golden-dataset-management

name	golden-dataset-management
description	Backup, restore, and validate golden datasets for AI/ML systems - ensuring test data integrity and preventing catastrophic data loss
version	2.0.0
author	YG Starter AI Agent Hub
tags	["golden-dataset","backup","data-protection","testing","regression",2025]

Golden Dataset Management

Protect and maintain high-quality test datasets for AI/ML systems

Overview

A golden dataset is a curated collection of high-quality examples used for:

• Regression testing: Ensure new code doesn't break existing functionality
• Retrieval evaluation: Measure search quality (precision, recall, MRR)
• Model benchmarking: Compare different models/approaches
• Reproducibility: Consistent results across environments

When to use this skill:

• Building test datasets for RAG systems
• Implementing backup/restore for critical data
• Validating data integrity (URL contracts, embeddings)
• Migrating data between environments

---

Example Production Dataset

Typical Stats:

• 100-500 documents (curated technical content)
• 400-2000 chunks (embedded text segments)
• 200-1000 test queries (with expected results)
• 85-95% pass rate (retrieval quality metric)

Purpose:

• Test hybrid search (vector + BM25 + RRF)
• Validate metadata boosting strategies
• Detect regressions in retrieval quality
• Benchmark new embedding models
• A/B test different chunking strategies

---

Core Concepts

1. Data Integrity Contracts

The URL Contract:

Golden dataset analyses MUST store real canonical URLs, not placeholders.

# WRONG - Placeholder URL (breaks restore)
analysis.url = "https://project.dev/placeholder/123"

# CORRECT - Real canonical URL (enables re-fetch if needed)
analysis.url = "https://docs.python.org/3/library/asyncio.html"

Why this matters:

• Enables re-fetching content if embeddings need regeneration
• Allows validation that source content hasn't changed
• Provides audit trail for data provenance

Verification:

# Check for placeholder URLs
def verify_url_contract(analyses: list[Analysis]) -> list[str]:
    """Find analyses with placeholder URLs."""
    invalid = []
    for analysis in analyses:
        if "project.dev" in analysis.url or "placeholder" in analysis.url:
            invalid.append(analysis.id)
    return invalid

---

2. Backup Strategies

Strategy 1: JSON Backup (Recommended)

Pros:

• Version controlled (commit to git)
• Human-readable (easy to inspect)
• Portable (works across DB versions)
• Incremental diffs (see what changed)

Cons:

• Must regenerate embeddings on restore
• Larger file size than SQL dump

Recommended: Use JSON backup for version control.

Strategy 2: SQL Dump

Pros:

• Fast restore (includes embeddings)
• Exact replica (binary-identical)
• Native PostgreSQL format

Cons:

• Not version controlled (binary format)
• DB version dependent
• No easy inspection

Use case: Local snapshots, not version control.

---

3. Backup Format

{
  "version": "1.0",
  "created_at": "2025-12-19T10:30:00Z",
  "metadata": {
    "total_analyses": 98,
    "total_chunks": 415,
    "total_artifacts": 98
  },
  "analyses": [
    {
      "id": "550e8400-e29b-41d4-a716-446655440000",
      "url": "https://docs.python.org/3/library/asyncio.html",
      "content_type": "documentation",
      "status": "completed",
      "created_at": "2025-11-15T08:20:00Z",
      "findings": [
        {
          "agent": "security_agent"
           
           
           
        
      
       
        
           
           
           
           
           
           
          
        
      
       
         
         
         
         
      
    
  

Key Design Decisions:

• Embeddings excluded (regenerate on restore with current model)
• Nested structure (analyses → chunks → artifacts)
• Metadata for validation
• ISO timestamps for reproducibility

---

Backup Implementation

Script Structure

# backend/scripts/backup_golden_dataset.py

import asyncio
import json
from datetime import datetime, UTC
from pathlib import Path
from sqlalchemy import select
from app.db.session import get_session
from app.db.models import Analysis, Chunk, Artifact

BACKUP_DIR = Path("backend/data")
BACKUP_FILE = BACKUP_DIR / "golden_dataset_backup.json"
METADATA_FILE = BACKUP_DIR / "golden_dataset_metadata.json"

async def backup_golden_dataset():
    """Backup golden dataset to JSON."""

    async with get_session() as session:
        # Fetch all completed analyses
        query = (
            select(Analysis)
            .where(Analysis.status == "completed")
            .order_by(Analysis.created_at)
        )
        result = await session.execute(query)
        analyses = result.scalars().all()

        # Serialize to JSON
        backup_data = {
            "version": "1.0",
            "created_at": datetime.now(UTC).isoformat(),
            "metadata": {
                "total_analyses": len(analyses),
                "total_chunks": sum(len(a.chunks) for a in analyses),
                "total_artifacts": len([a for a  analyses  a.artifact])
            },
            : [
                serialize_analysis(a)  a  analyses
            ]
        }

        
        BACKUP_DIR.mkdir(exist_ok=)
         (BACKUP_FILE, )  f:
            json.dump(backup_data, f, indent=, default=)

        
         (METADATA_FILE, )  f:
            json.dump(backup_data[], f, indent=)

        ()
        ()
        ()

 () -> :
    
     {
        : (analysis.),
        : analysis.url,
        : analysis.content_type,
        : analysis.status,
        : analysis.created_at.isoformat(),
        : [serialize_finding(f)  f  analysis.findings],
        : [serialize_chunk(c)  c  analysis.chunks],
        : serialize_artifact(analysis.artifact)  analysis.artifact  
    }

 () -> :
    
     {
        : (chunk.),
        : chunk.content,
        : chunk.section_title,
        : chunk.section_path,
        : chunk.content_type,
        : chunk.chunk_index
        
    }

Detailed Implementation: See templates/backup-script.py

---

Restore Implementation

Process Overview

1. Load JSON backup
2. Validate structure (version, required fields)
3. Create analyses (without embeddings yet)
4. Create chunks (without embeddings yet)
5. Generate embeddings (using current embedding model)
6. Create artifacts
7. Verify integrity (counts, URL contract)

Key Challenge: Regenerating Embeddings

async def restore_golden_dataset(replace: bool = False):
    """Restore golden dataset from JSON backup."""

    # Load backup
    with open(BACKUP_FILE) as f:
        backup_data = json.load(f)

    async with get_session() as session:
        if replace:
            # Delete existing data
            await session.execute(delete(Chunk))
            await session.execute(delete(Artifact))
            await session.execute(delete(Analysis))
            await session.commit()

        # Restore analyses and chunks
        from app.shared.services.embeddings import embed_text

        for analysis_data in backup_data["analyses"]:
            # Create analysis
            analysis = Analysis(
                id=UUID(analysis_data["id"]),
                url=analysis_data["url"],
                # ... other fields ...
            )
            session.add(analysis)

            # Create chunks with regenerated embeddings
            for chunk_data in analysis_data["chunks"]:
                # Regenerate embedding using CURRENT model
                embedding = await embed_text(chunk_data["content"])

                chunk = Chunk(
                    id=UUID(chunk_data["id"]),
                    analysis_id=analysis.,
                    content=chunk_data[],
                    embedding=embedding,  
                    
                )
                session.add(chunk)

             session.commit()

        ()

Why regenerate embeddings?

• Embedding models improve over time
• Ensures consistency with current model
• Smaller backup files (exclude large vectors)

Detailed Implementation: See references/backup-restore.md

---

Validation

Validation Checklist

async def verify_golden_dataset() -> dict:
    """Verify golden dataset integrity."""

    errors = []
    warnings = []

    async with get_session() as session:
        # 1. Check counts
        analysis_count = await session.scalar(select(func.count(Analysis.id)))
        chunk_count = await session.scalar(select(func.count(Chunk.id)))
        artifact_count = await session.scalar(select(func.count(Artifact.id)))

        expected = load_metadata()
        if analysis_count != expected["total_analyses"]:
            errors.append(f"Analysis count mismatch: {analysis_count} vs {expected['total_analyses']}")

        # 2. Check URL contract
        query = select(Analysis).where(
            Analysis.url.like("%project.dev%") |
            Analysis.url.like("%placeholder%")
        )
        result = await session.execute(query)
        invalid_urls = result.scalars().all()

        if invalid_urls:
            errors.append(f"Found {len(invalid_urls)} analyses with placeholder URLs")

        # 3. Check embeddings exist
        query = select(Chunk).where(Chunk.embedding.is_(None))
        result = await session.execute(query)
        missing_embeddings = result.scalars().all()

        if missing_embeddings:
            errors.append(f"Found {len(missing_embeddings)} chunks without embeddings")

        # 4. Check orphaned chunks
        query = select(Chunk).outerjoin(Analysis).where(Analysis..is_())
        result =  session.execute(query)
        orphaned = result.scalars().()

         orphaned:
            warnings.append()

         {
            : (errors) == ,
            : errors,
            : warnings,
            : {
                : analysis_count,
                : chunk_count,
                : artifact_count
            }
        }

Detailed Validation: See references/validation-contracts.md

---

CLI Usage

cd backend

# Backup golden dataset
uv run python scripts/backup_golden_dataset.py backup

# Verify backup integrity
uv run python scripts/backup_golden_dataset.py verify

# Restore from backup (WARNING: Deletes existing data)
uv run python scripts/backup_golden_dataset.py restore --replace

# Restore without deleting (adds to existing)
uv run python scripts/backup_golden_dataset.py restore

---

CI/CD Integration

Automated Backups

# .github/workflows/backup-golden-dataset.yml
name: Backup Golden Dataset

on:
  schedule:
    - cron: '0 2 * * 0'  # Weekly on Sunday at 2am
  workflow_dispatch:  # Manual trigger

jobs:
  backup:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          cd backend
          uv sync

      - name: Run backup
        env:
          DATABASE_URL: ${{ secrets.PROD_DATABASE_URL }}
        run: |
          cd backend
          uv run python scripts/backup_golden_dataset.py backup

      - name: Commit backup
        run: |
          git config user.name "GitHub Actions"
          git config user.email "[e-mail]"
          git add backend/data/golden_dataset_backup.json
          git add backend/data/golden_dataset_metadata.json
          git commit -m "chore: automated golden dataset backup"
          git push

---

Best Practices

1. Version Control Backups

# Commit backups to git
git add backend/data/golden_dataset_backup.json
git commit -m "chore: golden dataset backup (98 analyses, 415 chunks)"

2. Validate Before Deployment

# Pre-deployment check
uv run python scripts/backup_golden_dataset.py verify

# Should output:
# ✅ Validation passed
#    Analyses: 98
#    Chunks: 415
#    Artifacts: 98
#    No errors found

3. Test Restore in Staging

# Never test restore in production first!

# Staging environment
export DATABASE_URL=$STAGING_DATABASE_URL
uv run python scripts/backup_golden_dataset.py restore --replace

# Run tests to verify
uv run pytest tests/integration/test_retrieval_quality.py

4. Document Changes

// backend/data/golden_dataset_metadata.json
{
  "total_analyses": 98,
  "total_chunks": 415,
  "last_updated": "2025-12-19T10:30:00Z",
  "changes": [
    {
      "date": "2025-12-19",
      "action": "added",
      "count": 5,
      "description": "Added 5 new LangGraph tutorial analyses"
    },
    {
      "date": "2025-12-10",
      "action": "removed",
      "count": 2,
      "description": "Removed 2 outdated React 17 analyses"
    }
  ]
}

---

Disaster Recovery

Scenario 1: Accidental Deletion

# Oh no! Someone ran DELETE FROM analyses WHERE 1=1

# 1. Restore from backup
uv run python scripts/backup_golden_dataset.py restore --replace

# 2. Verify
uv run python scripts/backup_golden_dataset.py verify

# 3. Run tests
uv run pytest tests/integration/test_retrieval_quality.py

Scenario 2: Database Migration Gone Wrong

# Migration corrupted data

# 1. Rollback migration
alembic downgrade -1

# 2. Restore from backup
uv run python scripts/backup_golden_dataset.py restore --replace

# 3. Re-run migration (fixed)
alembic upgrade head

Scenario 3: New Environment Setup

# Fresh dev environment, need golden dataset

# 1. Clone repo (includes backup)
git clone https://github.com/your-org/project
cd project/backend

# 2. Setup DB
docker compose up -d postgres
alembic upgrade head

# 3. Restore golden dataset
uv run python scripts/backup_golden_dataset.py restore

# 4. Verify
uv run pytest tests/integration/test_retrieval_quality.py

---

References

Example Implementation Files

• backend/scripts/backup_golden_dataset.py - Main backup script
• backend/data/golden_dataset_backup.json - JSON backup (version controlled)
• backend/data/golden_dataset_metadata.json - Quick stats

Related Skills

• pgvector-search - Retrieval evaluation using golden dataset
• ai-native-development - Embedding generation for restore
• devops-deployment - CI/CD backup automation

---

2025 Best Practices Update

Incremental Backups

from pathlib import Path
import json
from datetime import datetime, UTC

class IncrementalBackup:
    """Incremental backup with change tracking."""

    def __init__(self, backup_dir: Path):
        self.backup_dir = backup_dir
        self.backup_dir.mkdir(exist_ok=True)

    async def create_incremental_backup(self) -> Path:
        """Create incremental backup (only changed documents)."""

        # Load previous backup hash map
        hash_file = self.backup_dir / "document_hashes.json"
        previous_hashes = {}
        if hash_file.exists():
            with open(hash_file) as f:
                previous_hashes = json.load(f)

        # Fetch current documents
        async with get_session() as session:
            query = select(Analysis).where(Analysis.status == "completed")
            result = await session.execute(query)
            analyses = result.scalars().all()

        # Find changed documents
        changed = []
        current_hashes = {}

        for analysis in analyses:
            # Calculate content hash
            content = json.dumps(serialize_analysis(analysis), sort_keys=True)
            current_hash = hashlib.sha256(content.encode()).hexdigest()
            current_hashes[str(analysis.)] = current_hash

            
             previous_hashes.get((analysis.)) != current_hash:
                changed.append(analysis)

        
        timestamp = datetime.now(UTC).strftime()
        backup_file = .backup_dir / 

         (backup_file, )  f:
            json.dump({
                : ,
                : ,
                : datetime.now(UTC).isoformat(),
                : (analyses),
                : (changed),
                : [serialize_analysis(a)  a  changed],
            }, f, indent=)

        
         (hash_file, )  f:
            json.dump(current_hashes, f, indent=)

        ()
         backup_file

Compression for Large Datasets

import gzip
import json

def save_compressed_backup(data: dict, path: Path):
    """Save compressed JSON backup."""
    with gzip.open(f"{path}.gz", "wt", encoding="utf-8") as f:
        json.dump(data, f, indent=2, default=str)

def load_compressed_backup(path: Path) -> dict:
    """Load compressed JSON backup."""
    with gzip.open(f"{path}.gz", "rt", encoding="utf-8") as f:
        return json.load(f)

---

Version: 2.0.0 (January 2025) Status: Production-ready patterns for AI/ML dataset management