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Restore complex prompts and add more models

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Sami Samhuri 2025-06-25 01:08:01 -04:00
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extract_tags.rb
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@ -11,7 +11,7 @@ require 'time'
class TagExtractor
OLLAMA_URL = 'http://localhost:11434/api/generate'
DEFAULT_MODELS = ['llava:7b']
DEFAULT_MODELS = ['llava:7b', 'qwen2.5vl:7b', 'bakllava:7b', 'minicpm-v:8b', 'llama3.2-vision:11b', 'llava:13b']
VALID_EXTENSIONS = %w[.jpg .jpeg .png .gif .bmp .tiff .tif].freeze
def initialize(options = {})

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prompts/08-memory-search-optimizer.txt Normal file
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@ -0,0 +1,13 @@
Create detailed keywords for video diary search. Users might search for: "happy moments", "food experiences", "family time", "adventures", "quiet moments", "celebrations", "daily life", "travel memories".
Keyword everything visible including:
- People: if present include 'people' with count descriptor (e.g. '3-people'), approximate ages, emotions on faces, what they're doing, how they're interacting
- Scene type: where this is happening, indoor/outdoor, public/private space
- Time: morning light, afternoon, golden hour, evening, night time
- Mood: the feeling of the moment (joyful, peaceful, exciting, intimate, festive, contemplative)
- Activities: eating, playing, working, relaxing, traveling, celebrating, exploring
- Details: specific foods visible, drinks, decorations, clothing styles, weather, season
- Colors: main colors that define the scene
- Special moments: laughter, hugs, cheers, surprises, achievements
Format: comma-separated keywords only, be specific rather than generic.

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prompts/11-smart-scene-decoder.txt Normal file
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You're analyzing frames for an AI-powered video diary search. Users search with natural language like "dinner with friends", "kids playing", "sunset at the beach", "birthday celebrations", "quiet morning coffee".
Extract and keyword:
HUMANS: if humans visible include 'people' and descriptive count (e.g. '4-people', 'couple', 'crowd'), estimated ages in decades (20s/30s/etc), primary emotion per person, body language, attire style. Skip if no humans present
ACTIONS: primary action, secondary actions, interactions, gestures
LOCATION: venue type, indoor/outdoor, architectural style, geographic region if evident
TEMPORAL: exact time if visible, otherwise: dawn/morning/noon/afternoon/dusk/night, season indicators
AMBIANCE: energy level 1-10, mood descriptors, lighting quality, color temperature
OBJECTS: enumerate all significant objects, food with cuisine type, beverages, decorations
CONTEXT: occasion type, relationship dynamics, cultural indicators
TECHNICAL: image quality descriptors, composition style
Output as comma-separated keywords. Prioritize specific over generic (e.g., "pepperoni pizza" not just "food").