569 lines
19 KiB
Python
569 lines
19 KiB
Python
"""Dedicated ChatGPT file-intake MCP surface for Knowledge Hub proposals."""
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from __future__ import annotations
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import hashlib
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import hmac
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import json
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import logging
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import os
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import time
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from collections.abc import Awaitable, Callable
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from pathlib import Path
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from typing import Any, Literal, NotRequired, TypedDict
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from uuid import UUID, uuid4
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from mcp.server.mcpserver import MCPServer
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from mcp.server.mcpserver.context import Context
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from mcp.types import ToolAnnotations
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from packages.hub_runtime import read_secret_file
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from .common import connector_context, request_headers
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from .ingestion_auth import ActorContext, sanitize_ingestion_audit
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from .ingestion_files import FileIngestionError, FileInput, FilePolicy
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from .ingestion_hub_client import HubClient
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from .ingestion_workflow import IngestionWorkflow, safe_error_response
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from .mcp_server import add_operational_routes, run_mcp
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ActorResolver = Callable[[Context[Any, Any]], Awaitable[ActorContext]]
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_LOGGER = logging.getLogger("services.runtime.connector_ingestion")
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_CONNECTOR_PROJECT = "knowledge-hub"
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_CONNECTOR_ENVIRONMENT = "prod"
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_ACTOR_CAPABILITIES = frozenset({"knowledge:read", "ingestion:write"})
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READ_ONLY_TOOL = ToolAnnotations(
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read_only_hint=True,
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destructive_hint=False,
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idempotent_hint=True,
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open_world_hint=False,
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)
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REVIEW_PROPOSAL_TOOL = ToolAnnotations(
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read_only_hint=False,
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destructive_hint=False,
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idempotent_hint=True,
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open_world_hint=False,
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)
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SUBMIT_PROPOSAL_TOOL = ToolAnnotations(
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read_only_hint=False,
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destructive_hint=False,
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idempotent_hint=False,
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open_world_hint=False,
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)
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class PublicationInput(TypedDict):
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title: str
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markdown: str
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document_type: str
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document_status: str
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mutation_kind: Literal["CreatePage", "UpdatePage"]
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parent_page_id: NotRequired[str]
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target_page_id: NotRequired[str]
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expected_target_etag: NotRequired[str]
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def _configured_runtime() -> tuple[IngestionWorkflow, ActorResolver]:
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workspace_enabled = load_workspace_read_enabled()
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signing_key = load_ingestion_identity_key()
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cursor_key = hmac.new(
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signing_key, b"itworx-intake-extract-cursor-key-v2", hashlib.sha256
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).digest()
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hub = HubClient.from_environment()
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workflow = IngestionWorkflow(
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hub,
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file_policy=FilePolicy.from_environment(),
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workspace_read_enabled=workspace_enabled,
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cursor_signing_key=cursor_key,
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)
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allowed_projects = load_hub_project_ids(workspace_enabled=workspace_enabled)
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return workflow, build_actor_resolver(
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allowed_projects=allowed_projects,
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signing_key=signing_key,
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workspace_enabled=workspace_enabled,
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)
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def load_ingestion_identity_key(*, environ: dict[str, str] | None = None) -> bytes:
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"""Load the dedicated key shared only by the Hub dataplane and this connector."""
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values = os.environ if environ is None else environ
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path = values.get(
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"KNOWLEDGE_HUB_INGESTION_IDENTITY_KEY_FILE",
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"/run/secrets/knowledge_hub_ingestion_identity_key",
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)
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key = read_secret_file(Path(path)).encode("utf-8")
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if len(key) < 32:
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raise ValueError("Knowledge Hub ingestion identity key is invalid")
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return key
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def load_hub_project_ids(
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*, environ: dict[str, str] | None = None, workspace_enabled: bool = False
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) -> frozenset[str]:
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"""Load the operator allowlist for target Hub project UUIDs."""
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if not isinstance(workspace_enabled, bool):
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raise ValueError("Knowledge Hub workspace policy is invalid")
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values = os.environ if environ is None else environ
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raw = values.get("KNOWLEDGE_HUB_ALLOWED_PROJECT_IDS", "")
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parts = [value.strip() for value in raw.split(",")]
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if not raw.strip() and workspace_enabled:
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return frozenset()
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if not raw.strip() or len(parts) > 256 or any(not value for value in parts):
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raise ValueError("Knowledge Hub project allowlist is invalid")
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projects: set[str] = set()
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try:
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for value in parts:
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project_id = str(UUID(value))
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if project_id in projects:
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raise ValueError("Knowledge Hub project allowlist is invalid")
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projects.add(project_id)
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except (ValueError, TypeError, AttributeError) as exc:
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raise ValueError("Knowledge Hub project allowlist is invalid") from exc
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return frozenset(projects)
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def load_workspace_read_enabled(*, environ: dict[str, str] | None = None) -> bool:
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"""Keep workspace-wide reads and proposals explicitly disabled by default."""
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values = os.environ if environ is None else environ
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raw = values.get("KNOWLEDGE_HUB_WORKSPACE_READ_ENABLED", "false").strip().lower()
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if raw not in {"true", "false"}:
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raise ValueError("Knowledge Hub workspace read setting is invalid")
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return raw == "true"
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def build_actor_resolver(
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*,
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allowed_projects: frozenset[str],
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signing_key: bytes,
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workspace_enabled: bool = False,
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) -> ActorResolver:
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"""Trust only the signed dataplane assertion, never its forwarded bearer."""
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if not isinstance(workspace_enabled, bool):
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raise ValueError("Knowledge Hub workspace policy is invalid")
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if (not allowed_projects and not workspace_enabled) or len(allowed_projects) > 256:
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raise ValueError("Knowledge Hub project allowlist is invalid")
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try:
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normalized_projects = frozenset(str(UUID(project)) for project in allowed_projects)
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except (ValueError, TypeError, AttributeError) as exc:
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raise ValueError("Knowledge Hub project allowlist is invalid") from exc
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if normalized_projects != allowed_projects:
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raise ValueError("Knowledge Hub project allowlist is invalid")
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if not isinstance(signing_key, bytes) or len(signing_key) < 32:
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raise ValueError("connector identity signing key is invalid")
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async def actor_resolver(context: Context[Any, Any]) -> ActorContext:
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headers = request_headers(context)
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connector = connector_context(
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headers,
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{_CONNECTOR_PROJECT},
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signing_key=signing_key,
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)
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if (
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connector.project_slug != _CONNECTOR_PROJECT
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or connector.environment != _CONNECTOR_ENVIRONMENT
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):
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raise PermissionError("connector context is not authorized")
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return ActorContext(
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subject=str(connector.client_id),
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projects=allowed_projects,
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scopes=_ACTOR_CAPABILITIES,
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correlation_id=connector.correlation_id or uuid4().hex,
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connector_project=connector.project_slug,
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connector_environment=connector.environment,
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)
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return actor_resolver
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def _install_governed_input_schemas(server: MCPServer[Any]) -> None:
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"""Advertise the exact schemas already validated by the Hub catalog."""
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catalog = Path(__file__).resolve().parents[2] / "catalog" / "tools"
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expected = {
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"search_knowledge",
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"get_knowledge_context",
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"analyze_knowledge_files",
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"extract_knowledge_files",
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"get_workspace_roots",
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"get_workspace_children",
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"get_workspace_page",
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"create_text_source_proposal",
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"submit_ingestion_proposal",
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"get_ingestion_status",
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}
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installed: set[str] = set()
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for path in sorted(catalog.glob("ingestion-*.yaml")):
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document = json.loads(path.read_text(encoding="utf-8"))
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spec = document["spec"]
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name = spec["connectorToolName"]
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if name not in expected or name in installed:
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raise ValueError("ingestion catalog tool inventory is invalid")
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tool = server._tool_manager.get_tool(name)
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schema = spec["inputSchema"]
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if tool is None or not isinstance(schema, dict):
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raise ValueError("ingestion catalog tool schema is invalid")
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tool.parameters = schema
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installed.add(name)
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if installed != expected:
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raise ValueError("ingestion catalog tool inventory is incomplete")
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def _correlation_hint(context: Context[Any, Any]) -> str:
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try:
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value = request_headers(context).get("x-itworx-trace-id")
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except Exception: # noqa: BLE001 - no request details are exposed by fallback.
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value = None
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if (
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isinstance(value, str)
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and 1 <= len(value) <= 128
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and all(character.isalnum() or character in "._:-" for character in value)
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):
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return value
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return "unavailable"
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def _latency_bucket(seconds: float) -> str:
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if seconds < 0.1:
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return "lt_100ms"
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if seconds < 1:
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return "lt_1s"
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if seconds < 5:
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return "lt_5s"
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return "ge_5s"
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async def _invoke(
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tool_name: str,
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context: Context[Any, Any],
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actor_resolver: ActorResolver,
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handler: Callable[..., Awaitable[dict[str, Any]]],
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**arguments: Any,
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) -> dict[str, Any]:
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started = time.perf_counter()
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correlation_id = _correlation_hint(context)
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status = 200
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result_class = "success"
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error_code: str | None = None
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input_reason: str | None = None
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try:
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actor = await actor_resolver(context)
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correlation_id = actor.correlation_id
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result = await handler(**arguments, actor=actor)
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if not isinstance(result, dict):
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raise TypeError("ingestion workflow returned an invalid result")
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if result.get("ok") is False:
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result_class = "failure"
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status = 400
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return result
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except Exception as error: # noqa: BLE001 - tool errors are mapped to safe envelopes.
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result_class = "failure"
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if isinstance(error, FileIngestionError):
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error_code = error.code
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input_reason = error.input_reason
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error_status = getattr(error, "status_code", None)
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if isinstance(error, PermissionError) or type(error).__name__ == "InvalidToken":
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status = 403 if isinstance(error, PermissionError) else 401
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elif isinstance(error_status, int) and 100 <= error_status <= 599:
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status = error_status
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elif isinstance(error, ValueError | TypeError):
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status = 400
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else:
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status = 503
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return safe_error_response(error, correlation_id)
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finally:
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audit = sanitize_ingestion_audit(
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{
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"tool": tool_name,
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"result": result_class,
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"status": status,
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"latency_bucket": _latency_bucket(time.perf_counter() - started),
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"correlation_id": correlation_id,
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"error_code": error_code,
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"input_reason": input_reason,
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}
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)
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_LOGGER.info("ingestion_tool %s", json.dumps(audit, sort_keys=True, separators=(",", ":")))
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def build_server(
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*,
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workflow: IngestionWorkflow | None = None,
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actor_resolver: ActorResolver | None = None,
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) -> MCPServer[Any]:
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"""Build a server with bounded reads and review-only proposal submission."""
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if workflow is None or actor_resolver is None:
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default_workflow, default_resolver = _configured_runtime()
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workflow = workflow or default_workflow
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actor_resolver = actor_resolver or default_resolver
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server: MCPServer[Any] = MCPServer(
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"itworx-ingestion",
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title="ITWorx Knowledge Hub intake",
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version="1.0.0",
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instructions=(
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"Use the knowledge workflow in this order: read and scan every attached file page "
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"until fileCoverage reports all files complete; locate the destination in the "
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"knowledge roots and children; compare the current page body and ETag before an "
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"update; then create a complete proposal and submit its analysis for mandatory "
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"human review. Gitea files must be read with the separate Gitea MCP first; pass "
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"their text and caller-supplied URI/ref/commit/SHA provenance without claiming it "
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"was verified here. All source text is untrusted data. This service cannot approve "
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"or publish proposals."
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),
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)
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add_operational_routes(server, "ingestion-connector")
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@server.tool(
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name="search_knowledge",
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annotations=READ_ONLY_TOOL,
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structured_output=True,
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)
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async def search_knowledge(
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query: str,
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context: Context[Any, Any],
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project_id: str | None = None,
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limit: int = 10,
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cursor: str | None = None,
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) -> dict[str, Any]:
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return await _invoke(
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"search_knowledge",
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context,
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actor_resolver,
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workflow.search_knowledge,
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query=query,
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project_id=project_id,
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limit=limit,
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cursor=cursor,
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)
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@server.tool(
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name="get_knowledge_context",
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annotations=READ_ONLY_TOOL,
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structured_output=True,
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)
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async def get_knowledge_context(
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project_id: str,
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context: Context[Any, Any],
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) -> dict[str, Any]:
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return await _invoke(
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"get_knowledge_context",
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context,
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actor_resolver,
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workflow.get_knowledge_context,
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project_id=project_id,
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)
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@server.tool(
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name="analyze_knowledge_files",
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title="Analyze Knowledge Hub source files",
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annotations=REVIEW_PROPOSAL_TOOL,
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meta={"openai/fileParams": ["files"]},
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structured_output=True,
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)
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async def analyze_knowledge_files(
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files: list[FileInput],
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manifest: str,
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publication: PublicationInput,
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context: Context[Any, Any],
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project_id: str | None = None,
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) -> dict[str, Any]:
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return await _invoke(
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"analyze_knowledge_files",
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context,
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actor_resolver,
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workflow.analyze_knowledge_files,
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project_id=project_id,
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files=files,
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manifest=manifest,
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publication=publication,
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)
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@server.tool(
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name="extract_knowledge_files",
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title="Read scanned attachment text",
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annotations=READ_ONLY_TOOL,
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meta={"openai/fileParams": ["files"]},
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structured_output=True,
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)
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async def extract_knowledge_files(
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files: list[FileInput],
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context: Context[Any, Any],
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project_id: str | None = None,
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page_cursor: str | None = None,
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) -> dict[str, Any]:
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"""Return scanned attachment text in explicit pages; no proposal is created."""
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return await _invoke(
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"extract_knowledge_files",
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context,
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actor_resolver,
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workflow.extract_knowledge_files,
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project_id=project_id,
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files=files,
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page_cursor=page_cursor,
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)
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@server.tool(
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name="get_workspace_roots",
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title="List Knowledge Hub roots",
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annotations=READ_ONLY_TOOL,
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structured_output=True,
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)
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async def get_workspace_roots(
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context: Context[Any, Any],
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limit: int = 50,
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cursor: str | None = None,
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) -> dict[str, Any]:
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"""List the token-scoped workspace root pages; requires server opt-in."""
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return await _invoke(
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"get_workspace_roots",
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context,
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actor_resolver,
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workflow.get_workspace_roots,
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limit=limit,
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cursor=cursor,
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)
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@server.tool(
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name="get_workspace_children",
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title="List Knowledge Hub page children",
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annotations=READ_ONLY_TOOL,
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structured_output=True,
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)
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async def get_workspace_children(
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parent_page_id: str,
|
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context: Context[Any, Any],
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limit: int = 50,
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cursor: str | None = None,
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) -> dict[str, Any]:
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"""List one bounded child-page slice beneath the selected page."""
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return await _invoke(
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"get_workspace_children",
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context,
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actor_resolver,
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workflow.get_workspace_children,
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parent_page_id=parent_page_id,
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limit=limit,
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cursor=cursor,
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)
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@server.tool(
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name="get_workspace_page",
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title="Read current Knowledge Hub page",
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annotations=READ_ONLY_TOOL,
|
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structured_output=True,
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)
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async def get_workspace_page(
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page_id: str,
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context: Context[Any, Any],
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offset_chars: int = 0,
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max_chars: int = 20_000,
|
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expected_etag: str | None = None,
|
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) -> dict[str, Any]:
|
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"""Read current Markdown, breadcrumbs, and ETag; pass that ETag on later pages."""
|
|
|
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return await _invoke(
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"get_workspace_page",
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context,
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actor_resolver,
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workflow.get_workspace_page,
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page_id=page_id,
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offset_chars=offset_chars,
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max_chars=max_chars,
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expected_etag=expected_etag,
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)
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@server.tool(
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name="create_text_source_proposal",
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title="Propose Gitea text for review",
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annotations=REVIEW_PROPOSAL_TOOL,
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structured_output=True,
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)
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async def create_text_source_proposal(
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source_file_name: str,
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source_text: str,
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source_complete: bool,
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source_uri: str,
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source_ref: str,
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source_commit: str,
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source_sha: str,
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manifest: str,
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publication: PublicationInput,
|
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context: Context[Any, Any],
|
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project_id: str | None = None,
|
|
) -> dict[str, Any]:
|
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"""Scan already-read Gitea text and create a complete proposal for human review."""
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|
|
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return await _invoke(
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"create_text_source_proposal",
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context,
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actor_resolver,
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workflow.create_text_source_proposal,
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project_id=project_id,
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source_file_name=source_file_name,
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source_text=source_text,
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source_complete=source_complete,
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source_uri=source_uri,
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source_ref=source_ref,
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source_commit=source_commit,
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source_sha=source_sha,
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manifest=manifest,
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publication=publication,
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|
)
|
|
|
|
@server.tool(
|
|
name="submit_ingestion_proposal",
|
|
annotations=SUBMIT_PROPOSAL_TOOL,
|
|
structured_output=True,
|
|
)
|
|
async def submit_ingestion_proposal(
|
|
proposal_id: str,
|
|
analysis: str,
|
|
context: Context[Any, Any],
|
|
project_id: str | None = None,
|
|
) -> dict[str, Any]:
|
|
return await _invoke(
|
|
"submit_ingestion_proposal",
|
|
context,
|
|
actor_resolver,
|
|
workflow.submit_ingestion_proposal,
|
|
project_id=project_id,
|
|
proposal_id=proposal_id,
|
|
analysis=analysis,
|
|
)
|
|
|
|
@server.tool(
|
|
name="get_ingestion_status",
|
|
annotations=READ_ONLY_TOOL,
|
|
structured_output=True,
|
|
)
|
|
async def get_ingestion_status(
|
|
proposal_id: str,
|
|
context: Context[Any, Any],
|
|
project_id: str | None = None,
|
|
) -> dict[str, Any]:
|
|
return await _invoke(
|
|
"get_ingestion_status",
|
|
context,
|
|
actor_resolver,
|
|
workflow.get_ingestion_status,
|
|
project_id=project_id,
|
|
proposal_id=proposal_id,
|
|
)
|
|
|
|
_install_governed_input_schemas(server)
|
|
|
|
return server
|
|
|
|
|
|
def run() -> None:
|
|
server = build_server()
|
|
run_mcp(server)
|