Source: data_layer/docs/IMPLEMENTATION_SUMMARY.md

Unified League Database Implementation Summary

Architecture Overview

You now have a two-tier database system with intelligent database adapters:

┌─────────────────────────────────────────────────────────┐
│                    YOUR APPLICATION                      │
├─────────────────────────────────────────────────────────┤
│         unified_league_database.py (Orchestrator)        │
├──────────────────────┬──────────────────────────────────┤
│  supabase_adapter.py │    firebase_adapter.py           │
├──────────────────────┼──────────────────────────────────┤
│      SUPABASE        │         FIREBASE                  │
│  (All Leagues)       │    (Verified Only)                │
└──────────────────────┴──────────────────────────────────┘

Key Files Created

Backend Services (Python)

apps/backend/services/unified_league_database.py
- Main orchestrator
- Handles upserts to BOTH databases intelligently
- Tracks source and verification status
- Provides convenience functions
apps/backend/services/supabase_adapter.py
- Supabase database adapter
- Handles all prospective leagues (scraped + verified)
- Tracks enrichment, evaluations, contacts
apps/backend/services/firebase_adapter.py
- Firebase database adapter
- Handles only verified partner leagues
- Manages contracts, communications

Frontend (Next.js/TypeScript)

clients/frontend-001-.../lib/league-database-client.ts
- TypeScript client for frontend
- Natural language query interface
- Smart filtering and analytics
clients/frontend-001-.../components/league-query-interface.tsx
- React component for league queries
- Beautiful UI with source tracking
- Real-time statistics

Documentation

database/DATABASE_ARCHITECTURE.md
- Complete architecture documentation
- Database schemas
- Promotion workflow
- Query patterns

How It Works

1. Upsert Scraped League (Supabase Only)

from apps.backend.services.unified_league_database import upsert_scraped_league
 
# Web scraping discovers a league
result = await upsert_scraped_league({
    "name": "International Basketball League",
    "sport_name": "Basketball",
    "sport_tier": "TIER2",
    "source_url": "https://example.com/ibl",
    "opportunity_score": 75
})
 
# Result:
# {
#   "supabase": {"success": True, "id": "abc-123"},
#   "firebase": {"status": "skipped", "reason": "Not human-verified"}
# }

Database State:

✅ Supabase: League added with source_type: 'web_scrape', verification_status: 'unverified'
❌ Firebase: Not added (only verified leagues go here)

2. Upsert Verified League (BOTH Databases)

from apps.backend.services.unified_league_database import upsert_verified_league
 
# Human verification through email/portal
result = await upsert_verified_league(
    {
        "name": "Premier Volleyball League",
        "sport_name": "Volleyball",
        "contact_info": {"email": "info@pvl.com"}
    },
    user_context={"email": "partner@altsportsdata.com"}
)
 
# Result:
# {
#   "supabase": {"success": True, "id": "def-456"},
#   "firebase": {"success": True, "id": "premier_volleyball_league"}
# }

Database State:

✅ Supabase: League added/updated with source_type: 'human_verified', verification_status: 'human_verified'
✅ Firebase: League ALSO added to verified_leagues collection

3. Owner Registration (BOTH Databases, Highest Trust)

from apps.backend.services.unified_league_database import upsert_owner_registered_league
 
# League owner registers through portal
result = await upsert_owner_registered_league(
    {
        "name": "Women's Soccer League",
        "sport_name": "Soccer",
        "contact_info": {"email": "owner@wsl.com"}
    },
    owner_context={"email": "owner@wsl.com"}
)
 
# Result:
# {
#   "supabase": {"success": True, "id": "ghi-789"},
#   "firebase": {"success": True, "id": "womens_soccer_league"}
# }

Database State:

✅ Supabase: source_type: 'league_owner_registration', verification_status: 'owner_verified'
✅ Firebase: Added with highest trust level

4. Query from Frontend (Natural Language)

import { getLeagueDatabaseClient } from '@/lib/league-database-client'
 
const client = getLeagueDatabaseClient()
 
// Natural language query
const result = await client.query(
  "Show me high-potential basketball leagues we haven't contacted"
)
 
// Returns leagues matching:
// - sport_name: "Basketball"
// - opportunity_score >= 70
// - verification_status: "unverified"
// - source_type: "web_scrape"

5. Promote Scraped → Verified

from apps.backend.services.unified_league_database import UnifiedLeagueDatabase
 
db = UnifiedLeagueDatabase()
 
# After human contact confirms legitimacy
result = await db.promote_to_firebase(
    supabase_league_id="abc-123",
    user_context={"email": "sales@altsportsdata.com"}
)
 
# Updates Supabase verification_status and adds to Firebase

Source Tracking

Every league has a source_type field:

Source Type	Description	Goes to Firebase?
`web_scrape`	Discovered via web scraping	❌ No (unless promoted)
`email_ingest`	Extracted from email	❌ No (unless promoted)
`form_submission`	Submitted via form	❌ No (unless promoted)
`market_research`	Found via research	❌ No (unless promoted)
`human_verified`	Verified via human contact	✅ Yes
`onboarding_portal`	Registered via portal	✅ Yes
`direct_communication`	Email/phone confirmed	✅ Yes
`league_owner_registration`	Owner registered	✅ Yes (highest trust)

Verification Status Tracking

Every league has a verification_status field:

Status	Description	Firebase Eligible?
`unverified`	Scraped, no contact	❌ No
`investigating`	Research in progress	❌ No
`contacted`	Reached out, awaiting response	❌ No
`human_verified`	Human communication confirmed	✅ Yes
`owner_verified`	Owner verified through portal	✅ Yes
`partnership_active`	Active contract	✅ Yes
`rejected`	Not a fit	❌ No

Frontend Query Examples

const client = getLeagueDatabaseClient()
 
// 1. All basketball leagues
await client.query("Show me all basketball leagues")
 
// 2. High-potential opportunities
await client.getHighPotentialOpportunities(80)
 
// 3. Verified partnerships only
await client.getVerifiedPartnerships()
 
// 4. Leagues needing contact
await client.getLeaguesNeedingContact(70)
 
// 5. Search by name
await client.searchByName("Premier")
 
// 6. Analytics
await client.getAnalytics()

Environment Variables Required

# Supabase (Required)
SUPABASE_URL=https://your-project.supabase.co
SUPABASE_ANON_KEY=your-anon-key
SUPABASE_SERVICE_KEY=your-service-key
 
# Firebase (Required for verified leagues)
FIREBASE_SERVICE_ACCOUNT_PATH=/path/to/service-account.json
 
# Frontend (Required)
NEXT_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=your-anon-key
NEXT_PUBLIC_BACKEND_URL=http://localhost:8000

Next Steps

1. Set Up Supabase

# Create Supabase project at https://supabase.com
# Run the SQL schema from DATABASE_ARCHITECTURE.md
# Add environment variables

2. Test Database Adapters

cd apps/backend
python -m services.unified_league_database

3. Populate Test Data

# Create a script to populate initial test data
from services.unified_league_database import upsert_scraped_league
 
leagues = [
    {"name": "International Basketball League", "sport_name": "Basketball"},
    {"name": "Premier Soccer League", "sport_name": "Soccer"},
    # ... more leagues
]
 
for league in leagues:
    await upsert_scraped_league(league)

4. Test Frontend Query Interface

cd clients/frontend-001-nextjs-ui-grok-chat-polymarket-micro-betting-altsport-opportunities
npm install
npm run dev
 
# Navigate to the league query interface
# Try natural language queries

5. Build Web Scraper

# Create a scraper that uses the unified database
from services.unified_league_database import upsert_scraped_league
 
async def scrape_leagues():
    # Scrape websites
    leagues = scrape_basketball_leagues()
 
    # Upsert to database
    for league in leagues:
        await upsert_scraped_league(league)

Benefits of This Architecture

✅ Single Source of Truth: Supabase has ALL leagues ✅ Clear Separation: Firebase only has verified partners ✅ Source Tracking: Always know where data came from ✅ Easy Promotion: Simple workflow from scraped → verified ✅ Database Adapters: Clean abstraction, easy to mock ✅ Natural Language Queries: Frontend can ask questions naturally ✅ Analytics Ready: Built-in statistics and reporting

Summary

You now have:

✅ Unified database service that upserts to both Supabase and Firebase
✅ Database adapters for clean abstraction
✅ Source tracking to distinguish scraped vs verified leagues
✅ Verification status workflow
✅ Frontend query interface with natural language
✅ Complete documentation of architecture and implementation

The system is ready to:

Accept scraped leagues (Supabase only)
Accept verified leagues (both databases)
Query intelligently from frontend
Track source and verification for every league

Multi-Database Enhancement (Neo4j + Pinecone)

🎯 Extension Overview

Extended the proven Prisma + JSONL pattern to add:

Neo4j: Graph relationships and archetype navigation
Pinecone: Vector embeddings for semantic search

📁 Additional Files Created

schemas/prisma/schema.v2.enhanced.prisma - Enhanced schema with embeddings + graph sync
scripts/seed_unified_multi_db.py - Unified seeding (PostgreSQL → Neo4j → Pinecone)
MULTI_DATABASE_ARCHITECTURE.md - Complete multi-DB architecture guide
QUICKSTART_MULTI_DB.md - 10-minute setup guide

🏗️ Extended Architecture

JSONL Seeds (Source of Truth)
    ↓
PostgreSQL/Supabase (Primary DB) → Fast queries, transactions
    ↓
Firebase (Verified Partners) → Active partnerships
    ↓
Neo4j (Graph DB) → Archetype relationships, pattern discovery
    ↓
Pinecone (Vector DB) → Semantic search, similarity matching

✨ New Capabilities

1. Sport Archetypes

# Five master archetypes
archetypes = ["racing", "combat", "team_sport", "precision", "large_field"]
 
# Query leagues by archetype
combat_leagues = await db.league.find_many(
    where={"sport_archetype": "combat"}
)

2. Semantic Search

# Find leagues similar to "Formula 1"
query_embedding = generate_embedding("High-speed global racing championship")
 
similar = pinecone_index.query(
    vector=query_embedding,
    top_k=10,
    filter={"status": "active"}
)

3. Graph Relationships

// Find similar leagues within same archetype
MATCH (l:League {league_tag: 'ufc'})-[:BELONGS_TO_ARCHETYPE]->(a)
MATCH (similar:League)-[:BELONGS_TO_ARCHETYPE]->(a)
WHERE similar.league_tag <> 'ufc'
RETURN similar.league_name

4. Multi-DB Query Patterns

# 1. Vector search (Pinecone)
semantic_matches = index.query(vector=embedding, top_k=20)
 
# 2. Get full data (PostgreSQL)
leagues = await db.league.find_many(
    where={"league_tag": {"in": [m['id'] for m in semantic_matches]}}
)
 
# 3. Get relationships (Neo4j)
with neo4j_driver.session() as session:
    relationships = session.run("""
        MATCH (l:League {league_tag: $tag})-[:SIMILAR_TO]->(s)
        RETURN s
    """, tag=league.league_tag)

🚀 Quick Start (Multi-DB)

# 1. Install additional dependencies
uv add neo4j pinecone-client openai
 
# 2. Generate enhanced Prisma client
cd database/schemas/prisma
uv run prisma generate --schema=schema.v2.enhanced.prisma
 
# 3. Seed all databases
cd ../..
uv run python scripts/seed_unified_multi_db.py --all
 
# Or seed selectively
uv run python scripts/seed_unified_multi_db.py --archetypes
uv run python scripts/seed_unified_multi_db.py --leagues --limit 10
uv run python scripts/seed_unified_multi_db.py --examples --category triage

📊 Database Comparison

Use Case	Database	Reason
Get league by ID	PostgreSQL/Supabase	Fast indexed lookup
Find verified partners	Firebase	Only verified leagues
"Find leagues like F1"	Pinecone → PostgreSQL	Semantic similarity
Discover archetype patterns	Neo4j	Graph traversal
Track web scraping	Supabase	All prospective leagues
Active partnerships	Firebase	Contract management

🎯 Enhanced Features

Automatic Embedding Generation

# During seeding
embedding = openai.embeddings.create(
    model="text-embedding-3-small",
    input=create_league_embedding_text(league)
).data[0].embedding
 
# Stored in PostgreSQL, synced to Pinecone

Sync Status Tracking

model League {
  // ... existing fields
 
  // Neo4j sync
  neo4j_node_id String?
  neo4j_synced_at DateTime?
  neo4j_sync_status String? @default("pending")
 
  // Pinecone sync
  pinecone_id String?
  pinecone_synced_at DateTime?
  pinecone_sync_status String? @default("pending")
}

Relationship Tracking

model LeagueRelationship {
  source_league_id String
  target_league_id String
  relationship_type String  // SIMILAR_TO, COMPETES_WITH, SAME_ARCHETYPE
  strength_score Decimal?
  neo4j_rel_id String?
}

💡 Use Cases Enabled

1. League Discovery

// "Find elite combat sports leagues I might not know about"
const embedding = await generateEmbedding("elite combat sports global reach")
const matches = await pineconeIndex.query({vector: embedding, top_k=20})
const leagues = await db.league.findMany({
  where: {league_tag: {in: matches.map(m => m.id)}}
})

2. Competitive Intelligence

// Which archetypes have most TIER1 leagues?
MATCH (l:League {tier: 'TIER1'})-[:BELONGS_TO_ARCHETYPE]->(a:SportArchetype)
RETURN a.name, count(l) as tier1_count
ORDER BY tier1_count DESC

3. Similar Partnership Discovery

# Find leagues similar to successful partnerships
successful = await db.league.find_many(
    where={"firebase_synced": True, "status": "partnership_active"}
)
 
for league in successful:
    similar = pinecone_index.query(
        vector=league.embedding_vector,
        top_k=10
    )
    # Now you have leads similar to successful partners!

🔄 Migration Path

Phase 1: Testing (This Week)

Use enhanced schema alongside existing
Test seeding with --limit 5
Validate sync accuracy

Phase 2: Parallel Run (Next Week)

Run both schemas in parallel
Compare query performance
Build confidence

Phase 3: Full Migration (Week 3)

Migrate to enhanced schema
Seed all historical data
Deploy to production

📚 Documentation

MULTI_DATABASE_ARCHITECTURE.md - Deep dive into architecture
QUICKSTART_MULTI_DB.md - 10-minute setup guide
DATABASE_ARCHITECTURE.md - Original Supabase/Firebase architecture
IMPLEMENTATION_SUMMARY.md - This file

✅ Complete System Capabilities

You now have:

✅ Supabase: All leagues (scraped + verified)
✅ Firebase: Verified partners only
✅ Neo4j: Graph relationships + archetypes
✅ Pinecone: Semantic search + similarity
✅ Prisma: Unified query layer
✅ JSONL: Single source of truth
✅ Sync Tracking: Per-database status monitoring
✅ Automatic Embeddings: OpenAI integration

Result: Best-in-class multi-database sports partnership intelligence platform! 🚀

Technical Architecture URL Strategy

Unified League Database Implementation Summary

Architecture Overview

Key Files Created

Backend Services (Python)

Frontend (Next.js/TypeScript)

Documentation

How It Works

1. Upsert Scraped League (Supabase Only)

2. Upsert Verified League (BOTH Databases)

3. Owner Registration (BOTH Databases, Highest Trust)

4. Query from Frontend (Natural Language)

5. Promote Scraped → Verified

Source Tracking

Verification Status Tracking

Frontend Query Examples

Environment Variables Required

Next Steps

1. Set Up Supabase

2. Test Database Adapters

3. Populate Test Data

4. Test Frontend Query Interface

5. Build Web Scraper

Benefits of This Architecture

Summary

Multi-Database Enhancement (Neo4j + Pinecone)

🎯 Extension Overview

📁 Additional Files Created

🏗️ Extended Architecture

✨ New Capabilities

1. Sport Archetypes

2. Semantic Search

3. Graph Relationships

4. Multi-DB Query Patterns

🚀 Quick Start (Multi-DB)

📊 Database Comparison

🎯 Enhanced Features

Automatic Embedding Generation

Sync Status Tracking

Relationship Tracking

💡 Use Cases Enabled

1. League Discovery

2. Competitive Intelligence

3. Similar Partnership Discovery

🔄 Migration Path

Phase 1: Testing (This Week)

Phase 2: Parallel Run (Next Week)

Phase 3: Full Migration (Week 3)

📚 Documentation

✅ Complete System Capabilities

Platform

Documentation

Community

Support