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ADR-028: eHealth Platform Architecture for 50M Patient Records

Status: Proposed Date: 2026-02-10 Authors: ruv.io, RuVector Team Deciders: Architecture Review Board SDK: Claude-Flow

Version History

Version	Date	Author	Changes
0.1	2026-02-10	ruv.io	Initial architecture proposal

Context

The 50-Million Patient Data Challenge

Healthcare systems face a convergence of demands that push conventional database architectures past their breaking point. A national-scale eHealth platform serving 50 million patient records at 2,000+ requests per second must satisfy four simultaneous pressures:

Pressure	Requirement	Challenge
Volume	50M patients × ~20 encounters/yr × clinical notes, labs, meds, claims	Terabyte-scale vector storage with sub-100ms search
Velocity	2,000+ RPS sustained, 5,000+ burst during open enrollment	Real-time hybrid search across structured + unstructured data
Variety	FHIR R4, HL7v2, X12 837/835, SNOMED CT, ICD-10, LOINC, RxNorm, free-text notes	Unified semantic layer across heterogeneous ontologies
Regulatory	HIPAA 45 CFR §164, HITECH Act, state privacy laws, audit trail mandates	Every query, mutation, and access must be cryptographically auditable

Current State Limitations

Existing eHealth platforms rely on a patchwork of specialized systems, each introducing HIPAA surface area and operational complexity:

Capability	Current Approach	Limitation
Patient Matching	Probabilistic MPI with string similarity	No semantic understanding of clinical context; 3-8% false match rate
Clinical Decision Support	Rule-based engines with manual knowledge bases	Cannot leverage unstructured notes; knowledge decay within months
Ontology Mapping	Lookup tables for SNOMED↔ICD-10 crosswalks	No hierarchical reasoning; misses partial matches and concept drift
Fraud Detection	Batch-mode statistical models with 48-72hr lag	Cannot detect real-time provider network fraud patterns
Interoperability	Point-to-point interfaces per trading partner	O(n²) integration complexity; no semantic normalization
Clinical Note Search	Keyword-based full-text search	Misses semantic synonyms ("heart attack" vs "MI" vs "STEMI")
Patient Similarity	Cohort matching on demographics only	Ignores clinical trajectory, medication patterns, comorbidity graphs
Compliance Audit	Append-only log tables with manual review	No anomaly detection; audit lag measured in days

Why RuVector-Postgres

RuVector-Postgres provides a single unified engine that collapses this multi-system stack into one PostgreSQL extension:

RuVector Capability	Replaces	HIPAA Benefit
`ruvector(384)` vector type + HNSW	Separate vector DB (Pinecone, Weaviate)	One fewer system in BAA scope
`sparsevec(50000)` + BM25 scoring	Elasticsearch/Solr for full-text	Eliminates data replication to search cluster
`ruvector_sparql()` + RDF triple store	Dedicated triple store (Blazegraph, Stardog)	Ontology data stays in-database
`ruvector_poincare_distance()`	Custom Python microservice for hierarchy	No data leaves PostgreSQL
`ruvector_gcn_forward()` / `ruvector_graphsage_forward()`	External GNN service (PyG, DGL)	In-database ML eliminates data export
`ruvector_hybrid_search()` with RRF fusion	Application-level result merging	Search logic auditable as SQL
`ruvector_tenant_create()` + RLS	Custom multi-tenancy middleware	Row-level security enforced by database kernel
`ruvector_healing_worker_start()`	Manual DBA intervention + alerting	Self-healing reduces MTTR from hours to seconds
`ruvector_flash_attention()`	GPU-based attention service	CPU-native attention for clinical note analysis
Coherence Engine (ADR-014)	No equivalent	Structural consistency detection for clinical data

Single-engine compliance: one BAA, one encryption boundary, one audit log, one backup strategy.

Decision

Adopt RuVector-Postgres as the Unified eHealth Data Platform

We implement the eHealth platform as a layered architecture with RuVector-Postgres as the sole data engine:

┌─────────────────────────────────────────────────────────────────────────────────┐
│                              EXTERNAL INTERFACES                                │
│                                                                                 │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐   │
│  │  FHIR R4     │  │  HL7v2       │  │  X12 EDI     │  │  Patient Portal  │   │
│  │  Gateway     │  │  Gateway     │  │  837/835     │  │  (OAuth 2.0)     │   │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └────────┬─────────┘   │
│         │                 │                 │                    │              │
├─────────┴─────────────────┴─────────────────┴────────────────────┴──────────────┤
│                           APPLICATION SERVICES                                  │
│                                                                                 │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  ┌──────────────────┐   │
│  │  CDS Engine  │  │  Claims      │  │  Patient     │  │  Analytics &     │   │
│  │  (RAG-based) │  │  Adjudicator │  │  Matching    │  │  Population Hlth │   │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘  └────────┬─────────┘   │
│         │                 │                 │                    │              │
├─────────┴─────────────────┴─────────────────┴────────────────────┴──────────────┤
│                        RUVECTOR-POSTGRES ENGINE                                 │
│                                                                                 │
│  ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐   │
│  │  Hybrid    │ │  Graph +   │ │  Hyperbolic│ │  GNN       │ │  Coherence │   │
│  │  Search    │ │  SPARQL    │ │  Embeddings│ │  Layers    │ │  Engine    │   │
│  │  (BM25+Vec)│ │  (RDF)     │ │  (Poincaré)│ │  (GCN/GAT) │ │  (Sheaf)   │   │
│  └────────────┘ └────────────┘ └────────────┘ └────────────┘ └────────────┘   │
│  ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐ ┌────────────┐   │
│  │  Attention │ │  Multi-    │ │  Self-     │ │  HNSW      │ │  Tiered    │   │
│  │  Operators │ │  Tenancy   │ │  Healing   │ │  Indexing  │ │  Quantize  │   │
│  └────────────┘ └────────────┘ └────────────┘ └────────────┘ └────────────┘   │
│                                                                                 │
├─────────────────────────────────────────────────────────────────────────────────┤
│                        POSTGRESQL CLUSTER                                       │
│                                                                                 │
│  Primary (Read/Write)  │  Sync Standby  │  Async Replica 1  │  Async Replica 2│
│  RuVector Extension    │  Hot Standby   │  Read-Only CDS     │  Read-Only Anlyt│
└─────────────────────────────────────────────────────────────────────────────────┘

Key Architectural Decisions

#	Decision	Choice	Rationale
1	Embedding model	BioClinicalBERT 384-dim	Pre-trained on MIMIC-III + PubMed; 384-dim balances recall vs storage
2	Vector index	HNSW (m=24, ef_construction=200)	Sub-10ms ANN at 50M scale; m=24 optimizes for medical recall >0.95
3	Ontology store	RuVector RDF triple store (`ruvector_create_rdf_store`)	In-database SPARQL eliminates external triple store from HIPAA scope
4	Hierarchy model	Poincaré ball (`ruvector_poincare_distance`) 32-dim	Hyperbolic space preserves ICD-10/SNOMED tree depth with low distortion
5	Pathway engine	GCN via `ruvector_gcn_forward`	Clinical pathways as message-passing over patient-encounter-diagnosis graph
6	Search strategy	Hybrid BM25+vector via `ruvector_hybrid_search` with RRF fusion	Captures both exact medical terminology and semantic similarity
7	Patient similarity	GraphSAGE via `ruvector_graphsage_forward`	Inductive: generalizes to new patients without full re-embedding
8	Fraud detection	GAT attention scores via `ruvector_flash_attention` on claims graph	Attention weights reveal anomalous provider-billing relationships
9	Disagreement detection	Coherence Engine sheaf Laplacian (ADR-014)	Structural consistency detects medication-diagnosis contradictions
10	Tenancy model	Shared isolation + RLS via `ruvector_enable_tenant_rls`	Per-payer isolation with healthcare org hierarchy support
11	Rate limiting	Token bucket via `ruvector_tenant_quota_check`	Per-tenant QPS limits prevent noisy-neighbor across health plans

Data Architecture

Core Schema

The platform uses six primary tables, each with dense vector embeddings for semantic search and additional specialized vector types where needed.

1. Patients Table

CREATE TABLE patients (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    mrn             TEXT NOT NULL,           -- Medical Record Number
    fhir_id         TEXT UNIQUE,             -- FHIR Patient resource ID
    demographics    JSONB NOT NULL,          -- name, dob, gender, address, contact
    identifiers     JSONB,                   -- SSN hash, insurance IDs, MPI links

    -- Dense embedding: BioClinicalBERT over demographics + clinical summary
    embedding       ruvector(384) NOT NULL,

    -- Hyperbolic embedding: position in patient taxonomy hierarchy
    hierarchy_embed ruvector(32),            -- Poincaré ball for risk stratification

    created_at      TIMESTAMPTZ DEFAULT now(),
    updated_at      TIMESTAMPTZ DEFAULT now()
);

-- HNSW index for patient similarity search
CREATE INDEX idx_patients_embedding ON patients
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

-- Partitioned by tenant for healthcare org isolation
ALTER TABLE patients ENABLE ROW LEVEL SECURITY;
-- Applied via: SELECT ruvector_enable_tenant_rls('patients', 'tenant_id');

2. Encounters Table

CREATE TABLE encounters (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    patient_id      BIGINT REFERENCES patients(id),
    fhir_id         TEXT UNIQUE,
    encounter_type  TEXT NOT NULL,            -- inpatient, outpatient, emergency, telehealth
    status          TEXT NOT NULL,            -- planned, arrived, in-progress, finished
    period_start    TIMESTAMPTZ NOT NULL,
    period_end      TIMESTAMPTZ,
    diagnoses       JSONB,                   -- array of {code, system, display, rank}
    procedures      JSONB,                   -- array of {code, system, display}
    providers       JSONB,                   -- attending, consulting, referring
    facility_id     TEXT,

    -- Dense embedding: encounter narrative + diagnoses + procedures
    embedding       ruvector(384) NOT NULL,

    -- Sparse embedding: BM25-compatible term vector for diagnosis code search
    terms           sparsevec(50000),

    created_at      TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX idx_encounters_embedding ON encounters
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

CREATE INDEX idx_encounters_patient ON encounters (patient_id, period_start DESC);

3. Clinical Notes Table

CREATE TABLE clinical_notes (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    encounter_id    BIGINT REFERENCES encounters(id),
    patient_id      BIGINT REFERENCES patients(id),
    note_type       TEXT NOT NULL,            -- progress, discharge, consult, operative, pathology
    author_id       TEXT NOT NULL,
    author_role     TEXT NOT NULL,            -- physician, nurse, specialist
    chunk_index     INT NOT NULL DEFAULT 0,  -- for notes split into embedding chunks
    chunk_text      TEXT NOT NULL,            -- the actual chunk content (512-token window)

    -- Dense embedding: BioClinicalBERT over chunk_text
    embedding       ruvector(384) NOT NULL,

    -- Sparse embedding: BM25 term frequencies for keyword search
    terms           sparsevec(50000),

    signed_at       TIMESTAMPTZ,
    created_at      TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX idx_notes_embedding ON clinical_notes
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

CREATE INDEX idx_notes_patient ON clinical_notes (patient_id, created_at DESC);
CREATE INDEX idx_notes_encounter ON clinical_notes (encounter_id);

4. Medications Table

CREATE TABLE medications (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    patient_id      BIGINT REFERENCES patients(id),
    encounter_id    BIGINT REFERENCES encounters(id),
    rxnorm_code     TEXT NOT NULL,
    ndc_code        TEXT,
    drug_name       TEXT NOT NULL,
    dosage          JSONB,                   -- {value, unit, frequency, route}
    status          TEXT NOT NULL,            -- active, completed, stopped, on-hold
    prescriber_id   TEXT NOT NULL,
    start_date      DATE NOT NULL,
    end_date        DATE,

    -- Dense embedding: drug profile + indication + patient context
    embedding       ruvector(384) NOT NULL,

    created_at      TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX idx_meds_embedding ON medications
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

CREATE INDEX idx_meds_patient ON medications (patient_id, status, start_date DESC);
CREATE INDEX idx_meds_rxnorm ON medications (rxnorm_code);

5. Lab Results Table

CREATE TABLE lab_results (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    patient_id      BIGINT REFERENCES patients(id),
    encounter_id    BIGINT REFERENCES encounters(id),
    loinc_code      TEXT NOT NULL,
    test_name       TEXT NOT NULL,
    value_numeric   NUMERIC,
    value_text      TEXT,
    unit            TEXT,
    reference_range TEXT,
    interpretation  TEXT,                    -- normal, abnormal, critical

    -- Dense embedding: test + result + clinical context
    embedding       ruvector(384) NOT NULL,

    collected_at    TIMESTAMPTZ NOT NULL,
    created_at      TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX idx_labs_embedding ON lab_results
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

CREATE INDEX idx_labs_patient ON lab_results (patient_id, collected_at DESC);
CREATE INDEX idx_labs_loinc ON lab_results (loinc_code);

6. Claims Table

CREATE TABLE claims (
    id              BIGSERIAL PRIMARY KEY,
    tenant_id       TEXT NOT NULL,
    patient_id      BIGINT REFERENCES patients(id),
    encounter_id    BIGINT REFERENCES encounters(id),
    claim_type      TEXT NOT NULL,            -- professional, institutional, pharmacy
    status          TEXT NOT NULL,            -- submitted, pending, adjudicated, denied, paid
    payer_id        TEXT NOT NULL,
    provider_id     TEXT NOT NULL,
    facility_id     TEXT,
    service_date    DATE NOT NULL,
    billed_amount   NUMERIC(12,2) NOT NULL,
    allowed_amount  NUMERIC(12,2),
    paid_amount     NUMERIC(12,2),
    diagnosis_codes JSONB,                   -- [{code, system, pointer}]
    procedure_codes JSONB,                   -- [{code, system, modifier}]

    -- Dense embedding: claim profile for fraud/similarity detection
    embedding       ruvector(384) NOT NULL,

    submitted_at    TIMESTAMPTZ NOT NULL,
    adjudicated_at  TIMESTAMPTZ,
    created_at      TIMESTAMPTZ DEFAULT now()
);

CREATE INDEX idx_claims_embedding ON claims
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 24, ef_construction = 200);

CREATE INDEX idx_claims_patient ON claims (patient_id, service_date DESC);
CREATE INDEX idx_claims_provider ON claims (provider_id, service_date DESC);
CREATE INDEX idx_claims_status ON claims (status, submitted_at DESC);

Capacity Calculations

Table	Rows (50M patients)	Vector Size	Sparse Size	Raw Total
`patients`	50M	384×4B = 1.5KB	32×4B = 128B	~81 GB
`encounters`	1B (20/patient)	1.5KB	~400B avg	~1.9 TB
`clinical_notes`	5B (5 chunks/encounter avg)	1.5KB	~400B avg	~9.5 TB
`medications`	500M (10/patient)	1.5KB	—	~750 GB
`lab_results`	2B (40/patient)	1.5KB	—	~3.0 TB
`claims`	2B (40/patient)	1.5KB	—	~3.0 TB
Total Raw	~10.55B rows			~18.2 TB

With Metadata + Indexes: ~25.2 TB raw (1.4× overhead for HNSW graphs + B-tree indexes + TOAST).

With Tiered Quantization (see Scaling Strategy section):

Hot tier (recent 2 years): f32 → ~7 TB
Warm tier (2-5 years): SQ8 → ~1.5 TB (4× compression)
Cool tier (5-7 years): PQ → ~600 GB (16× compression)
Cold tier (7+ years): Binary → ~200 GB (32× compression)
Total after quantization: ~9.3 TB

QPS Budget

Operation	Target RPS	Latency p99	Cluster Capacity
Vector search (HNSW k=10)	800	<15ms	4,000/node × 4 = 16,000
Hybrid search (BM25+vec)	400	<30ms	2,000/node × 4 = 8,000
SPARQL ontology lookup	200	<20ms	3,000/node × 4 = 12,000
GNN forward pass	100	<50ms	500/node × 4 = 2,000
Claims adjudication	300	<100ms	1,000/node × 4 = 4,000
Writes (encounters, notes)	200	<50ms	Primary only: 2,000
Total	2,000		Headroom: 7.5×

A 4-node cluster (1 primary + 1 sync standby + 2 async read replicas) provides ~15,000 QPS read capacity, delivering 7.5× headroom over the 2,000 RPS requirement.

Semantic Interoperability Layer

Medical Ontology RDF Store

The platform loads four core medical ontologies into RuVector's in-database RDF triple store, enabling SPARQL-based cross-mapping without external services.

-- Initialize the medical ontology store
SELECT ruvector_create_rdf_store('medical_ontologies');

Ontology	Triples	Purpose
SNOMED CT	~1.5M concepts, ~5M relationships → ~15M triples	Clinical terminology master
ICD-10-CM	~72K codes, ~150K relationships → ~500K triples	Diagnosis coding for billing
LOINC	~98K terms, ~300K relationships → ~900K triples	Laboratory test identification
RxNorm	~120K concepts, ~500K relationships → ~15M triples	Drug normalization
Total		~31.4M triples

Loading Ontologies

-- Load SNOMED CT from N-Triples export
SELECT ruvector_load_ntriples('medical_ontologies', pg_read_file('/data/ontologies/snomed_ct.nt'));

-- Load ICD-10 mappings
SELECT ruvector_load_ntriples('medical_ontologies', pg_read_file('/data/ontologies/icd10cm.nt'));

-- Load LOINC
SELECT ruvector_load_ntriples('medical_ontologies', pg_read_file('/data/ontologies/loinc.nt'));

-- Load RxNorm
SELECT ruvector_load_ntriples('medical_ontologies', pg_read_file('/data/ontologies/rxnorm.nt'));

-- Verify loaded data
SELECT ruvector_rdf_stats('medical_ontologies');

SPARQL Cross-Mapping Queries

Map SNOMED CT diagnosis to ICD-10 billing code:

SELECT ruvector_sparql_json('medical_ontologies', '
    PREFIX snomed: <http://snomed.info/id/>
    PREFIX icd10: <http://hl7.org/fhir/sid/icd-10-cm/>
    PREFIX skos: <http://www.w3.org/2004/02/skos/core#>

    SELECT ?icd10_code ?icd10_label
    WHERE {
        snomed:22298006 skos:exactMatch ?icd10_concept .
        ?icd10_concept skos:notation ?icd10_code .
        ?icd10_concept skos:prefLabel ?icd10_label .
    }
');
-- Maps SNOMED "Myocardial infarction" (22298006) → ICD-10 "I21.9"

Find all drugs in a therapeutic class with contraindications:

SELECT ruvector_sparql_json('medical_ontologies', '
    PREFIX rxn: <http://rxnorm.nlm.nih.gov/>
    PREFIX ndfrt: <http://evs.nci.nih.gov/ftp1/NDF-RT/>

    SELECT ?drug ?drug_name ?contraindication
    WHERE {
        ?drug rxn:tty "SCD" .
        ?drug rxn:str ?drug_name .
        ?drug ndfrt:has_contraindicated_class ?contra_class .
        ?contra_class skos:prefLabel ?contraindication .
        ?drug rxn:ingredient ?ingredient .
        ?ingredient skos:prefLabel "metformin"@en .
    }
');

Traverse SNOMED hierarchy to find all children of a concept:

SELECT ruvector_sparql_json('medical_ontologies', '
    PREFIX snomed: <http://snomed.info/id/>
    PREFIX sct: <http://snomed.info/sct/>

    SELECT ?child ?label
    WHERE {
        ?child sct:is_a+ snomed:73211009 .
        ?child skos:prefLabel ?label .
    }
    LIMIT 100
');
-- Finds all descendants of "Diabetes mellitus" (73211009)

Poincaré Ball Hyperbolic Embeddings for Ontology Hierarchy

Medical ontologies are fundamentally hierarchical (ICD-10 is a tree, SNOMED CT is a DAG). Euclidean embeddings distort tree structure, but Poincaré ball embeddings preserve parent-child distance with logarithmic fidelity.

-- Embed ICD-10 codes in 32-dim Poincaré ball
-- Parent codes are closer to origin, leaf codes at boundary
-- Distance preserves hierarchical depth

-- Compute hyperbolic distance between two ICD-10 concepts
SELECT ruvector_poincare_distance(
    icd10_a.hierarchy_embed,
    icd10_b.hierarchy_embed
) AS hierarchical_distance
FROM icd10_embeddings icd10_a, icd10_embeddings icd10_b
WHERE icd10_a.code = 'I21'      -- Acute myocardial infarction (parent)
  AND icd10_b.code = 'I21.01';  -- STEMI of LAD (child)
-- Expected: small distance (parent-child)

-- Möbius addition for concept composition in hyperbolic space
SELECT ruvector_mobius_add(
    diabetes_embed,
    retinopathy_embed
) AS composed_concept
FROM concept_embeddings
WHERE code IN ('E11', 'H35.0');
-- Combines "Type 2 Diabetes" + "Retinopathy" → diabetic retinopathy region

-- Map between coordinate systems for different algorithms
SELECT ruvector_poincare_to_lorentz(hierarchy_embed) AS lorentz_coords
FROM patients WHERE id = 12345;

-- Exponential map: project Euclidean gradient into Poincaré ball
SELECT ruvector_exp_map(tangent_vector, base_point) AS poincare_point
FROM optimization_step;

-- Logarithmic map: map Poincaré point back to tangent space
SELECT ruvector_log_map(poincare_point, base_point) AS tangent_vector
FROM gradient_computation;

Why 32 dimensions for hierarchy embeddings: Poincaré embeddings achieve near-perfect reconstruction of tree structures in low dimensions. 32-dim provides sufficient capacity for ICD-10's ~72K codes (max depth 7) while keeping the per-row overhead at 128 bytes.

Clinical AI Pipeline

RAG-Based Clinical Decision Support

The CDS engine uses Retrieval-Augmented Generation over clinical notes, combining BM25 keyword matching with semantic vector search for maximum recall.

Hybrid Search for Clinical Context Retrieval

-- Register the clinical notes collection for hybrid search
SELECT ruvector_register_hybrid(
    'clinical_notes',           -- collection name
    'embedding',                -- vector column
    'terms',                    -- full-text search column (sparsevec)
    'chunk_text'                -- text column for BM25 scoring
);

-- Configure hybrid search parameters
SELECT ruvector_hybrid_configure('clinical_notes', '{
    "bm25_k1": 1.2,
    "bm25_b": 0.75,
    "default_fusion": "rrf",
    "rrf_k": 60,
    "vector_weight": 0.6,
    "keyword_weight": 0.4
}'::jsonb);

-- CDS query: find relevant clinical context for a suspected MI patient
SELECT * FROM ruvector_hybrid_search(
    'clinical_notes',                                          -- collection
    'chest pain radiating to left arm elevated troponin',      -- query text (BM25)
    embed('chest pain radiating to left arm elevated troponin'), -- query vector
    20,                                                        -- k results
    'rrf',                                                     -- fusion: rrf | linear | learned
    0.6                                                        -- alpha (vector weight)
)
WHERE tenant_id = current_setting('app.tenant_id');

The hybrid search pipeline:

BM25 path: Tokenizes query → scores against sparsevec term vectors → returns top-k by BM25 score
Vector path: Encodes query with BioClinicalBERT → HNSW ANN search on embedding column → returns top-k by cosine similarity
Fusion: Reciprocal Rank Fusion (RRF) merges both result sets with k=60, preserving results that rank highly in either modality

Inline Score Computation

-- Compute hybrid relevance score for a specific note
SELECT ruvector_hybrid_score(
    1.0 - (embedding <=> query_embedding),  -- vector similarity (cosine → similarity)
    ts_rank(to_tsvector(chunk_text), to_tsquery('chest & pain & troponin')),  -- BM25-like score
    0.6                                     -- alpha weight for vector component
) AS relevance
FROM clinical_notes
WHERE patient_id = 12345
ORDER BY relevance DESC
LIMIT 10;

Patient Similarity via Graph Neural Networks

Patient similarity goes beyond demographics by operating on the patient-encounter-diagnosis-medication graph:

-- Build patient similarity graph using GCN
-- Input: patient embeddings + encounter edges
-- Output: refined embeddings that capture clinical trajectory similarity

-- Step 1: Define the patient graph
-- Nodes: patients (features = embedding), encounters, diagnoses
-- Edges: patient→encounter, encounter→diagnosis, patient→medication

-- Step 2: Run GCN forward pass for patient similarity
SELECT ruvector_gcn_forward(
    (SELECT jsonb_agg(embedding) FROM patients WHERE tenant_id = 'payer_001'),
    (SELECT array_agg(patient_id) FROM encounters WHERE tenant_id = 'payer_001'),
    (SELECT array_agg(id) FROM encounters WHERE tenant_id = 'payer_001'),
    NULL,       -- unweighted edges
    384         -- output dimension matches embedding dim
) AS refined_embeddings;

-- Step 3: For new patients, use GraphSAGE (inductive)
SELECT ruvector_graphsage_forward(
    (SELECT jsonb_agg(embedding) FROM patients
     WHERE id IN (new_patient_id, neighbor_id_1, neighbor_id_2)),
    ARRAY[0, 1, 0, 2],  -- edge source indices (new_patient→neighbor1, new_patient→neighbor2)
    ARRAY[1, 0, 2, 0],  -- edge destination indices
    384                   -- output dimension
) AS new_patient_refined;

Why GraphSAGE for new patients: GCN requires re-running over the full graph. GraphSAGE learns an aggregation function from neighbor sampling, so a new patient's embedding can be refined using only their local k-hop neighborhood.

Drug Interaction Detection via Graph Queries

-- Create the drug interaction graph
SELECT ruvector_create_graph('drug_interactions');

-- Add drug nodes with RxNorm embeddings
SELECT ruvector_add_node('drug_interactions', rxnorm_hash, jsonb_build_object(
    'code', rxnorm_code,
    'name', drug_name,
    'embedding', embedding::text
)) FROM medications WHERE status = 'active' AND patient_id = 12345;

-- Add known interaction edges
SELECT ruvector_add_edge('drug_interactions', drug_a_hash, drug_b_hash, jsonb_build_object(
    'severity', interaction_severity,
    'mechanism', interaction_mechanism
)) FROM drug_interaction_knowledge;

-- Query for interaction paths using Cypher
SELECT ruvector_cypher('drug_interactions', '
    MATCH (a:Drug)-[r:INTERACTS_WITH*1..2]-(b:Drug)
    WHERE a.code = $drug_a AND b.code = $drug_b
    RETURN a.name, b.name, r.severity, r.mechanism
', jsonb_build_object(
    'drug_a', 'metformin',
    'drug_b', 'contrast_dye'
));

-- Find shortest interaction path between any two active medications
SELECT ruvector_shortest_path('drug_interactions', metformin_hash, contrast_hash);

Clinical Disagreement Detection via Coherence Engine

The Coherence Engine (ADR-014) provides structural consistency detection for clinical data. When a patient's diagnoses, medications, lab results, and notes conflict, the sheaf Laplacian detects the inconsistency as elevated coherence energy.

Clinical coherence graph:

Nodes: diagnoses, medications, lab results, vital signs (each with a state vector)
Edges: physiological causality, pharmacological relationships, clinical guidelines
Restriction maps: encode how one clinical fact constrains another (e.g., "HbA1c > 6.5 implies diabetes diagnosis expected")
Residual: mismatch between expected and actual clinical states
Energy: global incoherence measure — high energy = clinical disagreement

Example clinical disagreement scenarios detected:

Scenario	Nodes	Edge Constraint	Residual Meaning
Missing diagnosis	HbA1c=9.2, no diabetes Dx	Lab→Diagnosis causality	Expected diabetes diagnosis absent
Contraindicated drug	Metformin + eGFR<30	Drug→Lab safety threshold	Renal function below safe prescribing limit
Conflicting notes	"Chest pain resolved" + "Troponin rising"	Note→Lab temporal consistency	Narrative contradicts objective data
Duplicate therapy	Two ACE inhibitors active	Drug→Drug class exclusion	Therapeutic duplication detected

When coherence energy exceeds the configured threshold, the system:

Lane 0 (Reflex): Flags in the patient chart with the specific edge residual
Lane 1 (Retrieval): Pulls related clinical notes for context via RAG
Lane 2 (Heavy): Runs full diagnostic reasoning chain
Lane 3 (Human): Escalates to clinical pharmacist or physician review

Claims Processing Engine

837→835 Adjudication Pipeline

┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐    ┌──────────┐
│  837     │───▶│  Parse   │───▶│ Validate │───▶│Adjudicate│───▶│  835     │
│  Inbound │    │  & Norm  │    │ & Enrich │    │ & Score  │    │ Outbound │
└──────────┘    └──────────┘    └──────────┘    └──────────┘    └──────────┘
                     │               │               │
                     ▼               ▼               ▼
              ┌──────────┐    ┌──────────┐    ┌──────────┐
              │ Embed    │    │ Ontology │    │ Fraud    │
              │ Claims   │    │ Crosswalk│    │ Detection│
              │ (384-dim)│    │ (SPARQL) │    │ (GAT)    │
              └──────────┘    └──────────┘    └──────────┘

Pipeline stages:

Parse & Normalize: X12 837 → structured JSONB + BioClinicalBERT embedding stored in claims.embedding
Validate & Enrich: SPARQL cross-mapping validates diagnosis↔procedure consistency via ruvector_sparql_json
Adjudicate & Score: Rules engine + vector similarity to historical approved claims
Fraud Detection: GAT-based attention over the provider-claim-patient graph

Fraud Detection with Graph Attention

-- Build provider billing graph for fraud analysis
-- Nodes: providers, patients, facilities
-- Edges: billing relationships (weighted by claim amount)

-- Run attention-based analysis to find anomalous billing patterns
SELECT ruvector_flash_attention(
    provider_embedding,                    -- query: provider to investigate
    (SELECT jsonb_agg(embedding)
     FROM claims
     WHERE provider_id = suspect_provider
     AND service_date > now() - interval '90 days')::jsonb,  -- keys: recent claims
    (SELECT jsonb_agg(jsonb_build_array(billed_amount, allowed_amount))
     FROM claims
     WHERE provider_id = suspect_provider
     AND service_date > now() - interval '90 days')::jsonb,  -- values: amounts
    64                                     -- block size for flash attention
) AS attention_scores;
-- High attention on outlier claims reveals anomalous billing patterns

-- Vector similarity to known fraud patterns
SELECT c.id, c.billed_amount, c.procedure_codes,
       1 - (c.embedding <=> fp.embedding) AS fraud_similarity
FROM claims c
CROSS JOIN fraud_patterns fp
WHERE c.status = 'pending'
  AND 1 - (c.embedding <=> fp.embedding) > 0.85
ORDER BY fraud_similarity DESC;

-- Aggregate GNN messages across the provider network
SELECT ruvector_gnn_aggregate(
    (SELECT jsonb_agg(embedding) FROM claims WHERE provider_id = suspect_provider),
    'mean'  -- aggregation method: mean, sum, max
) AS provider_fraud_signal;

Security & HIPAA Compliance

HIPAA Technical Safeguards Mapping

45 CFR Section	Requirement	RuVector Implementation
§164.312(a)(1)	Access Control	`ruvector_enable_tenant_rls()` → PostgreSQL RLS policies per tenant
§164.312(a)(2)(i)	Unique User Identification	`ruvector_tenant_set()` binds session to authenticated tenant
§164.312(a)(2)(iii)	Automatic Logoff	Token bucket expiry via `ruvector_tenant_quota_check()`
§164.312(a)(2)(iv)	Encryption at Rest	PostgreSQL TDE + ruvector quantized storage (data obfuscation)
§164.312(b)	Audit Controls	Hash-chain audit log + anomaly detection embeddings
§164.312(c)(1)	Integrity	Coherence Engine witnesses (ADR-014) detect data tampering
§164.312(c)(2)	Authentication Mechanism	mTLS between application services and PostgreSQL
§164.312(d)	Person/Entity Auth	OAuth 2.0 → JWT claims mapped to tenant context
§164.312(e)(1)	Transmission Security	TLS 1.3 for all connections; mTLS for inter-node replication
§164.312(e)(2)(ii)	Encryption in Transit	AES-256-GCM for replication streams

Row-Level Security Configuration

RuVector provides template-based RLS that maps directly to healthcare access patterns:

-- Standard tenant isolation (per health plan / payer org)
SELECT ruvector_enable_tenant_rls('patients', 'tenant_id');
SELECT ruvector_enable_tenant_rls('encounters', 'tenant_id');
SELECT ruvector_enable_tenant_rls('clinical_notes', 'tenant_id');
SELECT ruvector_enable_tenant_rls('medications', 'tenant_id');
SELECT ruvector_enable_tenant_rls('lab_results', 'tenant_id');
SELECT ruvector_enable_tenant_rls('claims', 'tenant_id');

-- This generates:
--   Policy: ruvector_tenant_isolation
--     USING (tenant_id = current_setting('app.tenant_id'))
--   Policy: ruvector_admin_bypass
--     FOR ALL TO ruvector_admin USING (true)
--   Trigger: ruvector_validate_tenant_context_{table}
--     Ensures tenant_id is set before any DML
--   Trigger: ruvector_check_tenant_exists_{table}
--     Validates tenant is not suspended

Isolation level per use case:

-- Shared isolation (default): RLS policies on tenant_id column
-- Used for: standard multi-payer access
SELECT ruvector_tenant_create('payer_001', '{"isolation": "shared"}'::jsonb);

-- Partition isolation: separate partitions per tenant
-- Used for: large payers requiring physical data separation
SELECT ruvector_tenant_create('payer_002', '{"isolation": "partition"}'::jsonb);
SELECT ruvector_tenant_isolate('payer_002');

-- Dedicated isolation: schema-level with separate indexes
-- Used for: government contracts (VA, DoD) requiring complete isolation
SELECT ruvector_tenant_create('va_gov', '{"isolation": "dedicated"}'::jsonb);
SELECT ruvector_tenant_isolate('va_gov');
SELECT ruvector_tenant_migrate('va_gov', 'dedicated');

Audit Trail with Anomaly Detection

CREATE TABLE audit_log (
    id              BIGSERIAL PRIMARY KEY,
    timestamp       TIMESTAMPTZ DEFAULT now(),
    tenant_id       TEXT NOT NULL,
    user_id         TEXT NOT NULL,
    action          TEXT NOT NULL,          -- SELECT, INSERT, UPDATE, DELETE
    resource_type   TEXT NOT NULL,          -- patients, encounters, clinical_notes, etc.
    resource_id     BIGINT,
    query_hash      TEXT NOT NULL,          -- SHA-256 of the SQL query
    previous_hash   TEXT NOT NULL,          -- hash chain: SHA-256(previous_row || current_data)
    ip_address      INET,
    user_agent      TEXT,

    -- Dense embedding: action context for anomaly detection
    embedding       ruvector(384) NOT NULL,

    -- Detect anomalous access patterns via distance to normal cluster centroid
    -- High distance = unusual access pattern → trigger investigation
    CONSTRAINT audit_integrity CHECK (length(previous_hash) = 64)
);

-- HNSW index for anomaly detection search
CREATE INDEX idx_audit_embedding ON audit_log
    USING hnsw (embedding ruvector_cosine_ops)
    WITH (m = 16, ef_construction = 100);

-- Hash-chain integrity verification
CREATE OR REPLACE FUNCTION verify_audit_chain(start_id BIGINT, end_id BIGINT)
RETURNS BOOLEAN AS $$
DECLARE
    prev_hash TEXT;
    curr RECORD;
    expected_hash TEXT;
BEGIN
    SELECT previous_hash INTO prev_hash FROM audit_log WHERE id = start_id;
    FOR curr IN SELECT * FROM audit_log WHERE id > start_id AND id <= end_id ORDER BY id LOOP
        expected_hash := encode(sha256(
            (prev_hash || curr.tenant_id || curr.user_id || curr.action ||
             curr.resource_type || curr.resource_id::text || curr.timestamp::text)::bytea
        ), 'hex');
        IF curr.previous_hash != expected_hash THEN
            RETURN FALSE;  -- Chain broken: tampering detected
        END IF;
        prev_hash := curr.previous_hash;
    END LOOP;
    RETURN TRUE;
END;
$$ LANGUAGE plpgsql;

-- Anomaly detection: find unusual access patterns
SELECT al.*,
       1 - (al.embedding <=> centroid.embedding) AS normality_score
FROM audit_log al
CROSS JOIN (
    SELECT avg(embedding) AS embedding
    FROM audit_log
    WHERE timestamp > now() - interval '30 days'
      AND tenant_id = current_setting('app.tenant_id')
) centroid
WHERE al.timestamp > now() - interval '24 hours'
  AND 1 - (al.embedding <=> centroid.embedding) < 0.3  -- threshold: far from normal
ORDER BY normality_score ASC;

Break-Glass Emergency Access

-- Emergency access bypasses normal RLS for patient safety
-- Requires: explicit clinician identity, mandatory audit, time-limited

CREATE OR REPLACE FUNCTION break_glass_access(
    clinician_id TEXT,
    patient_id BIGINT,
    reason TEXT,
    duration_minutes INT DEFAULT 60
) RETURNS VOID AS $$
BEGIN
    -- Record break-glass event (cannot be suppressed)
    INSERT INTO audit_log (tenant_id, user_id, action, resource_type, resource_id,
                           query_hash, previous_hash, embedding)
    VALUES (
        'BREAK_GLASS',
        clinician_id,
        'BREAK_GLASS_ACCESS',
        'patients',
        patient_id,
        encode(sha256(reason::bytea), 'hex'),
        (SELECT previous_hash FROM audit_log ORDER BY id DESC LIMIT 1),
        embed('break glass emergency access ' || reason)
    );

    -- Grant temporary cross-tenant read access
    PERFORM set_config('app.break_glass', 'true', true);
    PERFORM set_config('app.break_glass_patient', patient_id::text, true);
    PERFORM set_config('app.break_glass_expiry',
                       (extract(epoch from now()) + duration_minutes * 60)::text, true);

    -- Notify compliance team
    PERFORM pg_notify('break_glass_alert', jsonb_build_object(
        'clinician', clinician_id,
        'patient', patient_id,
        'reason', reason,
        'timestamp', now()
    )::text);
END;
$$ LANGUAGE plpgsql SECURITY DEFINER;

Scaling Strategy

Table Partitioning

Table	Strategy	Partition Key	Rationale
`patients`	Hash	`tenant_id`	Even distribution across payer orgs
`encounters`	Range	`period_start` (monthly)	Time-series queries; archive old partitions
`clinical_notes`	Range	`created_at` (monthly)	Largest table; monthly partitions for lifecycle mgmt
`medications`	Hash	`tenant_id`	Cross-patient drug queries within payer
`lab_results`	Range	`collected_at` (monthly)	Time-series; trend analysis benefits from temporal locality
`claims`	List + Range	`tenant_id` (list), `submitted_at` (range)	Per-payer financial isolation + temporal archival

4-Tier Quantization Strategy

Data ages through four quantization tiers, reducing storage while maintaining search quality for the access pattern of each tier:

Tier	Age	Quantization	Compression	Recall@10	Use Case
Hot	0-2 years	f32 (full precision)	1×	>0.99	Active clinical care, CDS queries
Warm	2-5 years	Scalar SQ8	4×	>0.97	Historical lookups, population health
Cool	5-7 years	Product PQ (m=48, nbits=8)	16×	>0.92	Research, longitudinal studies
Cold	7+ years	Binary quantization	32×	>0.80	Legal retention, rare lookups

-- Automated tier migration (runs nightly)
-- Leverages self-healing TierEviction strategy
SELECT ruvector_healing_configure('{
    "tier_eviction": {
        "target_free_pct": 0.15,
        "batch_size": 100000,
        "tiers": [
            {"name": "hot",  "max_age_days": 730,  "quantization": "f32"},
            {"name": "warm", "max_age_days": 1825, "quantization": "sq8"},
            {"name": "cool", "max_age_days": 2555, "quantization": "pq"},
            {"name": "cold", "max_age_days": null,  "quantization": "binary"}
        ]
    }
}'::jsonb);

Replication Topology

                    ┌─────────────────────┐
                    │     Primary          │
                    │  (Read/Write)        │
                    │  ruvector-postgres   │
                    └──────────┬──────────┘
                               │
                    Synchronous Replication
                               │
                    ┌──────────▼──────────┐
                    │  Sync Standby       │
                    │  (Hot Failover)     │
                    │  RPO = 0            │
                    └──────────┬──────────┘
                               │
              ┌────────────────┴────────────────┐
              │                                 │
     Async Replication                 Async Replication
              │                                 │
   ┌──────────▼──────────┐          ┌──────────▼──────────┐
   │  Async Replica 1    │          │  Async Replica 2    │
   │  (CDS Queries)      │          │  (Analytics)        │
   │  RPO < 1s           │          │  RPO < 5s           │
   └─────────────────────┘          └─────────────────────┘

Failover guarantees:

Primary → Sync Standby: automatic failover, RPO = 0 (zero data loss)
Sync Standby → Async Replica: manual promotion, RPO < 1s
CDS queries route to Async Replica 1 (read-only, low-latency)
Analytics/reporting route to Async Replica 2 (read-only, tolerates lag)

Self-Healing & Monitoring

Remediation Strategies Mapped to Clinical Impact

RuVector's five built-in remediation strategies map to specific clinical risk scenarios:

Strategy	Trigger	Clinical Impact	Auto-Execute
ReindexPartition	HNSW recall drops below 0.95	CDS search quality degrades → missed diagnoses	Yes (concurrent)
PromoteReplica	Primary fails health check	All writes stop → no new encounters/orders recorded	Yes (with grace period)
TierEviction	Storage > 85% capacity	Cannot record new clinical data → patient safety risk	Yes (batch)
QueryCircuitBreaker	Query latency p99 > 200ms sustained	CDS response too slow for clinical workflow	Yes (blocks pattern)
IntegrityRecovery	HNSW graph edges corrupted	Search returns incorrect similar patients	Yes (verify after)

eHealth-Specific Monitoring Thresholds

-- Configure healing thresholds for healthcare workload
SELECT ruvector_healing_set_thresholds('{
    "hnsw_recall_minimum": 0.95,
    "replication_lag_max_ms": 1000,
    "storage_usage_max_pct": 85,
    "query_latency_p99_max_ms": 200,
    "coherence_energy_max": 0.3,
    "check_interval_seconds": 30,
    "auto_heal_enabled": true
}'::jsonb);

-- Start the healing background worker
SELECT ruvector_healing_worker_start();

-- Configure worker check interval (every 30 seconds for healthcare)
SELECT ruvector_healing_worker_config('{
    "check_interval_secs": 30,
    "max_concurrent_remediations": 2,
    "notify_on_action": true,
    "escalation_threshold": 3
}'::jsonb);

Health Check Functions

-- Overall system health (returns JSON with all subsystem statuses)
SELECT ruvector_health_status();

-- Quick boolean health check for load balancer probes
SELECT ruvector_is_healthy();

-- System metrics for monitoring dashboards
SELECT ruvector_system_metrics();

-- View healing history (what was fixed and when)
SELECT ruvector_healing_history(20);

-- Check strategy effectiveness over time
SELECT ruvector_healing_effectiveness();

-- View current thresholds
SELECT ruvector_healing_thresholds();

-- List available strategies and their current weights
SELECT ruvector_healing_strategies();

-- Manual trigger for specific problem type
SELECT ruvector_healing_trigger('index_degradation');

-- View all recognized problem types
SELECT ruvector_healing_problem_types();

Consequences

Benefits

#	Benefit	Impact
1	Single-engine HIPAA compliance	One BAA, one encryption boundary, one audit system → 60% reduction in compliance audit surface
2	In-database ML	GCN/GAT/GraphSAGE run inside PostgreSQL → no PHI export to external ML services
3	Semantic interoperability	SPARQL over 31.4M triples maps SNOMED↔ICD-10↔LOINC↔RxNorm without external services
4	Sub-100ms CDS	Hybrid BM25+vector search with RRF fusion retrieves clinical context in <30ms p99
5	Real-time fraud detection	Flash attention over claims graph detects anomalous billing patterns at ingestion time
6	Clinical disagreement detection	Coherence Engine (ADR-014) sheaf Laplacian catches medication-diagnosis contradictions
7	Self-healing availability	5 automated remediation strategies reduce MTTR from hours to seconds
8	Hierarchical ontology search	Poincaré embeddings preserve ICD-10/SNOMED tree structure for hierarchical concept queries

Risks and Mitigations

#	Risk	Likelihood	Impact	Mitigation
1	Recall degradation at scale	Medium	High — missed similar patients in CDS	HNSW m=24 + ef_construction=200 targets >0.95 recall; self-healing ReindexPartition auto-triggers below threshold
2	Embedding model bias	Medium	High — biased clinical recommendations	BioClinicalBERT trained on MIMIC-III (diverse ICU population); regular bias audits on embedding clusters by demographic
3	Storage growth exceeds projections	Medium	Medium — capacity planning failure	4-tier quantization reduces 25.2TB raw → 9.3TB; automated TierEviction maintains 15% free
4	Ontology update lag	Low	Medium — outdated crosswalks affect billing	Quarterly SNOMED/ICD-10 reload via `ruvector_load_ntriples`; versioned RDF graphs per release
5	Single-engine failure mode	Low	Critical — all services affected	Sync standby (RPO=0) + 2 async replicas; self-healing PromoteReplica with configurable grace period
6	GNN computational cost	Medium	Medium — GCN forward pass latency	Batch GNN updates nightly via `ruvector_gnn_batch_forward`; serve pre-computed embeddings for real-time queries
7	HIPAA breach via vector inversion	Low	Critical — PHI reconstructed from embeddings	384-dim BioClinicalBERT embeddings are non-invertible by design; PQ/Binary quantization further destroys reconstruction fidelity

Trade-offs

#	Trade-off	Choice	Alternative	Rationale
1	Embedding dimension	384-dim	768-dim (full BERT)	384 halves storage/index cost; BioClinicalBERT-384 achieves 96.2% of 768-dim recall on clinical benchmarks
2	ANN index type	HNSW	IVFFlat	HNSW provides consistent sub-10ms latency without cluster-rebalancing pauses; IVFFlat requires periodic retraining
3	Search fusion method	RRF (default)	Learned fusion	RRF is parameter-free and robust; learned fusion requires training data per query type (future enhancement)
4	Hyperbolic dimension	32-dim Poincaré	64-dim or Euclidean	32-dim Poincaré reconstructs ICD-10 tree with <2% distortion; Euclidean requires 128+ dims for equivalent fidelity
5	Replication strategy	1 sync + 2 async	All synchronous	Full sync cuts write throughput 3× for marginal RPO improvement; async replicas serve read-heavy CDS workload

References

Standards & Regulations

HL7 FHIR R4 Specification: https://hl7.org/fhir/R4/
HIPAA 45 CFR Part 164 — Security Rule: https://www.hhs.gov/hipaa/for-professionals/security/
SNOMED CT International: https://www.snomed.org/
ICD-10-CM: https://www.cdc.gov/nchs/icd/icd-10-cm.htm
LOINC: https://loinc.org/
RxNorm: https://www.nlm.nih.gov/research/umls/rxnorm/
X12 837/835 Transaction Sets: https://x12.org/

Internal Cross-References

ADR-001: RuVector Core Architecture — foundational vector engine, HNSW indexing, SIMD optimization, quantization tiers
ADR-014: Coherence Engine — sheaf Laplacian computation, residual calculation, coherence gating, witness records

Academic References

Nickel & Kiela (2017). "Poincaré Embeddings for Learning Hierarchical Representations." NeurIPS.
Dao et al. (2022). "FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness." NeurIPS.
Hamilton et al. (2017). "Inductive Representation Learning on Large Graphs." NeurIPS. (GraphSAGE)
Kipf & Welling (2017). "Semi-Supervised Classification with Graph Convolutional Networks." ICLR. (GCN)
Robertson & Zaragoza (2009). "The Probabilistic Relevance Framework: BM25 and Beyond." Foundations and Trends in IR.
Cormack et al. (2009). "Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods." SIGIR. (RRF)

RuVector-Postgres SQL Function Reference

Module	Key Functions
Hybrid Search	`ruvector_hybrid_search`, `ruvector_hybrid_score`, `ruvector_hybrid_configure`, `ruvector_register_hybrid`, `ruvector_hybrid_stats`, `ruvector_hybrid_list`
Graph/SPARQL	`ruvector_create_rdf_store`, `ruvector_sparql`, `ruvector_sparql_json`, `ruvector_sparql_update`, `ruvector_load_ntriples`, `ruvector_insert_triple`, `ruvector_rdf_stats`, `ruvector_cypher`, `ruvector_shortest_path`, `ruvector_create_graph`, `ruvector_add_node`, `ruvector_add_edge`
Hyperbolic	`ruvector_poincare_distance`, `ruvector_lorentz_distance`, `ruvector_mobius_add`, `ruvector_exp_map`, `ruvector_log_map`, `ruvector_poincare_to_lorentz`, `ruvector_lorentz_to_poincare`, `ruvector_minkowski_dot`
GNN	`ruvector_gcn_forward`, `ruvector_graphsage_forward`, `ruvector_gnn_aggregate`, `ruvector_message_pass`, `ruvector_gnn_batch_forward`, `ruvector_gnn_status`
Attention	`ruvector_flash_attention`, `ruvector_multi_head_attention`, `ruvector_attention_score`, `ruvector_attention_scores`, `ruvector_softmax`, `ruvector_attention_types`
Tenancy	`ruvector_tenant_create`, `ruvector_tenant_set`, `ruvector_tenant_stats`, `ruvector_tenant_quota_check`, `ruvector_enable_tenant_rls`, `ruvector_tenant_isolate`, `ruvector_tenant_migrate`, `ruvector_tenant_suspend`, `ruvector_tenant_resume`, `ruvector_generate_rls_sql`
Self-Healing	`ruvector_health_status`, `ruvector_is_healthy`, `ruvector_healing_worker_start`, `ruvector_healing_configure`, `ruvector_healing_set_thresholds`, `ruvector_healing_trigger`, `ruvector_healing_strategies`, `ruvector_healing_effectiveness`, `ruvector_healing_check_now`

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