feat(prompts): implement Phase 1 - Foundation (FR-1)
Implement addressable artifacts with content-based identity and change detection. Core Features: - Artifact model with SHA-256 content digests - ArtifactReference for cross-space addressing - IArtifactRepository interface for pluggable storage - SQLiteArtifactRepository implementation - ArtifactService for high-level operations - Content digest calculation utilities Database: - prompt_artifacts table with indexes - Support for artifact metadata and types - UNIQUE constraint on space_id+name Tests (41 passing): - 26 model tests (metadata, artifacts, references, digests) - 15 repository tests (CRUD, queries, constraints) Implements: - FR-1.1: Unique addressability by name and ID - FR-1.2: Content digest computation and storage - FR-1.3: Cross-space artifact references Files Created: - markitect/prompts/models.py - markitect/prompts/repositories/interfaces.py - markitect/prompts/repositories/sqlite.py - markitect/prompts/services/artifact_service.py - migrations/prompts/001_create_artifacts_table.sql - tests/unit/prompts/test_artifact_models.py - tests/unit/prompts/test_artifact_repository.py Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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markitect/prompts/models.py
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279
markitect/prompts/models.py
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"""
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Core domain models for Prompt Dependency Resolution.
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This module provides foundational data models for addressable artifacts,
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references, and content hashing for change detection.
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"""
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import hashlib
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import uuid
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from dataclasses import dataclass, field
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from datetime import datetime
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from typing import Dict, Any, Optional
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from enum import Enum
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class ArtifactType(Enum):
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"""Type classification for artifacts."""
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CONTENT = "content" # Regular content artifact
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TEMPLATE = "template" # Prompt template
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GENERATED = "generated" # LLM-generated artifact
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SCHEMA = "schema" # Validation schema
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CONFIG = "config" # Configuration artifact
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@dataclass
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class ArtifactMetadata:
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"""
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Extensible metadata for artifacts.
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Attributes:
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description: Human-readable description
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tags: List of tags for categorization
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author: Author identifier
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version: Version string (optional)
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custom: Dictionary for custom metadata fields
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"""
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description: Optional[str] = None
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tags: list[str] = field(default_factory=list)
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author: Optional[str] = None
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version: Optional[str] = None
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custom: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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"""Convert metadata to dictionary for serialization."""
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return {
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"description": self.description,
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"tags": self.tags,
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"author": self.author,
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"version": self.version,
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"custom": self.custom,
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}
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "ArtifactMetadata":
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"""Create metadata from dictionary."""
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return cls(
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description=data.get("description"),
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tags=data.get("tags", []),
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author=data.get("author"),
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version=data.get("version"),
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custom=data.get("custom", {}),
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)
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@dataclass
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class Artifact:
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"""
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Core artifact entity with content-based addressing.
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Implements FR-1: InformationSpace Addressability
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- Unique persistent identifier
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- Content digest for change detection
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- Cross-space references via space_id
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Attributes:
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id: Unique identifier (UUID)
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space_id: ID of containing InformationSpace
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name: Human-readable name (unique within space)
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artifact_type: Classification of artifact
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content_digest: SHA-256 hash of content
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content_size: Size of content in bytes
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metadata: Extensible metadata
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created_at: Creation timestamp
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updated_at: Last modification timestamp
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"""
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id: str
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space_id: str
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name: str
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artifact_type: ArtifactType
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content_digest: str
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content_size: int = 0
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metadata: ArtifactMetadata = field(default_factory=ArtifactMetadata)
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created_at: datetime = field(default_factory=datetime.utcnow)
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updated_at: datetime = field(default_factory=datetime.utcnow)
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@classmethod
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def create(
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cls,
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space_id: str,
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name: str,
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content: str,
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artifact_type: ArtifactType = ArtifactType.CONTENT,
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metadata: Optional[ArtifactMetadata] = None,
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) -> "Artifact":
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"""
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Create a new artifact with automatic ID and digest generation.
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Args:
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space_id: ID of containing space
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name: Artifact name
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content: Artifact content
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artifact_type: Type classification
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metadata: Optional metadata
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Returns:
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New Artifact instance with computed digest
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"""
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artifact_id = str(uuid.uuid4())
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content_digest = calculate_content_digest(content)
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content_size = len(content.encode('utf-8'))
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return cls(
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id=artifact_id,
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space_id=space_id,
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name=name,
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artifact_type=artifact_type,
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content_digest=content_digest,
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content_size=content_size,
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metadata=metadata or ArtifactMetadata(),
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)
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def update_content(self, new_content: str) -> None:
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"""
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Update artifact content and recalculate digest.
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Args:
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new_content: New content string
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"""
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self.content_digest = calculate_content_digest(new_content)
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self.content_size = len(new_content.encode('utf-8'))
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self.updated_at = datetime.utcnow()
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def has_changed(self, current_digest: str) -> bool:
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"""
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Check if content has changed by comparing digests.
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Args:
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current_digest: Digest to compare against
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Returns:
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True if digests differ
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"""
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return self.content_digest != current_digest
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def to_dict(self) -> Dict[str, Any]:
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"""Convert artifact to dictionary for serialization."""
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return {
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"id": self.id,
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"space_id": self.space_id,
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"name": self.name,
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"artifact_type": self.artifact_type.value,
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"content_digest": self.content_digest,
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"content_size": self.content_size,
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"metadata": self.metadata.to_dict(),
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"created_at": self.created_at.isoformat(),
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"updated_at": self.updated_at.isoformat(),
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}
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@classmethod
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def from_dict(cls, data: Dict[str, Any]) -> "Artifact":
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"""Create artifact from dictionary."""
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return cls(
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id=data["id"],
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space_id=data["space_id"],
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name=data["name"],
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artifact_type=ArtifactType(data["artifact_type"]),
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content_digest=data["content_digest"],
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content_size=data.get("content_size", 0),
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metadata=ArtifactMetadata.from_dict(data.get("metadata", {})),
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created_at=datetime.fromisoformat(data["created_at"]),
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updated_at=datetime.fromisoformat(data["updated_at"]),
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)
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@dataclass
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class ArtifactReference:
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"""
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Reference to an artifact, possibly in another space.
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Implements FR-1.3: Cross-space artifact references
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Attributes:
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name: Artifact name to resolve
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space_id: Optional specific space ID (if not provided, uses resolution order)
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version: Optional version constraint
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"""
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name: str
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space_id: Optional[str] = None
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version: Optional[str] = None
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def __str__(self) -> str:
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"""String representation for display and debugging."""
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if self.space_id:
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ref = f"{self.space_id}:{self.name}"
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else:
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ref = self.name
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if self.version:
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ref = f"{ref}@{self.version}"
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return ref
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@classmethod
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def parse(cls, reference_str: str) -> "ArtifactReference":
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"""
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Parse a reference string into components.
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Formats:
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- "artifact-name"
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- "space-id:artifact-name"
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- "artifact-name@version"
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- "space-id:artifact-name@version"
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Args:
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reference_str: Reference string to parse
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Returns:
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Parsed ArtifactReference
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Raises:
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ValueError: If reference string is malformed
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"""
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# Split version if present
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if '@' in reference_str:
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ref_part, version = reference_str.rsplit('@', 1)
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else:
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ref_part, version = reference_str, None
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# Split space if present
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if ':' in ref_part:
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space_id, name = ref_part.split(':', 1)
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else:
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space_id, name = None, ref_part
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if not name:
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raise ValueError(f"Invalid artifact reference: {reference_str}")
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return cls(name=name, space_id=space_id, version=version)
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def calculate_content_digest(content: str) -> str:
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"""
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Calculate SHA-256 digest of content.
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Implements FR-1.2: Content digest computation
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Args:
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content: Content string to hash
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Returns:
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Hexadecimal SHA-256 digest
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"""
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return hashlib.sha256(content.encode('utf-8')).hexdigest()
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def calculate_bundle_digest(components: Dict[str, str]) -> str:
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"""
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Calculate digest of multiple components (for InputBundleHash).
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Args:
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components: Dictionary of component name -> digest
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Returns:
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Hexadecimal SHA-256 digest of sorted components
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"""
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# Sort components for deterministic hashing
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sorted_items = sorted(components.items())
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bundle_str = ':'.join(f"{k}={v}" for k, v in sorted_items)
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return hashlib.sha256(bundle_str.encode('utf-8')).hexdigest()
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