.env.example

# FiveK dataset subset size (number of images to index)
FIVEK_SUBSET_SIZE=500

# Paths (optional; defaults relative to project root)
# FIVEK_DATASET_DIR=./fivek_dataset
# FIVEK_LRCAT_PATH=./fivek_dataset/raw_photos/fivek.lrcat
# FIVEK_RAW_PHOTOS_DIR=./fivek_dataset/raw_photos

# Azure AI Search (for vector index)
# AZURE_SEARCH_ENDPOINT=https://<your-service>.search.windows.net
# AZURE_SEARCH_KEY=<your-admin-key>
# AZURE_SEARCH_INDEX_NAME=fivek-vectors

# Embedding: local CLIP (uses Mac MPS / CUDA when available) or Azure Vision
# EMBEDDING_MODEL=openai/clip-vit-base-patch32
# EMBEDDING_DIM=512

# Optional: Azure AI Vision multimodal embeddings (skips local CLIP; no GPU needed)
# AZURE_VISION_ENDPOINT=https://<your-vision>.cognitiveservices.azure.com
# AZURE_VISION_KEY=<your-key>
# AZURE_VISION_MODEL_VERSION=2023-04-15

# Azure OpenAI (LLM for pipeline: analyze image + expert recipe → suggested edits)
# AZURE_OPENAI_ENDPOINT=https://<your-resource>.cognitiveservices.azure.com/
# AZURE_OPENAI_KEY=<your-key>
# AZURE_OPENAI_DEPLOYMENT=gpt-4o
# AZURE_OPENAI_API_VERSION=2024-12-01-preview

# Optional: external editing API (if set, run_pipeline.py --api calls it; else edits applied locally)
# EDITING_API_URL=https://photo-editing-xxx.azurewebsites.net/api/apply-edits