first ingestion/normalization slice

docs/ingestion-implementation.md

@@ -0,0 +1,84 @@
+# Ingestion Implementation Note
+
+Date: 2026-05-06
+
+Status: first implementation slice for `KONT-WP-0006`.
+
+## Purpose
+
+This note records the first governed ingestion implementation built on top of
+the asset registry service. It turns local source files into stable knowledge
+assets with source and normalized representations while preserving source
+provenance, actor context, auditability, and inspectable ingestion job state.
+
+## Implemented Package Shape
+
+```text
+src/kontextual_engine/
+  core/ingestion.py
+  ports/ingestion.py
+  services/ingestion_service.py
+  adapters/local_files/
+  adapters/builtin_extractors/
+  adapters/markitect_tool/
+```
+
+The new `AssetIngestionService` is separate from the older artifact-era
+`IngestionService` compatibility facade in `src/kontextual_engine/ingestion.py`.
+
+## Implemented Capabilities
+
+- Durable `IngestionJob` state with queued, running, completed, failed,
+  partially completed, retried, quarantined, and canceled statuses.
+- Structured ingestion failures with retriable flags and diagnostic details.
+- Connector and extractor port contracts owned by the engine.
+- Local file connector with source references, checksums, media type detection,
+  file metadata, and directory file iteration.
+- Plain text extractor producing a normalized engine representation.
+- Markitect markdown extractor adapter boundary that delegates markdown parsing,
+  headings, sections, frontmatter, and snapshot identity to `markitect-tool`
+  when available.
+- Synchronous first-run ingestion flow that creates governed assets through
+  `AssetRegistryService`.
+- Source and normalized `AssetRepresentation` records for ingested files.
+- Metadata records for connector, extractor, source digest, source media type,
+  size, and extraction metadata.
+- Failed unsupported-media ingestion records job failure without adding an asset
+  to the trusted registry.
+- Directory ingestion with per-file child jobs and partial result accounting.
+- In-memory and SQLite job persistence.
+
+## Current SQLite Additions
+
+- `ingestion_jobs`
+
+The table stores indexed status, actor, correlation ID, timestamps, and a JSON
+payload for the full job contract.
+
+## markitect-tool Boundary
+
+Markdown ingestion uses `MarkitectMarkdownExtractor`, which imports
+`markitect-tool` only inside the adapter. The engine preserves Markitect output
+as normalized structure and adapter metadata; it does not make Markitect
+document classes part of the engine domain model.
+
+## Not Yet Implemented
+
+- Asynchronous job runner and queue dispatch.
+- Re-ingestion reconciliation for existing assets.
+- Identity policies that preserve asset identity across source moves.
+- PDF, office document, and dataset extractors.
+- Deep normalized structure for tables, links, embedded references, and fields
+  beyond extractor-provided metadata.
+- Quarantine policy checks beyond unsupported/failed extraction paths.
+
+## Test Coverage
+
+`tests/test_asset_ingestion_service.py` covers:
+
+- plain text local-file ingestion into governed assets,
+- source and normalized representation creation,
+- job persistence and status inspection,
+- unsupported media failure without trusted asset creation,
+- directory partial success/failure accounting,
+- SQLite reload preserving ingestion jobs and ingested asset state.

src/kontextual_engine/__init__.py

@@ -22,15 +22,22 @@ from .core import (
     AuditEvent,
     AuditOutcome,
     Classification,
+    ConnectorCapability,
     ContextEntity,
     ContextEntityType,
     CoreRelationship,
     DerivedArtifactLineage,
+    ExtractionResult,
+    ExtractorCapability,
     IdempotencyRecord,
     IdempotencyStatus,
+    IngestionFailure,
+    IngestionJob,
+    IngestionJobStatus,
     KnowledgeAsset,
     LifecycleState,
     MetadataRecord,
+    NormalizedDocument,
     OperationContext,
     PolicyDecision,
     PolicyEffect,
@@ -38,6 +45,7 @@ from .core import (
     RepresentationKind,
     Sensitivity,
     SourceReference,
+    SourcePayload,
     VersionChangeType,
 )
 from .errors import (
@@ -50,10 +58,23 @@ from .errors import (
     ValidationError,
 )
 from .ingestion import IngestionRequest, IngestionResult, IngestionService
-from .ports import AllowAllPolicyGateway, AssetRegistryRepository, PolicyGateway
+from .ports import (
+    AllowAllPolicyGateway,
+    AssetRegistryRepository,
+    DirectorySourceConnector,
+    FormatExtractor,
+    PolicyGateway,
+    SourceConnector,
+)
 from .query import QueryEngine, QueryResult
 from .relationships import RelationshipGraph
-from .services import AssetChangeResult, AssetRegistryService, RelationshipChangeResult
+from .services import (
+    AssetChangeResult,
+    AssetIngestionResult,
+    AssetIngestionService,
+    AssetRegistryService,
+    RelationshipChangeResult,
+)
 from .storage import InMemoryKnowledgeRepository
 from .workflows import (
     InputBundle,
@@ -76,6 +97,8 @@ __all__ = [
     "ActorType",
     "AssetRepresentation",
     "AssetChangeResult",
+    "AssetIngestionResult",
+    "AssetIngestionService",
     "AssetRegistryRepository",
     "AssetRegistryService",
     "AssetVersion",
@@ -83,6 +106,7 @@ __all__ = [
     "AuditOutcome",
     "AuthorizationError",
     "Classification",
+    "ConnectorCapability",
     "Collection",
     "ContextAssembler",
     "ContextEntity",
@@ -92,12 +116,19 @@ __all__ = [
     "CoreRelationship",
     "DerivedArtifactLineage",
     "Diagnostic",
+    "DirectorySourceConnector",
     "DuplicateResourceError",
+    "ExtractionResult",
+    "ExtractorCapability",
+    "FormatExtractor",
     "InMemoryAssetRegistryRepository",
     "InMemoryKnowledgeRepository",
     "IngestionRequest",
     "IngestionResult",
     "IngestionService",
+    "IngestionFailure",
+    "IngestionJob",
+    "IngestionJobStatus",
     "InputBundle",
     "IdempotencyRecord",
     "IdempotencyStatus",
@@ -105,6 +136,7 @@ __all__ = [
     "KontextualError",
     "LifecycleState",
     "MetadataRecord",
+    "NormalizedDocument",
     "NotFoundError",
     "OperationRun",
     "OperationStage",
@@ -124,6 +156,8 @@ __all__ = [
     "RunStatus",
     "Sensitivity",
     "SourceReference",
+    "SourceConnector",
+    "SourcePayload",
     "SQLiteAssetRegistryRepository",
     "ValidationError",
     "VersionChangeType",

src/kontextual_engine/adapters/builtin_extractors/__init__.py

@@ -0,0 +1,5 @@
+"""Built-in baseline format extractors."""
+
+from .text import PlainTextExtractor
+
+__all__ = ["PlainTextExtractor"]

src/kontextual_engine/adapters/builtin_extractors/text.py

@@ -0,0 +1,42 @@
+"""Plain text normalization extractor."""
+
+from __future__ import annotations
+
+from kontextual_engine.core import ExtractionResult, ExtractorCapability, NormalizedDocument, SourcePayload
+
+
+class PlainTextExtractor:
+    name = "plain-text"
+    media_types = ("text/plain",)
+
+    def capabilities(self) -> ExtractorCapability:
+        return ExtractorCapability(
+            extractor_name=self.name,
+            media_types=self.media_types,
+            extraction_depth="text",
+            produces_structure=False,
+        )
+
+    def supports(self, media_type: str) -> bool:
+        return media_type in self.media_types or media_type.startswith("text/plain")
+
+    def extract(self, payload: SourcePayload) -> ExtractionResult:
+        text = payload.read_text()
+        normalized = NormalizedDocument(
+            title=payload.title,
+            text=text,
+            fields={"line_count": len(text.splitlines())},
+            confidence=1.0,
+            extractor_metadata={
+                "extractor": self.name,
+                "source_media_type": payload.media_type,
+            },
+        )
+        return ExtractionResult(
+            normalized=normalized,
+            metadata={
+                "extractor": self.name,
+                "source_digest": payload.content_digest,
+                "source_size_bytes": payload.size_bytes,
+            },
+        )

src/kontextual_engine/adapters/local_files/__init__.py

@@ -0,0 +1,5 @@
+"""Local filesystem ingestion connector."""
+
+from .connector import LocalFileConnector
+
+__all__ = ["LocalFileConnector"]

src/kontextual_engine/adapters/local_files/connector.py

@@ -0,0 +1,77 @@
+"""Local file and directory source connector."""
+
+from __future__ import annotations
+
+import mimetypes
+from pathlib import Path
+from typing import Any
+
+from kontextual_engine.core import ConnectorCapability, SourcePayload, SourceReference, content_digest
+from kontextual_engine.errors import NotFoundError, ValidationError
+
+
+class LocalFileConnector:
+    name = "local_file"
+
+    def capabilities(self) -> ConnectorCapability:
+        return ConnectorCapability(
+            connector_name=self.name,
+            source_types=("file", "directory"),
+            supports_directories=True,
+            metadata={"uri_schemes": ["file", "path"]},
+        )
+
+    def fetch(self, source_uri: str) -> SourcePayload:
+        path = Path(source_uri).expanduser()
+        if not path.exists():
+            raise NotFoundError("Local source file not found", details={"path": str(path)})
+        if not path.is_file():
+            raise ValidationError("Local source is not a file", details={"path": str(path)})
+
+        content = path.read_bytes()
+        media_type = _guess_media_type(path)
+        source_ref = SourceReference(
+            source_system=self.name,
+            path=str(path),
+            checksum=content_digest(content),
+            connector_ref=f"{self.name}:{path.resolve()}",
+            metadata=_file_metadata(path),
+        )
+        return SourcePayload(
+            connector_name=self.name,
+            source_uri=str(path),
+            source_ref=source_ref,
+            media_type=media_type,
+            content=content,
+            title=path.stem,
+            metadata={"filename": path.name, **_file_metadata(path)},
+        )
+
+    def iter_files(self, source_uri: str, *, recursive: bool = True) -> list[str]:
+        root = Path(source_uri).expanduser()
+        if not root.exists():
+            raise NotFoundError("Local source directory not found", details={"path": str(root)})
+        if root.is_file():
+            return [str(root)]
+        if not root.is_dir():
+            raise ValidationError("Local source is not a directory", details={"path": str(root)})
+        pattern = "**/*" if recursive else "*"
+        return sorted(str(path) for path in root.glob(pattern) if path.is_file())
+
+
+def _guess_media_type(path: Path) -> str:
+    suffix = path.suffix.lower()
+    if suffix in {".md", ".markdown", ".mkd"}:
+        return "text/markdown"
+    if suffix in {".txt", ".text", ".log"}:
+        return "text/plain"
+    guessed, _ = mimetypes.guess_type(path.name)
+    return guessed or "application/octet-stream"
+
+
+def _file_metadata(path: Path) -> dict[str, Any]:
+    stat = path.stat()
+    return {
+        "size_bytes": stat.st_size,
+        "mtime_ns": stat.st_mtime_ns,
+    }

src/kontextual_engine/adapters/markitect_tool/__init__.py

@@ -0,0 +1,5 @@
+"""markitect-tool ingestion adapter boundary."""
+
+from .markdown import MarkitectMarkdownExtractor
+
+__all__ = ["MarkitectMarkdownExtractor"]

src/kontextual_engine/adapters/markitect_tool/markdown.py

@@ -0,0 +1,86 @@
+"""Markdown normalization through markitect-tool."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from kontextual_engine.core import ExtractionResult, ExtractorCapability, NormalizedDocument, SourcePayload
+from kontextual_engine.errors import AdapterUnavailableError
+
+
+class MarkitectMarkdownExtractor:
+    """Adapter boundary to markitect-tool; Markdown syntax logic stays external."""
+
+    name = "markitect-tool"
+    media_types = ("text/markdown", "text/x-markdown")
+
+    def capabilities(self) -> ExtractorCapability:
+        return ExtractorCapability(
+            extractor_name=self.name,
+            media_types=self.media_types,
+            extraction_depth="structure",
+            produces_structure=True,
+            optional_dependency="markitect-tool",
+            metadata={"delegates_markdown_syntax": True},
+        )
+
+    def supports(self, media_type: str) -> bool:
+        return media_type in self.media_types
+
+    def extract(self, payload: SourcePayload) -> ExtractionResult:
+        try:
+            import markitect_tool as mkt
+        except Exception as exc:  # pragma: no cover - depends on optional environment
+            raise AdapterUnavailableError(
+                "markitect-tool is required for markdown normalization",
+                details={"adapter": self.name, "media_type": payload.media_type},
+            ) from exc
+
+        source_path = payload.source_ref.path
+        text = payload.read_text()
+        document = self._parse_document(mkt, text, source_path)
+        serialized = document.to_dict() if hasattr(document, "to_dict") else {}
+        snapshot = self._snapshot(mkt, source_path)
+        structure = {
+            "frontmatter": dict(serialized.get("frontmatter", {})),
+            "headings": list(serialized.get("headings", [])),
+            "sections": list(serialized.get("sections", [])),
+        }
+        normalized = NormalizedDocument(
+            title=payload.title,
+            text=text,
+            structure=structure,
+            fields={
+                "frontmatter": dict(serialized.get("frontmatter", {})),
+                "heading_count": len(structure["headings"]),
+                "section_count": len(structure["sections"]),
+            },
+            confidence=1.0,
+            extractor_metadata={
+                "extractor": self.name,
+                "source_media_type": payload.media_type,
+                "snapshot": snapshot,
+            },
+        )
+        return ExtractionResult(
+            normalized=normalized,
+            metadata={
+                "extractor": self.name,
+                "frontmatter": structure["frontmatter"],
+                "headings": structure["headings"],
+                "snapshot": snapshot,
+                "source_digest": payload.content_digest,
+                "source_size_bytes": payload.size_bytes,
+            },
+        )
+
+    def _parse_document(self, mkt: Any, text: str, source_path: str | None) -> Any:
+        if source_path and Path(source_path).exists() and hasattr(mkt, "parse_markdown_file"):
+            return mkt.parse_markdown_file(Path(source_path))
+        return mkt.parse_markdown(text, source_path=source_path)
+
+    def _snapshot(self, mkt: Any, source_path: str | None) -> dict[str, Any]:
+        if not source_path or not Path(source_path).exists() or not hasattr(mkt, "snapshot_identity_for_file"):
+            return {}
+        return mkt.snapshot_identity_for_file(Path(source_path), parse_options={"profile": "default"}).to_dict()

src/kontextual_engine/adapters/memory/asset_registry.py

@@ -13,6 +13,8 @@ from kontextual_engine.core import (
     ContextEntity,
     CoreRelationship,
     IdempotencyRecord,
+    IngestionJob,
+    IngestionJobStatus,
     KnowledgeAsset,
     LifecycleState,
     MetadataRecord,
@@ -32,6 +34,7 @@ class InMemoryAssetRegistryRepository:
     versions: dict[str, list[AssetVersion]] = field(default_factory=dict)
     audit_events: dict[str, AuditEvent] = field(default_factory=dict)
     idempotency_records: dict[str, IdempotencyRecord] = field(default_factory=dict)
+    ingestion_jobs: dict[str, IngestionJob] = field(default_factory=dict)
 
     def save_actor(self, actor: Actor) -> Actor:
         self.actors[actor.id] = actor
@@ -190,3 +193,23 @@ class InMemoryAssetRegistryRepository:
 
     def get_idempotency_record(self, key: str) -> IdempotencyRecord | None:
         return self.idempotency_records.get(key)
+
+    def save_ingestion_job(self, job: IngestionJob) -> IngestionJob:
+        self.ingestion_jobs[job.job_id] = job
+        return job
+
+    def get_ingestion_job(self, job_id: str) -> IngestionJob:
+        try:
+            return self.ingestion_jobs[job_id]
+        except KeyError as exc:
+            raise NotFoundError("Ingestion job not found", details={"job_id": job_id}) from exc
+
+    def list_ingestion_jobs(
+        self,
+        *,
+        status: IngestionJobStatus | None = None,
+    ) -> list[IngestionJob]:
+        jobs: Iterable[IngestionJob] = self.ingestion_jobs.values()
+        if status is not None:
+            jobs = [job for job in jobs if job.status == status]
+        return sorted(jobs, key=lambda job: (job.created_at, job.job_id))

src/kontextual_engine/adapters/sqlite/asset_registry.py

@@ -15,6 +15,8 @@ from kontextual_engine.core import (
     ContextEntity,
     CoreRelationship,
     IdempotencyRecord,
+    IngestionJob,
+    IngestionJobStatus,
     KnowledgeAsset,
     LifecycleState,
     MetadataRecord,
@@ -381,6 +383,51 @@ class SQLiteAssetRegistryRepository:
             return None
         return IdempotencyRecord.from_dict(_loads(row["payload"]))
 
+    def save_ingestion_job(self, job: IngestionJob) -> IngestionJob:
+        with self._connect() as conn:
+            conn.execute(
+                """
+                insert into ingestion_jobs (id, status, actor_id, correlation_id, created_at, updated_at, payload)
+                values (?, ?, ?, ?, ?, ?, ?)
+                on conflict(id) do update set
+                  status=excluded.status,
+                  actor_id=excluded.actor_id,
+                  correlation_id=excluded.correlation_id,
+                  updated_at=excluded.updated_at,
+                  payload=excluded.payload
+                """,
+                (
+                    job.job_id,
+                    job.status.value,
+                    job.actor_id,
+                    job.correlation_id,
+                    job.created_at,
+                    job.updated_at,
+                    _json(job.to_dict()),
+                ),
+            )
+        return job
+
+    def get_ingestion_job(self, job_id: str) -> IngestionJob:
+        row = self._one("select payload from ingestion_jobs where id = ?", (job_id,))
+        if row is None:
+            raise NotFoundError("Ingestion job not found", details={"job_id": job_id})
+        return IngestionJob.from_dict(_loads(row["payload"]))
+
+    def list_ingestion_jobs(
+        self,
+        *,
+        status: IngestionJobStatus | None = None,
+    ) -> list[IngestionJob]:
+        if status is None:
+            rows = self._all("select payload from ingestion_jobs order by created_at, id", ())
+        else:
+            rows = self._all(
+                "select payload from ingestion_jobs where status = ? order by created_at, id",
+                (status.value,),
+            )
+        return [IngestionJob.from_dict(_loads(row["payload"])) for row in rows]
+
     def _initialize(self) -> None:
         with self._connect() as conn:
             conn.executescript(
@@ -449,6 +496,15 @@ class SQLiteAssetRegistryRepository:
                   status text not null,
                   payload text not null
                 );
+                create table if not exists ingestion_jobs (
+                  id text primary key,
+                  status text not null,
+                  actor_id text not null,
+                  correlation_id text not null,
+                  created_at text not null,
+                  updated_at text not null,
+                  payload text not null
+                );
                 create index if not exists idx_assets_lifecycle on assets(lifecycle);
                 create index if not exists idx_representations_asset on representations(asset_id);
                 create index if not exists idx_metadata_asset on metadata_records(asset_id);
@@ -458,6 +514,8 @@ class SQLiteAssetRegistryRepository:
                 create index if not exists idx_versions_asset on asset_versions(asset_id);
                 create index if not exists idx_audit_target on audit_events(target);
                 create index if not exists idx_audit_correlation on audit_events(correlation_id);
+                create index if not exists idx_ingestion_jobs_status on ingestion_jobs(status);
+                create index if not exists idx_ingestion_jobs_correlation on ingestion_jobs(correlation_id);
                 """
             )
 

src/kontextual_engine/core/__init__.py

@@ -4,6 +4,16 @@ from .actors import Actor, ActorType, OperationContext
 from .assets import AssetRepresentation, KnowledgeAsset, RepresentationKind
 from .audit import AuditEvent, AuditOutcome
 from .idempotency import IdempotencyRecord, IdempotencyStatus
+from .ingestion import (
+    ConnectorCapability,
+    ExtractionResult,
+    ExtractorCapability,
+    IngestionFailure,
+    IngestionJob,
+    IngestionJobStatus,
+    NormalizedDocument,
+    SourcePayload,
+)
 from .metadata import Classification, LifecycleState, MetadataRecord, Sensitivity
 from .policy import PolicyDecision, PolicyEffect
 from .primitives import content_digest, mapping_digest, new_id, stable_json_dumps, utc_now
@@ -28,15 +38,22 @@ __all__ = [
     "AuditEvent",
     "AuditOutcome",
     "Classification",
+    "ConnectorCapability",
     "ContextEntity",
     "ContextEntityType",
     "CoreRelationship",
     "DerivedArtifactLineage",
+    "ExtractionResult",
+    "ExtractorCapability",
     "IdempotencyRecord",
     "IdempotencyStatus",
+    "IngestionFailure",
+    "IngestionJob",
+    "IngestionJobStatus",
     "KnowledgeAsset",
     "LifecycleState",
     "MetadataRecord",
+    "NormalizedDocument",
     "OperationContext",
     "PolicyDecision",
     "PolicyEffect",
@@ -44,6 +61,7 @@ __all__ = [
     "RepresentationKind",
     "Sensitivity",
     "SourceReference",
+    "SourcePayload",
     "VersionChangeType",
     "content_digest",
     "mapping_digest",

src/kontextual_engine/core/ingestion.py

@@ -0,0 +1,308 @@
+"""Ingestion job and normalized content primitives."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field, replace
+from enum import Enum
+from typing import Any
+
+from .primitives import compact_dict, content_digest, mapping_digest, new_id, stable_json_dumps, utc_now
+from .provenance import SourceReference
+
+
+class IngestionJobStatus(str, Enum):
+    QUEUED = "queued"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"
+    PARTIALLY_COMPLETED = "partially_completed"
+    RETRIED = "retried"
+    QUARANTINED = "quarantined"
+    CANCELED = "canceled"
+
+
+@dataclass(frozen=True)
+class IngestionFailure:
+    code: str
+    message: str
+    retriable: bool = False
+    details: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return compact_dict(
+            {
+                "code": self.code,
+                "message": self.message,
+                "retriable": self.retriable,
+                "details": dict(self.details),
+            }
+        )
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "IngestionFailure":
+        return cls(
+            code=data["code"],
+            message=data["message"],
+            retriable=bool(data.get("retriable", False)),
+            details=dict(data.get("details", {})),
+        )
+
+
+@dataclass(frozen=True)
+class ConnectorCapability:
+    connector_name: str
+    source_types: tuple[str, ...]
+    supports_directories: bool = False
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return compact_dict(
+            {
+                "connector_name": self.connector_name,
+                "source_types": list(self.source_types),
+                "supports_directories": self.supports_directories,
+                "metadata": dict(self.metadata),
+            }
+        )
+
+
+@dataclass(frozen=True)
+class ExtractorCapability:
+    extractor_name: str
+    media_types: tuple[str, ...]
+    extraction_depth: str = "text"
+    produces_structure: bool = False
+    optional_dependency: str | None = None
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return compact_dict(
+            {
+                "extractor_name": self.extractor_name,
+                "media_types": list(self.media_types),
+                "extraction_depth": self.extraction_depth,
+                "produces_structure": self.produces_structure,
+                "optional_dependency": self.optional_dependency,
+                "metadata": dict(self.metadata),
+            }
+        )
+
+
+@dataclass(frozen=True)
+class SourcePayload:
+    connector_name: str
+    source_uri: str
+    source_ref: SourceReference
+    media_type: str
+    content: bytes
+    title: str
+    metadata: dict[str, Any] = field(default_factory=dict)
+    permission_context: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def content_digest(self) -> str:
+        return content_digest(self.content)
+
+    @property
+    def size_bytes(self) -> int:
+        return len(self.content)
+
+    def read_text(self, encoding: str = "utf-8") -> str:
+        return self.content.decode(encoding)
+
+
+@dataclass(frozen=True)
+class NormalizedDocument:
+    text: str
+    media_type: str = "application/vnd.kontextual.normalized+json"
+    title: str | None = None
+    structure: dict[str, Any] = field(default_factory=dict)
+    tables: list[dict[str, Any]] = field(default_factory=list)
+    links: list[dict[str, Any]] = field(default_factory=list)
+    fields: dict[str, Any] = field(default_factory=dict)
+    confidence: float | None = None
+    unsupported_elements: list[dict[str, Any]] = field(default_factory=list)
+    extractor_metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def normalized_hash(self) -> str:
+        return mapping_digest(self.to_dict(include_hash=False))
+
+    def to_dict(self, *, include_hash: bool = True) -> dict[str, Any]:
+        data = compact_dict(
+            {
+                "title": self.title,
+                "text": self.text,
+                "media_type": self.media_type,
+                "structure": dict(self.structure),
+                "tables": list(self.tables),
+                "links": list(self.links),
+                "fields": dict(self.fields),
+                "confidence": self.confidence,
+                "unsupported_elements": list(self.unsupported_elements),
+                "extractor_metadata": dict(self.extractor_metadata),
+            }
+        )
+        if include_hash:
+            data["normalized_hash"] = self.normalized_hash
+        return data
+
+    def to_json(self) -> str:
+        return stable_json_dumps(self.to_dict())
+
+
+@dataclass(frozen=True)
+class ExtractionResult:
+    normalized: NormalizedDocument
+    metadata: dict[str, Any] = field(default_factory=dict)
+    diagnostics: tuple[IngestionFailure, ...] = ()
+
+    def to_dict(self) -> dict[str, Any]:
+        return compact_dict(
+            {
+                "normalized": self.normalized.to_dict(),
+                "metadata": dict(self.metadata),
+                "diagnostics": [diagnostic.to_dict() for diagnostic in self.diagnostics],
+            }
+        )
+
+
+@dataclass(frozen=True)
+class IngestionJob:
+    input: dict[str, Any]
+    actor_id: str
+    correlation_id: str
+    status: IngestionJobStatus = IngestionJobStatus.QUEUED
+    source_ref: SourceReference | None = None
+    output_asset_ids: tuple[str, ...] = ()
+    failures: tuple[IngestionFailure, ...] = ()
+    partial_results: dict[str, Any] = field(default_factory=dict)
+    retry_options: dict[str, Any] = field(default_factory=dict)
+    retry_of_job_id: str | None = None
+    attempts: int = 1
+    metadata: dict[str, Any] = field(default_factory=dict)
+    job_id: str = field(default_factory=lambda: new_id("ingest"))
+    created_at: str = field(default_factory=lambda: utc_now().isoformat())
+    updated_at: str = field(default_factory=lambda: utc_now().isoformat())
+    completed_at: str | None = None
+
+    @classmethod
+    def create(
+        cls,
+        *,
+        input: dict[str, Any],
+        actor_id: str,
+        correlation_id: str,
+        retry_of_job_id: str | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> "IngestionJob":
+        return cls(
+            input=dict(input),
+            actor_id=actor_id,
+            correlation_id=correlation_id,
+            retry_of_job_id=retry_of_job_id,
+            metadata=dict(metadata or {}),
+        )
+
+    def running(self, *, source_ref: SourceReference | None = None) -> "IngestionJob":
+        return replace(
+            self,
+            status=IngestionJobStatus.RUNNING,
+            source_ref=source_ref or self.source_ref,
+            updated_at=utc_now().isoformat(),
+        )
+
+    def completed(
+        self,
+        *,
+        output_asset_ids: tuple[str, ...],
+        partial_results: dict[str, Any] | None = None,
+    ) -> "IngestionJob":
+        now = utc_now().isoformat()
+        return replace(
+            self,
+            status=IngestionJobStatus.COMPLETED,
+            output_asset_ids=tuple(output_asset_ids),
+            partial_results=dict(partial_results or self.partial_results),
+            updated_at=now,
+            completed_at=now,
+        )
+
+    def failed(
+        self,
+        failure: IngestionFailure,
+        *,
+        status: IngestionJobStatus = IngestionJobStatus.FAILED,
+        partial_results: dict[str, Any] | None = None,
+    ) -> "IngestionJob":
+        now = utc_now().isoformat()
+        return replace(
+            self,
+            status=status,
+            failures=self.failures + (failure,),
+            partial_results=dict(partial_results or self.partial_results),
+            updated_at=now,
+            completed_at=now,
+        )
+
+    def partially_completed(
+        self,
+        *,
+        output_asset_ids: tuple[str, ...],
+        failures: tuple[IngestionFailure, ...],
+        partial_results: dict[str, Any],
+    ) -> "IngestionJob":
+        now = utc_now().isoformat()
+        return replace(
+            self,
+            status=IngestionJobStatus.PARTIALLY_COMPLETED,
+            output_asset_ids=tuple(output_asset_ids),
+            failures=tuple(failures),
+            partial_results=dict(partial_results),
+            updated_at=now,
+            completed_at=now,
+        )
+
+    def to_dict(self) -> dict[str, Any]:
+        return compact_dict(
+            {
+                "job_id": self.job_id,
+                "status": self.status.value,
+                "input": dict(self.input),
+                "actor_id": self.actor_id,
+                "correlation_id": self.correlation_id,
+                "source_ref": self.source_ref.to_dict() if self.source_ref else None,
+                "output_asset_ids": list(self.output_asset_ids),
+                "failures": [failure.to_dict() for failure in self.failures],
+                "partial_results": dict(self.partial_results),
+                "retry_options": dict(self.retry_options),
+                "retry_of_job_id": self.retry_of_job_id,
+                "attempts": self.attempts,
+                "metadata": dict(self.metadata),
+                "created_at": self.created_at,
+                "updated_at": self.updated_at,
+                "completed_at": self.completed_at,
+            }
+        )
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "IngestionJob":
+        source_ref = data.get("source_ref")
+        return cls(
+            job_id=data["job_id"],
+            status=IngestionJobStatus(data["status"]),
+            input=dict(data.get("input", {})),
+            actor_id=data["actor_id"],
+            correlation_id=data["correlation_id"],
+            source_ref=SourceReference.from_dict(source_ref) if source_ref else None,
+            output_asset_ids=tuple(data.get("output_asset_ids", [])),
+            failures=tuple(IngestionFailure.from_dict(item) for item in data.get("failures", [])),
+            partial_results=dict(data.get("partial_results", {})),
+            retry_options=dict(data.get("retry_options", {})),
+            retry_of_job_id=data.get("retry_of_job_id"),
+            attempts=int(data.get("attempts", 1)),
+            metadata=dict(data.get("metadata", {})),
+            created_at=data["created_at"],
+            updated_at=data["updated_at"],
+            completed_at=data.get("completed_at"),
+        )

src/kontextual_engine/ports/__init__.py

@@ -1,11 +1,14 @@
 """Stable ports owned by the engine."""
 
+from .ingestion import DirectorySourceConnector, FormatExtractor, SourceConnector
 from .policy import AllowAllPolicyGateway, PolicyGateway
 from .repositories import AssetRegistryRepository
 
 __all__ = [
     "AllowAllPolicyGateway",
     "AssetRegistryRepository",
+    "DirectorySourceConnector",
+    "FormatExtractor",
     "PolicyGateway",
+    "SourceConnector",
 ]
-

src/kontextual_engine/ports/ingestion.py

@@ -0,0 +1,34 @@
+"""Connector and extractor ports for ingestion."""
+
+from __future__ import annotations
+
+from typing import Protocol
+
+from kontextual_engine.core import (
+    ConnectorCapability,
+    ExtractionResult,
+    ExtractorCapability,
+    SourcePayload,
+)
+
+
+class SourceConnector(Protocol):
+    name: str
+
+    def capabilities(self) -> ConnectorCapability: ...
+
+    def fetch(self, source_uri: str) -> SourcePayload: ...
+
+
+class DirectorySourceConnector(SourceConnector, Protocol):
+    def iter_files(self, source_uri: str, *, recursive: bool = True) -> list[str]: ...
+
+
+class FormatExtractor(Protocol):
+    name: str
+
+    def capabilities(self) -> ExtractorCapability: ...
+
+    def supports(self, media_type: str) -> bool: ...
+
+    def extract(self, payload: SourcePayload) -> ExtractionResult: ...

src/kontextual_engine/ports/repositories.py

@@ -12,6 +12,8 @@ from kontextual_engine.core import (
     ContextEntity,
     CoreRelationship,
     IdempotencyRecord,
+    IngestionJob,
+    IngestionJobStatus,
     KnowledgeAsset,
     LifecycleState,
     MetadataRecord,
@@ -71,3 +73,11 @@ class AssetRegistryRepository(Protocol):
 
     def save_idempotency_record(self, record: IdempotencyRecord) -> IdempotencyRecord: ...
     def get_idempotency_record(self, key: str) -> IdempotencyRecord | None: ...
+
+    def save_ingestion_job(self, job: IngestionJob) -> IngestionJob: ...
+    def get_ingestion_job(self, job_id: str) -> IngestionJob: ...
+    def list_ingestion_jobs(
+        self,
+        *,
+        status: IngestionJobStatus | None = None,
+    ) -> list[IngestionJob]: ...

src/kontextual_engine/services/__init__.py

@@ -1,5 +1,12 @@
 """Application services for the engine."""
 
 from .asset_service import AssetChangeResult, AssetRegistryService, RelationshipChangeResult
+from .ingestion_service import AssetIngestionResult, AssetIngestionService
 
-__all__ = ["AssetChangeResult", "AssetRegistryService", "RelationshipChangeResult"]
+__all__ = [
+    "AssetChangeResult",
+    "AssetIngestionResult",
+    "AssetIngestionService",
+    "AssetRegistryService",
+    "RelationshipChangeResult",
+]

src/kontextual_engine/services/ingestion_service.py

@@ -0,0 +1,304 @@
+"""Application service for governed asset ingestion."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable
+
+from kontextual_engine.adapters.builtin_extractors import PlainTextExtractor
+from kontextual_engine.adapters.local_files import LocalFileConnector
+from kontextual_engine.adapters.markitect_tool import MarkitectMarkdownExtractor
+from kontextual_engine.core import (
+    AssetRepresentation,
+    Classification,
+    IngestionFailure,
+    IngestionJob,
+    IngestionJobStatus,
+    KnowledgeAsset,
+    MetadataRecord,
+    OperationContext,
+    RepresentationKind,
+    Sensitivity,
+    SourcePayload,
+    mapping_digest,
+)
+from kontextual_engine.errors import AdapterUnavailableError, KontextualError
+from kontextual_engine.ports import AssetRegistryRepository, DirectorySourceConnector, FormatExtractor, SourceConnector
+
+from .asset_service import AssetChangeResult, AssetRegistryService
+
+
+@dataclass(frozen=True)
+class AssetIngestionResult:
+    job: IngestionJob
+    asset: KnowledgeAsset | None = None
+    asset_change: AssetChangeResult | None = None
+
+
+class AssetIngestionService:
+    def __init__(
+        self,
+        repository: AssetRegistryRepository,
+        *,
+        asset_service: AssetRegistryService | None = None,
+        connectors: Iterable[SourceConnector] | None = None,
+        extractors: Iterable[FormatExtractor] | None = None,
+    ) -> None:
+        self.repository = repository
+        self.asset_service = asset_service or AssetRegistryService(repository)
+        self.connectors = {connector.name: connector for connector in (connectors or [LocalFileConnector()])}
+        self.extractors = list(extractors or [PlainTextExtractor(), MarkitectMarkdownExtractor()])
+
+    def connector_capabilities(self) -> list[dict]:
+        return [connector.capabilities().to_dict() for connector in self.connectors.values()]
+
+    def extractor_capabilities(self) -> list[dict]:
+        return [extractor.capabilities().to_dict() for extractor in self.extractors]
+
+    def ingest_file(
+        self,
+        path: str | Path,
+        context: OperationContext,
+        *,
+        asset_id: str | None = None,
+        title: str | None = None,
+        classification: Classification | None = None,
+        idempotency_key: str | None = None,
+    ) -> AssetIngestionResult:
+        connector = self._connector("local_file")
+        job = IngestionJob.create(
+            input={"connector": connector.name, "source_uri": str(path), "mode": "file"},
+            actor_id=context.actor.id,
+            correlation_id=context.correlation_id,
+        )
+        self.repository.save_ingestion_job(job)
+        try:
+            payload = connector.fetch(str(path))
+            return self._ingest_payload(
+                job,
+                payload,
+                context,
+                asset_id=asset_id,
+                title=title,
+                classification=classification,
+                idempotency_key=idempotency_key,
+            )
+        except Exception as exc:
+            failed = job.failed(_failure_from_exception(exc))
+            self.repository.save_ingestion_job(failed)
+            return AssetIngestionResult(failed)
+
+    def ingest_directory(
+        self,
+        path: str | Path,
+        context: OperationContext,
+        *,
+        recursive: bool = True,
+        classification: Classification | None = None,
+    ) -> IngestionJob:
+        connector = self._directory_connector("local_file")
+        job = IngestionJob.create(
+            input={
+                "connector": connector.name,
+                "source_uri": str(path),
+                "mode": "directory",
+                "recursive": recursive,
+            },
+            actor_id=context.actor.id,
+            correlation_id=context.correlation_id,
+        )
+        job = job.running()
+        self.repository.save_ingestion_job(job)
+
+        output_asset_ids: list[str] = []
+        failures: list[IngestionFailure] = []
+        item_results: list[dict] = []
+        files = connector.iter_files(str(path), recursive=recursive)
+        for source_uri in files:
+            result = self.ingest_file(source_uri, context, classification=classification)
+            item = {
+                "source_uri": source_uri,
+                "job_id": result.job.job_id,
+                "status": result.job.status.value,
+            }
+            if result.asset is not None:
+                output_asset_ids.append(result.asset.id)
+                item["asset_id"] = result.asset.id
+            if result.job.failures:
+                failures.extend(result.job.failures)
+                item["failures"] = [failure.to_dict() for failure in result.job.failures]
+            item_results.append(item)
+
+        partial_results = {
+            "files_total": len(files),
+            "succeeded": sum(1 for item in item_results if item["status"] == IngestionJobStatus.COMPLETED.value),
+            "failed": sum(1 for item in item_results if item["status"] == IngestionJobStatus.FAILED.value),
+            "quarantined": sum(1 for item in item_results if item["status"] == IngestionJobStatus.QUARANTINED.value),
+            "skipped": 0,
+            "items": item_results,
+        }
+        if failures and output_asset_ids:
+            job = job.partially_completed(
+                output_asset_ids=tuple(output_asset_ids),
+                failures=tuple(failures),
+                partial_results=partial_results,
+            )
+        elif failures:
+            job = job.failed(
+                IngestionFailure(
+                    code="ingestion.directory_failed",
+                    message="Directory ingestion failed for all files",
+                    retriable=True,
+                    details=partial_results,
+                ),
+                partial_results=partial_results,
+            )
+        else:
+            job = job.completed(output_asset_ids=tuple(output_asset_ids), partial_results=partial_results)
+        self.repository.save_ingestion_job(job)
+        return job
+
+    def get_job(self, job_id: str) -> IngestionJob:
+        return self.repository.get_ingestion_job(job_id)
+
+    def list_jobs(self, *, status: IngestionJobStatus | None = None) -> list[IngestionJob]:
+        return self.repository.list_ingestion_jobs(status=status)
+
+    def _ingest_payload(
+        self,
+        job: IngestionJob,
+        payload: SourcePayload,
+        context: OperationContext,
+        *,
+        asset_id: str | None,
+        title: str | None,
+        classification: Classification | None,
+        idempotency_key: str | None,
+    ) -> AssetIngestionResult:
+        job = job.running(source_ref=payload.source_ref)
+        self.repository.save_ingestion_job(job)
+        extractor = self._extractor(payload.media_type)
+        extraction = extractor.extract(payload)
+        resolved_asset_id = asset_id or _stable_asset_id(payload)
+        source_representation = AssetRepresentation.from_content(
+            resolved_asset_id,
+            RepresentationKind.SOURCE,
+            payload.media_type,
+            payload.content,
+            storage_ref=payload.source_uri,
+            producer=payload.connector_name,
+            source_ref_id=payload.source_ref.id,
+            metadata={
+                "connector": payload.connector_name,
+                "source_digest": payload.content_digest,
+                "source_size_bytes": payload.size_bytes,
+                **payload.metadata,
+            },
+        )
+        normalized_representation = AssetRepresentation.from_content(
+            resolved_asset_id,
+            RepresentationKind.NORMALIZED,
+            extraction.normalized.media_type,
+            extraction.normalized.to_json(),
+            producer=extractor.name,
+            source_ref_id=payload.source_ref.id,
+            metadata={
+                "extractor": extractor.name,
+                "normalized_hash": extraction.normalized.normalized_hash,
+                **extraction.metadata,
+            },
+        )
+        asset_change = self.asset_service.create_asset(
+            title or payload.title,
+            classification or Classification(asset_type="document", sensitivity=Sensitivity.INTERNAL),
+            context,
+            asset_id=resolved_asset_id,
+            source_refs=[payload.source_ref],
+            representations=[source_representation, normalized_representation],
+            metadata_records=_metadata_records(payload, extractor.name, extraction.metadata),
+            idempotency_key=idempotency_key,
+        )
+        completed = job.completed(
+            output_asset_ids=(asset_change.asset.id,),
+            partial_results={
+                "connector": payload.connector_name,
+                "extractor": extractor.name,
+                "source_digest": payload.content_digest,
+                "representations": [
+                    source_representation.representation_id,
+                    normalized_representation.representation_id,
+                ],
+                "diagnostics": [diagnostic.to_dict() for diagnostic in extraction.diagnostics],
+            },
+        )
+        self.repository.save_ingestion_job(completed)
+        return AssetIngestionResult(completed, asset_change.asset, asset_change)
+
+    def _connector(self, name: str) -> SourceConnector:
+        try:
+            return self.connectors[name]
+        except KeyError as exc:
+            raise AdapterUnavailableError("Source connector is not registered", details={"connector": name}) from exc
+
+    def _directory_connector(self, name: str) -> DirectorySourceConnector:
+        connector = self._connector(name)
+        if not hasattr(connector, "iter_files"):
+            raise AdapterUnavailableError(
+                "Source connector does not support directory iteration",
+                details={"connector": name},
+            )
+        return connector  # type: ignore[return-value]
+
+    def _extractor(self, media_type: str) -> FormatExtractor:
+        for extractor in self.extractors:
+            if extractor.supports(media_type):
+                return extractor
+        raise AdapterUnavailableError(
+            "No extractor registered for media type",
+            details={"media_type": media_type},
+        )
+
+
+def _stable_asset_id(payload: SourcePayload) -> str:
+    digest = mapping_digest(
+        {
+            "source_system": payload.source_ref.source_system,
+            "path": payload.source_ref.path,
+            "uri": payload.source_ref.uri,
+            "external_id": payload.source_ref.external_id,
+            "connector_ref": payload.source_ref.connector_ref,
+        }
+    )
+    return f"asset-{digest.removeprefix('sha256:')[:20]}"
+
+
+def _metadata_records(
+    payload: SourcePayload,
+    extractor_name: str,
+    extraction_metadata: dict,
+) -> list[MetadataRecord]:
+    return [
+        MetadataRecord("source_media_type", payload.media_type, provenance={"producer": payload.connector_name}),
+        MetadataRecord("source_digest", payload.content_digest, provenance={"producer": payload.connector_name}),
+        MetadataRecord("source_size_bytes", payload.size_bytes, provenance={"producer": payload.connector_name}),
+        MetadataRecord("connector", payload.connector_name, provenance={"producer": payload.connector_name}, confirmed=True),
+        MetadataRecord("extractor", extractor_name, provenance={"producer": extractor_name}, confirmed=True),
+        MetadataRecord("extraction", dict(extraction_metadata), provenance={"producer": extractor_name}),
+    ]
+
+
+def _failure_from_exception(exc: Exception) -> IngestionFailure:
+    if isinstance(exc, KontextualError):
+        return IngestionFailure(
+            code=exc.code,
+            message=str(exc),
+            retriable=isinstance(exc, AdapterUnavailableError),
+            details=dict(exc.details),
+        )
+    return IngestionFailure(
+        code="ingestion.unexpected",
+        message=str(exc),
+        retriable=False,
+        details={"exception_type": type(exc).__name__},
+    )

tests/test_asset_ingestion_service.py

@@ -0,0 +1,108 @@
+from pathlib import Path
+
+from kontextual_engine import (
+    Actor,
+    ActorType,
+    AssetIngestionService,
+    Classification,
+    IngestionJobStatus,
+    InMemoryAssetRegistryRepository,
+    LifecycleState,
+    OperationContext,
+    RepresentationKind,
+    Sensitivity,
+    SQLiteAssetRegistryRepository,
+)
+
+
+def test_asset_ingestion_service_ingests_plain_text_file_as_governed_asset(tmp_path: Path) -> None:
+    source = tmp_path / "note.txt"
+    source.write_text("hello\nworld\n", encoding="utf-8")
+    repo = InMemoryAssetRegistryRepository()
+    service = AssetIngestionService(repo)
+
+    result = service.ingest_file(
+        source,
+        operation_context(),
+        asset_id="asset-note",
+        classification=Classification(asset_type="note", sensitivity=Sensitivity.INTERNAL),
+    )
+
+    assert result.job.status == IngestionJobStatus.COMPLETED
+    assert result.job.correlation_id == "corr-ingest"
+    assert result.job.output_asset_ids == ("asset-note",)
+    assert result.asset is not None
+    assert result.asset.source_refs[0].source_system == "local_file"
+    assert result.asset.source_refs[0].path == str(source)
+    assert repo.get_ingestion_job(result.job.job_id).status == IngestionJobStatus.COMPLETED
+    assert {item.kind for item in repo.list_representations(asset_id="asset-note")} == {
+        RepresentationKind.SOURCE,
+        RepresentationKind.NORMALIZED,
+    }
+    normalized = repo.list_representations(asset_id="asset-note", kind=RepresentationKind.NORMALIZED)[0]
+    assert normalized.media_type == "application/vnd.kontextual.normalized+json"
+    assert normalized.metadata["extractor"] == "plain-text"
+    assert repo.list_audit_events(target="asset:asset-note")[0].operation == "asset.create"
+
+
+def test_ingestion_failure_records_job_without_trusting_unsupported_asset(tmp_path: Path) -> None:
+    source = tmp_path / "blob.bin"
+    source.write_bytes(b"\x00\x01\x02")
+    repo = InMemoryAssetRegistryRepository()
+    service = AssetIngestionService(repo)
+
+    result = service.ingest_file(source, operation_context(), asset_id="asset-blob")
+
+    assert result.asset is None
+    assert result.job.status == IngestionJobStatus.FAILED
+    assert result.job.failures[0].code == "kontextual.adapter_unavailable"
+    assert result.job.failures[0].details["media_type"] == "application/octet-stream"
+    assert repo.list_assets() == []
+
+
+def test_directory_ingestion_reports_partial_results(tmp_path: Path) -> None:
+    (tmp_path / "one.txt").write_text("one", encoding="utf-8")
+    (tmp_path / "two.bin").write_bytes(b"\x00\x01")
+    repo = InMemoryAssetRegistryRepository()
+    service = AssetIngestionService(repo)
+
+    job = service.ingest_directory(tmp_path, operation_context(), recursive=False)
+
+    assert job.status == IngestionJobStatus.PARTIALLY_COMPLETED
+    assert job.partial_results["files_total"] == 2
+    assert job.partial_results["succeeded"] == 1
+    assert job.partial_results["failed"] == 1
+    assert len(job.output_asset_ids) == 1
+    assert len(job.failures) == 1
+
+
+def test_sqlite_ingestion_jobs_survive_reinstantiation(tmp_path: Path) -> None:
+    source = tmp_path / "policy.txt"
+    source.write_text("governed ingestion", encoding="utf-8")
+    db_path = tmp_path / "registry.sqlite"
+    repo = SQLiteAssetRegistryRepository(db_path)
+    service = AssetIngestionService(repo)
+
+    result = service.ingest_file(
+        source,
+        operation_context(),
+        asset_id="asset-policy",
+    )
+
+    reloaded = SQLiteAssetRegistryRepository(db_path)
+    job = reloaded.get_ingestion_job(result.job.job_id)
+
+    assert job.status == IngestionJobStatus.COMPLETED
+    assert job.output_asset_ids == ("asset-policy",)
+    assert reloaded.get_asset("asset-policy").lifecycle == LifecycleState.ACTIVE
+    assert len(reloaded.list_representations(asset_id="asset-policy")) == 2
+
+
+def operation_context() -> OperationContext:
+    actor = Actor.create(
+        ActorType.HUMAN,
+        actor_id="user-ingest",
+        display_name="Ingestion Tester",
+        groups=["engineering"],
+    )
+    return OperationContext.create(actor, correlation_id="corr-ingest")

workplans/KONT-WP-0006-multi-format-ingestion-normalization.md

@@ -4,13 +4,13 @@ type: workplan
 title: "Multi-Format Ingestion And Normalization"
 domain: markitect
 repo: kontextual-engine
-status: todo
+status: active
 owner: codex
 topic_slug: markitect
 planning_priority: high
 planning_order: 6
 created: "2026-05-05"
-updated: "2026-05-05"
+updated: "2026-05-06"
 state_hub_workstream_id: "270c83c0-eaed-4143-99d0-bb3fcfd23758"
 ---
 
@@ -45,11 +45,21 @@ needed, and snapshot identity. The engine should normalize Markitect results
 into its common representation and preserve source/adapter provenance rather
 than rebuilding Markdown syntax behavior.
 
+## Implementation Status
+
+As of 2026-05-06, the first ingestion slice is recorded in
+`docs/ingestion-implementation.md`. It establishes ingestion job primitives,
+connector/extractor ports, local file ingestion, plain text normalization,
+Markitect markdown adapter boundaries, directory partial-result reporting, and
+in-memory/SQLite job persistence. Remaining work is focused on async execution,
+re-ingestion identity reconciliation, richer structural extraction, quarantine
+policy checks, and non-text format adapters.
+
 ## I6.1 - Implement ingestion job model status and retry surface
 
 ```task
 id: KONT-WP-0006-T001
-status: todo
+status: done
 priority: high
 state_hub_task_id: "8e5e514a-6eef-42d9-a93c-2458b4c82753"
 ```
@@ -68,7 +78,7 @@ Acceptance:
 
 ```task
 id: KONT-WP-0006-T002
-status: todo
+status: done
 priority: high
 state_hub_task_id: "3eafdab5-478d-49d9-a17f-3cd7c8847cb1"
 ```
@@ -87,7 +97,7 @@ Acceptance:
 
 ```task
 id: KONT-WP-0006-T003
-status: todo
+status: in_progress
 priority: high
 state_hub_task_id: "d3e3d4d2-a581-4438-bee7-6fc4161d3925"
 ```
@@ -105,7 +115,7 @@ Acceptance:
 
 ```task
 id: KONT-WP-0006-T004
-status: todo
+status: in_progress
 priority: high
 state_hub_task_id: "63bf2f7e-705d-40ae-a160-75fc508ffb1f"
 ```