env.example

# ChatTag Filter Configuration (powered by LangChain)
# Copy this file to .env and fill in your values

# Required: LangChain model string in "provider:model" format.
# Pick one of (all 4 providers are installed by default):
FILTER_CHATTAG_MODEL=openai:gpt-4o-mini
# FILTER_CHATTAG_MODEL=google_genai:gemini-2.0-flash
# FILTER_CHATTAG_MODEL=anthropic:claude-3-5-sonnet-latest
# FILTER_CHATTAG_MODEL=ollama:llava

# Required: provider credentials (set whichever matches your model above).
# LangChain reads these provider-native env vars automatically.
OPENAI_API_KEY=your_openai_api_key_here
# GOOGLE_API_KEY=your_google_api_key_here
# ANTHROPIC_API_KEY=your_anthropic_api_key_here
# OLLAMA_HOST=http://localhost:11434

# Required: Path to prompt file
FILTER_PROMPT=./prompts/food_annotation_prompt.txt

# Input Source (choose one):
# For video processing:
VIDEO_PATH=/path/to/your/video.mp4
# Note: VideoIn uses !sync!no-loop;main for reliable processing

# For image processing (file or directory):
IMAGE_PATH=/path/to/your/images
# Optional: Image file pattern (default: jpg)
IMAGE_PATTERN=jpg
# Optional: Scan subdirectories recursively
FILTER_RECURSIVE=false

# Optional: LLM parameters
FILTER_MAX_TOKENS=1000
FILTER_TEMPERATURE=0.1

# Optional: Image processing
FILTER_MAX_IMAGE_SIZE=0  # 0 = keep original size, >0 = resize to max pixels
FILTER_IMAGE_QUALITY=85

# Optional: Output configuration
FILTER_SAVE_FRAMES=true
FILTER_OUTPUT_DIR=./output_frames

# Optional: Output schema (JSON string). LangChain enforces this via
# with_structured_output() — every provider returns the same shape.
FILTER_OUTPUT_SCHEMA={"lettuce": {"present": false, "confidence": 0.0}, "tomato": {"present": false, "confidence": 0.0}, "chicken": {"present": false, "confidence": 0.0}}

# Optional: Topic filtering
FILTER_TOPIC_PATTERN=.*
FILTER_EXCLUDE_TOPICS=debug,test

# Optional: No-ops mode (skip LLM calls for testing)
FILTER_NO_OPS=false