File size: 1,487 Bytes
38ab39c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # Architecture: Cora
## System Overview
```mermaid
graph TD
User([User]) --> UI[Gradio UI]
User --> API[FastAPI Persistence]
UI --> Engine[CoraEngine]
API --> Engine
Engine --> Curator[CoraCurator - LLM]
Engine --> Generator[HF Inference API - SDXL]
Engine --> Vision[CoraVision - CLIP/YOLO]
Engine --> Memory[CoraMemory - ChromaDB]
Memory --> DB[(ChromaDB SQL)]
Vision --> Embed[SentenceTransformers]
```
## Core Components
- **CoraEngine (`cora_engine.py`)**: The central orchestrator. Handles generation logic, model fallbacks, and RAG (Retrieval-Augmented Generation) fallback.
- **CoraCurator (`cora_curator.py`)**: Uses a Large Language Model to refine simple user prompts into rich, historical descriptions.
- **CoraVision (`cora_vision.py`)**: Handles visual processing, including generating CLIP embeddings for images/text and performing object detection with YOLO for automated tagging.
- **CoraMemory (`cora_memory.py`)**: Persistent storage using ChromaDB. Stores image paths, metadata, and embeddings for semantic search.
## Data Flow (Generation)
1. User enters prompt.
2. `CoraCurator` refines the prompt.
3. `CoraEngine` calls Hugging Face Inference API.
4. If primary model fails (e.g., 401, 503, or invalid data), it retries with a fallback model.
5. If both fail, it performs a RAG search in `CoraMemory` for the closest existing match.
6. The result is returned to the user and archived.
|