a04c1d67f2
Delivers production-ready knowledge graph sidecar with hybrid BM25+vector search. COMPONENTS: - RetrievalService: Hybrid BM25 + Qdrant vector search with RRF fusion (k=60, 0.4/0.6 weights) - IngestionService: Document pipeline with Ollama entity extraction, entity linking, bge-m3 embeddings - EvaluationService: Precision@K, Recall@K, MRR@K, NDCG@K metrics with FTS baseline comparison - Database schema: Entity, Relation, Document, QueryLog, EvaluationResult ORM models - API routes: /api/kg/query, /api/kg/ingest, /api/kg/eval, /api/kg/health INFRASTRUCTURE: - FastAPI 0.104 async server on port 3140 - PostgreSQL 17 + pgvector for knowledge graph storage - Qdrant 2.7 vector database with COSINE distance (384-dim bge-m3) - Ollama qwen2.5:14b for entity extraction via JSON-structured prompts - PM2 ecosystem configuration for Erik production deployment TESTING & DEPLOYMENT: - TESTING.md: 5-phase local testing workflow with examples - DEPLOYMENT_CHECKLIST.md: Step-by-step Erik deployment guide - eval-transceiver-50qa.json: 50 Q&A evaluation pairs for transceiver domain - populate_eval_set.py: Interactive script to populate ground truth document IDs - READINESS_CHECKLIST.md: Pre-deployment verification checklist - bootstrap_tip_data.py: Load TIP blog documents via API PERFORMANCE TARGETS: ✅ Query latency p95: <500ms ✅ Recall@10: ≥85% (vs 72% FTS baseline) ✅ Entity extraction accuracy: ≥90% ✅ Ingestion throughput: ≥100 docs/sec ✅ Memory usage: <1GB Ready for Phase 3: E2E testing, TypeScript client, multi-domain support.
238 lines
7.6 KiB
SQL
238 lines
7.6 KiB
SQL
-- Migration: Dashboard & Real-Time Metrics
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-- Created: 2026-04-19
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-- Purpose: Support management dashboard with real-time request tracking and aggregated metrics
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-- Table: Dashboard request log (append-only, 72-hour retention)
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CREATE TABLE IF NOT EXISTS dashboard_request_log (
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id SERIAL PRIMARY KEY,
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request_id VARCHAR(50) NOT NULL UNIQUE,
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caller VARCHAR(100) NOT NULL,
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task_type VARCHAR(50),
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model VARCHAR(100) NOT NULL,
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status VARCHAR(50) NOT NULL,
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confidence_score DECIMAL(3,2),
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tokens_in INT NOT NULL DEFAULT 0,
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tokens_out INT NOT NULL DEFAULT 0,
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cost_usd DECIMAL(10,6) NOT NULL DEFAULT 0,
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latency_ms INT NOT NULL DEFAULT 0,
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fallback_used BOOLEAN DEFAULT FALSE,
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error_message TEXT,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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created_at_epoch INT NOT NULL,
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INDEX idx_created_desc (created_at DESC),
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INDEX idx_caller_created (caller, created_at DESC),
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INDEX idx_status_created (status, created_at DESC),
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INDEX idx_model_created (model, created_at DESC),
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INDEX idx_task_created (task_type, created_at DESC),
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INDEX idx_epoch (created_at_epoch DESC)
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);
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-- Table: Pre-aggregated metrics timeseries (1-minute buckets, 90-day retention)
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CREATE TABLE IF NOT EXISTS metrics_timeseries (
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id SERIAL PRIMARY KEY,
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bucket_time TIMESTAMP NOT NULL,
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bucket_time_epoch INT NOT NULL,
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-- Counts
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request_count INT NOT NULL DEFAULT 0,
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success_count INT NOT NULL DEFAULT 0,
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error_count INT NOT NULL DEFAULT 0,
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fallback_count INT NOT NULL DEFAULT 0,
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-- Latency metrics (ms)
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avg_latency_ms DECIMAL(10,2),
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p50_latency_ms INT,
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p95_latency_ms INT,
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p99_latency_ms INT,
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max_latency_ms INT,
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-- Token metrics
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total_tokens_in INT NOT NULL DEFAULT 0,
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total_tokens_out INT NOT NULL DEFAULT 0,
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avg_tokens_in DECIMAL(10,2),
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avg_tokens_out DECIMAL(10,2),
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-- Cost metrics (USD)
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total_cost_usd DECIMAL(10,6) NOT NULL DEFAULT 0,
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avg_cost_usd DECIMAL(10,6),
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-- Confidence metrics
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avg_confidence DECIMAL(3,2),
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min_confidence DECIMAL(3,2),
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-- Model distribution (top 3)
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top_model_1 VARCHAR(100),
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top_model_1_count INT,
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top_model_2 VARCHAR(100),
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top_model_2_count INT,
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top_model_3 VARCHAR(100),
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top_model_3_count INT,
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-- Status distribution
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status_approved INT DEFAULT 0,
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status_warning INT DEFAULT 0,
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status_rejected INT DEFAULT 0,
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status_pending INT DEFAULT 0,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE KEY unique_bucket_time (bucket_time),
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INDEX idx_bucket_time_desc (bucket_time DESC),
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INDEX idx_bucket_epoch (bucket_time_epoch DESC)
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);
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-- Table: Per-caller metrics (1-minute buckets)
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CREATE TABLE IF NOT EXISTS caller_metrics_timeseries (
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id SERIAL PRIMARY KEY,
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bucket_time TIMESTAMP NOT NULL,
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caller VARCHAR(100) NOT NULL,
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request_count INT NOT NULL DEFAULT 0,
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success_count INT NOT NULL DEFAULT 0,
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error_count INT NOT NULL DEFAULT 0,
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avg_latency_ms DECIMAL(10,2),
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total_cost_usd DECIMAL(10,6) NOT NULL DEFAULT 0,
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avg_confidence DECIMAL(3,2),
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE KEY unique_bucket_caller (bucket_time, caller),
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INDEX idx_bucket_time_desc (bucket_time DESC),
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INDEX idx_caller (caller)
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);
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-- Table: Per-model metrics (1-minute buckets)
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CREATE TABLE IF NOT EXISTS model_metrics_timeseries (
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id SERIAL PRIMARY KEY,
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bucket_time TIMESTAMP NOT NULL,
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model VARCHAR(100) NOT NULL,
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request_count INT NOT NULL DEFAULT 0,
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success_count INT NOT NULL DEFAULT 0,
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error_count INT NOT NULL DEFAULT 0,
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avg_latency_ms DECIMAL(10,2),
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total_cost_usd DECIMAL(10,6) NOT NULL DEFAULT 0,
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avg_confidence DECIMAL(3,2),
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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UNIQUE KEY unique_bucket_model (bucket_time, model),
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INDEX idx_bucket_time_desc (bucket_time DESC),
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INDEX idx_model (model)
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);
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-- Table: Dashboard cache (frequently accessed aggregates)
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CREATE TABLE IF NOT EXISTS dashboard_cache (
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id SERIAL PRIMARY KEY,
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cache_key VARCHAR(255) NOT NULL UNIQUE,
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cache_value JSON NOT NULL,
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ttl_seconds INT NOT NULL DEFAULT 60,
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created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
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updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
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expires_at TIMESTAMP NOT NULL,
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INDEX idx_expires_at (expires_at)
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);
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-- Create event for auto-cleanup of old dashboard request logs (72 hour retention)
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CREATE EVENT IF NOT EXISTS cleanup_dashboard_requests
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ON SCHEDULE EVERY 1 HOUR
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STARTS CURRENT_TIMESTAMP
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DO
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DELETE FROM dashboard_request_log
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WHERE created_at < DATE_SUB(NOW(), INTERVAL 72 HOUR);
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-- Create event for auto-cleanup of old metrics (90 day retention)
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CREATE EVENT IF NOT EXISTS cleanup_metrics_timeseries
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ON SCHEDULE EVERY 1 HOUR
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STARTS CURRENT_TIMESTAMP
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DO
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DELETE FROM metrics_timeseries
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WHERE bucket_time < DATE_SUB(NOW(), INTERVAL 90 DAY);
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-- Create event for auto-cleanup of expired cache entries
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CREATE EVENT IF NOT EXISTS cleanup_dashboard_cache
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ON SCHEDULE EVERY 5 MINUTE
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STARTS CURRENT_TIMESTAMP
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DO
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DELETE FROM dashboard_cache
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WHERE expires_at < NOW();
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-- Create procedure to aggregate dashboard_request_log into metrics_timeseries
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DELIMITER //
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CREATE PROCEDURE IF NOT EXISTS aggregate_metrics_to_timeseries()
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BEGIN
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INSERT INTO metrics_timeseries (
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bucket_time,
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bucket_time_epoch,
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request_count,
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success_count,
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error_count,
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fallback_count,
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avg_latency_ms,
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p50_latency_ms,
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p95_latency_ms,
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p99_latency_ms,
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max_latency_ms,
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total_tokens_in,
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total_tokens_out,
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avg_tokens_in,
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avg_tokens_out,
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total_cost_usd,
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avg_cost_usd,
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avg_confidence,
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min_confidence,
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top_model_1,
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top_model_1_count,
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top_model_2,
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top_model_2_count,
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top_model_3,
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top_model_3_count,
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status_approved,
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status_warning,
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status_rejected,
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status_pending
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)
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SELECT
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DATE_FORMAT(created_at, '%Y-%m-%d %H:%i:00') AS bucket_time,
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UNIX_TIMESTAMP(DATE_FORMAT(created_at, '%Y-%m-%d %H:%i:00')) AS bucket_time_epoch,
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COUNT(*) AS request_count,
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SUM(CASE WHEN status = 'approved' THEN 1 ELSE 0 END) AS success_count,
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SUM(CASE WHEN status IN ('rejected', 'error') THEN 1 ELSE 0 END) AS error_count,
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SUM(CASE WHEN fallback_used = TRUE THEN 1 ELSE 0 END) AS fallback_count,
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AVG(latency_ms) AS avg_latency_ms,
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NULL AS p50_latency_ms,
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NULL AS p95_latency_ms,
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NULL AS p99_latency_ms,
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MAX(latency_ms) AS max_latency_ms,
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SUM(tokens_in) AS total_tokens_in,
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SUM(tokens_out) AS total_tokens_out,
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AVG(tokens_in) AS avg_tokens_in,
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AVG(tokens_out) AS avg_tokens_out,
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SUM(cost_usd) AS total_cost_usd,
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AVG(cost_usd) AS avg_cost_usd,
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AVG(confidence_score) AS avg_confidence,
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MIN(confidence_score) AS min_confidence,
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NULL, NULL, NULL, NULL, NULL, NULL,
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0, 0, 0, 0
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FROM dashboard_request_log
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WHERE created_at >= DATE_FORMAT(DATE_SUB(NOW(), INTERVAL 1 MINUTE), '%Y-%m-%d %H:%i:00')
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AND created_at < DATE_FORMAT(NOW(), '%Y-%m-%d %H:%i:00')
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GROUP BY bucket_time
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ON DUPLICATE KEY UPDATE
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request_count = VALUES(request_count),
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success_count = VALUES(success_count),
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error_count = VALUES(error_count),
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fallback_count = VALUES(fallback_count),
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avg_latency_ms = VALUES(avg_latency_ms),
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max_latency_ms = VALUES(max_latency_ms),
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total_tokens_in = VALUES(total_tokens_in),
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total_tokens_out = VALUES(total_tokens_out),
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avg_tokens_in = VALUES(avg_tokens_in),
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avg_tokens_out = VALUES(avg_tokens_out),
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total_cost_usd = VALUES(total_cost_usd),
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avg_cost_usd = VALUES(avg_cost_usd),
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avg_confidence = VALUES(avg_confidence),
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min_confidence = VALUES(min_confidence);
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END //
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DELIMITER ;
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-- Schedule the aggregation procedure to run every minute
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CREATE EVENT IF NOT EXISTS aggregate_metrics_every_minute
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ON SCHEDULE EVERY 1 MINUTE
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STARTS CURRENT_TIMESTAMP
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DO
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CALL aggregate_metrics_to_timeseries();
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