Add Themis application with custom widgets, views, and utilities

- Implemented custom form widgets for date, time, and datetime fields with DaisyUI styling.
- Created utility functions for formatting dates, times, and numbers according to user preferences.
- Developed views for profile settings, API key management, and notifications, including health check endpoints.
- Added URL configurations for Themis tests and main application routes.
- Established test cases for custom widgets to ensure proper functionality and integration.
- Defined project metadata and dependencies in pyproject.toml for package management.

mnemosyne/library/models.py

@@ -0,0 +1,251 @@
+"""
+Neo4j graph models for the Mnemosyne content library.
+
+All content data (libraries, collections, items, chunks, concepts, images)
+lives in Neo4j as a knowledge graph. These models use neomodel's StructuredNode
+OGM — they do NOT participate in Django's ORM or migrations.
+"""
+
+from neomodel import (
+    ArrayProperty,
+    DateTimeProperty,
+    FloatProperty,
+    IntegerProperty,
+    JSONProperty,
+    RelationshipTo,
+    StringProperty,
+    StructuredNode,
+    StructuredRel,
+    UniqueIdProperty,
+)
+
+
+# --- Relationship models ---
+
+
+class ReferencesRel(StructuredRel):
+    """Relationship properties for Item -> Concept REFERENCES edges."""
+
+    weight = FloatProperty(default=1.0)
+    context = StringProperty(default="")
+
+
+class RelatedToRel(StructuredRel):
+    """Relationship properties for Item -> Item RELATED_TO edges."""
+
+    relationship_type = StringProperty(default="")
+    weight = FloatProperty(default=1.0)
+
+
+class NearbyImageRel(StructuredRel):
+    """Relationship properties for Chunk -> Image HAS_NEARBY_IMAGE edges."""
+
+    proximity = StringProperty(default="same_page")  # same_page, inline, same_slide, same_chapter
+
+
+# --- Node models ---
+
+
+class Library(StructuredNode):
+    """
+    Top-level container representing a content library.
+
+    Each library has a type (fiction, technical, music, film, art, journal)
+    that drives chunking strategy, embedding instructions, and LLM prompts.
+    """
+
+    uid = UniqueIdProperty()
+    name = StringProperty(unique_index=True, required=True)
+    library_type = StringProperty(
+        required=True,
+        choices={
+            "fiction": "Fiction",
+            "technical": "Technical",
+            "music": "Music",
+            "film": "Film",
+            "art": "Art",
+            "journal": "Journal",
+        },
+    )
+    description = StringProperty(default="")
+
+    # Content-type configuration
+    chunking_config = JSONProperty(default={})
+    embedding_instruction = StringProperty(default="")
+    reranker_instruction = StringProperty(default="")
+    llm_context_prompt = StringProperty(default="")
+
+    created_at = DateTimeProperty(default_now=True)
+
+    # Relationships
+    collections = RelationshipTo("Collection", "CONTAINS")
+
+    def __str__(self):
+        return f"{self.name} ({self.library_type})"
+
+
+class Collection(StructuredNode):
+    """
+    A grouping of items within a library.
+
+    Examples: a book series, an album discography, a project folder.
+    """
+
+    uid = UniqueIdProperty()
+    name = StringProperty(required=True)
+    description = StringProperty(default="")
+    metadata = JSONProperty(default={})
+
+    created_at = DateTimeProperty(default_now=True)
+
+    # Relationships
+    items = RelationshipTo("Item", "CONTAINS")
+    library = RelationshipTo("Library", "BELONGS_TO")
+
+    def __str__(self):
+        return self.name
+
+
+class Item(StructuredNode):
+    """
+    An individual piece of content: a document, song, image set, journal entry, etc.
+
+    Items store their original file in S3 (via s3_key) and are chunked
+    for embedding and retrieval.
+    """
+
+    uid = UniqueIdProperty()
+    title = StringProperty(required=True)
+    item_type = StringProperty(default="")
+    s3_key = StringProperty(default="")
+    content_hash = StringProperty(index=True)
+    file_type = StringProperty(default="")
+    file_size = IntegerProperty(default=0)
+    metadata = JSONProperty(default={})
+
+    created_at = DateTimeProperty(default_now=True)
+    updated_at = DateTimeProperty(default_now=True)
+
+    # Embedding pipeline fields (Phase 2)
+    embedding_status = StringProperty(
+        default="pending",
+        choices={
+            "pending": "Pending",
+            "processing": "Processing",
+            "completed": "Completed",
+            "failed": "Failed",
+        },
+    )
+    embedding_model_name = StringProperty(default="")
+    chunk_count = IntegerProperty(default=0)
+    image_count = IntegerProperty(default=0)
+    error_message = StringProperty(default="")
+
+    # Relationships
+    chunks = RelationshipTo("Chunk", "HAS_CHUNK")
+    images = RelationshipTo("Image", "HAS_IMAGE")
+    concepts = RelationshipTo("Concept", "REFERENCES", model=ReferencesRel)
+    related_items = RelationshipTo("Item", "RELATED_TO", model=RelatedToRel)
+
+    def __str__(self):
+        return self.title
+
+
+class Chunk(StructuredNode):
+    """
+    A text chunk extracted from an Item for embedding and retrieval.
+
+    Chunk text is stored in S3; text_preview holds the first 500 chars
+    for Neo4j full-text indexing.
+    """
+
+    uid = UniqueIdProperty()
+    chunk_index = IntegerProperty(required=True)
+    chunk_s3_key = StringProperty(required=True)
+    chunk_size = IntegerProperty(default=0)
+    text_preview = StringProperty(default="")  # First 500 chars for full-text index
+    embedding = ArrayProperty(FloatProperty())  # 4096d vector
+
+    created_at = DateTimeProperty(default_now=True)
+
+    # Relationships
+    mentions = RelationshipTo("Concept", "MENTIONS")
+    nearby_images = RelationshipTo("Image", "HAS_NEARBY_IMAGE", model=NearbyImageRel)
+
+    def __str__(self):
+        return f"Chunk {self.chunk_index} ({self.uid})"
+
+
+class Concept(StructuredNode):
+    """
+    A named entity or topic extracted from content.
+
+    Concepts form the backbone of the knowledge graph, linking items
+    and chunks through shared references.
+    """
+
+    uid = UniqueIdProperty()
+    name = StringProperty(unique_index=True, required=True)
+    concept_type = StringProperty(
+        default="",
+        choices={
+            "person": "Person",
+            "place": "Place",
+            "topic": "Topic",
+            "technique": "Technique",
+            "theme": "Theme",
+        },
+    )
+    embedding = ArrayProperty(FloatProperty())  # 4096d vector
+
+    # Relationships
+    related_concepts = RelationshipTo("Concept", "RELATED_TO")
+
+    def __str__(self):
+        return self.name
+
+
+class Image(StructuredNode):
+    """
+    An image associated with an Item (cover art, diagram, photo, etc.).
+
+    The image file is stored in S3; embeddings enable multimodal search.
+    """
+
+    uid = UniqueIdProperty()
+    s3_key = StringProperty(required=True)
+    image_type = StringProperty(
+        default="",
+        choices={
+            "cover": "Cover",
+            "diagram": "Diagram",
+            "artwork": "Artwork",
+            "still": "Still",
+            "photo": "Photo",
+        },
+    )
+    description = StringProperty(default="")
+    metadata = JSONProperty(default={})
+
+    created_at = DateTimeProperty(default_now=True)
+
+    # Relationships
+    embeddings = RelationshipTo("ImageEmbedding", "HAS_EMBEDDING")
+
+    def __str__(self):
+        return f"Image {self.image_type} ({self.uid})"
+
+
+class ImageEmbedding(StructuredNode):
+    """
+    A multimodal embedding vector for an Image node.
+
+    Generated by Qwen3-VL for unified text+image vector space.
+    """
+
+    uid = UniqueIdProperty()
+    embedding = ArrayProperty(FloatProperty())  # 4096d multimodal vector
+    created_at = DateTimeProperty(default_now=True)
+
+    def __str__(self):
+        return f"ImageEmbedding ({self.uid})"