Graph Output

Charts are graphs.

Every natal chart Vedākṣha computes is simultaneously a property graph. The same computation that returns planetary longitudes also produces a ChartGraph — a typed, traversable graph with 10 node types and 13 edge types, ready for Neo4j, SurrealDB, or a vector store.

Ontology — 10 Node Types

ChartPlanetSignHouseNakshatraPadaPatternDashaPeriodYogaFixedStar

13 Edge Types — Relationships

Planet→IN_SIGN→Sign

Planet→IN_HOUSE→House

Planet→IN_NAKSHATRA→Nakshatra

Planet→IN_PADA→Pada

Planet→ASPECTS→Planet

Planet→RULES→Sign

Planet→PARTICIPATES_IN→Yoga

House→STARTS_IN→Sign

Chart→HAS_PLANET→Planet

Chart→HAS_PATTERN→Pattern

Chart→HAS_DASHA→DashaPeriod

DashaPeriod→HAS_ANTARDASHA→DashaPeriod

Chart→CONTAINS_FIXED_STAR→FixedStar

5 Emitter Formats

CypherNeo4j

MERGE statements with deterministic IDs. Drop-in to any Neo4j import pipeline.

SurrealQLSurrealDB

RELATE syntax with typed record IDs. Works with SurrealDB graph traversal queries.

JSON-LDLinked Data

Schema.org-compatible context with custom Vedic terms. SPARQL and RDF compatible.

JSONGeneral

Flat JSON representation of the full ChartGraph. Zero dependencies, portable everywhere.

Embedding TextVector Stores

RAG-optimized text chunks, one per node. Feed directly to OpenAI, Cohere, or any embedding model.

Deterministic IDs

Every node in a ChartGraph has an ID derived deterministically from its content — the same Julian Day and coordinates always produce the same graph with the same node IDs. This makes MERGE-based upserts in Neo4j and SurrealDB idempotent: re-import the same chart and nothing changes. It also means graph IDs can be used as stable foreign keys in relational or document stores.

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