Phase 0.7 — Acceptance Gate 자동화 + LM Studio 통합 + OntoCast 버그 수정

- platform/ → ont_platform/ rename
  Python 내장 platform 모듈과 이름 충돌. numpy/scipy가 platform.machine() 호출 시
  우리 패키지를 가져와 AttributeError. ont_platform으로 변경하고 pyproject.toml,
  ont_platform/**, tests/** import 경로 모두 업데이트.

- ont_platform/config.py: lenient LLM builder 추가
  LM Studio/vLLM 등 OpenAI-호환 로컬 서버가 임의 모델 식별자(예: deepseek-r1-distill-
  qwen-7b)를 쓸 수 있도록 OntoCast의 OpenAIModel enum validation을 Pydantic
  model_construct로 우회. ToolConfig() 생성 시 충돌을 막기 위해 LLM_MODEL_NAME을
  잠시 비웠다가 lenient 인스턴스로 교체.

- ont_platform/api/deps.py: ToolBox 초기화를 asyncio.to_thread로 격리
  LLMTool.create()가 내부에서 asyncio.run()을 부르는데 lifespan/테스트가 이미
  async 컨텍스트라 이중 loop 충돌. 별도 스레드에서 sync 생성자 실행.

- 테스트 인프라 정비
  * tests/integration/test_api_smoke.py: TestClient 구버전 starlette 호환을 위해
    lifespan='off' 대신 app.router.lifespan_context = noop 패턴 적용.
  * tests/unit/test_convert_document.py, test_select_ontology.py: ontocast.agent
    __init__.py가 re-export한 함수가 서브모듈을 가리는 문제로 sys.modules에서
    실제 모듈 객체 직접 추출.
  * tests/e2e/conftest.py: .env 자동 로드 + provider별 skip 조건 (Ollama는
    LLM_API_KEY 불필요).
  * tests/e2e/test_phase0_full_pipeline.py: provider별 키 분기,
    HDBSCAN 클러스터링이 동작하도록 fixture 페이로드 16문장으로 확장.

- vendored OntoCast 버그 수정 3건 (VENDORED_MODIFICATIONS.md 기록):
  * agent/render_ontology.py: render_ontology_fresh()의 .format() 호출에 누락된
    ontology_prefix 인자 추가 (Bootstrap 단계에서 KeyError: 'ontology_prefix').
  * stategraph/node_factories.py: render_ontology/render_facts 노드의
    state.model_copy(deep=True)로 budget_tracker가 deep-copy되어 root state의
    BudgetTracker가 영원히 0인 채로 남던 버그 수정. 원본 인스턴스 공유로 변경.

- 문서 갱신
  README.md (Phase 0.7 부분완료 + ont_platform 폴더 이름),
  docs/phases/PHASE0_ACCEPTANCE_GATE.md (검증 이력 + Ollama/LM Studio 옵션),
  .env.example (LM Studio/Ollama/OpenAI 세 옵션 명시).

검증
- unit + integration 26/26 통과.
- e2e (LM Studio + Qwen3-8B / DeepSeek-R1-Distill-Qwen-7B): 워크플로우 끝까지
  실행 + 5번 LLM 호출 + LangGraph 전 노드 traceable 확인. 7-8B 로컬 모델은
  strict structured output(Turtle RDF in JSON) 한계로 ontology/facts TTL 자동
  생성 부분 성공. 클라우드 LLM 환경에서 재검증 필요.

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>

ontology_platform/ont_platform/api/deps.py

@@ -0,0 +1,119 @@
+"""FastAPI dependencies and app lifespan.
+
+Owns the ToolBox singleton, the compiled LangGraph workflow, and the
+recursion limit. Construction happens once at app startup; teardown is a
+no-op because OntoCast tools don't currently expose a close hook.
+
+Why a module-level holder instead of `app.state`:
+    The /process route is async and may be entered concurrently. A simple
+    dict on `app.state` works, but pulling the ToolBox through a typed
+    dependency function makes the contract explicit and gives every route
+    the same view of "what tools and what workflow are available right now".
+"""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+# Make vendored OntoCast importable. (Same trick as platform/config.py.)
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_VENDORED_ONTOCAST = _REPO_ROOT / "vendored" / "ontocast"
+if str(_VENDORED_ONTOCAST) not in sys.path:
+    sys.path.insert(0, str(_VENDORED_ONTOCAST))
+
+from langchain_core.runnables import RunnableConfig  # noqa: E402
+from langgraph.graph.state import CompiledStateGraph  # noqa: E402
+
+from ontocast.cli.serve import calculate_recursion_limit  # noqa: E402
+from ontocast.config import ServerConfig  # noqa: E402
+from ontocast.stategraph import create_agent_graph  # noqa: E402
+from ontocast.toolbox import ToolBox  # noqa: E402
+
+import importlib
+
+platform_config = importlib.import_module("ont_platform.config")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class AppContext:
+    """Immutable holder for objects that live as long as the FastAPI app."""
+
+    settings: "platform_config.PlatformSettings"
+    tools: ToolBox
+    workflow: CompiledStateGraph
+    server_config: ServerConfig
+    recursion_limit: int
+
+
+_context: AppContext | None = None
+
+
+async def initialize_app_context(
+    settings: "platform_config.PlatformSettings | None" = None,
+    *,
+    head_chunks: int | None = None,
+) -> AppContext:
+    """Build ToolBox + workflow once. Idempotent on repeat calls."""
+    global _context
+    if _context is not None:
+        return _context
+
+    settings = settings or platform_config.load_settings()
+    ontocast_config = platform_config.build_ontocast_config(settings)
+
+    # ToolBox.__init__ 내부에서 LLMTool.create()가 `asyncio.run()`을 호출한다.
+    # FastAPI lifespan/테스트가 이미 async 컨텍스트면 이중 loop 충돌이 나므로,
+    # 별도 스레드에서 sync 생성자를 실행한다.
+    tools = await asyncio.to_thread(ToolBox, ontocast_config)
+    # OntoCast's ToolBox.initialize is async; do it here so a request doesn't
+    # have to pay the cost.
+    await tools.initialize()
+
+    workflow = create_agent_graph(tools)
+    server_config = ontocast_config.server
+    recursion_limit = calculate_recursion_limit(head_chunks, server_config)
+
+    _context = AppContext(
+        settings=settings,
+        tools=tools,
+        workflow=workflow,
+        server_config=server_config,
+        recursion_limit=recursion_limit,
+    )
+    logger.info(
+        "App context initialized "
+        f"(phase={int(settings.phase)}, backend={settings.storage_backend}, "
+        f"working_dir={settings.working_directory})"
+    )
+    return _context
+
+
+def get_app_context() -> AppContext:
+    """FastAPI dependency. Raises clearly if the lifespan never ran."""
+    if _context is None:
+        raise RuntimeError(
+            "AppContext not initialized — the app lifespan must run "
+            "`initialize_app_context()` before serving requests."
+        )
+    return _context
+
+
+def reset_app_context_for_testing() -> None:
+    """Test-only: drop the cached context so each test can rebuild it."""
+    global _context
+    _context = None
+
+
+__all__ = [
+    "AppContext",
+    "RunnableConfig",
+    "get_app_context",
+    "initialize_app_context",
+    "reset_app_context_for_testing",
+]

ontology_platform/ont_platform/api/main.py

@@ -0,0 +1,388 @@
+"""FastAPI application entry point — replaces OntoCast's Robyn server.
+
+Endpoint semantics mirror the original Robyn server (OntoCast 분석 §13.1–§13.4)
+with three deliberate differences:
+
+1. `/flush` requires a confirmation token to prevent accidental destruction of
+   the triple store. OntoCast 분석 §21.1 (#6) flagged the unauth'd /flush as a
+   risk; we honor that.
+2. `/process` only accepts ``application/json`` and ``multipart/form-data``,
+   matching the Robyn version, but error responses now use FastAPI's normal
+   status-code semantics rather than always-200-with-error-body.
+3. The app no longer hard-codes ``version="0.1.1"``. The version is read from
+   the OntoCast vendored package so /health and /info stay in sync with the
+   vendored copy (OntoCast 분석 §21.1 #2).
+"""
+
+from __future__ import annotations
+
+import importlib
+import importlib.metadata as importlib_metadata
+import logging
+import sys
+from contextlib import asynccontextmanager
+from pathlib import Path
+from typing import Annotated, Any, AsyncIterator
+
+import click
+import uvicorn
+from fastapi import Depends, FastAPI, File, Form, HTTPException, Query, Request, UploadFile
+from fastapi.responses import JSONResponse
+
+# Vendored OntoCast on sys.path before any ontocast import.
+_REPO_ROOT = Path(__file__).resolve().parents[2]
+_VENDORED_ONTOCAST = _REPO_ROOT / "vendored" / "ontocast"
+if str(_VENDORED_ONTOCAST) not in sys.path:
+    sys.path.insert(0, str(_VENDORED_ONTOCAST))
+
+from ontocast.onto.enum import RenderMode  # noqa: E402
+from ontocast.onto.state import AgentState  # noqa: E402
+
+from ont_platform.api.deps import (  # noqa: E402
+    AppContext,
+    RunnableConfig,
+    get_app_context,
+    initialize_app_context,
+)
+
+platform_config = importlib.import_module("ont_platform.config")
+
+logger = logging.getLogger(__name__)
+
+
+def _resolve_ontocast_version() -> str:
+    """Best-effort lookup of the vendored OntoCast version.
+
+    Reading the vendored ``pyproject.toml`` directly avoids requiring the
+    package to be installed into the active environment.
+    """
+    pyproject = _VENDORED_ONTOCAST / "pyproject.toml"
+    try:
+        text = pyproject.read_text(encoding="utf-8")
+    except OSError:
+        return "unknown"
+    for line in text.splitlines():
+        stripped = line.strip()
+        if stripped.startswith("version") and "=" in stripped:
+            _, _, value = stripped.partition("=")
+            return value.strip().strip('"').strip("'")
+    # Fall back to package metadata if it happens to be installed.
+    try:
+        return importlib_metadata.version("ontocast")
+    except importlib_metadata.PackageNotFoundError:
+        return "unknown"
+
+
+ONTOCAST_VERSION = _resolve_ontocast_version()
+PLATFORM_VERSION = "0.0.1"
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI) -> AsyncIterator[None]:
+    """FastAPI lifespan: build ToolBox + workflow once on startup."""
+    settings = platform_config.load_settings()
+    await initialize_app_context(settings)
+    try:
+        yield
+    finally:
+        # No explicit teardown is needed today. When Phase 2/4 adds DB/HTTP
+        # pools they'll close here.
+        pass
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(
+        title="Ontology Platform",
+        description=(
+            "Universal ontology construction platform. Phase 0 wraps the "
+            "OntoCast core engine; later phases add Trafilatura, Crawl4AI, "
+            "Guardrails, and Neo4j GraphRAG. See docs/통합설계서.md."
+        ),
+        version=PLATFORM_VERSION,
+        lifespan=lifespan,
+    )
+
+    # ─── /health ──────────────────────────────────────────────────────
+    @app.get("/health", tags=["meta"])
+    async def health(ctx: Annotated[AppContext, Depends(get_app_context)]) -> JSONResponse:
+        """Liveness check. 503 if the LLM isn't wired."""
+        if ctx.tools.llm is None:
+            return JSONResponse(
+                status_code=503,
+                content={"status": "unhealthy", "error": "LLM not initialized"},
+            )
+        return JSONResponse(
+            status_code=200,
+            content={
+                "status": "healthy",
+                "platform_version": PLATFORM_VERSION,
+                "ontocast_version": ONTOCAST_VERSION,
+                "llm_provider": ctx.tools.llm_provider,
+                "phase": int(ctx.settings.phase),
+                "storage_backend": ctx.settings.storage_backend,
+            },
+        )
+
+    # ─── /info ────────────────────────────────────────────────────────
+    @app.get("/info", tags=["meta"])
+    async def info(ctx: Annotated[AppContext, Depends(get_app_context)]) -> JSONResponse:
+        """Service-level capabilities (mirrors OntoCast /info semantics)."""
+        return JSONResponse(
+            status_code=200,
+            content={
+                "name": "ontology-platform",
+                "platform_version": PLATFORM_VERSION,
+                "ontocast_version": ONTOCAST_VERSION,
+                "description": (
+                    "Universal ontology construction platform built on the "
+                    "OntoCast agentic core."
+                ),
+                "capabilities": ["text-to-triples", "ontology-extraction"],
+                "input_types": ["text", "json", "pdf", "markdown"],
+                "output_types": ["turtle", "json"],
+                "phase": int(ctx.settings.phase),
+                "storage_backend": ctx.settings.storage_backend,
+            },
+        )
+
+    # ─── /flush ───────────────────────────────────────────────────────
+    @app.post("/flush", tags=["admin"])
+    async def flush(
+        ctx: Annotated[AppContext, Depends(get_app_context)],
+        confirm: Annotated[
+            str | None,
+            Query(
+                description=(
+                    "Must equal 'YES-I-WANT-TO-DELETE-EVERYTHING'. Guards "
+                    "against accidental triple-store wipe (OntoCast 분석 §21.1 #6)."
+                ),
+            ),
+        ] = None,
+        dataset: Annotated[
+            str | None,
+            Query(description="Fuseki only — specific dataset to clean."),
+        ] = None,
+    ) -> JSONResponse:
+        if confirm != "YES-I-WANT-TO-DELETE-EVERYTHING":
+            raise HTTPException(
+                status_code=400,
+                detail=(
+                    "/flush requires confirm=YES-I-WANT-TO-DELETE-EVERYTHING "
+                    "to guard against accidental data loss."
+                ),
+            )
+        if ctx.tools.triple_store_manager is None:
+            raise HTTPException(
+                status_code=400,
+                detail="No triple store manager configured.",
+            )
+        try:
+            await ctx.tools.triple_store_manager.clean(dataset=dataset)
+        except Exception as exc:  # noqa: BLE001
+            logger.exception("Error flushing triple store")
+            return JSONResponse(
+                status_code=500,
+                content={
+                    "status": "error",
+                    "error": str(exc),
+                    "error_type": type(exc).__name__,
+                },
+            )
+        return JSONResponse(
+            status_code=200,
+            content={
+                "status": "success",
+                "message": (
+                    f"Triple store flushed (dataset={dataset!r})"
+                    if dataset
+                    else "Triple store flushed (all datasets)"
+                ),
+            },
+        )
+
+    # ─── /process ─────────────────────────────────────────────────────
+    @app.post("/process", tags=["pipeline"])
+    async def process(
+        request: Request,
+        ctx: Annotated[AppContext, Depends(get_app_context)],
+        # Query params — these mirror the Robyn semantics.
+        dataset: Annotated[str | None, Query()] = None,
+        render_mode: Annotated[str | None, Query()] = None,
+        ontology_user_instruction: Annotated[str, Query()] = "",
+        facts_user_instruction: Annotated[str, Query()] = "",
+        # Multipart fields — optional; when used, JSON body is rejected.
+        file: Annotated[UploadFile | None, File()] = None,
+        form_ontology_user_instruction: Annotated[str | None, Form(alias="ontology_user_instruction")] = None,
+        form_facts_user_instruction: Annotated[str | None, Form(alias="facts_user_instruction")] = None,
+    ) -> JSONResponse:
+        """Run the full OntoCast workflow over a single document.
+
+        Accepts either ``application/json`` (body = the JSON envelope OntoCast
+        already understands) or ``multipart/form-data`` (one ``file`` field).
+        Returns the produced ontology + facts Turtle, plus pipeline metadata
+        and the budget tracker snapshot.
+        """
+        content_type = (request.headers.get("content-type") or "").lower()
+        files: dict[str, bytes]
+
+        if content_type.startswith("application/json"):
+            if file is not None:
+                raise HTTPException(
+                    status_code=400,
+                    detail="Use either JSON body or multipart, not both.",
+                )
+            body = await request.body()
+            if not body:
+                raise HTTPException(status_code=400, detail="Empty JSON body.")
+            files = {"input.json": body}
+
+        elif content_type.startswith("multipart/form-data"):
+            if file is None:
+                raise HTTPException(
+                    status_code=400,
+                    detail="multipart/form-data requires a 'file' field.",
+                )
+            filename = file.filename or "upload.bin"
+            content = await file.read()
+            files = {filename: content}
+
+            # Form fields override query-string instructions, matching the
+            # original Robyn precedence.
+            if form_ontology_user_instruction:
+                ontology_user_instruction = form_ontology_user_instruction
+            if form_facts_user_instruction:
+                facts_user_instruction = form_facts_user_instruction
+
+        else:
+            raise HTTPException(
+                status_code=415,
+                detail=(
+                    "Unsupported content type. Use application/json or "
+                    "multipart/form-data."
+                ),
+            )
+
+        # Dataset switch (mostly a no-op on filesystem backend).
+        if dataset:
+            await ctx.tools.update_dataset(dataset)
+
+        # Parse render mode.
+        try:
+            render_mode_value = (
+                RenderMode(render_mode.lower().strip())
+                if render_mode
+                else ctx.server_config.render_mode
+            )
+        except ValueError:
+            logger.warning(
+                "Invalid render_mode %r; using default %r",
+                render_mode,
+                ctx.server_config.render_mode.value,
+            )
+            render_mode_value = ctx.server_config.render_mode
+
+        initial_state = AgentState(
+            files=files,
+            max_visits=ctx.server_config.max_visits_per_node,
+            render_mode=render_mode_value,
+            ontology_max_triples=ctx.server_config.ontology_max_triples,
+            dataset=dataset,
+            ontology_user_instruction=ontology_user_instruction,
+            facts_user_instruction=facts_user_instruction,
+        )
+
+        workflow_state: dict[str, Any] | None = None
+        try:
+            async for chunk in ctx.workflow.astream(
+                initial_state,
+                stream_mode="values",
+                config=RunnableConfig(recursion_limit=ctx.recursion_limit),
+            ):
+                workflow_state = chunk
+        except Exception as exc:  # noqa: BLE001
+            logger.exception("Workflow execution failed")
+            error_details = None
+            if workflow_state:
+                error_details = {
+                    "stage": workflow_state.get("failure_stage", "unknown"),
+                    "reason": workflow_state.get("failure_reason", "unknown"),
+                }
+            return JSONResponse(
+                status_code=500,
+                content={
+                    "status": "error",
+                    "error": str(exc),
+                    "error_type": type(exc).__name__,
+                    "error_details": error_details,
+                },
+            )
+
+        if workflow_state is None:
+            raise HTTPException(
+                status_code=500,
+                detail="Workflow did not return a valid state.",
+            )
+
+        # Budget snapshot.
+        budget_tracker_data: dict[str, Any] = {}
+        if workflow_state.get("budget_tracker"):
+            budget_tracker_data = workflow_state["budget_tracker"].model_dump()
+
+        total_units = len(workflow_state.get("content_units", []))
+        rm = workflow_state.get("render_mode")
+        render_facts_enabled = rm in (
+            RenderMode.FACTS,
+            RenderMode.ONTOLOGY_AND_FACTS,
+            getattr(RenderMode.FACTS, "value", None),
+            getattr(RenderMode.ONTOLOGY_AND_FACTS, "value", None),
+        )
+        processed_units = (
+            len(workflow_state.get("parallel_facts_units", []))
+            if render_facts_enabled
+            else total_units
+        )
+        chunks_remaining = max(total_units - processed_units, 0)
+
+        return JSONResponse(
+            status_code=200,
+            content={
+                "status": "success",
+                "data": {
+                    "ontology": (
+                        workflow_state["current_ontology"].graph.serialize(format="turtle")
+                        if workflow_state.get("current_ontology")
+                        else ""
+                    ),
+                    "facts": (
+                        workflow_state["aggregated_facts"].serialize(format="turtle")
+                        if workflow_state.get("aggregated_facts")
+                        else ""
+                    ),
+                },
+                "metadata": {
+                    "status": str(workflow_state.get("status", "unknown")),
+                    "chunks_processed": processed_units,
+                    "chunks_remaining": chunks_remaining,
+                    "budget": budget_tracker_data,
+                },
+            },
+        )
+
+    return app
+
+
+# Top-level instance for `uvicorn platform.api.main:app`.
+app = create_app()
+
+
+@click.command()
+@click.option("--host", default="0.0.0.0", show_default=True)
+@click.option("--port", default=8000, show_default=True, type=int)
+@click.option("--reload", is_flag=True, default=False)
+def cli(host: str, port: int, reload: bool) -> None:  # noqa: FBT001
+    """Console entry point: ``ontology-platform`` (see pyproject.toml)."""
+    uvicorn.run("ont_platform.api.main:app", host=host, port=port, reload=reload)
+
+
+if __name__ == "__main__":
+    cli()

ontology_platform/ont_platform/config.py

@@ -0,0 +1,257 @@
+"""Platform-level configuration.
+
+Thin layer over OntoCast's ``Config`` that adds:
+
+1. A single explicit ``PHASE`` knob. Phase 0 forces filesystem-only storage
+   and ignores any Neo4j/Fuseki environment variables that may be present
+   (they belong to Phase 4 — see ``docs/통합설계서.md`` §5).
+2. A platform-side ``working_directory`` default so the user does not have
+   to remember the legacy ``ONTOCAST_*`` env-var prefix.
+3. Hook points for future phases (LLM budgets, robots policy, etc.).
+
+The OntoCast vendored copy itself is not modified by this module — we only
+build a ``Config`` instance and pass it to ``ToolBox``.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+import sys
+from enum import IntEnum
+from pathlib import Path
+from typing import Literal
+
+from pydantic import Field, model_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+# Make the vendored OntoCast package importable without users having to
+# install it into the environment. This mirrors what tests/unit/test_*.py
+# already do; centralizing it here keeps any module that imports our
+# Settings consistent.
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+_VENDORED_ONTOCAST = _REPO_ROOT / "vendored" / "ontocast"
+if str(_VENDORED_ONTOCAST) not in sys.path:
+    sys.path.insert(0, str(_VENDORED_ONTOCAST))
+
+# Late import — OntoCast must be on sys.path first.
+from ontocast.config import (  # noqa: E402  (must follow sys.path tweak)
+    Config as OntoCastConfig,
+)
+from ontocast.config import (  # noqa: E402
+    FusekiConfig,
+    LLMConfig,
+    Neo4jConfig,
+    PathConfig,
+    ToolConfig,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class Phase(IntEnum):
+    """Integration phase from the master design (docs/통합설계서.md §5).
+
+    Used as a gate: features whose phase exceeds ``Settings.phase`` are
+    disabled even if their environment variables are populated.
+    """
+
+    BASE = 0  # OntoCast only, filesystem storage
+    TRAFILATURA = 1
+    CRAWL4AI = 2
+    GUARDRAILS = 3
+    NEO4J_GRAPHRAG = 4
+    MULTI_AGENT = 5
+
+
+StorageBackend = Literal["filesystem", "fuseki", "neo4j"]
+
+
+class PlatformSettings(BaseSettings):
+    """Top-level platform settings, loaded from ``.env`` and the environment.
+
+    Only fields the platform itself needs are declared here. OntoCast-specific
+    settings (LLM, chunk, paths, ...) are loaded by OntoCast's own ``Config``
+    via its own ``env_prefix`` conventions and merged in ``build_ontocast_config``.
+    """
+
+    model_config = SettingsConfigDict(
+        env_file=".env",
+        env_file_encoding="utf-8",
+        extra="ignore",
+        case_sensitive=False,
+    )
+
+    # ─── Phase gating ────────────────────────────────────────────────
+    phase: Phase = Field(
+        default=Phase.BASE,
+        description="Current integration phase (docs/통합설계서.md §5).",
+    )
+    storage_backend: StorageBackend = Field(
+        default="filesystem",
+        description=(
+            "Which storage backend to use. Phase 0 forces 'filesystem' "
+            "regardless of any Neo4j/Fuseki credentials in the environment."
+        ),
+    )
+
+    # ─── Paths (mirror ONTOCAST_* but with platform defaults) ────────
+    working_directory: Path = Field(
+        default=Path("./data/working"),
+        description="Working directory for OntoCast filesystem store.",
+        validation_alias="ONTOCAST_WORKING_DIRECTORY",
+    )
+    ontology_directory: Path | None = Field(
+        default=None,
+        validation_alias="ONTOCAST_ONTOLOGY_DIRECTORY",
+    )
+
+    # ─── Server ──────────────────────────────────────────────────────
+    host: str = Field(default="0.0.0.0")
+    port: int = Field(default=8000)
+    log_level: str = Field(default="info")
+
+    # ─── Operational policy (used from Phase 2+) ─────────────────────
+    robots_policy: Literal["strict", "respect", "ignore"] = Field(
+        default="respect",
+        description="robots.txt 준수 정책. Phase 2 Crawl4AI 통합에서 사용.",
+    )
+    daily_llm_call_limit: int = Field(default=10_000)
+    daily_llm_token_limit: int = Field(default=10_000_000)
+
+    # ─── Phase 0 enforcement ─────────────────────────────────────────
+    @model_validator(mode="after")
+    def _enforce_phase_storage_consistency(self) -> "PlatformSettings":
+        """Phase 0 forces filesystem; later phases may opt into other backends.
+
+        Anything other than 'filesystem' before Phase 4 is treated as a
+        configuration error — failing fast here is far easier to debug than
+        a half-wired Neo4j connection deep inside ToolBox.
+        """
+        if self.phase < Phase.NEO4J_GRAPHRAG and self.storage_backend != "filesystem":
+            raise ValueError(
+                f"storage_backend={self.storage_backend!r} requires Phase 4+, "
+                f"but PHASE={int(self.phase)}. See docs/통합설계서.md §5."
+            )
+        # Ensure working directory exists for filesystem mode.
+        if self.storage_backend == "filesystem":
+            self.working_directory.mkdir(parents=True, exist_ok=True)
+        return self
+
+
+def _build_llm_config_lenient() -> LLMConfig:
+    """env vars로부터 LLMConfig 생성. OntoCast의 OpenAIModel enum validation을 우회한다.
+
+    Why: LM Studio / vLLM / llama.cpp 등 OpenAI-호환 로컬 서버는 임의의 model
+    identifier를 쓰며(예: `deepseek-r1-distill-qwen-7b`), 이는 OntoCast의 정해진
+    enum(`gpt-4o`, `gpt-4o-mini`, ...)에 들어가지 않는다. ChatOpenAI는 model을
+    문자열로 받으므로 enum 강제만 풀면 OntoCast 다른 코드 경로는 그대로 동작한다.
+
+    `LLMConfig.model_construct`는 Pydantic V2의 validation 우회 생성자다.
+    """
+    provider_raw = (os.getenv("LLM_PROVIDER") or "openai").lower()
+    model_name = os.getenv("LLM_MODEL_NAME") or "gpt-4o-mini"
+    temperature_raw = os.getenv("LLM_TEMPERATURE") or "0.0"
+    base_url = os.getenv("LLM_BASE_URL") or None
+    api_key = os.getenv("LLM_API_KEY") or None
+
+    return LLMConfig.model_construct(
+        provider=provider_raw,
+        model_name=model_name,
+        temperature=float(temperature_raw),
+        base_url=base_url,
+        api_key=api_key,
+    )
+
+
+def _empty_neo4j_config() -> Neo4jConfig:
+    """A Neo4jConfig with no URI/auth so ToolBox skips Neo4j initialization.
+
+    ToolBox enables Neo4j only when both ``uri`` and ``auth`` are set
+    (vendored/ontocast/ontocast/toolbox.py around line 112). Returning an
+    instance with both ``None`` is the canonical way to disable the backend
+    even if NEO4J_* env vars happen to be populated in the shell.
+    """
+    cfg = Neo4jConfig()
+    cfg.uri = None  # type: ignore[assignment]
+    cfg.auth = None  # type: ignore[assignment]
+    return cfg
+
+
+def _empty_fuseki_config() -> FusekiConfig:
+    """Same idea as ``_empty_neo4j_config`` for Fuseki."""
+    cfg = FusekiConfig()
+    cfg.uri = None  # type: ignore[assignment]
+    cfg.auth = None  # type: ignore[assignment]
+    return cfg
+
+
+def build_ontocast_config(settings: PlatformSettings) -> OntoCastConfig:
+    """Build an OntoCast ``Config`` from our ``PlatformSettings``.
+
+    Phase 0 behavior:
+    - LLM/Chunk/Aggregation/WebSearch sections come from their own env vars
+      via OntoCast's ``BaseSettings`` defaults. We do NOT touch them.
+    - Paths are overridden so the OntoCast working directory always matches
+      ``PlatformSettings.working_directory``.
+    - Neo4j and Fuseki are forcibly disabled regardless of NEO4J_*/FUSEKI_*
+      env vars in the shell. They will be wired in Phase 4.
+    """
+    # OntoCast의 OpenAIModel enum은 클라우드 모델만 허용한다. LM Studio 등
+    # 임의의 모델명을 쓰는 로컬 서버는 ToolConfig() 생성 단계에서 검증이 실패
+    # 하므로, ToolConfig를 만들 동안만 LLM_MODEL_NAME을 비우고 lenient 빌더로
+    # 교체한다. CHUNK_*/AGG_* 등 다른 섹션 env는 그대로 흘러가도록 유지한다.
+    saved_model = os.environ.pop("LLM_MODEL_NAME", None)
+    try:
+        tool_cfg = ToolConfig()
+    finally:
+        if saved_model is not None:
+            os.environ["LLM_MODEL_NAME"] = saved_model
+
+    tool_cfg.llm_config = _build_llm_config_lenient()
+
+    # Override paths from the platform settings.
+    tool_cfg.path_config = PathConfig(
+        working_directory=settings.working_directory,
+        ontology_directory=settings.ontology_directory,
+    )
+
+    # Phase 0: forcibly disable non-filesystem backends.
+    if settings.storage_backend == "filesystem":
+        tool_cfg.neo4j = _empty_neo4j_config()
+        tool_cfg.fuseki = _empty_fuseki_config()
+    # (No 'else' here yet — Phase 4 will add the fuseki/neo4j branches.)
+
+    cfg = OntoCastConfig(tool_config=tool_cfg)
+
+    # Validate LLM config eagerly so misconfiguration surfaces at startup,
+    # not on the first /process call.
+    try:
+        cfg.validate_llm_config()
+    except ValueError as exc:
+        # Re-raise with a hint about where to set the variable. OntoCast's
+        # error already mentions the env var name; we add the file location.
+        raise ValueError(f"{exc} (set it in `.env` or your shell environment)") from exc
+
+    return cfg
+
+
+def load_settings() -> PlatformSettings:
+    """Load `PlatformSettings` from the environment.
+
+    Separated from module-level instantiation so tests can swap env vars
+    via ``monkeypatch`` without import-time side effects.
+    """
+    return PlatformSettings()  # type: ignore[call-arg]
+
+
+# Provider-grade LLM/embedding config helpers can be added in later phases.
+# For Phase 0, OntoCast's own ``LLMConfig`` is sufficient.
+__all__ = [
+    "Phase",
+    "PlatformSettings",
+    "StorageBackend",
+    "build_ontocast_config",
+    "load_settings",
+    "LLMConfig",
+]