Search Pinterest for visual references. Returns list of pins with image URLs and metadata. Args: query: e.g. 'quiet luxury beige coat editorial' limit: max pins to return (default 20)

Download images from a pinterest_search result to local filesystem. Saves to {PINTEREST_DATA_DIR}/pinterest/{date}/{query_slug}/ Args: search_result: output dict from pinterest_search max_images: max images to download (default 10)

Analyze images with LLM vision. Returns structured visual tags per image. Tags: lighting_type, composition_type, camera_distance, mood, palette, segment, shot_type, garment_focus, styling_signals, brand_feel, overall_quality. Args: image_paths: local file paths to images model: optional OpenRouter model override (default from PINTEREST_VISION_MODEL env)

Store visual analyses in ChromaDB vector base for future semantic retrieval. Note: on first run, ChromaDB will download an embedding model (~90 MB). Args: analyses: output list from pinterest_analyze query: optional label for what was searched

Full visual intelligence pipeline: search → download → analyze → store. Note: on first run with ingest=True, ChromaDB will download an embedding model (~90 MB). Args: query: search query, e.g. 'minimal editorial white shirt studio' limit: max pins to search (default 15) max_download: max images to download and analyze (default 8) analyze: run LLM vision analysis (default True) ingest: store results in vector base (default True)

Semantic search across stored visual references. Find past analyses by style, mood, segment, or free-text description. Args: query: e.g. 'dark editorial masculine streetwear close-up' n_results: number of results to return (default 10) segment: optional filter (luxury / premium / contemporary / streetwear) shot_type: optional filter (campaign editorial / e-commerce product / lookbook / ...) mood: optional filter by mood string