🎯 START HERE - Data Layer Navigation

Welcome to the Data Layer! This guide helps you navigate the complete architecture.

📖 Reading Order

1️⃣ First Time Here? → Read This File (You are here!)

2️⃣ Want Overview? → README.md

• Quick start guide
• Architecture at a glance
• Common tasks
• Time: 10 minutes

3️⃣ Understand Architecture? → DATA_FABRIC_ARCHITECTURE.md

• Complete system design
• Data flow diagrams
• Storage strategy
• Code examples
• Time: 30-45 minutes

4️⃣ Ready to Build? → IMPLEMENTATION_GUIDE.md

• Week-by-week plan
• Step-by-step instructions
• Code templates
• Testing strategy
• Time: Reference as you build

5️⃣ Need Task Breakdown? → ../database/DATABASE_ORGANIZATION_TASKS.md

• 35+ detailed tasks
• 8 implementation phases
• Success criteria for each
• Time: Reference during implementation

6️⃣ Quick Lookups? → ../database/WHERE_DOES_IT_GO.md

• Decision tree for file placement
• Quick reference table
• Common scenarios
• Time: 2-5 minutes per lookup

🎯 Choose Your Path

Path A: I Want to Understand First

START_HERE.md (this file)
    ↓
README.md (overview)
    ↓
DATA_FABRIC_ARCHITECTURE.md (deep dive)
    ↓
Ready to implement!

Time: 1 hour
Best For: Architects, team leads, reviewers

Path B: I Want to Start Building Now

START_HERE.md (this file)
    ↓
IMPLEMENTATION_GUIDE.md (Week 1, Day 1)
    ↓
Start creating directories
    ↓
Reference docs as needed

Time: Jump right in
Best For: Implementers, developers with tight deadline

Path C: I Need to See the Big Picture

START_HERE.md (this file)
    ↓
DELIVERY_SUMMARY.md (what was delivered)
    ↓
README.md (how it works)
    ↓
Decide next steps

Time: 20 minutes
Best For: Decision makers, stakeholders

🗺️ Full Document Map

Core Documents (data_layer/)

📁 data_layer/
│
├── 🎯 START_HERE.md                       ← You are here
├── 📖 README.md                            ← Main overview
├── 🏗️ DATA_FABRIC_ARCHITECTURE.md         ← Complete spec
├── 🚀 IMPLEMENTATION_GUIDE.md              ← Build guide
├── 📦 DELIVERY_SUMMARY.md                  ← What you got
├── 🏷️ NAMING_STRATEGY.md                   ← Why "data_fabric"
└── 📋 COMPREHENSIVE_ORGANIZATION_PLAN.md   ← Original plan

Supporting Documents (database/)

📁 database/
│
├── 📝 DATABASE_ORGANIZATION_TASKS.md       ← 35+ tasks
├── 🔍 WHERE_DOES_IT_GO.md                  ← Quick reference
└── ✅ IMPLEMENTATION_CHECKLIST.md          ← Weekly checklist

🎓 Concepts Quick Reference

What is "Data Fabric"?

Answer: An architecture that unifies data across multiple storage systems with intelligent metadata and automated orchestration.

Your system IS a data fabric because it has:

1. ✅ Unified access (single directory → multiple databases)
2. ✅ Active metadata (schemas drive generation)
3. ✅ Knowledge graph (vector embeddings)
4. ✅ Automation (sync scripts)

What are the 3 Tiers?

DEFINITIONS (Source)          → Git-tracked, canonical data
    ↓
WEAVE (Transform)             → Python code that builds/generates
    ↓
VIEWS (Materialized)          → PostgreSQL, LangMem, Redis, files

Think of it as:

• Definitions = The blueprint
• Weave = The factory
• Views = The products

What's the Key Innovation?

Single Source → Multiple Outputs

One config file generates:

• ✅ Training examples (JSONL)
• ✅ Database records (PostgreSQL JSONB)
• ✅ Vector embeddings (LangMem)
• ✅ Cache entries (Redis)
• ✅ Prompt content (dynamic injection)

Change once, updates everywhere!

🚀 Quick Wins

Win #1: Generate Pydantic from JSON Schema (15 min)

# 1. Place JSON Schema in canonical location
cp my-schema.schema.json data_layer/definitions/schemas/canonical/
 
# 2. Run generator
python data_layer/weave/builders/schemas/generate_pydantic.py
 
# 3. Import and use!
from data_layer.definitions.schemas.generated.pydantic import MySchema
validated = MySchema(**data)  # Type-safe!

Win #2: Auto-Generate Training Examples from Config (20 min)

# 1. Edit config
vim data_layer/definitions/config/business/pricing/tier_presets.v1.json
 
# 2. Generate examples
python data_layer/weave/builders/examples/config_to_examples.py
 
# 3. See output
cat data_layer/definitions/examples/generated/pricing-examples.jsonl
# Now has 50+ training examples automatically created!

Win #3: Build Dynamic Prompt with Live Config (30 min)

from data_layer.weave.builders.prompts import classification_builder
 
# Builds prompt with ACTUAL config values, not hardcoded
builder = classification_builder.ClassificationPromptBuilder()
prompt = builder.build_tier_classifier(
    league_data={"name": "UFC", "sport": "MMA"}
)
 
# Prompt contains:
# - Real scoring weights from scoring_model.v1.json (0.25, 0.20, etc.)
# - Real tier thresholds from same config (82, 68, 52)
# - Retrieved examples from LangMem (if configured)
 
# Never hardcode values again!

🎯 Common Questions

Q: Where do I put a new business config file?

A: data_layer/definitions/config/business/{category}/

See: WHERE_DOES_IT_GO.md for decision tree

Q: How do I generate Pydantic, TypeScript, Zod from JSON Schema?

A: Run python data_layer/weave/builders/schemas/generate_all.py

See: IMPLEMENTATION_GUIDE.md Week 1

Q: How do I sync configs to PostgreSQL/LangMem?

A: Run python data_layer/scripts/sync/sync_all.py

See: IMPLEMENTATION_GUIDE.md Week 3

Q: Where do hand-curated examples go?

A: data_layer/definitions/examples/seeds/{category}/

See: DATA_FABRIC_ARCHITECTURE.md Examples Section

Q: Where do auto-generated examples go?

A: data_layer/definitions/examples/generated/

See: IMPLEMENTATION_GUIDE.md Week 2

Q: How long does implementation take?

A: 4 weeks (60-80 hours) if following the guide

See: DELIVERY_SUMMARY.md

🛠️ Implementation Phases Summary

💡 Key Principles

1. Single Source of Truth

Rule: All canonical data in definitions/
Why: Never edit views/ directly - always regenerate from source

2. Everything is Retrievable

Rule: Embed configs, examples, prompts as vectors
Why: Semantic search for intelligent AI context

3. Type Safety Everywhere

Rule: One JSON Schema → Pydantic + TypeScript + Zod + Drizzle
Why: Catch errors early, guarantee consistency

4. Generation Over Duplication

Rule: Generate artifacts, don't copy
Why: Single source, automatic propagation

5. Multi-Storage Optimization

Rule: Store data optimally for access pattern
Why: PostgreSQL (queries), LangMem (semantic), Redis (speed)

🎨 Visual Summary

Before This Architecture

😵 Schemas scattered across 5+ locations
😵 Hardcoded values in prompts
😵 Manual example selection
😵 Inconsistent validation (backend ≠ frontend)
😵 Configs stored in unknown locations

After This Architecture

✅ One schema → Pydantic + TypeScript + Zod + Drizzle
✅ One config → Examples + Prompts + DB + Embeddings
✅ Semantic retrieval → Intelligent few-shot learning
✅ Type-safe end-to-end from single source
✅ Clear organization: definitions → weave → views

🎯 Your Next Action

Choose one:

Option A: Understand First (Recommended)

1. Read README.md (10 min)
2. Read DATA_FABRIC_ARCHITECTURE.md (45 min)
3. Review IMPLEMENTATION_GUIDE.md (skim)
4. Start Week 1, Day 1 implementation

Option B: Build Now

1. Open IMPLEMENTATION_GUIDE.md Week 1
2. Follow step-by-step instructions
3. Reference other docs as needed

Option C: Present to Team

1. Read DELIVERY_SUMMARY.md (15 min)
2. Show architecture diagrams from DATA_FABRIC_ARCHITECTURE.md
3. Present implementation timeline from IMPLEMENTATION_GUIDE.md

🤝 Support

Stuck? Check these:

1. Quick Lookup: WHERE_DOES_IT_GO.md
2. Task Details: DATABASE_ORGANIZATION_TASKS.md
3. Weekly Progress: IMPLEMENTATION_CHECKLIST.md
4. Architecture Questions: DATA_FABRIC_ARCHITECTURE.md

🎉 What You're Building

A production-ready data fabric that:

✅ Generates type-safe code from schemas
✅ Builds dynamic prompts from components
✅ Creates training data from configs
✅ Embeds everything for semantic retrieval
✅ Syncs across multiple storage backends
✅ Validates end-to-end (backend + frontend)
✅ Maintains single source of truth

This is enterprise-grade. Let's build it! 🚀

Last Updated: 2025-10-16
Status: Ready to implement
Estimated Time: 4 weeks