Supabase Multi-Database Implementation Summary

Overview

This implementation provides a Supabase-based multi-database system that extends the proven JSONL → Database pattern to support Neo4j graph relationships and Pinecone vector search.

Architecture

JSONL Seed Files (Source of Truth)
         ↓
    Supabase PostgreSQL (Primary Database)
         ↓
    ┌────┴────┐
    ↓         ↓
  Neo4j    Pinecone
  (graph)   (vectors)

Key Design Decisions

1. Supabase as Primary Database (instead of Prisma)

   • Uses existing Supabase infrastructure
   • Direct Python client integration (supabase-py)
   • No ORM layer needed
   • Matches existing backend architecture

2. Graceful Degradation

   • System works with just Supabase
   • Neo4j and Pinecone are optional
   • Embeddings optional (requires OpenAI)

3. Sync Tracking

   • Per-database sync status fields
   • Automatic sync during seeding
   • Retry-able failures

Implementation Files

Core Scripts

1. seed_supabase_multi_db.py (550 lines)

   • Unified seeding across all databases
   • Auto-generates OpenAI embeddings
   • Syncs to Neo4j (relationships) and Pinecone (vectors)
   • Tracks sync status in Supabase

2. validate_supabase_multi_db.py (280 lines)

   • Validates all database connections
   • Auto-loads .env from project root
   • Reports sync health and status
   • Saves results to JSON

3. seed.examples.py (existing, 250 lines)

   • Seeds few-shot examples from JSONL
   • Compatible with Supabase schema

Documentation

1. QUICKSTART_SUPABASE.md

   • 10-minute setup guide
   • SQL schema for Supabase tables
   • Environment configuration
   • Sample queries

2. INDEX.md (updated)

   • Complete navigation guide
   • References Supabase scripts
   • Updated learning path

3. SUPABASE_IMPLEMENTATION_SUMMARY.md

   • This file - implementation overview

Database Schema

Sport Archetypes Table

CREATE TABLE sport_archetypes (
  id UUID PRIMARY KEY,
  archetype_name TEXT UNIQUE,
  display_name TEXT,
  code_pattern TEXT,
  characteristics JSONB,
  embedding_vector JSONB,
  neo4j_node_id TEXT,
  created_at TIMESTAMPTZ,
  updated_at TIMESTAMPTZ
);

5 Master Archetypes:

• racing - Racing & Speed Sports
• combat - Combat Sports
• team_sport - Team Sports
• precision - Precision & Target Sports
• large_field - Large Field Competitions

Prospective Leagues Table

CREATE TABLE prospective_leagues (
  id UUID PRIMARY KEY,
  league_tag TEXT UNIQUE,
  league_name TEXT,
  sport_name TEXT,
  sport_tier TEXT,
  sport_archetype TEXT,
 
  -- Embedding
  embedding_vector JSONB,
  embedding_model TEXT,
 
  -- Neo4j sync
  neo4j_node_id TEXT,
  neo4j_sync_status TEXT,
  neo4j_synced_at TIMESTAMPTZ,
 
  -- Pinecone sync
  pinecone_id TEXT,
  pinecone_namespace TEXT,
  pinecone_sync_status TEXT,
  pinecone_synced_at TIMESTAMPTZ,
 
  created_at TIMESTAMPTZ,
  updated_at TIMESTAMPTZ
);

Few-Shot Examples Table

CREATE TABLE few_shot_examples (
  id UUID PRIMARY KEY,
  example_id TEXT UNIQUE,
  category TEXT,
  scenario TEXT,
  sport TEXT,
  tier TEXT,
  complexity TEXT,
  quality_score DECIMAL,
  usage_count INTEGER,
  input_data JSONB,
  output_data JSONB,
  tags JSONB,
  embedding_vector JSONB,
  pinecone_sync_status TEXT,
  created_at TIMESTAMPTZ,
  updated_at TIMESTAMPTZ
);

Seeding Workflow

1. Archetypes (Required)

python scripts/seed_supabase_multi_db.py --archetypes

Process:

1. Generate embeddings for each archetype
2. Upsert to Supabase sport_archetypes table
3. Sync to Neo4j as SportArchetype nodes

2. Leagues (Optional)

python scripts/seed_supabase_multi_db.py --leagues

Process:

1. Read JSON files from seeds/ directory
2. Generate embedding from league name + sport + description
3. Upsert to Supabase prospective_leagues table
4. Create Neo4j League node
5. Create BELONGS_TO_ARCHETYPE relationship
6. Upsert vector to Pinecone altsports-leagues index
7. Update sync status in Supabase

3. Examples (Optional)

python scripts/seed_supabase_multi_db.py --examples

Process:

1. Read JSONL files from seed.examples-kb/data/
2. Generate embeddings from scenario + input
3. Upsert to Supabase few_shot_examples table
4. Upsert vector to Pinecone altsports-examples index

Neo4j Graph Model

Nodes

• SportArchetype - 5 master archetype nodes
• League - Individual league nodes

Relationships

• (League)-[:BELONGS_TO_ARCHETYPE]->(SportArchetype)

Sample Cypher Queries

// Get all combat sports leagues
MATCH (l:League)-[:BELONGS_TO_ARCHETYPE]->(a:SportArchetype {name: "combat"})
RETURN l.league_name, l.status
 
// Count leagues per archetype
MATCH (l:League)-[:BELONGS_TO_ARCHETYPE]->(a:SportArchetype)
RETURN a.name, count(l) as league_count
ORDER BY league_count DESC

Pinecone Vector Search

Indexes

1. altsports-leagues (dimension: 1536)

   • League embeddings
   • Metadata: league_name, sport_name, sport_archetype, tier

2. altsports-examples (dimension: 1536)

   • Example embeddings
   • Namespaces by category (triage, contract_generation, etc.)

Sample Queries

from pinecone import Pinecone
from openai import OpenAI
 
# Generate query embedding
openai_client = OpenAI()
response = openai_client.embeddings.create(
    model="text-embedding-3-small",
    input="Find high-speed racing leagues"
)
query_vector = response.data[0].embedding
 
# Search Pinecone
pc = Pinecone(api_key=os.getenv('PINECONE_API_KEY'))
index = pc.Index('altsports-leagues')
 
results = index.query(
    vector=query_vector,
    top_k=10,
    include_metadata=True
)
 
# Get full data from Supabase
league_tags = [match['id'] for match in results['matches']]
supabase_results = supabase.table('prospective_leagues')\
    .select('*')\
    .in_('league_tag', league_tags)\
    .execute()

Multi-Database Query Patterns

Pattern 1: Semantic Search → Supabase

# 1. Vector search in Pinecone
pinecone_results = index.query(vector=query_embedding, top_k=10)
 
# 2. Get full records from Supabase
league_tags = [r['id'] for r in pinecone_results['matches']]
leagues = supabase.table('prospective_leagues')\
    .select('*')\
    .in_('league_tag', league_tags)\
    .execute()

Pattern 2: Supabase → Neo4j Relationships

# 1. Get league from Supabase
league = supabase.table('prospective_leagues')\
    .select('*')\
    .eq('league_tag', 'ufc')\
    .single()\
    .execute()
 
# 2. Get archetype siblings from Neo4j
with neo4j_driver.session() as session:
    result = session.run("""
        MATCH (l:League {league_tag: $tag})-[:BELONGS_TO_ARCHETYPE]->(a:SportArchetype)
        MATCH (sibling:League)-[:BELONGS_TO_ARCHETYPE]->(a)
        WHERE sibling.league_tag <> $tag
        RETURN sibling.league_name
    """, tag='ufc')

Pattern 3: Combined Multi-DB Query

# 1. Semantic search (Pinecone)
pinecone_matches = index.query(vector=embedding, top_k=20)
 
# 2. Get full data (Supabase)
leagues = supabase.table('prospective_leagues')\
    .select('*')\
    .in_('league_tag', [m['id'] for m in pinecone_matches])\
    .execute()
 
# 3. For each, get graph relationships (Neo4j)
for league in leagues.data:
    with neo4j_driver.session() as session:
        siblings = session.run("""
            MATCH (l:League {league_tag: $tag})-[:BELONGS_TO_ARCHETYPE]->(a)
            MATCH (sibling)-[:BELONGS_TO_ARCHETYPE]->(a)
            RETURN sibling.league_name
        """, tag=league['league_tag'])

Environment Setup

Required

SUPABASE_URL=postgresql://postgres:password@db.project.supabase.co:5432/postgres
SUPABASE_API_KEY=your_anon_key

Optional

# For embeddings
OPENAI_API_KEY=sk-...
 
# For graph relationships
NEO4J_URI=bolt://localhost:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=...
 
# For vector search
PINECONE_API_KEY=...

Usage Examples

Setup and Validation

# Validate setup
python database/scripts/validate_supabase_multi_db.py
 
# Check results
cat database/validation_results.json

Seeding

# Seed everything
python database/scripts/seed_supabase_multi_db.py --all
 
# Seed specific components
python database/scripts/seed_supabase_multi_db.py --archetypes
python database/scripts/seed_supabase_multi_db.py --leagues --limit 10
python database/scripts/seed_supabase_multi_db.py --examples --category triage

Querying

from supabase import create_client
import os
 
# Connect
supabase = create_client(
    os.getenv('SUPABASE_URL'),
    os.getenv('SUPABASE_API_KEY')
)
 
# Query combat sports
result = supabase.table('prospective_leagues')\
    .select('*')\
    .eq('sport_archetype', 'combat')\
    .execute()
 
print(f"Found {len(result.data)} combat sports leagues")

Key Features

1. Sport Archetype System

• 5 master categories for all sports
• Code pattern mapping (RaceEvent, FightEvent, etc.)
• Characteristics metadata (JSONB)
• Graph navigation via Neo4j

2. Embedding Generation

• Auto-generates OpenAI embeddings during seeding
• text-embedding-3-small (1536 dimensions)
• Stored as JSONB in Supabase
• Synced to Pinecone for vector search

3. Sync Tracking

• Per-database sync status (pending/synced/failed)
• Timestamps for sync operations
• Foreign key references (neo4j_node_id, pinecone_id)
• Queryable sync health

4. Graceful Degradation

• Works with just Supabase
• Neo4j optional (graph features disabled)
• Pinecone optional (semantic search disabled)
• OpenAI optional (no embeddings generated)

Comparison to Original Prisma Approach

Why Supabase?

• Already in use by existing backend
• No new dependencies (prisma)
• Direct SQL control
• Matches existing architecture

Next Steps

1. Create Supabase Tables - Run SQL from QUICKSTART_SUPABASE.md
2. Validate Setup - Run validate_supabase_multi_db.py
3. Seed Archetypes - Run with --archetypes flag
4. Add League Seeds - Create JSON files in seeds/
5. Test Queries - Use examples from QUICKSTART_SUPABASE.md

Troubleshooting

Supabase Connection Issues

• Ensure SUPABASE_URL is the PostgreSQL connection string (starts with postgresql://)
• Validator auto-extracts HTTP URL for Python client
• Check SUPABASE_API_KEY is the anon key from dashboard

Neo4j Not Required

• Neo4j is optional
• System works without it
• Graph features simply disabled

Embedding Generation Fails

• Ensure OPENAI_API_KEY is set
• Check OpenAI account credits
• Embeddings are optional

Files Created

1. scripts/seed_supabase_multi_db.py - Unified seeding
2. scripts/validate_supabase_multi_db.py - Setup validator
3. QUICKSTART_SUPABASE.md - Quick start guide
4. SUPABASE_IMPLEMENTATION_SUMMARY.md - This file
5. INDEX.md (updated) - Navigation guide

Architecture Benefits

1. Backwards Compatible - Works with existing Supabase setup
2. No New Dependencies - Uses existing supabase-py client
3. Optional Extensions - Neo4j and Pinecone are opt-in
4. Sync Tracking - Built-in sync status management
5. Graceful Degradation - Works even if optional DBs unavailable

Status: Production Ready 🚀 Pattern: JSONL → Supabase → Neo4j + Pinecone Built with: Supabase + Neo4j + Pinecone + OpenAI