Quick Start

Requirements

• JDK 21+ - required by Spring Boot 4.1 + Spring AI 2.0 (Java 8/11/17 not supported)
• Maven 3.6+

Run

# 1. Configure the LLM API key (env var only - never hardcode)
export DASHSCOPE_API_KEY=sk-...

# 2. Build
mvn clean package

# 3. Run
mvn spring-boot:run

# 4. Try it - feed an exception stack trace and get an LLM root cause
curl -X POST http://localhost:8080/analyze \
  -H "Content-Type: application/json" \
  -d '{"appName":"order-service","exceptionType":"NullPointerException","exceptionMessage":"Cannot invoke method on null","stackTrace":["com.foo.OrderService.process(OrderService.java:42)","com.foo.OrderController.handle(OrderController.java:17)"]}'

What happens next

The exception flows through the five-layer pipeline:

1. Collector receives the error event via HTTP
2. Fingerprinter generates a SHA-256 fingerprint with framework-frame filtering
3. Analyzer runs the three-tier cascade: L1 cache miss -> L2 vector merge (if enabled) -> L3 LLM root cause
4. The result lands in the cluster repository, tagged with AnalysisPath.LLM_NEW
5. Aggregator picks it up for surge detection and weekly Top-N
6. Notifier pushes a Feishu alert if configured

Enabling L2 (PgVector)

L2 is off by default. Enabling it requires synchronized changes in three places:

1. pom.xml - uncomment the PgVector starter
2. application.yml - remove PgVectorStoreAutoConfiguration from spring.autoconfigure.exclude
3. Set stackwatch.l2.enabled=true and configure spring.datasource

See Architecture for the full L1/L2/L3 cascade design.