Seed Data¶

This runbook documents the full seed pipeline for populating Curaway's databases with initial data. Seeds must be run in a specific order because later seeds depend on records created by earlier ones.

Seed Order¶

Run the seeds in this exact sequence:

# 1. Base seed: tenant + consent records
python -m app.seed

# 2. Providers: 42 providers across 8 countries
python -m app.seed_providers

# 3. Demo data: demo patient Aisha + sample cases
python -m app.seed_demo

# 4. Knowledge graph: Neo4j provider-procedure-country graph
python -m app.seed_graph

# 5. Embeddings: Qdrant vector embeddings for semantic search
python -m app.seed_embeddings

# 6. Doctors: 8 doctor profiles + language services
python -m app.seed_doctors

# 7. Storefront: treatment categories, provider content, completeness scores
python -m app.seed_storefront

Or as a single command:

python -m app.seed && \
python -m app.seed_providers && \
python -m app.seed_demo && \
python -m app.seed_graph && \
python -m app.seed_embeddings && \
python -m app.seed_doctors && \
python -m app.seed_storefront

Seed Details¶

1. python -m app.seed -- Base Seed¶

Creates the foundational records that all other seeds depend on.

Creates:

Idempotent: Yes -- skips records that already exist.

Dependencies: None. This must run first.

2. python -m app.seed_providers -- Provider Seed¶

Populates the providers table with 42 medical providers across 8 countries.

Creates:

Each provider record includes:

• Name, country, city
• Specialties (array)
• Accreditations (JCI, ISO 9001, etc.)
• Description (used for embedding generation)
• Accepted insurance networks
• Languages spoken at the facility
• Average procedure costs (range)

Idempotent: Yes -- uses upsert by provider ID.

Dependencies: Requires app.seed (needs tenant record).

3. python -m app.seed_demo -- Demo Data Seed¶

Creates a demo patient and sample cases for testing and demonstrations.

Creates:

Idempotent: Yes -- checks for existing demo patient by email before creating.

Dependencies: Requires app.seed and app.seed_providers.

4. python -m app.seed_graph -- Knowledge Graph Seed¶

Populates the Neo4j knowledge graph with provider-procedure-country relationships.

Creates:

Idempotent: Yes -- uses MERGE in Cypher to avoid duplicates.

Dependencies: Requires app.seed_providers (reads provider data from PostgreSQL).

5. python -m app.seed_embeddings -- Vector Embedding Seed¶

Generates text embeddings for all providers and upserts them into Qdrant.

Creates:

Each point includes:

• Vector: Embedding of the provider's description + specialties + procedures
• Payload: provider_id, name, country, specialties, accreditations

Cost: ~42 API calls to OpenAI embedding endpoint (~$0.01 total).

Idempotent: Yes -- upserts by provider ID.

Dependencies: Requires app.seed_providers and a valid OPENAI_API_KEY.

6. python -m app.seed_doctors -- Doctor Seed¶

Creates doctor profiles and their procedure associations.

Creates:

Each doctor record includes:

• Full name, specialty, license number
• Languages spoken
• Years of experience
• Associated provider (foreign key)
• Procedures performed (many-to-many)
• Education and certifications

Additionally seeds language services for each provider based on the languages spoken by their doctors.

Idempotent: Yes -- checks for existing doctors by license number.

Dependencies: Requires app.seed_providers.

7. python -m app.seed_storefront -- Storefront Seed¶

Seeds 12 treatment categories, updates 6 demo providers with storefront content (taglines, facilities, cultural support, travel info), and computes completeness scores for all 42 providers.

Creates:

Idempotent: Yes -- upserts by slug/ID.

Dependencies: Requires app.seed_providers and app.seed_doctors.

Default Tenant¶

All seeded data belongs to the tenant tenant-apollo-001. This is the default development and demo tenant used across all environments.

Resetting All Data¶

To wipe and re-seed everything from scratch:

# 1. Reset PostgreSQL
alembic downgrade base
alembic upgrade head

# 2. Clear Neo4j
python -c "
from neo4j import GraphDatabase
import os
driver = GraphDatabase.driver(os.environ['NEO4J_URI'],
    auth=(os.environ['NEO4J_USERNAME'], os.environ['NEO4J_PASSWORD']))
with driver.session() as s:
    s.run('MATCH (n) DETACH DELETE n')
driver.close()
"

# 3. Clear Qdrant
python -c "
from qdrant_client import QdrantClient
import os
client = QdrantClient(url=os.environ['QDRANT_URL'], api_key=os.environ['QDRANT_API_KEY'])
client.delete_collection('providers')
"

# 4. Run full seed sequence
python -m app.seed && \
python -m app.seed_providers && \
python -m app.seed_demo && \
python -m app.seed_graph && \
python -m app.seed_embeddings && \
python -m app.seed_doctors && \
python -m app.seed_storefront

Verifying Seeds¶

After running the full seed sequence, verify each database:

# PostgreSQL: check record counts
python -c "
from app.db import get_session
# Expected: 1 tenant, 42 providers, 1 patient, 3 cases, 8 doctors
"

# Neo4j: check node counts (see database-ops.md for Cypher queries)

# Qdrant: check collection (see database-ops.md for Python snippet)

See the database operations runbook for detailed verification queries.