feat: add migrations, test scripts, and utility tools
- Add database migrations (006-028) for face recognition, identity, file_uuid - Add test scripts for ASR, face, search, processing - Add portal frontend (Tauri) - Add config, benchmark, and monitoring utilities - Add model checkpoints and pretrained model references
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Warren
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-- ================================================================
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-- Migration 006: Face Recognition Tables
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-- Version: 006
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-- Date: 2026-03-30
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-- Description: Add tables for face recognition feature storage
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-- Includes face embeddings, identities, and clusters
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-- ================================================================
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-- 6.1: Enable pgvector extension if not already enabled
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CREATE EXTENSION IF NOT EXISTS vector;
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-- 6.2: Create face_identities table
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CREATE TABLE IF NOT EXISTS face_identities (
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id SERIAL PRIMARY KEY,
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face_id VARCHAR(255) NOT NULL UNIQUE,
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name VARCHAR(255),
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embedding VECTOR(512), -- InsightFace default embedding dimension
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attributes JSONB,
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metadata JSONB DEFAULT '{}'::jsonb,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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is_active BOOLEAN DEFAULT TRUE,
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-- Indexes for performance
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CONSTRAINT face_identities_face_id_key UNIQUE (face_id)
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);
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-- 6.3: Create face_detections table
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CREATE TABLE IF NOT EXISTS face_detections (
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id SERIAL PRIMARY KEY,
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video_uuid VARCHAR(255) NOT NULL,
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frame_number BIGINT NOT NULL,
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timestamp_secs DOUBLE PRECISION NOT NULL,
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face_id VARCHAR(255),
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x INTEGER NOT NULL,
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y INTEGER NOT NULL,
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width INTEGER NOT NULL,
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height INTEGER NOT NULL,
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confidence DOUBLE PRECISION NOT NULL,
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embedding VECTOR(512),
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attributes JSONB,
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identity_id INTEGER REFERENCES face_identities(id) ON DELETE SET NULL,
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identity_confidence DOUBLE PRECISION,
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cluster_id VARCHAR(255),
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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-- Ensure unique detection per frame
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CONSTRAINT unique_detection_per_frame UNIQUE (video_uuid, frame_number, x, y, width, height)
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);
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-- 6.4: Create face_clusters table
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CREATE TABLE IF NOT EXISTS face_clusters (
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id SERIAL PRIMARY KEY,
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cluster_id VARCHAR(255) NOT NULL,
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video_uuid VARCHAR(255) NOT NULL,
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centroid VECTOR(512),
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size INTEGER NOT NULL DEFAULT 0,
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representative_face_id VARCHAR(255),
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metadata JSONB DEFAULT '{}'::jsonb,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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-- Constraints
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CONSTRAINT face_clusters_cluster_id_key UNIQUE (cluster_id)
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);
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-- 6.5: Create face_recognition_results table
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CREATE TABLE IF NOT EXISTS face_recognition_results (
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id SERIAL PRIMARY KEY,
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video_uuid VARCHAR(255) NOT NULL UNIQUE,
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frame_count BIGINT NOT NULL DEFAULT 0,
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fps DOUBLE PRECISION NOT NULL DEFAULT 0.0,
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total_faces INTEGER NOT NULL DEFAULT 0,
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recognized_faces INTEGER NOT NULL DEFAULT 0,
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clusters_count INTEGER NOT NULL DEFAULT 0,
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result_data JSONB NOT NULL,
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processing_time_secs DOUBLE PRECISION,
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created_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
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-- Constraints
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CONSTRAINT face_recognition_results_video_uuid_key UNIQUE (video_uuid)
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);
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-- 6.6: Create face_similarity_search function
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CREATE OR REPLACE FUNCTION find_similar_faces(
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query_embedding VECTOR(512),
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similarity_threshold DOUBLE PRECISION DEFAULT 0.6,
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limit_count INTEGER DEFAULT 10
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)
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RETURNS TABLE (
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face_id VARCHAR(255),
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name VARCHAR(255),
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similarity DOUBLE PRECISION,
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attributes JSONB,
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metadata JSONB
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) AS $$
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BEGIN
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RETURN QUERY
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SELECT
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fi.face_id,
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fi.name,
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1 - (fi.embedding <=> query_embedding) AS similarity,
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fi.attributes,
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fi.metadata
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FROM face_identities fi
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WHERE fi.is_active = TRUE
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AND fi.embedding IS NOT NULL
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AND 1 - (fi.embedding <=> query_embedding) >= similarity_threshold
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ORDER BY fi.embedding <=> query_embedding
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LIMIT limit_count;
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END;
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$$ LANGUAGE plpgsql;
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-- 6.7: Create function to update face cluster centroids
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CREATE OR REPLACE FUNCTION update_cluster_centroid(cluster_uuid VARCHAR(255))
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RETURNS VOID AS $$
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DECLARE
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new_centroid VECTOR(512);
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BEGIN
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-- Calculate new centroid from all face embeddings in the cluster
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SELECT AVG(embedding) INTO new_centroid
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FROM face_detections
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WHERE cluster_id = cluster_uuid
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AND embedding IS NOT NULL;
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-- Update cluster centroid
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UPDATE face_clusters
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SET centroid = new_centroid,
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size = (SELECT COUNT(*) FROM face_detections WHERE cluster_id = cluster_uuid)
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WHERE cluster_id = cluster_uuid;
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END;
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$$ LANGUAGE plpgsql;
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-- 6.8: Create function to find or create face identity
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CREATE OR REPLACE FUNCTION find_or_create_face_identity(
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p_face_id VARCHAR(255),
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p_name VARCHAR(255) DEFAULT NULL,
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p_embedding VECTOR(512) DEFAULT NULL,
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p_attributes JSONB DEFAULT NULL,
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p_metadata JSONB DEFAULT '{}'::jsonb
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)
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RETURNS INTEGER AS $$
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DECLARE
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v_id INTEGER;
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BEGIN
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-- Try to find existing face identity
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SELECT id INTO v_id
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FROM face_identities
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WHERE face_id = p_face_id;
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IF v_id IS NULL THEN
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-- Create new face identity
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INSERT INTO face_identities (face_id, name, embedding, attributes, metadata)
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VALUES (p_face_id, p_name, p_embedding, p_attributes, p_metadata)
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RETURNING id INTO v_id;
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ELSE
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-- Update existing face identity
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UPDATE face_identities
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SET
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name = COALESCE(p_name, name),
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embedding = COALESCE(p_embedding, embedding),
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attributes = COALESCE(p_attributes, attributes),
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metadata = COALESCE(p_metadata, metadata),
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updated_at = CURRENT_TIMESTAMP
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WHERE id = v_id;
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END IF;
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RETURN v_id;
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END;
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$$ LANGUAGE plpgsql;
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-- 6.9: Create indexes for performance
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CREATE INDEX IF NOT EXISTS idx_face_detections_video_uuid ON face_detections(video_uuid);
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CREATE INDEX IF NOT EXISTS idx_face_detections_face_id ON face_detections(face_id);
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CREATE INDEX IF NOT EXISTS idx_face_detections_frame ON face_detections(video_uuid, frame_number);
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CREATE INDEX IF NOT EXISTS idx_face_detections_identity ON face_detections(identity_id);
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CREATE INDEX IF NOT EXISTS idx_face_detections_cluster ON face_detections(cluster_id);
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CREATE INDEX IF NOT EXISTS idx_face_clusters_video_uuid ON face_clusters(video_uuid);
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CREATE INDEX IF NOT EXISTS idx_face_recognition_results_created_at ON face_recognition_results(created_at);
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-- 6.10: Create indexes for vector similarity search
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CREATE INDEX IF NOT EXISTS idx_face_identities_embedding
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ON face_identities USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = 100);
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CREATE INDEX IF NOT EXISTS idx_face_detections_embedding
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ON face_detections USING ivfflat (embedding vector_cosine_ops)
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WITH (lists = 100);
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-- 6.11: Add comments
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COMMENT ON TABLE face_identities IS 'Stores registered face identities with embeddings';
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COMMENT ON TABLE face_detections IS 'Stores individual face detections from videos';
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COMMENT ON TABLE face_clusters IS 'Stores face clusters from video analysis';
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COMMENT ON TABLE face_recognition_results IS 'Stores face recognition processing results';
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COMMENT ON FUNCTION find_similar_faces IS 'Finds similar faces based on embedding similarity';
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COMMENT ON FUNCTION update_cluster_centroid IS 'Updates cluster centroid from member embeddings';
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COMMENT ON FUNCTION find_or_create_face_identity IS 'Finds or creates a face identity record';
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-- 6.12: Create trigger to update updated_at timestamp
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CREATE OR REPLACE FUNCTION update_updated_at_column()
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RETURNS TRIGGER AS $$
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BEGIN
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NEW.updated_at = CURRENT_TIMESTAMP;
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RETURN NEW;
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END;
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$$ LANGUAGE plpgsql;
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-- Create triggers only if they don't exist
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DO $$
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BEGIN
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IF NOT EXISTS (
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SELECT 1 FROM pg_trigger
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WHERE tgname = 'update_face_identities_updated_at'
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AND tgrelid = 'face_identities'::regclass
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) THEN
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CREATE TRIGGER update_face_identities_updated_at
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BEFORE UPDATE ON face_identities
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FOR EACH ROW
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EXECUTE FUNCTION update_updated_at_column();
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END IF;
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IF NOT EXISTS (
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SELECT 1 FROM pg_trigger
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WHERE tgname = 'update_face_recognition_results_updated_at'
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AND tgrelid = 'face_recognition_results'::regclass
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) THEN
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CREATE TRIGGER update_face_recognition_results_updated_at
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BEFORE UPDATE ON face_recognition_results
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FOR EACH ROW
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EXECUTE FUNCTION update_updated_at_column();
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END IF;
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END $$;
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