imbajin commented on code in PR #543: URL: https://github.com/apache/hugegraph-doc/pull/543#discussion_r4326028568
########## content/en/docs/quickstart/hugegraph-ai/ontogeny.md: ########## @@ -0,0 +1,91 @@ +--- +title: "Ontogeny" +linkTitle: "Ontogeny" +weight: 7 +--- + +`ontogeny` — Greek *onto-* (being, the root of *ontology*) + *genesis* (birth, development): in biology the word names the **full-life-cycle development of an individual organism**. The name is the thesis: an ontology here is not a static schema but a living thing — declared in Git, compiled into tables and a graph, and grown through use. It is a **self-improving operational ontology platform**: it models an enterprise's business semantics (objects, properties, links) and business actions (actions, functions, policies) as declarative, Git-native YAML, and serves them to applications, analytics and AI agents through one governed API — optionally projected onto [Apache HugeGraph](https://github.com/apache/hugegraph) for deep-graph queries. + +The whole system on one board — this is the console's Overview dashboard, and the map for everything below: + + + +*Top: the eight first-class DSL citizens (Action = the only write gate, EvalSuite = the selection box). Middle: the online pipeline (build → explore → act → control → audit) and the offline RSI loop, joined by the agent spine — its reads/writes cross the same gates, its traces and approvals flow back as evolution signals. Bottom: the supporting layer and the three cross-cutting invariants (ownership, one gate, Git as source).* + +## Key Features + +- **Git-native ontology** — modeling is YAML + pull requests; consensus happens in code review, not a locked GUI +- **Single-process kernel** — `ontogeny serve` = Registry + query engine + Action runtime on SQLite / Postgres +- **Agent-first** — a governed MCP tool surface at `/mcp`; agents and humans share one policy surface, writes pass human approval gates +- **Self-improving (RSI)** — observe → mutate (LLM-generated DSL diffs) → select (deterministic eval suites) → release (T0 auto-merge / T1 PR / T2 canary); *mutation is outsourced to the LLM, the selection function is not* +- **Constitutional guardrails** — machine proposals travel the same Git/CI channel as humans; policy relaxation, markings and tier definitions always require human review +- **Bilingual enterprise console** — React 19 + TypeScript: ontology canvas (browse *and* edit), graph exploration, action runner, audit, self-evolution console + +Modeling is code review, not form-filling — the ontology canvas shows every object, link, action and function of the domain, with references highlighted on hover: + + + +## Quick Start + +### Option 1: Docker (Recommended) + +```bash +git clone -b ontogeny https://github.com/apache/hugegraph-ai.git +cd hugegraph-ai + +docker compose up -d # self-contained demo (SQLite + seeded data) +# docker compose --profile postgres up -d # Postgres-backed Review Comment: Resolved: Clarified docker compose profiles in both EN and CN docs to accurately state that postgres starts a postgres container while the demo image uses SQLite, and graph requires configuring projection via the admin API. ########## content/en/docs/quickstart/hugegraph-ai/ontogeny.md: ########## @@ -0,0 +1,91 @@ +--- +title: "Ontogeny" +linkTitle: "Ontogeny" +weight: 7 +--- + +`ontogeny` — Greek *onto-* (being, the root of *ontology*) + *genesis* (birth, development): in biology the word names the **full-life-cycle development of an individual organism**. The name is the thesis: an ontology here is not a static schema but a living thing — declared in Git, compiled into tables and a graph, and grown through use. It is a **self-improving operational ontology platform**: it models an enterprise's business semantics (objects, properties, links) and business actions (actions, functions, policies) as declarative, Git-native YAML, and serves them to applications, analytics and AI agents through one governed API — optionally projected onto [Apache HugeGraph](https://github.com/apache/hugegraph) for deep-graph queries. + +The whole system on one board — this is the console's Overview dashboard, and the map for everything below: + + + +*Top: the eight first-class DSL citizens (Action = the only write gate, EvalSuite = the selection box). Middle: the online pipeline (build → explore → act → control → audit) and the offline RSI loop, joined by the agent spine — its reads/writes cross the same gates, its traces and approvals flow back as evolution signals. Bottom: the supporting layer and the three cross-cutting invariants (ownership, one gate, Git as source).* + +## Key Features + +- **Git-native ontology** — modeling is YAML + pull requests; consensus happens in code review, not a locked GUI +- **Single-process kernel** — `ontogeny serve` = Registry + query engine + Action runtime on SQLite / Postgres +- **Agent-first** — a governed MCP tool surface at `/mcp`; agents and humans share one policy surface, writes pass human approval gates +- **Self-improving (RSI)** — observe → mutate (LLM-generated DSL diffs) → select (deterministic eval suites) → release (T0 auto-merge / T1 PR / T2 canary); *mutation is outsourced to the LLM, the selection function is not* +- **Constitutional guardrails** — machine proposals travel the same Git/CI channel as humans; policy relaxation, markings and tier definitions always require human review +- **Bilingual enterprise console** — React 19 + TypeScript: ontology canvas (browse *and* edit), graph exploration, action runner, audit, self-evolution console + +Modeling is code review, not form-filling — the ontology canvas shows every object, link, action and function of the domain, with references highlighted on hover: + + + +## Quick Start + +### Option 1: Docker (Recommended) + +```bash +git clone -b ontogeny https://github.com/apache/hugegraph-ai.git +cd hugegraph-ai + +docker compose up -d # self-contained demo (SQLite + seeded data) +# docker compose --profile postgres up -d # Postgres-backed +# docker compose --profile graph up -d # + HugeGraph projection +``` + +Console + API: **http://localhost:8000** — the first boot creates an `admin` account and prints the generated password to the container log (or preset `ONTOGENY_ADMIN_PASSWORD`). One command gets you a live dashboard seeded with a real manufacturing domain — the ontology model, the agent surface and the RSI loop at a glance: + + + +### Option 2: From Source + +```bash +# backend (with the MCP extra so /mcp is served) Review Comment: Resolved: Added the missing `git clone -b ontogeny` and `cd hugegraph-ai` commands to the EN From Source block. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
