMLParser
+import logging
+
+logger = logging.getLogger(__name__)
+
+# Configuration constants
+# Minimum pattern length to avoid false positives from short substrings
+MIN_PATTERN_MATCH_LENGTH = 3
+# Exact match bonus for column mapping priority
+EXACT_MATCH_BONUS = 100
+
+# Swedish column name mappings
+# Extended to support multiple invoice types: product invoices, rental invoices, utility bills
+COLUMN_MAPPINGS = {
+    "article_number": [
+        "art nummer",
+        "artikelnummer",
+        "artikel",
+        "artnr",
+        "art.nr",
+        "art nr",
+        "objektnummer",  # Rental: property reference
+        "objekt",
+    ],
+    "description": [
+        "beskrivning",
+        "produktbeskrivning",
+        "produkt",
+        "tjänst",
+        "text",
+        "benämning",
+        "vara/tjänst",
+        "vara",
+        # Rental invoice specific
+        "specifikation",
+        "spec",
+        "hyresperiod",  # Rental period
+        "period",
+        "typ",  # Type of charge
+        # Utility bills
+        "förbrukning",  # Consumption
+        "avläsning",  # Meter reading
+    ],
+    "quantity": ["antal", "qty", "st", "pcs", "kvantitet", "m²", "kvm"],
+    "unit": ["enhet", "unit"],
+    "unit_price": ["á-pris", "a-pris", "pris", "styckpris", "enhetspris", "à pris"],
+    "amount": [
+        "belopp",
+        "summa",
+        "total",
+        "netto",
+        "rad summa",
+        # Rental specific
+        "hyra",  # Rent
+        "avgift",  # Fee
+        "kostnad",  # Cost
+        "debitering",  # Charge
+        "totalt",  # Total
+    ],
+    "vat_rate": ["moms", "moms%", "vat", "skatt", "moms %"],
+    # Additional field for rental: deductions/adjustments
+    "deduction": [
+        "avdrag",  # Deduction
+        "rabatt",  # Discount
+        "kredit",  # Credit
+    ],
+}
+
+# Keywords that indicate NOT a line items table
+SUMMARY_KEYWORDS = [
+    "frakt",
+    "faktura.avg",
+    "fakturavg",
+    "exkl.moms",
+    "att betala",
+    "öresavr",
+    "bankgiro",
+    "plusgiro",
+    "ocr",
+    "forfallodatum",
+    "förfallodatum",
+]
+
+
+class _TableHTMLParser(HTMLParser):
+    """Internal HTML parser for tables."""
+
+    def __init__(self):
+        super().__init__()
+        self.rows: list[list[str]] = []
+        self.current_row: list[str] = []
+        self.current_cell: str = ""
+        self.in_td = False
+        self.in_thead = False
+        self.header_row: list[str] = []
+
+    def handle_starttag(self, tag, attrs):
+        if tag == "tr":
+            self.current_row = []
+        elif tag in ("td", "th"):
+            self.in_td = True
+            self.current_cell = ""
+        elif tag == "thead":
+            self.in_thead = True
+
+    def handle_endtag(self, tag):
+        if tag in ("td", "th"):
+            self.in_td = False
+            self.current_row.append(self.current_cell.strip())
+        elif tag == "tr":
+            if self.current_row:
+                if self.in_thead:
+                    self.header_row = self.current_row
+                else:
+                    self.rows.append(self.current_row)
+        elif tag == "thead":
+            self.in_thead = False
+
+    def handle_data(self, data):
+        if self.in_td:
+            self.current_cell += data
+
+
+class HTMLTableParser:
+    """Parse HTML tables into structured data."""
+
+    def parse(self, html: str) -> tuple[list[str], list[list[str]]]:
+        """
+        Parse HTML table and return header and rows.
+
+        Args:
+            html: HTML string containing table.
+
+        Returns:
+            Tuple of (header_row, data_rows).
+        """
+        parser = _TableHTMLParser()
+        parser.feed(html)
+        return parser.header_row, parser.rows
+
+
+class ColumnMapper:
+    """Map column headers to field names."""
+
+    def __init__(self, mappings: dict[str, list[str]] | None = None):
+        """
+        Initialize column mapper.
+
+        Args:
+            mappings: Custom column mappings. Uses Swedish defaults if None.
+        """
+        self.mappings = mappings or COLUMN_MAPPINGS
+
+    def map(self, headers: list[str]) -> dict[int, str]:
+        """
+        Map column indices to field names.
+
+        Args:
+            headers: List of column header strings.
+
+        Returns:
+            Dictionary mapping column index to field name.
+        """
+        mapping = {}
+        for idx, header in enumerate(headers):
+            normalized = self._normalize(header)
+
+            if not normalized.strip():
+                continue
+
+            best_match = None
+            best_match_len = 0
+
+            for field_name, patterns in self.mappings.items():
+                for pattern in patterns:
+                    if pattern == normalized:
+                        # Exact match gets highest priority
+                        best_match = field_name
+                        best_match_len = len(pattern) + EXACT_MATCH_BONUS
+                        break
+                    elif pattern in normalized and len(pattern) > best_match_len:
+                        # Partial match requires minimum length to avoid false positives
+                        if len(pattern) >= MIN_PATTERN_MATCH_LENGTH:
+                            best_match = field_name
+                            best_match_len = len(pattern)
+
+                if best_match_len > EXACT_MATCH_BONUS:
+                    # Found exact match, no need to check other fields
+                    break
+
+            if best_match:
+                mapping[idx] = best_match
+
+        return mapping
+
+    def _normalize(self, header: str) -> str:
+        """Normalize header text for matching."""
+        return header.lower().strip().replace(".", "").replace("-", " ")

packages/backend/backend/table/merged_cell_handler.py

@@ -0,0 +1,423 @@
+"""
+Merged Cell Handler
+
+Handles detection and extraction of data from tables with merged cells,
+a common issue with PP-StructureV3 OCR output.
+"""
+
+import re
+import logging
+from typing import TYPE_CHECKING
+
+from .models import LineItem
+
+if TYPE_CHECKING:
+    from .html_table_parser import ColumnMapper
+
+logger = logging.getLogger(__name__)
+
+# Minimum positive amount to consider as line item (filters noise like row indices)
+MIN_AMOUNT_THRESHOLD = 100
+
+
+class MergedCellHandler:
+    """Handles tables with vertically merged cells from PP-StructureV3."""
+
+    def __init__(self, mapper: "ColumnMapper"):
+        """
+        Initialize handler.
+
+        Args:
+            mapper: ColumnMapper instance for header keyword detection.
+        """
+        self.mapper = mapper
+
+    def has_vertically_merged_cells(self, rows: list[list[str]]) -> bool:
+        """
+        Check if table rows contain vertically merged data in single cells.
+
+        PP-StructureV3 sometimes merges multiple table rows into single cells, e.g.:
+        ["Produktnr 1457280 1457280 1060381", "", "Antal 6ST 6ST 1ST", "Pris 127,20 127,20 159,20"]
+
+        Detection: cells contain repeating patterns of numbers or keywords suggesting multiple lines.
+        """
+        if not rows:
+            return False
+
+        for row in rows:
+            for cell in row:
+                if not cell or len(cell) < 20:
+                    continue
+
+                # Check for multiple product numbers (7+ digit patterns)
+                product_nums = re.findall(r"\b\d{7}\b", cell)
+                if len(product_nums) >= 2:
+                    logger.debug(f"has_vertically_merged_cells: found {len(product_nums)} product numbers in cell")
+                    return True
+
+                # Check for multiple prices (Swedish format: 123,45 or 1 234,56)
+                prices = re.findall(r"\b\d{1,3}(?:\s?\d{3})*[,\.]\d{2}\b", cell)
+                if len(prices) >= 3:
+                    logger.debug(f"has_vertically_merged_cells: found {len(prices)} prices in cell")
+                    return True
+
+                # Check for multiple quantity patterns (e.g., "6ST 6ST 1ST")
+                quantities = re.findall(r"\b\d+\s*(?:ST|st|PCS|pcs)\b", cell)
+                if len(quantities) >= 2:
+                    logger.debug(f"has_vertically_merged_cells: found {len(quantities)} quantities in cell")
+                    return True
+
+        return False
+
+    def split_merged_rows(
+        self, rows: list[list[str]]
+    ) -> tuple[list[str], list[list[str]]]:
+        """
+        Split vertically merged cells back into separate rows.
+
+        Handles complex cases where PP-StructureV3 merges content across
+        multiple HTML rows. For example, 5 line items might be spread across
+        3 HTML rows with content mixed together.
+
+        Strategy:
+        1. Merge all row content per column
+        2. Detect how many actual data rows exist (by counting product numbers)
+        3. Split each column's content into that many lines
+
+        Returns header and data rows.
+        """
+        if not rows:
+            return [], []
+
+        # Filter out completely empty rows
+        non_empty_rows = [r for r in rows if any(cell.strip() for cell in r)]
+        if not non_empty_rows:
+            return [], rows
+
+        # Determine column count
+        col_count = max(len(r) for r in non_empty_rows)
+
+        # Merge content from all rows for each column
+        merged_columns = []
+        for col_idx in range(col_count):
+            col_content = []
+            for row in non_empty_rows:
+                if col_idx < len(row) and row[col_idx].strip():
+                    col_content.append(row[col_idx].strip())
+            merged_columns.append(" ".join(col_content))
+
+        logger.debug(f"split_merged_rows: merged columns = {merged_columns}")
+
+        # Count how many actual data rows we should have
+        # Use the column with most product numbers as reference
+        expected_rows = self._count_expected_rows(merged_columns)
+        logger.debug(f"split_merged_rows: expecting {expected_rows} data rows")
+
+        if expected_rows <= 1:
+            # Not enough data for splitting
+            return [], rows
+
+        # Split each column based on expected row count
+        split_columns = []
+        for col_idx, col_text in enumerate(merged_columns):
+            if not col_text.strip():
+                split_columns.append([""] * (expected_rows + 1))  # +1 for header
+                continue
+            lines = self._split_cell_content_for_rows(col_text, expected_rows)
+            split_columns.append(lines)
+
+        # Ensure all columns have same number of lines (immutable approach)
+        max_lines = max(len(col) for col in split_columns)
+        split_columns = [
+            col + [""] * (max_lines - len(col))
+            for col in split_columns
+        ]
+
+        logger.debug(f"split_merged_rows: split into {max_lines} lines total")
+
+        # First line is header, rest are data rows
+        header = [col[0] for col in split_columns]
+        data_rows = []
+        for line_idx in range(1, max_lines):
+            row = [col[line_idx] if line_idx < len(col) else "" for col in split_columns]
+            if any(cell.strip() for cell in row):
+                data_rows.append(row)
+
+        logger.debug(f"split_merged_rows: header={header}, data_rows count={len(data_rows)}")
+        return header, data_rows
+
+    def _count_expected_rows(self, merged_columns: list[str]) -> int:
+        """
+        Count how many data rows should exist based on content patterns.
+
+        Returns the maximum count found from:
+        - Product numbers (7 digits)
+        - Quantity patterns (number + ST/PCS)
+        - Amount patterns (in columns likely to be totals)
+        """
+        max_count = 0
+
+        for col_text in merged_columns:
+            if not col_text:
+                continue
+
+            # Count product numbers (most reliable indicator)
+            product_nums = re.findall(r"\b\d{7}\b", col_text)
+            max_count = max(max_count, len(product_nums))
+
+            # Count quantities (e.g., "6ST 6ST 1ST 1ST 1ST")
+            quantities = re.findall(r"\b\d+\s*(?:ST|st|PCS|pcs)\b", col_text)
+            max_count = max(max_count, len(quantities))
+
+        return max_count
+
+    def _split_cell_content_for_rows(self, cell: str, expected_rows: int) -> list[str]:
+        """
+        Split cell content knowing how many data rows we expect.
+
+        This is smarter than split_cell_content because it knows the target count.
+        """
+        cell = cell.strip()
+
+        # Try product number split first
+        product_pattern = re.compile(r"(\b\d{7}\b)")
+        products = product_pattern.findall(cell)
+        if len(products) == expected_rows:
+            parts = product_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            # Include description text after each product number
+            values = []
+            for i in range(1, len(parts), 2):  # Odd indices are product numbers
+                if i < len(parts):
+                    prod_num = parts[i].strip()
+                    # Check if there's description text after
+                    desc = parts[i + 1].strip() if i + 1 < len(parts) else ""
+                    # If description looks like text (not another pattern), include it
+                    if desc and not re.match(r"^\d{7}$", desc):
+                        # Truncate at next product number pattern if any
+                        desc_clean = re.split(r"\d{7}", desc)[0].strip()
+                        if desc_clean:
+                            values.append(f"{prod_num} {desc_clean}")
+                        else:
+                            values.append(prod_num)
+                    else:
+                        values.append(prod_num)
+            if len(values) == expected_rows:
+                return [header] + values
+
+        # Try quantity split
+        qty_pattern = re.compile(r"(\b\d+\s*(?:ST|st|PCS|pcs|M|m|KG|kg)\b)")
+        quantities = qty_pattern.findall(cell)
+        if len(quantities) == expected_rows:
+            parts = qty_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            values = [p.strip() for p in parts[1:] if p.strip() and qty_pattern.match(p)]
+            if len(values) == expected_rows:
+                return [header] + values
+
+        # Try amount split for discount+totalsumma columns
+        cell_lower = cell.lower()
+        has_discount = any(kw in cell_lower for kw in ["rabatt", "discount"])
+        has_total = any(kw in cell_lower for kw in ["totalsumma", "total", "summa", "belopp"])
+
+        if has_discount and has_total:
+            # Extract only amounts (3+ digit numbers), skip discount percentages
+            amount_pattern = re.compile(r"\b(\d{3,}[,\.]\d{2})\b")
+            amounts = amount_pattern.findall(cell)
+            if len(amounts) >= expected_rows:
+                # Take the last expected_rows amounts (they are likely the totals)
+                return ["Totalsumma"] + amounts[:expected_rows]
+
+        # Try price split
+        price_pattern = re.compile(r"(\b\d{1,3}(?:\s?\d{3})*[,\.]\d{2}\b)")
+        prices = price_pattern.findall(cell)
+        if len(prices) >= expected_rows:
+            parts = price_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            values = [p.strip() for p in parts[1:] if p.strip() and price_pattern.match(p)]
+            if len(values) >= expected_rows:
+                return [header] + values[:expected_rows]
+
+        # Fall back to original single-value behavior
+        return [cell]
+
+    def split_cell_content(self, cell: str) -> list[str]:
+        """
+        Split a cell containing merged multi-line content.
+
+        Strategies:
+        1. Look for product number patterns (7 digits)
+        2. Look for quantity patterns (number + ST/PCS)
+        3. Look for price patterns (with decimal)
+        4. Handle interleaved discount+amount patterns
+        """
+        cell = cell.strip()
+
+        # Strategy 1: Split by product numbers (common pattern: "Produktnr 1234567 1234568")
+        product_pattern = re.compile(r"(\b\d{7}\b)")
+        products = product_pattern.findall(cell)
+        if len(products) >= 2:
+            # Extract header (text before first product number) and values
+            parts = product_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            values = [p for p in parts[1:] if p.strip() and re.match(r"\d{7}", p)]
+            return [header] + values
+
+        # Strategy 2: Split by quantities (e.g., "Antal 6ST 6ST 1ST")
+        qty_pattern = re.compile(r"(\b\d+\s*(?:ST|st|PCS|pcs|M|m|KG|kg)\b)")
+        quantities = qty_pattern.findall(cell)
+        if len(quantities) >= 2:
+            parts = qty_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            values = [p.strip() for p in parts[1:] if p.strip() and qty_pattern.match(p)]
+            return [header] + values
+
+        # Strategy 3: Handle interleaved discount+amount (e.g., "Rabatt i% Totalsumma 10,0 686,88 10,0 686,88")
+        # Check if header contains two keywords indicating merged columns
+        cell_lower = cell.lower()
+        has_discount_header = any(kw in cell_lower for kw in ["rabatt", "discount"])
+        has_amount_header = any(kw in cell_lower for kw in ["totalsumma", "summa", "belopp", "total"])
+
+        if has_discount_header and has_amount_header:
+            # Extract all numbers and pair them (discount, amount, discount, amount, ...)
+            # Pattern for amounts: 3+ digit numbers with decimals (e.g., 686,88)
+            amount_pattern = re.compile(r"\b(\d{3,}[,\.]\d{2})\b")
+            amounts = amount_pattern.findall(cell)
+
+            if len(amounts) >= 2:
+                # Return header as "Totalsumma" (amount header) so it maps to amount field, not deduction
+                # This avoids the "Rabatt" keyword causing is_deduction=True
+                header = "Totalsumma"
+                return [header] + amounts
+
+        # Strategy 4: Split by prices (e.g., "Pris 127,20 127,20 159,20")
+        price_pattern = re.compile(r"(\b\d{1,3}(?:\s?\d{3})*[,\.]\d{2}\b)")
+        prices = price_pattern.findall(cell)
+        if len(prices) >= 2:
+            parts = price_pattern.split(cell)
+            header = parts[0].strip() if parts else ""
+            values = [p.strip() for p in parts[1:] if p.strip() and price_pattern.match(p)]
+            return [header] + values
+
+        # No pattern detected, return as single value
+        return [cell]
+
+    def has_merged_header(self, header: list[str] | None) -> bool:
+        """
+        Check if header appears to be a merged cell containing multiple column names.
+
+        This happens when OCR merges table headers into a single cell, e.g.:
+        "Specifikation 0218103-1201 2 rum och kök Hyra Avdrag" instead of separate columns.
+
+        Also handles cases where PP-StructureV3 produces headers like:
+        ["Specifikation ... Hyra Avdrag", "", "", ""] with empty trailing cells.
+        """
+        if header is None or not header:
+            return False
+
+        # Filter out empty cells to find the actual content
+        non_empty_cells = [h for h in header if h.strip()]
+
+        # Check if we have a single non-empty cell that contains multiple keywords
+        if len(non_empty_cells) == 1:
+            header_text = non_empty_cells[0].lower()
+            # Count how many column keywords are in this single cell
+            keyword_count = 0
+            for patterns in self.mapper.mappings.values():
+                for pattern in patterns:
+                    if pattern in header_text:
+                        keyword_count += 1
+                        break  # Only count once per field type
+
+            logger.debug(f"has_merged_header: header_text='{header_text}', keyword_count={keyword_count}")
+            return keyword_count >= 2
+
+        return False
+
+    def extract_from_merged_cells(
+        self, header: list[str], rows: list[list[str]]
+    ) -> list[LineItem]:
+        """
+        Extract line items from tables with merged cells.
+
+        For poorly OCR'd tables like:
+        Header: ["Specifikation 0218103-1201 2 rum och kök Hyra Avdrag"]
+        Row 1: ["", "", "", "8159"]      <- amount row
+        Row 2: ["", "", "", "-2 000"]    <- deduction row (separate line item)
+
+        Or:
+        Row: ["", "", "", "8159 -2 000"]  <- both in same row -> 2 line items
+
+        Each amount becomes its own line item. Negative amounts are marked as is_deduction=True.
+        """
+        items = []
+
+        # Amount pattern for Swedish format - match numbers like "8159" or "8 159" or "-2000" or "-2 000"
+        amount_pattern = re.compile(
+            r"(-?\d[\d\s]*(?:[,\.]\d+)?)"
+        )
+
+        # Try to parse header cell for description info
+        header_text = " ".join(h for h in header if h.strip()) if header else ""
+        logger.debug(f"extract_from_merged_cells: header_text='{header_text}'")
+        logger.debug(f"extract_from_merged_cells: rows={rows}")
+
+        # Extract description from header
+        description = None
+        article_number = None
+
+        # Look for object number pattern (e.g., "0218103-1201")
+        obj_match = re.search(r"(\d{7}-\d{4})", header_text)
+        if obj_match:
+            article_number = obj_match.group(1)
+
+        # Look for description after object number
+        desc_match = re.search(r"\d{7}-\d{4}\s+(.+?)(?:\s+(?:Hyra|Avdrag|Belopp))", header_text, re.IGNORECASE)
+        if desc_match:
+            description = desc_match.group(1).strip()
+
+        row_index = 0
+        for row in rows:
+            # Combine all non-empty cells in the row
+            row_text = " ".join(cell.strip() for cell in row if cell.strip())
+            logger.debug(f"extract_from_merged_cells: row text='{row_text}'")
+
+            if not row_text:
+                continue
+
+            # Find all amounts in the row
+            amounts = amount_pattern.findall(row_text)
+            logger.debug(f"extract_from_merged_cells: amounts={amounts}")
+
+            for amt_str in amounts:
+                # Clean the amount string
+                cleaned = amt_str.replace(" ", "").strip()
+                if not cleaned or cleaned == "-":
+                    continue
+
+                is_deduction = cleaned.startswith("-")
+
+                # Skip small positive numbers that are likely not amounts
+                # (e.g., row indices, small percentages)
+                if not is_deduction:
+                    try:
+                        val = float(cleaned.replace(",", "."))
+                        if val < MIN_AMOUNT_THRESHOLD:
+                            continue
+                    except ValueError:
+                        continue
+
+                # Create a line item for each amount
+                item = LineItem(
+                    row_index=row_index,
+                    description=description if row_index == 0 else "Avdrag" if is_deduction else None,
+                    article_number=article_number if row_index == 0 else None,
+                    amount=cleaned,
+                    is_deduction=is_deduction,
+                    confidence=0.7,
+                )
+                items.append(item)
+                row_index += 1
+                logger.debug(f"extract_from_merged_cells: created item amount={cleaned}, is_deduction={is_deduction}")
+
+        return items

packages/backend/backend/table/models.py

@@ -0,0 +1,61 @@
+"""
+Line Items Data Models
+
+Dataclasses for line item extraction results.
+"""
+
+from dataclasses import dataclass
+from decimal import Decimal, InvalidOperation
+
+
+@dataclass
+class LineItem:
+    """Single line item from invoice."""
+
+    row_index: int
+    description: str | None = None
+    quantity: str | None = None
+    unit: str | None = None
+    unit_price: str | None = None
+    amount: str | None = None
+    article_number: str | None = None
+    vat_rate: str | None = None
+    is_deduction: bool = False  # True if this row is a deduction/discount
+    confidence: float = 0.9
+
+
+@dataclass
+class LineItemsResult:
+    """Result of line items extraction."""
+
+    items: list[LineItem]
+    header_row: list[str]
+    raw_html: str
+    is_reversed: bool = False
+
+    @property
+    def total_amount(self) -> str | None:
+        """Calculate total amount from line items (deduction rows have negative amounts)."""
+        if not self.items:
+            return None
+
+        total = Decimal("0")
+        for item in self.items:
+            if item.amount:
+                try:
+                    # Parse Swedish number format (1 234,56)
+                    amount_str = item.amount.replace(" ", "").replace(",", ".")
+                    total += Decimal(amount_str)
+                except InvalidOperation:
+                    pass
+
+        if total == 0:
+            return None
+
+        # Format back to Swedish format
+        formatted = f"{total:,.2f}".replace(",", " ").replace(".", ",")
+        # Fix the space/comma swap
+        parts = formatted.rsplit(",", 1)
+        if len(parts) == 2:
+            return parts[0].replace(" ", " ") + "," + parts[1]
+        return formatted

packages/backend/backend/table/structure_detector.py

@@ -158,36 +158,36 @@ class TableDetector:
             return tables
 
         # Log raw result type for debugging
-        logger.info(f"PP-StructureV3 raw results type: {type(results).__name__}")
+        logger.debug(f"PP-StructureV3 raw results type: {type(results).__name__}")
 
         # Handle case where results is a single dict-like object (PaddleX 3.x)
         # rather than a list of results
         if hasattr(results, "get") and not isinstance(results, list):
             # Single result object - wrap in list for uniform processing
-            logger.info("Results is dict-like, wrapping in list")
+            logger.debug("Results is dict-like, wrapping in list")
             results = [results]
         elif hasattr(results, "__iter__") and not isinstance(results, (list, tuple)):
             # Iterator or generator - convert to list
             try:
                 results = list(results)
-                logger.info(f"Converted iterator to list with {len(results)} items")
+                logger.debug(f"Converted iterator to list with {len(results)} items")
             except Exception as e:
                 logger.warning(f"Failed to convert results to list: {e}")
                 return tables
 
-        logger.info(f"Processing {len(results)} result(s)")
+        logger.debug(f"Processing {len(results)} result(s)")
 
         for i, result in enumerate(results):
             try:
                 result_type = type(result).__name__
                 has_get = hasattr(result, "get")
                 has_layout = hasattr(result, "layout_elements")
-                logger.info(f"Result[{i}]: type={result_type}, has_get={has_get}, has_layout_elements={has_layout}")
+                logger.debug(f"Result[{i}]: type={result_type}, has_get={has_get}, has_layout_elements={has_layout}")
 
                 # Try PaddleX 3.x API first (dict-like with table_res_list)
                 if has_get:
                     parsed = self._parse_paddlex_result(result)
-                    logger.info(f"Result[{i}]: parsed {len(parsed)} tables via PaddleX path")
+                    logger.debug(f"Result[{i}]: parsed {len(parsed)} tables via PaddleX path")
                     tables.extend(parsed)
                     continue
 
@@ -201,14 +201,14 @@ class TableDetector:
                         if table_result and table_result.confidence >= self.config.min_confidence:
                             tables.append(table_result)
                             legacy_count += 1
-                    logger.info(f"Result[{i}]: parsed {legacy_count} tables via legacy path")
+                    logger.debug(f"Result[{i}]: parsed {legacy_count} tables via legacy path")
                 else:
                     logger.warning(f"Result[{i}]: no recognized API (not dict-like and no layout_elements)")
             except Exception as e:
                 logger.warning(f"Failed to parse result: {type(result).__name__}, error: {e}")
                 continue
 
-        logger.info(f"Total tables detected: {len(tables)}")
+        logger.debug(f"Total tables detected: {len(tables)}")
         return tables
 
     def _parse_paddlex_result(self, result: Any) -> list[TableDetectionResult]:
@@ -223,7 +223,7 @@ class TableDetector:
                 result_keys = list(result.keys())
             elif hasattr(result, "__dict__"):
                 result_keys = list(result.__dict__.keys())
-            logger.info(f"Parsing PaddleX result: type={result_type}, keys={result_keys}")
+            logger.debug(f"Parsing PaddleX result: type={result_type}, keys={result_keys}")
 
             # Get table results from PaddleX 3.x API
             # Handle both dict.get() and attribute access
@@ -234,8 +234,8 @@ class TableDetector:
                 table_res_list = getattr(result, "table_res_list", None)
                 parsing_res_list = getattr(result, "parsing_res_list", [])
 
-            logger.info(f"table_res_list: {type(table_res_list).__name__}, count={len(table_res_list) if table_res_list else 0}")
-            logger.info(f"parsing_res_list: {type(parsing_res_list).__name__}, count={len(parsing_res_list) if parsing_res_list else 0}")
+            logger.debug(f"table_res_list: {type(table_res_list).__name__}, count={len(table_res_list) if table_res_list else 0}")
+            logger.debug(f"parsing_res_list: {type(parsing_res_list).__name__}, count={len(parsing_res_list) if parsing_res_list else 0}")
 
             if not table_res_list:
                 # Log available keys/attributes for debugging
@@ -330,7 +330,7 @@ class TableDetector:
                     # Default confidence for PaddleX 3.x results
                     confidence = 0.9
 
-                    logger.info(f"Table {i}: html_len={len(html)}, cells={len(cells)}")
+                    logger.debug(f"Table {i}: html_len={len(html)}, cells={len(cells)}")
                     tables.append(TableDetectionResult(
                         bbox=(float(bbox[0]), float(bbox[1]), float(bbox[2]), float(bbox[3])),
                         html=html,
@@ -467,14 +467,14 @@ class TableDetector:
         if not pdf_path.exists():
             raise FileNotFoundError(f"PDF not found: {pdf_path}")
 
-        logger.info(f"detect_from_pdf: {pdf_path}, page={page_number}, dpi={dpi}")
+        logger.debug(f"detect_from_pdf: {pdf_path}, page={page_number}, dpi={dpi}")
 
         # Render specific page
         for page_no, image_bytes in render_pdf_to_images(str(pdf_path), dpi=dpi):
             if page_no == page_number:
                 image = Image.open(io.BytesIO(image_bytes))
                 image_array = np.array(image)
-                logger.info(f"detect_from_pdf: rendered page {page_no}, image shape={image_array.shape}")
+                logger.debug(f"detect_from_pdf: rendered page {page_no}, image shape={image_array.shape}")
                 return self.detect(image_array)
 
         raise ValueError(f"Page {page_number} not found in PDF")

packages/backend/backend/table/text_line_items_extractor.py

@@ -15,6 +15,11 @@ import logging
 
 logger = logging.getLogger(__name__)
 
+# Configuration constants
+DEFAULT_ROW_TOLERANCE = 15.0  # Max vertical distance (pixels) to consider same row
+MIN_ITEMS_FOR_VALID_EXTRACTION = 2  # Minimum items required for valid extraction
+MIN_TEXT_ELEMENTS_FOR_EXTRACTION = 5  # Minimum text elements needed to attempt extraction
+
 
 @dataclass
 class TextElement:
@@ -65,7 +70,10 @@ class TextLineItemsResult:
     extraction_method: str = "text_spatial"
 
 
-# Swedish amount pattern: 1 234,56 or 1234.56 or 1,234.56
+# Amount pattern matches Swedish, US, and simple numeric formats
+# Handles: "1 234,56", "1,234.56", "1234.56", "100 kr", "50:-", "-100,00"
+# Does NOT handle: amounts with more than 2 decimal places, scientific notation
+# See tests in test_text_line_items_extractor.py::TestAmountPattern
 AMOUNT_PATTERN = re.compile(
     r"(?<![0-9])(?:"
     r"-?\d{1,3}(?:\s\d{3})*(?:,\d{2})?"  # Swedish: 1 234,56
@@ -128,17 +136,17 @@ class TextLineItemsExtractor:
 
     def __init__(
         self,
-        row_tolerance: float = 15.0,  # Max vertical distance to consider same row
-        min_items_for_valid: int = 2,  # Minimum items to consider extraction valid
+        row_tolerance: float = DEFAULT_ROW_TOLERANCE,
+        min_items_for_valid: int = MIN_ITEMS_FOR_VALID_EXTRACTION,
     ):
         """
         Initialize extractor.
 
         Args:
             row_tolerance: Maximum vertical distance (pixels) between elements
-                          to consider them on the same row.
+                          to consider them on the same row. Default: 15.0
             min_items_for_valid: Minimum number of line items required for
-                                extraction to be considered successful.
+                                extraction to be considered successful. Default: 2
         """
         self.row_tolerance = row_tolerance
         self.min_items_for_valid = min_items_for_valid
@@ -161,10 +169,13 @@ class TextLineItemsExtractor:
 
         # Extract text elements from parsing results
         text_elements = self._extract_text_elements(parsing_res_list)
-        logger.info(f"TextLineItemsExtractor: found {len(text_elements)} text elements")
+        logger.debug(f"TextLineItemsExtractor: found {len(text_elements)} text elements")
 
-        if len(text_elements) < 5:  # Need at least a few elements
-            logger.debug("Too few text elements for line item extraction")
+        if len(text_elements) < MIN_TEXT_ELEMENTS_FOR_EXTRACTION:
+            logger.debug(
+                f"Too few text elements ({len(text_elements)}) for line item extraction, "
+                f"need at least {MIN_TEXT_ELEMENTS_FOR_EXTRACTION}"
+            )
             return None
 
         return self.extract_from_text_elements(text_elements)
@@ -183,11 +194,11 @@ class TextLineItemsExtractor:
         """
         # Group elements by row
         rows = self._group_by_row(text_elements)
-        logger.info(f"TextLineItemsExtractor: grouped into {len(rows)} rows")
+        logger.debug(f"TextLineItemsExtractor: grouped into {len(rows)} rows")
 
         # Find the line items section
         item_rows = self._identify_line_item_rows(rows)
-        logger.info(f"TextLineItemsExtractor: identified {len(item_rows)} potential item rows")
+        logger.debug(f"TextLineItemsExtractor: identified {len(item_rows)} potential item rows")
 
         if len(item_rows) < self.min_items_for_valid:
             logger.debug(f"Found only {len(item_rows)} item rows, need at least {self.min_items_for_valid}")
@@ -195,7 +206,7 @@ class TextLineItemsExtractor:
 
         # Extract structured items
         items = self._parse_line_items(item_rows)
-        logger.info(f"TextLineItemsExtractor: extracted {len(items)} line items")
+        logger.debug(f"TextLineItemsExtractor: extracted {len(items)} line items")
 
         if len(items) < self.min_items_for_valid:
             return None
@@ -209,7 +220,11 @@ class TextLineItemsExtractor:
     def _extract_text_elements(
         self, parsing_res_list: list[dict[str, Any]]
     ) -> list[TextElement]:
-        """Extract TextElement objects from parsing_res_list."""
+        """Extract TextElement objects from parsing_res_list.
+
+        Handles both dict and LayoutBlock object formats from PP-StructureV3.
+        Gracefully skips invalid elements with appropriate logging.
+        """
         elements = []
 
         for elem in parsing_res_list:
@@ -220,11 +235,15 @@ class TextLineItemsExtractor:
                     bbox = elem.get("bbox", [])
                     # Try both 'text' and 'content' keys
                     text = elem.get("text", "") or elem.get("content", "")
-                else:
+                elif hasattr(elem, "label"):
                     label = getattr(elem, "label", "")
                     bbox = getattr(elem, "bbox", [])
                     # LayoutBlock objects use 'content' attribute
                     text = getattr(elem, "content", "") or getattr(elem, "text", "")
+                else:
+                    # Element is neither dict nor has expected attributes
+                    logger.debug(f"Skipping element with unexpected type: {type(elem).__name__}")
+                    continue
 
                 # Only process text elements (skip images, tables, etc.)
                 if label not in ("text", "paragraph_title", "aside_text"):
@@ -232,6 +251,7 @@ class TextLineItemsExtractor:
 
                 # Validate bbox
                 if not self._valid_bbox(bbox):
+                    logger.debug(f"Skipping element with invalid bbox: {bbox}")
                     continue
 
                 # Clean text
@@ -250,8 +270,13 @@ class TextLineItemsExtractor:
                         ),
                     )
                 )
+            except (KeyError, TypeError, ValueError, AttributeError) as e:
+                # Expected format issues - log at debug level
+                logger.debug(f"Skipping element due to format issue: {e}")
+                continue
             except Exception as e:
-                logger.debug(f"Failed to parse element: {e}")
+                # Unexpected errors - log at warning level for visibility
+                logger.warning(f"Unexpected error parsing element: {type(e).__name__}: {e}")
                 continue
 
         return elements
@@ -270,6 +295,7 @@ class TextLineItemsExtractor:
         Group text elements into rows based on vertical position.
 
         Elements within row_tolerance of each other are considered same row.
+        Uses dynamic average center_y to handle varying element heights more accurately.
         """
         if not elements:
             return []
@@ -277,22 +303,22 @@ class TextLineItemsExtractor:
         # Sort by vertical position
         sorted_elements = sorted(elements, key=lambda e: e.center_y)
 
-        rows = []
-        current_row = [sorted_elements[0]]
-        current_y = sorted_elements[0].center_y
+        rows: list[list[TextElement]] = []
+        current_row: list[TextElement] = [sorted_elements[0]]
 
         for elem in sorted_elements[1:]:
-            if abs(elem.center_y - current_y) <= self.row_tolerance:
-                # Same row
+            # Calculate dynamic average center_y for current row
+            avg_center_y = sum(e.center_y for e in current_row) / len(current_row)
+
+            if abs(elem.center_y - avg_center_y) <= self.row_tolerance:
+                # Same row - add element and recalculate average on next iteration
                 current_row.append(elem)
             else:
-                # New row
-                if current_row:
-                    # Sort row by horizontal position
+                # New row - finalize current row
+                # Sort row by horizontal position (left to right)
                 current_row.sort(key=lambda e: e.center_x)
                 rows.append(current_row)
                 current_row = [elem]
-                current_y = elem.center_y
 
         # Don't forget last row
         if current_row:

packages/backend/backend/validation/llm_validator.py

@@ -7,6 +7,7 @@ the autolabel results to identify potential errors.
 
 import json
 import base64
+import logging
 import os
 from pathlib import Path
 from typing import Optional, Dict, Any, List
@@ -14,6 +15,8 @@ from dataclasses import dataclass, asdict
 from datetime import datetime
 
 import psycopg2
+
+logger = logging.getLogger(__name__)
 from psycopg2.extras import execute_values
 
 from shared.config import DEFAULT_DPI
@@ -648,7 +651,7 @@ Return ONLY the JSON object, no other text."""
         docs = self.get_documents_with_failed_matches(limit=limit)
 
         if verbose:
-            print(f"Found {len(docs)} documents with failed matches to validate")
+            logger.info("Found %d documents with failed matches to validate", len(docs))
 
         results = []
         for i, doc in enumerate(docs):
@@ -656,16 +659,16 @@ Return ONLY the JSON object, no other text."""
 
             if verbose:
                 failed_fields = [f['field'] for f in doc['failed_fields']]
-                print(f"[{i+1}/{len(docs)}] Validating {doc_id[:8]}... (failed: {', '.join(failed_fields)})")
+                logger.info("[%d/%d] Validating %s... (failed: %s)", i+1, len(docs), doc_id[:8], ', '.join(failed_fields))
 
             result = self.validate_document(doc_id, provider, model)
             results.append(result)
 
             if verbose:
                 if result.error:
-                    print(f"  ERROR: {result.error}")
+                    logger.error("  ERROR: %s", result.error)
                 else:
-                    print(f"  OK ({result.processing_time_ms:.0f}ms)")
+                    logger.info("  OK (%.0fms)", result.processing_time_ms)
 
         return results
 

packages/backend/backend/web/api/v1/admin/documents.py

@@ -39,6 +39,26 @@ from backend.web.schemas.common import ErrorResponse
 
 logger = logging.getLogger(__name__)
 
+# PDF magic bytes - all valid PDF files must start with this sequence
+PDF_MAGIC_BYTES = b"%PDF"
+
+
+def validate_pdf_magic_bytes(content: bytes) -> None:
+    """Validate that file content has valid PDF magic bytes.
+
+    PDF files must start with the bytes '%PDF' (0x25 0x50 0x44 0x46).
+    This validation prevents attackers from uploading malicious files
+    (executables, scripts) by simply renaming them to .pdf extension.
+
+    Args:
+        content: The raw file content to validate.
+
+    Raises:
+        ValueError: If the content does not start with valid PDF magic bytes.
+    """
+    if not content or not content.startswith(PDF_MAGIC_BYTES):
+        raise ValueError("Invalid PDF file: does not have valid PDF header")
+
 
 def _validate_uuid(value: str, name: str = "ID") -> None:
     """Validate UUID format."""
@@ -135,6 +155,14 @@ def create_documents_router(storage_config: StorageConfig) -> APIRouter:
             logger.error(f"Failed to read uploaded file: {e}")
             raise HTTPException(status_code=400, detail="Failed to read file")
 
+        # Validate PDF magic bytes (only for PDF files)
+        if file_ext == ".pdf":
+            try:
+                validate_pdf_magic_bytes(content)
+            except ValueError as e:
+                logger.warning(f"PDF magic bytes validation failed: {e}")
+                raise HTTPException(status_code=400, detail=str(e))
+
         # Get page count (for PDF)
         page_count = 1
         if file_ext == ".pdf":

packages/backend/backend/web/api/v1/admin/training/__init__.py

@@ -12,6 +12,7 @@ from .documents import register_document_routes
 from .export import register_export_routes
 from .datasets import register_dataset_routes
 from .models import register_model_routes
+from .pool import register_pool_routes
 
 
 def create_training_router() -> APIRouter:
@@ -23,6 +24,7 @@ def create_training_router() -> APIRouter:
     register_export_routes(router)
     register_dataset_routes(router)
     register_model_routes(router)
+    register_pool_routes(router)
 
     return router
 

packages/backend/backend/web/api/v1/admin/training/datasets.py

@@ -12,6 +12,7 @@ from backend.web.core.auth import (
     AnnotationRepoDep,
     ModelVersionRepoDep,
     TrainingTaskRepoDep,
+    FineTunePoolRepoDep,
 )
 from backend.web.schemas.admin import (
     DatasetCreateRequest,
@@ -233,6 +234,7 @@ def register_dataset_routes(router: APIRouter) -> None:
         datasets_repo: DatasetRepoDep,
         models: ModelVersionRepoDep,
         tasks: TrainingTaskRepoDep,
+        pool: FineTunePoolRepoDep,
     ) -> TrainingTaskResponse:
         """Create a training task from a dataset.
 
@@ -261,13 +263,39 @@ def register_dataset_routes(router: APIRouter) -> None:
                     status_code=404,
                     detail=f"Base model version not found: {base_model_version_id}",
                 )
+            # Chain prevention: never fine-tune from a fine-tuned model
+            if getattr(base_model, "model_type", "base") == "finetune":
+                original_base_id = getattr(base_model, "base_model_version_id", None)
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Cannot chain fine-tunes. Model {base_model.version} is already "
+                        f"a fine-tuned model. Select the original base model instead"
+                        f"{f' (base_model_version_id: {original_base_id})' if original_base_id else ''}."
+                    ),
+                )
+            # Pool threshold: require minimum verified pool entries for fine-tuning
+            from backend.web.services.data_mixer import MIN_POOL_SIZE
+
+            verified_count = pool.get_pool_count(verified_only=True)
+            if verified_count < MIN_POOL_SIZE:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Fine-tuning requires at least {MIN_POOL_SIZE} verified pool entries "
+                        f"(currently {verified_count}). Add more documents to the fine-tune "
+                        f"pool and verify them before starting fine-tuning."
+                    ),
+                )
+
             # Store the resolved model path for the training worker
             config_dict["base_model_path"] = base_model.model_path
             config_dict["base_model_version"] = base_model.version
             logger.info(
-                "Incremental training: using model %s (%s) as base",
+                "Fine-tuning: using base model %s (%s) with %d verified pool entries",
                 base_model.version,
                 base_model.model_path,
+                verified_count,
             )
 
         task_id = tasks.create(

packages/backend/backend/web/api/v1/admin/training/export.py

@@ -11,6 +11,7 @@ from backend.web.schemas.admin import (
     ExportResponse,
 )
 from backend.web.schemas.common import ErrorResponse
+from shared.bbox import expand_bbox
 
 logger = logging.getLogger(__name__)
 
@@ -102,12 +103,47 @@ def register_export_routes(router: APIRouter) -> None:
                     dst_image.write_bytes(image_content)
                     total_images += 1
 
+                    # Get image dimensions for bbox expansion
+                    img_dims = storage.get_admin_image_dimensions(doc_id, page_num)
+                    if img_dims is None:
+                        # Fall back to standard A4 at 300 DPI if dimensions unavailable
+                        img_width, img_height = 2480, 3508
+                    else:
+                        img_width, img_height = img_dims
+
                     label_name = f"{doc.document_id}_page{page_num}.txt"
                     label_path = export_dir / "labels" / split / label_name
 
                     with open(label_path, "w") as f:
                         for ann in page_annotations:
-                            line = f"{ann.class_id} {ann.x_center:.6f} {ann.y_center:.6f} {ann.width:.6f} {ann.height:.6f}\n"
+                            # Convert normalized coords to pixel coords
+                            half_w = (ann.width * img_width) / 2
+                            half_h = (ann.height * img_height) / 2
+                            x0 = ann.x_center * img_width - half_w
+                            y0 = ann.y_center * img_height - half_h
+                            x1 = ann.x_center * img_width + half_w
+                            y1 = ann.y_center * img_height + half_h
+
+                            # Apply uniform bbox expansion
+                            ex0, ey0, ex1, ey1 = expand_bbox(
+                                bbox=(x0, y0, x1, y1),
+                                image_width=img_width,
+                                image_height=img_height,
+                            )
+
+                            # Convert back to normalized YOLO format
+                            new_x_center = (ex0 + ex1) / 2 / img_width
+                            new_y_center = (ey0 + ey1) / 2 / img_height
+                            new_width = (ex1 - ex0) / img_width
+                            new_height = (ey1 - ey0) / img_height
+
+                            # Clamp to valid range
+                            new_x_center = max(0, min(1, new_x_center))
+                            new_y_center = max(0, min(1, new_y_center))
+                            new_width = max(0, min(1, new_width))
+                            new_height = max(0, min(1, new_height))
+
+                            line = f"{ann.class_id} {new_x_center:.6f} {new_y_center:.6f} {new_width:.6f} {new_height:.6f}\n"
                             f.write(line)
                             total_annotations += 1
 

packages/backend/backend/web/api/v1/admin/training/models.py

@@ -88,6 +88,9 @@ def register_model_routes(router: APIRouter) -> None:
                     name=m.name,
                     status=m.status,
                     is_active=m.is_active,
+                    model_type=getattr(m, "model_type", "base"),
+                    base_model_version_id=str(m.base_model_version_id) if getattr(m, "base_model_version_id", None) else None,
+                    gating_status=getattr(m, "gating_status", "pending"),
                     metrics_mAP=m.metrics_mAP,
                     document_count=m.document_count,
                     trained_at=m.trained_at,
@@ -121,6 +124,9 @@ def register_model_routes(router: APIRouter) -> None:
                 name=model.name,
                 status=model.status,
                 is_active=model.is_active,
+                model_type=getattr(model, "model_type", "base"),
+                base_model_version_id=str(model.base_model_version_id) if getattr(model, "base_model_version_id", None) else None,
+                gating_status=getattr(model, "gating_status", "pending"),
                 metrics_mAP=model.metrics_mAP,
                 document_count=model.document_count,
                 trained_at=model.trained_at,
@@ -153,6 +159,10 @@ def register_model_routes(router: APIRouter) -> None:
             model_path=model.model_path,
             status=model.status,
             is_active=model.is_active,
+            model_type=getattr(model, "model_type", "base"),
+            base_model_version_id=str(model.base_model_version_id) if getattr(model, "base_model_version_id", None) else None,
+            base_training_dataset_id=str(model.base_training_dataset_id) if getattr(model, "base_training_dataset_id", None) else None,
+            gating_status=getattr(model, "gating_status", "pending"),
             task_id=str(model.task_id) if model.task_id else None,
             dataset_id=str(model.dataset_id) if model.dataset_id else None,
             metrics_mAP=model.metrics_mAP,
@@ -209,6 +219,25 @@ def register_model_routes(router: APIRouter) -> None:
     ) -> ModelVersionResponse:
         """Activate a model version for inference."""
         _validate_uuid(version_id, "version_id")
+
+        # Check gating status before activation (for fine-tuned models)
+        pre_check = models.get(version_id)
+        if not pre_check:
+            raise HTTPException(status_code=404, detail="Model version not found")
+        model_type = getattr(pre_check, "model_type", "base")
+        gating_status = getattr(pre_check, "gating_status", "skipped")
+        if model_type == "finetune":
+            if gating_status == "reject":
+                raise HTTPException(
+                    status_code=400,
+                    detail="Model failed deployment gating validation. Cannot activate a rejected model.",
+                )
+            if gating_status == "pending":
+                raise HTTPException(
+                    status_code=400,
+                    detail="Model gating validation not yet completed. Wait for validation to finish.",
+                )
+
         model = models.activate(version_id)
         if not model:
             raise HTTPException(status_code=404, detail="Model version not found")
@@ -227,6 +256,8 @@ def register_model_routes(router: APIRouter) -> None:
         message = "Model version activated for inference"
         if model_reloaded:
             message += " (model reloaded)"
+        if gating_status == "review":
+            message += " (WARNING: gating status is REVIEW - manual inspection recommended)"
 
         return ModelVersionResponse(
             version_id=str(model.version_id),

packages/backend/backend/web/api/v1/admin/training/pool.py

@@ -0,0 +1,159 @@
+"""Fine-Tune Pool Endpoints."""
+
+import logging
+from typing import Annotated
+
+from fastapi import APIRouter, HTTPException, Query
+
+from backend.web.core.auth import AdminTokenDep, FineTunePoolRepoDep, DocumentRepoDep
+from backend.web.schemas.admin.pool import (
+    PoolAddRequest,
+    PoolEntryItem,
+    PoolEntryResponse,
+    PoolListResponse,
+    PoolStatsResponse,
+)
+
+from ._utils import _validate_uuid
+
+logger = logging.getLogger(__name__)
+
+
+def register_pool_routes(router: APIRouter) -> None:
+    """Register fine-tune pool endpoints on the router."""
+
+    @router.post(
+        "/pool",
+        response_model=PoolEntryResponse,
+        summary="Add document to fine-tune pool",
+        description="Add a labeled document to the fine-tune pool for future fine-tuning.",
+    )
+    async def add_to_pool(
+        request: PoolAddRequest,
+        admin_token: AdminTokenDep,
+        pool: FineTunePoolRepoDep,
+        docs: DocumentRepoDep,
+    ) -> PoolEntryResponse:
+        """Add a document to the fine-tune pool."""
+        _validate_uuid(request.document_id, "document_id")
+
+        # Verify document exists
+        doc = docs.get(request.document_id)
+        if not doc:
+            raise HTTPException(status_code=404, detail="Document not found")
+
+        # Check if already in pool
+        existing = pool.get_by_document(request.document_id)
+        if existing:
+            raise HTTPException(
+                status_code=409,
+                detail=f"Document already in fine-tune pool (entry_id: {existing.entry_id})",
+            )
+
+        entry = pool.add_document(
+            document_id=request.document_id,
+            added_by=admin_token,
+            reason=request.reason,
+        )
+
+        return PoolEntryResponse(
+            entry_id=str(entry.entry_id),
+            message="Document added to fine-tune pool",
+        )
+
+    @router.get(
+        "/pool",
+        response_model=PoolListResponse,
+        summary="List fine-tune pool entries",
+    )
+    async def list_pool_entries(
+        admin_token: AdminTokenDep,
+        pool: FineTunePoolRepoDep,
+        verified_only: Annotated[bool, Query(description="Filter to verified only")] = False,
+        limit: Annotated[int, Query(ge=1, le=100)] = 20,
+        offset: Annotated[int, Query(ge=0)] = 0,
+    ) -> PoolListResponse:
+        """List entries in the fine-tune pool."""
+        entries, total = pool.get_paginated(
+            verified_only=verified_only,
+            limit=limit,
+            offset=offset,
+        )
+
+        return PoolListResponse(
+            total=total,
+            limit=limit,
+            offset=offset,
+            entries=[
+                PoolEntryItem(
+                    entry_id=str(e.entry_id),
+                    document_id=str(e.document_id),
+                    added_by=e.added_by,
+                    reason=e.reason,
+                    is_verified=e.is_verified,
+                    verified_at=e.verified_at,
+                    verified_by=e.verified_by,
+                    created_at=e.created_at,
+                )
+                for e in entries
+            ],
+        )
+
+    @router.get(
+        "/pool/stats",
+        response_model=PoolStatsResponse,
+        summary="Get fine-tune pool statistics",
+    )
+    async def get_pool_stats(
+        admin_token: AdminTokenDep,
+        pool: FineTunePoolRepoDep,
+    ) -> PoolStatsResponse:
+        """Get statistics about the fine-tune pool."""
+        total = pool.get_pool_count(verified_only=False)
+        verified = pool.get_pool_count(verified_only=True)
+
+        return PoolStatsResponse(
+            total_entries=total,
+            verified_entries=verified,
+            unverified_entries=total - verified,
+            is_ready=verified >= 50,
+        )
+
+    @router.post(
+        "/pool/{entry_id}/verify",
+        response_model=PoolEntryResponse,
+        summary="Verify a pool entry",
+        description="Mark a pool entry as verified (human-reviewed).",
+    )
+    async def verify_pool_entry(
+        entry_id: str,
+        admin_token: AdminTokenDep,
+        pool: FineTunePoolRepoDep,
+    ) -> PoolEntryResponse:
+        """Mark a pool entry as verified."""
+        _validate_uuid(entry_id, "entry_id")
+        entry = pool.verify_entry(entry_id, verified_by=admin_token)
+        if not entry:
+            raise HTTPException(status_code=404, detail="Pool entry not found")
+
+        return PoolEntryResponse(
+            entry_id=str(entry.entry_id),
+            message="Pool entry verified",
+        )
+
+    @router.delete(
+        "/pool/{entry_id}",
+        summary="Remove from fine-tune pool",
+    )
+    async def remove_from_pool(
+        entry_id: str,
+        admin_token: AdminTokenDep,
+        pool: FineTunePoolRepoDep,
+    ) -> dict:
+        """Remove a document from the fine-tune pool."""
+        _validate_uuid(entry_id, "entry_id")
+        success = pool.remove_entry(entry_id)
+        if not success:
+            raise HTTPException(status_code=404, detail="Pool entry not found")
+
+        return {"message": "Entry removed from fine-tune pool"}

packages/backend/backend/web/core/auth.py

@@ -17,6 +17,7 @@ from backend.data.repositories import (
     TrainingTaskRepository,
     ModelVersionRepository,
     BatchUploadRepository,
+    FineTunePoolRepository,
 )
 
 
@@ -95,6 +96,12 @@ def get_batch_upload_repository() -> BatchUploadRepository:
     return BatchUploadRepository()
 
 
+@lru_cache(maxsize=1)
+def get_finetune_pool_repository() -> FineTunePoolRepository:
+    """Get the FineTunePoolRepository instance (thread-safe singleton)."""
+    return FineTunePoolRepository()
+
+
 def reset_all_repositories() -> None:
     """Reset all repository instances (for testing)."""
     get_token_repository.cache_clear()
@@ -104,6 +111,7 @@ def reset_all_repositories() -> None:
     get_training_task_repository.cache_clear()
     get_model_version_repository.cache_clear()
     get_batch_upload_repository.cache_clear()
+    get_finetune_pool_repository.cache_clear()
 
 
 # Repository dependency type aliases
@@ -113,3 +121,4 @@ DatasetRepoDep = Annotated[DatasetRepository, Depends(get_dataset_repository)]
 TrainingTaskRepoDep = Annotated[TrainingTaskRepository, Depends(get_training_task_repository)]
 ModelVersionRepoDep = Annotated[ModelVersionRepository, Depends(get_model_version_repository)]
 BatchUploadRepoDep = Annotated[BatchUploadRepository, Depends(get_batch_upload_repository)]
+FineTunePoolRepoDep = Annotated[FineTunePoolRepository, Depends(get_finetune_pool_repository)]

packages/backend/backend/web/core/scheduler.py

@@ -16,6 +16,7 @@ from backend.data.repositories import (
     ModelVersionRepository,
     DocumentRepository,
     AnnotationRepository,
+    FineTunePoolRepository,
 )
 from backend.web.core.task_interface import TaskRunner, TaskStatus
 from backend.web.services.storage_helpers import get_storage_helper
@@ -47,6 +48,7 @@ class TrainingScheduler(TaskRunner):
         self._model_versions = ModelVersionRepository()
         self._documents = DocumentRepository()
         self._annotations = AnnotationRepository()
+        self._pool = FineTunePoolRepository()
 
     @property
     def name(self) -> str:
@@ -168,7 +170,7 @@ class TrainingScheduler(TaskRunner):
 
         try:
             # Get training configuration
-            model_name = config.get("model_name", "yolo11n.pt")
+            model_name = config.get("model_name", "yolo26s.pt")
             base_model_path = config.get("base_model_path")  # For incremental training
             epochs = config.get("epochs", 100)
             batch_size = config.get("batch_size", 16)
@@ -182,14 +184,19 @@ class TrainingScheduler(TaskRunner):
             augmentation_multiplier = config.get("augmentation_multiplier", 2)
 
             # Determine which model to use as base
-            if base_model_path:
-                # Incremental training: use existing trained model
+            is_finetune = bool(base_model_path)
+            if is_finetune:
+                # Fine-tuning: use existing trained model as base
                 if not Path(base_model_path).exists():
                     raise ValueError(f"Base model not found: {base_model_path}")
                 effective_model = base_model_path
+                # Override parameters for fine-tuning (best practices)
+                epochs = config.get("epochs", 10)
+                learning_rate = config.get("learning_rate", 0.001)
                 self._training_tasks.add_log(
                     task_id, "INFO",
-                    f"Incremental training from: {base_model_path}",
+                    f"Fine-tuning from: {base_model_path} "
+                    f"(epochs={epochs}, freeze=10, cos_lr=true)",
                 )
             else:
                 # Train from pretrained model
@@ -229,10 +236,16 @@ class TrainingScheduler(TaskRunner):
                         f"(total: {aug_result['total_images']})",
                     )
 
+            # Build mixed dataset for fine-tuning (pool samples + old data)
+            if is_finetune and dataset_id:
+                data_yaml, dataset_path = self._build_mixed_finetune_dataset(
+                    task_id, dataset_path, data_yaml,
+                )
+
             # Run YOLO training
             result = self._run_yolo_training(
                 task_id=task_id,
-                model_name=effective_model,  # Use base model or pretrained model
+                model_name=effective_model,
                 data_yaml=data_yaml,
                 epochs=epochs,
                 batch_size=batch_size,
@@ -240,6 +253,8 @@ class TrainingScheduler(TaskRunner):
                 learning_rate=learning_rate,
                 device=device,
                 project_name=project_name,
+                freeze=10 if is_finetune else 0,
+                cos_lr=is_finetune,
             )
 
             # Update task with results
@@ -261,13 +276,23 @@ class TrainingScheduler(TaskRunner):
                 )
 
             # Auto-create model version for the completed training
-            self._create_model_version_from_training(
+            model_version = self._create_model_version_from_training(
                 task_id=task_id,
                 config=config,
                 dataset_id=dataset_id,
                 result=result,
             )
 
+            # Auto-run gating validation for fine-tuned models
+            if is_finetune and model_version:
+                self._run_gating_after_finetune(
+                    task_id=task_id,
+                    model_version=model_version,
+                    config=config,
+                    data_yaml=data_yaml,
+                    result=result,
+                )
+
         except Exception as e:
             logger.error(f"Training task {task_id} failed: {e}")
             self._training_tasks.add_log(task_id, "ERROR", f"Training failed: {e}")
@@ -286,13 +311,16 @@ class TrainingScheduler(TaskRunner):
         config: dict[str, Any],
         dataset_id: str | None,
         result: dict[str, Any],
-    ) -> None:
-        """Create a model version entry from completed training."""
+    ) -> Any | None:
+        """Create a model version entry from completed training.
+
+        Returns the created model version, or None on failure.
+        """
         try:
             model_path = result.get("model_path")
             if not model_path:
                 logger.warning(f"No model path in training result for task {task_id}")
-                return
+                return None
 
             # Get task info for name
             task = self._training_tasks.get(task_id)
@@ -322,6 +350,12 @@ class TrainingScheduler(TaskRunner):
                 if dataset:
                     document_count = dataset.total_documents
 
+            # Determine model lineage
+            is_finetune = bool(config.get("base_model_path"))
+            model_type = "finetune" if is_finetune else "base"
+            base_model_version_id = config.get("base_model_version_id") if is_finetune else None
+            gating_status = "pending" if is_finetune else "skipped"
+
             # Create model version
             model_version = self._model_versions.create(
                 version=version,
@@ -337,6 +371,10 @@ class TrainingScheduler(TaskRunner):
                 training_config=config,
                 file_size=file_size,
                 trained_at=datetime.utcnow(),
+                model_type=model_type,
+                base_model_version_id=base_model_version_id,
+                base_training_dataset_id=dataset_id if not is_finetune else None,
+                gating_status=gating_status,
             )
 
             logger.info(
@@ -349,12 +387,105 @@ class TrainingScheduler(TaskRunner):
                 f"Model version {version} created (mAP: {mAP_display})",
             )
 
+            return model_version
+
         except Exception as e:
             logger.error(f"Failed to create model version for task {task_id}: {e}")
             self._training_tasks.add_log(
                 task_id, "WARNING",
                 f"Failed to auto-create model version: {e}",
             )
+            return None
+
+    def _build_mixed_finetune_dataset(
+        self,
+        task_id: str,
+        base_dataset_path: Path,
+        original_data_yaml: str,
+    ) -> tuple[str, Path]:
+        """Build a mixed dataset for fine-tuning.
+
+        Combines verified pool samples with randomly sampled old training data.
+
+        Returns:
+            Tuple of (data_yaml path, dataset_path) for the mixed dataset.
+            Falls back to original if mixing fails or pool is empty.
+        """
+        try:
+            from backend.web.services.data_mixer import build_mixed_dataset
+
+            pool_doc_ids = self._pool.get_all_document_ids(verified_only=True)
+            if not pool_doc_ids:
+                self._training_tasks.add_log(
+                    task_id, "INFO",
+                    "No verified pool entries found, using original dataset",
+                )
+                return original_data_yaml, base_dataset_path
+
+            mixed_output = base_dataset_path.parent / f"mixed_{task_id[:8]}"
+            mix_result = build_mixed_dataset(
+                pool_document_ids=pool_doc_ids,
+                base_dataset_path=base_dataset_path,
+                output_dir=mixed_output,
+            )
+
+            self._training_tasks.add_log(
+                task_id, "INFO",
+                f"Data mixing: {mix_result['new_images']} new + "
+                f"{mix_result['old_images']} old = {mix_result['total_images']} total "
+                f"(ratio: {mix_result['mixing_ratio']}x)",
+            )
+
+            return mix_result["data_yaml"], mixed_output
+
+        except Exception as e:
+            logger.error(f"Data mixing failed for task {task_id}: {e}")
+            self._training_tasks.add_log(
+                task_id, "WARNING",
+                f"Data mixing failed: {e}. Using original dataset.",
+            )
+            return original_data_yaml, base_dataset_path
+
+    def _run_gating_after_finetune(
+        self,
+        task_id: str,
+        model_version: Any,
+        config: dict[str, Any],
+        data_yaml: str,
+        result: dict[str, Any],
+    ) -> None:
+        """Run gating validation after a fine-tune training completes."""
+        try:
+            from backend.web.services.gating_validator import run_gating_validation
+
+            model_path = result.get("model_path")
+            base_model_version_id = config.get("base_model_version_id")
+            version_id = str(model_version.version_id)
+
+            self._training_tasks.add_log(
+                task_id, "INFO", "Running deployment gating validation...",
+            )
+
+            gating_result = run_gating_validation(
+                model_version_id=version_id,
+                new_model_path=model_path,
+                base_model_version_id=base_model_version_id,
+                data_yaml=data_yaml,
+                task_id=task_id,
+            )
+
+            self._training_tasks.add_log(
+                task_id, "INFO",
+                f"Gating result: {gating_result.overall_status} "
+                f"(gate1={gating_result.gate1_status}, gate2={gating_result.gate2_status})",
+            )
+
+        except Exception as e:
+            logger.error(f"Gating validation failed for task {task_id}: {e}")
+            self._training_tasks.add_log(
+                task_id, "WARNING",
+                f"Gating validation failed: {e}. Model remains in 'pending' state.",
+            )
 
     def _export_training_data(self, task_id: str) -> dict[str, Any] | None:
         """Export training data for a task."""
@@ -456,6 +587,8 @@ names: {list(FIELD_CLASSES.values())}
         learning_rate: float,
         device: str,
         project_name: str,
+        freeze: int = 0,
+        cos_lr: bool = False,
     ) -> dict[str, Any]:
         """Run YOLO training using shared trainer."""
         from shared.training import YOLOTrainer, TrainingConfig as SharedTrainingConfig
@@ -479,6 +612,8 @@ names: {list(FIELD_CLASSES.values())}
             project="runs/train",
             name=f"{project_name}/task_{task_id[:8]}",
             workers=0,
+            freeze=freeze,
+            cos_lr=cos_lr,
         )
 
         # Run training using shared trainer

packages/backend/backend/web/schemas/admin/__init__.py

@@ -11,6 +11,7 @@ from .annotations import *  # noqa: F401, F403
 from .training import *  # noqa: F401, F403
 from .datasets import *  # noqa: F401, F403
 from .models import *  # noqa: F401, F403
+from .pool import *  # noqa: F401, F403
 from .dashboard import *  # noqa: F401, F403
 
 # Resolve forward references for DocumentDetailResponse

packages/backend/backend/web/schemas/admin/models.py

@@ -40,6 +40,9 @@ class ModelVersionItem(BaseModel):
     name: str = Field(..., description="Model name")
     status: str = Field(..., description="Status (active, inactive, archived)")
     is_active: bool = Field(..., description="Is currently active for inference")
+    model_type: str = Field(default="base", description="Model type (base or finetune)")
+    base_model_version_id: str | None = Field(None, description="Base model version UUID (for fine-tuned models)")
+    gating_status: str = Field(default="pending", description="Deployment gating status")
     metrics_mAP: float | None = Field(None, description="Mean Average Precision")
     document_count: int = Field(..., description="Documents used in training")
     trained_at: datetime | None = Field(None, description="Training completion time")
@@ -66,6 +69,10 @@ class ModelVersionDetailResponse(BaseModel):
     model_path: str = Field(..., description="Path to model file")
     status: str = Field(..., description="Status (active, inactive, archived)")
     is_active: bool = Field(..., description="Is currently active for inference")
+    model_type: str = Field(default="base", description="Model type (base or finetune)")
+    base_model_version_id: str | None = Field(None, description="Base model version UUID")
+    base_training_dataset_id: str | None = Field(None, description="Base training dataset UUID")
+    gating_status: str = Field(default="pending", description="Deployment gating status")
     task_id: str | None = Field(None, description="Training task UUID")
     dataset_id: str | None = Field(None, description="Dataset UUID")
     metrics_mAP: float | None = Field(None, description="Mean Average Precision")

packages/backend/backend/web/schemas/admin/pool.py

@@ -0,0 +1,72 @@
+"""Admin Fine-Tune Pool Schemas."""
+
+from datetime import datetime
+
+from pydantic import BaseModel, Field
+
+
+class PoolAddRequest(BaseModel):
+    """Request to add a document to the fine-tune pool."""
+
+    document_id: str = Field(..., description="Document UUID to add")
+    reason: str = Field(
+        default="user_reported_failure",
+        description="Reason: user_reported_failure, manual_addition",
+    )
+
+
+class PoolEntryItem(BaseModel):
+    """Fine-tune pool entry."""
+
+    entry_id: str = Field(..., description="Entry UUID")
+    document_id: str = Field(..., description="Document UUID")
+    added_by: str | None = Field(None, description="Who added this entry")
+    reason: str | None = Field(None, description="Reason for adding")
+    is_verified: bool = Field(..., description="Whether entry has been verified")
+    verified_at: datetime | None = Field(None, description="Verification timestamp")
+    verified_by: str | None = Field(None, description="Who verified")
+    created_at: datetime = Field(..., description="Creation timestamp")
+
+
+class PoolListResponse(BaseModel):
+    """Paginated pool entry list."""
+
+    total: int = Field(..., ge=0, description="Total entries")
+    limit: int = Field(..., ge=1, description="Page size")
+    offset: int = Field(..., ge=0, description="Current offset")
+    entries: list[PoolEntryItem] = Field(default_factory=list, description="Pool entries")
+
+
+class PoolStatsResponse(BaseModel):
+    """Pool statistics."""
+
+    total_entries: int = Field(..., ge=0, description="Total pool entries")
+    verified_entries: int = Field(..., ge=0, description="Verified entries")
+    unverified_entries: int = Field(..., ge=0, description="Unverified entries")
+    is_ready: bool = Field(..., description="Whether pool has >= 50 verified entries for fine-tuning")
+    min_required: int = Field(default=50, description="Minimum verified entries required")
+
+
+class PoolEntryResponse(BaseModel):
+    """Response for pool entry operation."""
+
+    entry_id: str = Field(..., description="Entry UUID")
+    message: str = Field(..., description="Status message")
+
+
+class GatingResultItem(BaseModel):
+    """Gating validation result."""
+
+    result_id: str = Field(..., description="Result UUID")
+    model_version_id: str = Field(..., description="Model version UUID")
+    gate1_status: str = Field(..., description="Gate 1 status")
+    gate1_original_mAP: float | None = Field(None, description="Original model mAP")
+    gate1_new_mAP: float | None = Field(None, description="New model mAP")
+    gate1_mAP_drop: float | None = Field(None, description="mAP drop percentage")
+    gate2_status: str = Field(..., description="Gate 2 status")
+    gate2_detection_rate: float | None = Field(None, description="Detection rate on new samples")
+    gate2_total_samples: int | None = Field(None, description="Total new samples tested")
+    gate2_detected_samples: int | None = Field(None, description="Samples correctly detected")
+    overall_status: str = Field(..., description="Overall gating status")
+    reviewer_notes: str | None = Field(None, description="Reviewer notes")
+    created_at: datetime = Field(..., description="Creation timestamp")

packages/backend/backend/web/schemas/admin/training.py

@@ -12,7 +12,7 @@ from .enums import TrainingStatus, TrainingType
 class TrainingConfig(BaseModel):
     """Training configuration."""
 
-    model_name: str = Field(default="yolo11n.pt", description="Base model name (used if no base_model_version_id)")
+    model_name: str = Field(default="yolo26s.pt", description="Base model name (used if no base_model_version_id)")
     base_model_version_id: str | None = Field(
         default=None,
         description="Model version UUID to use as base for incremental training. If set, uses this model instead of model_name.",

packages/backend/backend/web/services/autolabel.py

@@ -37,7 +37,7 @@ class AutoLabelService:
     def ocr_engine(self) -> OCREngine:
         """Lazy initialization of OCR engine."""
         if self._ocr_engine is None:
-            self._ocr_engine = OCREngine(lang="en")
+            self._ocr_engine = OCREngine(lang="sv")
         return self._ocr_engine
 
     def auto_label_document(

packages/backend/backend/web/services/data_mixer.py

@@ -0,0 +1,199 @@
+"""
+Data Mixing Service
+
+Mixes fine-tune pool samples with randomly sampled old training data
+following best practices for incremental YOLO fine-tuning.
+"""
+
+import logging
+import random
+import shutil
+from pathlib import Path
+from typing import Any
+from uuid import UUID
+
+logger = logging.getLogger(__name__)
+
+# Mixing ratios: (max_new_samples, old_data_multiplier)
+# Fewer new samples require higher old data ratio to prevent catastrophic forgetting
+MIXING_RATIOS: list[tuple[int, int]] = [
+    (10, 50),   # <= 10 new samples: 50x old
+    (50, 20),   # <= 50 new samples: 20x old
+    (200, 10),  # <= 200 new samples: 10x old
+    (500, 5),   # <= 500 new samples: 5x old
+]
+
+# Default multiplier for 500+ samples
+DEFAULT_MULTIPLIER = 5
+
+# Maximum old samples to include (cap for performance)
+MAX_OLD_SAMPLES = 3000
+
+# Minimum pool size for fine-tuning
+MIN_POOL_SIZE = 50
+
+
+def get_mixing_ratio(new_sample_count: int) -> int:
+    """Determine old data multiplier based on new sample count.
+
+    Args:
+        new_sample_count: Number of new samples in the fine-tune pool.
+
+    Returns:
+        Multiplier for old data sampling.
+    """
+    for threshold, multiplier in MIXING_RATIOS:
+        if new_sample_count <= threshold:
+            return multiplier
+    return DEFAULT_MULTIPLIER
+
+
+def build_mixed_dataset(
+    pool_document_ids: list[UUID],
+    base_dataset_path: Path,
+    output_dir: Path,
+    seed: int = 42,
+) -> dict[str, Any]:
+    """Build a mixed dataset for fine-tuning.
+
+    Combines ALL fine-tune pool samples with randomly sampled old data
+    from the base training dataset.
+
+    Args:
+        pool_document_ids: Document IDs from the fine-tune pool.
+        base_dataset_path: Path to the base training dataset directory.
+        output_dir: Output directory for the mixed dataset.
+        seed: Random seed for reproducible sampling.
+
+    Returns:
+        Dictionary with dataset info (data_yaml path, counts).
+    """
+    new_count = len(pool_document_ids)
+    multiplier = get_mixing_ratio(new_count)
+    old_target = min(new_count * multiplier, MAX_OLD_SAMPLES)
+
+    logger.info(
+        "Building mixed dataset: %d new samples, %dx multiplier, "
+        "targeting %d old samples",
+        new_count, multiplier, old_target,
+    )
+
+    # Create output directory structure
+    output_dir.mkdir(parents=True, exist_ok=True)
+    for split in ("train", "val"):
+        (output_dir / "images" / split).mkdir(parents=True, exist_ok=True)
+        (output_dir / "labels" / split).mkdir(parents=True, exist_ok=True)
+
+    # Collect old training images from base dataset
+    old_train_images = _collect_images(base_dataset_path / "images" / "train")
+    old_val_images = _collect_images(base_dataset_path / "images" / "val")
+
+    # Randomly sample old data
+    rng = random.Random(seed)
+    all_old_images = old_train_images + old_val_images
+    if len(all_old_images) > old_target:
+        sampled_old = rng.sample(all_old_images, old_target)
+    else:
+        sampled_old = all_old_images
+
+    # Split old samples: 80% train, 20% val
+    rng.shuffle(sampled_old)
+    old_train_count = int(len(sampled_old) * 0.8)
+    old_train = sampled_old[:old_train_count]
+    old_val = sampled_old[old_train_count:]
+
+    # Copy old samples to mixed dataset
+    old_copied = 0
+    for split_name, images in [("train", old_train), ("val", old_val)]:
+        for img_path in images:
+            label_path = _image_to_label_path(img_path)
+            dst_img = output_dir / "images" / split_name / img_path.name
+            dst_label = output_dir / "labels" / split_name / label_path.name
+            if img_path.exists():
+                shutil.copy2(img_path, dst_img)
+                old_copied += 1
+            if label_path.exists():
+                shutil.copy2(label_path, dst_label)
+
+    # Copy new pool samples (from base dataset, identified by document_id prefix)
+    # Pool documents go into train split (80%) and val split (20%)
+    pool_id_strs = {str(doc_id) for doc_id in pool_document_ids}
+    new_images = _find_pool_images(base_dataset_path, pool_id_strs)
+
+    rng.shuffle(new_images)
+    new_train_count = int(len(new_images) * 0.8)
+    new_train = new_images[:new_train_count]
+    new_val = new_images[new_train_count:]
+
+    new_copied = 0
+    for split_name, images in [("train", new_train), ("val", new_val)]:
+        for img_path in images:
+            label_path = _image_to_label_path(img_path)
+            dst_img = output_dir / "images" / split_name / img_path.name
+            dst_label = output_dir / "labels" / split_name / label_path.name
+            if img_path.exists() and not dst_img.exists():
+                shutil.copy2(img_path, dst_img)
+                new_copied += 1
+            if label_path.exists() and not dst_label.exists():
+                shutil.copy2(label_path, dst_label)
+
+    # Generate data.yaml
+    from shared.fields import FIELD_CLASSES
+
+    yaml_path = output_dir / "data.yaml"
+    yaml_content = (
+        f"path: {output_dir.absolute()}\n"
+        f"train: images/train\n"
+        f"val: images/val\n"
+        f"\n"
+        f"nc: {len(FIELD_CLASSES)}\n"
+        f"names: {list(FIELD_CLASSES.values())}\n"
+    )
+    yaml_path.write_text(yaml_content)
+
+    total_images = old_copied + new_copied
+    logger.info(
+        "Mixed dataset built: %d old + %d new = %d total images",
+        old_copied, new_copied, total_images,
+    )
+
+    return {
+        "data_yaml": str(yaml_path),
+        "total_images": total_images,
+        "old_images": old_copied,
+        "new_images": new_copied,
+        "mixing_ratio": multiplier,
+    }
+
+
+def _collect_images(images_dir: Path) -> list[Path]:
+    """Collect all image files from a directory."""
+    if not images_dir.exists():
+        return []
+    return sorted(images_dir.glob("*.png")) + sorted(images_dir.glob("*.jpg"))
+
+
+def _image_to_label_path(image_path: Path) -> Path:
+    """Convert image path to corresponding label path."""
+    labels_dir = image_path.parent.parent.parent / "labels" / image_path.parent.name
+    return labels_dir / image_path.with_suffix(".txt").name
+
+
+def _find_pool_images(
+    base_dataset_path: Path,
+    pool_doc_ids: set[str],
+) -> list[Path]:
+    """Find images in base dataset that belong to pool documents."""
+    images: list[Path] = []
+    for split in ("train", "val", "test"):
+        split_dir = base_dataset_path / "images" / split
+        if not split_dir.exists():
+            continue
+        for img_path in split_dir.iterdir():
+            if not img_path.is_file():
+                continue
+            # Image filenames are like: {doc_id}_page{N}.png
+            doc_id = img_path.stem.rsplit("_page", 1)[0]
+            if doc_id in pool_doc_ids:
+                images.append(img_path)
+    return images