Add vision analysis capabilities to the embedding pipeline

- Introduced a new vision analysis service to classify, describe, and extract text from images.
- Enhanced the Image model with fields for OCR text, vision model name, and analysis status.
- Added a new "nonfiction" library type with specific chunking and embedding configurations.
- Updated content types to include vision prompts for various library types.
- Integrated vision analysis into the embedding pipeline, allowing for image analysis during document processing.
- Implemented metrics to track vision analysis performance and usage.
- Updated UI components to display vision analysis results and statuses in item details and the embedding dashboard.
- Added migration for new vision model fields and usage tracking.

mnemosyne/library/models.py

@@ -60,6 +60,7 @@ class Library(StructuredNode):
         required=True,
         choices={
             "fiction": "Fiction",
+            "nonfiction": "Non-Fiction",
             "technical": "Technical",
             "music": "Music",
             "film": "Film",
@@ -219,6 +220,12 @@ class Image(StructuredNode):
         choices={
             "cover": "Cover",
             "diagram": "Diagram",
+            "chart": "Chart",
+            "table": "Table",
+            "screenshot": "Screenshot",
+            "illustration": "Illustration",
+            "map": "Map",
+            "portrait": "Portrait",
             "artwork": "Artwork",
             "still": "Still",
             "photo": "Photo",
@@ -227,10 +234,24 @@ class Image(StructuredNode):
     description = StringProperty(default="")
     metadata = JSONProperty(default={})
 
+    # Vision analysis fields (Phase 2B)
+    ocr_text = StringProperty(default="")  # Visible text extracted by vision model
+    vision_model_name = StringProperty(default="")  # Which vision model analyzed this
+    analysis_status = StringProperty(
+        default="pending",
+        choices={
+            "pending": "Pending",
+            "completed": "Completed",
+            "failed": "Failed",
+            "skipped": "Skipped",
+        },
+    )
+
     created_at = DateTimeProperty(default_now=True)
 
     # Relationships
     embeddings = RelationshipTo("ImageEmbedding", "HAS_EMBEDDING")
+    concepts = RelationshipTo("Concept", "DEPICTS")
 
     def __str__(self):
         return f"Image {self.image_type} ({self.uid})"