Copilot commented on code in PR #1319:
URL: https://github.com/apache/dubbo-admin/pull/1319#discussion_r2314112281
##########
ai/internal/agent/flow.go:
##########
@@ -0,0 +1,179 @@
+package agent
+
+import (
+ "context"
+
+ "dubbo-admin-ai/internal/config"
+ "dubbo-admin-ai/internal/manager"
+ "dubbo-admin-ai/internal/schema"
+ "dubbo-admin-ai/internal/tools"
+ "errors"
+ "fmt"
+ "log"
+ "os"
+
+ "github.com/firebase/genkit/go/core/logger"
+
+ "github.com/firebase/genkit/go/ai"
+ "github.com/firebase/genkit/go/core"
+ "github.com/firebase/genkit/go/genkit"
+)
+
+// 公开的 Flow 变量，以便在编排器中调用
+var (
+ ReActFlow *core.Flow[schema.ReActIn, schema.ReActOut, struct{}]
+ ThinkingFlow *core.Flow[schema.ThinkIn, *schema.ThinkOut, struct{}]
+ ActFlow *core.Flow[*schema.ActIn, schema.ActOut, struct{}]
+
+ ThinkPrompt *ai.Prompt
+)
+
+var g *genkit.Genkit
+
+// The order of initialization cannot change
+func InitAgent() (err error) {
+ if err = manager.LoadEnvVars(); err != nil {
+ return err
+ }
+
+ manager.InitLogger()
+
+ if g, err = manager.GetGlobalGenkit(); err != nil {
+ return err
+ }
+
+ tools.RegisterAllMockTools(g)
+
+ if err = InitFlows(g); err != nil {
+ return err
+ }
+
+ return nil
+}
+
+func InitFlows(registry *genkit.Genkit) error {
+ if registry == nil {
+ return fmt.Errorf("registry is nil")
+ }
+ g = registry
+
+ data, err := os.ReadFile(config.PROMPT_DIR_PATH + "/agentSystem.prompt")
+ if err != nil {
+ return fmt.Errorf("failed to read agentSystem prompt: %w", err)
+ }
+ systemPromptText := string(data)
+
+ mockTools, err := tools.AllMockToolRef()
+ if err != nil {
+ log.Fatalf("failed to get mock mock_tools: %v", err)
+ }
+ ThinkPrompt, err = genkit.DefinePrompt(g, "agentThinking",
+ ai.WithSystem(systemPromptText),
+ ai.WithInputType(schema.ThinkIn{}),
+ ai.WithOutputType(schema.ThinkOut{}),
+ ai.WithPrompt("{{userInput}}"),
+ ai.WithTools(mockTools...),
+ )
+
+ if err != nil {
+ return fmt.Errorf("failed to define agentThink prompt: %w", err)
+ }
+
+ ReActFlow = genkit.DefineFlow(g, "reAct", reAct)
+ ThinkingFlow = genkit.DefineFlow(g, "thinking", thinking)
+ ActFlow = genkit.DefineFlow(g, "act", act)
+
+ return nil
+}
+
+// Flow 的核心函数实现 `fn` (不对外导出)
+// ----------------------------------------------------------------------------
+// 1. agentOrchestrator: 总指挥/编排器的核心逻辑
+// ----------------------------------------------------------------------------
+func reAct(ctx context.Context, reActInput schema.ReActIn) (reActOut
schema.ReActOut, err error) {
+ //TODO: 输入数据意图解析
+
+ thinkingInput := reActInput
+ // 主协调循环 (Reconciliation Loop)
+ for i := 0; i < config.MAX_REACT_ITERATIONS; i++ {
+
+ // a. 调用 thinkingFlow
+ thinkingResp, err := ThinkingFlow.Run(ctx, thinkingInput)
+ if err != nil {
+ manager.GetLogger().Error("failed to run thinking
flow", "error", err)
+ return "", err
+ }
+ if thinkingResp == nil {
+ manager.GetLogger().Error("expected non-nil response")
+ return "", errors.New("expected non-nil response")
+ }
+
+ for _, r := range thinkingResp.ToolRequests {
+ manager.GetLogger().Info(r.String())
+ }
+
+ reActOut = thinkingResp.String()
+
+ // b. 检查是否有最终答案
+ if thinkingResp.IsStop() {
+ break
+ }
+
+ // c. 调用 actFlow
+ actOutArray, err := ActFlow.Run(ctx, thinkingResp)
+ if err != nil {
+ logger.FromContext(ctx).Error("failed to run act flow",
"error", err)
+ return "", err
+ }
+
+ // TODO: 这里将来会集成SessionMemory，暂时简化处理
+ _ = actOutArray // 占位符，避免未使用变量错误
+ }
+
+ return reActOut, nil
+}
+
+// ----------------------------------------------------------------------------
+// 2. thinking: 大脑/思考者的核心逻辑
+// ----------------------------------------------------------------------------
+func thinking(ctx context.Context, input schema.ThinkIn) (*schema.ThinkOut,
error) {
+ resp, err := ThinkPrompt.Execute(ctx,
+ ai.WithInput(input),
+ )
+
+ if err != nil {
+ return nil, fmt.Errorf("failed to execute agentThink prompt:
%w", err)
+ }
+
+ // 解析输出
+ var response *schema.ThinkOut = nil
Review Comment:
[nitpick] Explicit initialization to nil is unnecessary for pointer types.
Simply declare as `var response *schema.ThinkOut` for cleaner code.
```suggestion
var response *schema.ThinkOut
```
##########
ai/internal/tools/mock_tools.go:
##########
@@ -0,0 +1,623 @@
+package tools
+
+import (
+ "fmt"
+ "log"
+
+ "github.com/firebase/genkit/go/ai"
+ "github.com/firebase/genkit/go/genkit"
+)
+
+// ================================================
+// Prometheus Query Service Latency Tool
+// ================================================
+type ToolOutput interface {
+ Tool() string
+}
+
+type PrometheusServiceLatencyInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to query"`
+ TimeRangeMinutes int `json:"timeRangeMinutes"
jsonschema:"required,description=Time range in minutes"`
+ Quantile float64 `json:"quantile"
jsonschema:"required,description=Quantile to query (e.g., 0.99 or 0.95)"`
+}
+
+type PrometheusServiceLatencyOutput struct {
+ ToolName string `json:"toolName"`
+ Quantile float64 `json:"quantile"`
+ ValueMillis int `json:"valueMillis"`
+ Summary string `json:"summary"`
+}
+
+func (o PrometheusServiceLatencyOutput) Tool() string {
+ return o.ToolName
+}
+
+func prometheusQueryServiceLatency(ctx *ai.ToolContext, input
PrometheusServiceLatencyInput) (PrometheusServiceLatencyOutput, error) {
+ log.Printf("Tool 'prometheus_query_service_latency' called for service:
%s", input.ServiceName)
+
+ // Mock data based on the example in prompt
+ valueMillis := 3500
+ if input.ServiceName != "order-service" {
+ valueMillis = 850
+ }
+
+ return PrometheusServiceLatencyOutput{
+ ToolName: "prometheus_query_service_latency",
+ Quantile: input.Quantile,
+ ValueMillis: valueMillis,
+ Summary: fmt.Sprintf("服务 %s 在过去%d分钟内的 P%.0f 延迟为 %dms",
input.ServiceName, input.TimeRangeMinutes, input.Quantile*100, valueMillis),
+ }, nil
+}
+
+// ================================================
+// Prometheus Query Service Traffic Tool
+// ================================================
+
+type PrometheusServiceTrafficInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to query"`
+ TimeRangeMinutes int `json:"timeRangeMinutes"
jsonschema:"required,description=Time range in minutes"`
+}
+
+type PrometheusServiceTrafficOutput struct {
+ ToolName string `json:"toolName"`
+ RequestRateQPS float64 `json:"requestRateQPS"`
+ ErrorRatePercentage float64 `json:"errorRatePercentage"`
+ Summary string `json:"summary"`
+}
+
+func (o PrometheusServiceTrafficOutput) Tool() string {
+ return o.ToolName
+}
+
+func prometheusQueryServiceTraffic(ctx *ai.ToolContext, input
PrometheusServiceTrafficInput) (PrometheusServiceTrafficOutput, error) {
+ log.Printf("Tool 'prometheus_query_service_traffic' called for service:
%s", input.ServiceName)
+
+ return PrometheusServiceTrafficOutput{
+ ToolName: "prometheus_query_service_traffic",
+ RequestRateQPS: 250.0,
+ ErrorRatePercentage: 5.2,
+ Summary: fmt.Sprintf("服务 %s 的 QPS 为 250, 错误率为
5.2%%", input.ServiceName),
+ }, nil
+}
+
+// ================================================
+// Query Timeseries Database Tool
+// ================================================
+
+type QueryTimeseriesDatabaseInput struct {
+ PromqlQuery string `json:"promqlQuery"
jsonschema:"required,description=PromQL query to execute"`
+}
+
+type TimeseriesMetric struct {
+ Pod string `json:"pod"`
+}
+
+type TimeseriesValue struct {
+ Timestamp int64 `json:"timestamp"`
+ Value string `json:"value"`
+}
+
+type TimeseriesResult struct {
+ Metric TimeseriesMetric `json:"metric"`
+ Value TimeseriesValue `json:"value"`
+}
+
+type QueryTimeseriesDatabaseOutput struct {
+ ToolName string `json:"toolName"`
+ Query string `json:"query"`
+ Results []TimeseriesResult `json:"results"`
+ Summary string `json:"summary"`
+}
+
+func (o QueryTimeseriesDatabaseOutput) Tool() string {
+ return o.ToolName
+}
+
+func queryTimeseriesDatabase(ctx *ai.ToolContext, input
QueryTimeseriesDatabaseInput) (QueryTimeseriesDatabaseOutput, error) {
+ log.Printf("Tool 'query_timeseries_database' called with query: %s",
input.PromqlQuery)
+
+ return QueryTimeseriesDatabaseOutput{
+ ToolName: "query_timeseries_database",
+ Query: input.PromqlQuery,
+ Results: []TimeseriesResult{
+ {
+ Metric: TimeseriesMetric{Pod:
"order-service-pod-1"},
+ Value: TimeseriesValue{Timestamp: 1692192000,
Value: "3.5"},
+ },
+ {
+ Metric: TimeseriesMetric{Pod:
"order-service-pod-2"},
+ Value: TimeseriesValue{Timestamp: 1692192000,
Value: "3.2"},
+ },
+ },
+ Summary: "查询返回了 2 个时间序列",
+ }, nil
+}
+
+// ================================================
+// Application Performance Profiling Tool
+// ================================================
+
+type ApplicationPerformanceProfilingInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to profile"`
+ PodName string `json:"podName"
jsonschema:"required,description=The specific pod name to profile"`
+ DurationSeconds int `json:"durationSeconds"
jsonschema:"required,description=Duration of profiling in seconds"`
+}
+
+type PerformanceHotspot struct {
+ CPUTimePercentage float64 `json:"cpuTimePercentage"`
+ StackTrace []string `json:"stackTrace"`
+}
+
+type ApplicationPerformanceProfilingOutput struct {
+ ToolName string `json:"toolName"`
+ Status string `json:"status"`
+ TotalSamples int `json:"totalSamples"`
+ Hotspots []PerformanceHotspot `json:"hotspots"`
+ Summary string `json:"summary"`
+}
+
+func (o ApplicationPerformanceProfilingOutput) Tool() string {
+ return o.ToolName
+}
+
+func applicationPerformanceProfiling(ctx *ai.ToolContext, input
ApplicationPerformanceProfilingInput) (ApplicationPerformanceProfilingOutput,
error) {
+ log.Printf("Tool 'application_performance_profiling' called for
service: %s, pod: %s", input.ServiceName, input.PodName)
+
+ return ApplicationPerformanceProfilingOutput{
+ ToolName: "application_performance_profiling",
+ Status: "completed",
+ TotalSamples: 10000,
+ Hotspots: []PerformanceHotspot{
+ {
+ CPUTimePercentage: 45.5,
+ StackTrace: []string{
+
"com.example.OrderService.processOrder()",
+ "com.example.DatabaseClient.query()",
+
"java.sql.PreparedStatement.executeQuery()",
+ },
+ },
+ },
+ Summary: "性能分析显示，45.5%的CPU时间消耗在数据库查询调用链上",
+ }, nil
+}
+
+// ================================================
+// JVM Performance Analysis Tool
+// ================================================
+
+type JVMPerformanceAnalysisInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the Java service to analyze"`
+ PodName string `json:"podName" jsonschema:"required,description=The
specific pod name to analyze"`
+}
+
+type JVMPerformanceAnalysisOutput struct {
+ ToolName string `json:"toolName"`
+ FullGcCountLastHour int `json:"fullGcCountLastHour"`
+ FullGcTimeAvgMillis int `json:"fullGcTimeAvgMillis"`
+ HeapUsagePercentage float64 `json:"heapUsagePercentage"`
+ Summary string `json:"summary"`
+}
+
+func (o JVMPerformanceAnalysisOutput) Tool() string {
+ return o.ToolName
+}
+
+func jvmPerformanceAnalysis(ctx *ai.ToolContext, input
JVMPerformanceAnalysisInput) (JVMPerformanceAnalysisOutput, error) {
+ log.Printf("Tool 'jvm_performance_analysis' called for service: %s,
pod: %s", input.ServiceName, input.PodName)
+
+ return JVMPerformanceAnalysisOutput{
+ ToolName: "jvm_performance_analysis",
+ FullGcCountLastHour: 15,
+ FullGcTimeAvgMillis: 1200,
+ HeapUsagePercentage: 85.5,
+ Summary: "GC activity is high, average Full GC time
is 1200ms",
+ }, nil
+}
+
+// ================================================
+// Trace Dependency View Tool
+// ================================================
+
+type TraceDependencyViewInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to analyze
dependencies"`
+}
+
+type TraceDependencyViewOutput struct {
+ ToolName string `json:"toolName"`
+ UpstreamServices []string `json:"upstreamServices"`
+ DownstreamServices []string `json:"downstreamServices"`
+}
+
+func (o TraceDependencyViewOutput) Tool() string {
+ return o.ToolName
+}
+
+func traceDependencyView(ctx *ai.ToolContext, input TraceDependencyViewInput)
(TraceDependencyViewOutput, error) {
+ log.Printf("Tool 'trace_dependency_view' called for service: %s",
input.ServiceName)
+
+ return TraceDependencyViewOutput{
+ ToolName: "trace_dependency_view",
+ UpstreamServices: []string{"api-gateway", "user-service"},
+ DownstreamServices: []string{"mysql-orders-db", "redis-cache",
"payment-service"},
+ }, nil
+}
+
+// ================================================
+// Trace Latency Analysis Tool
+// ================================================
+
+type TraceLatencyAnalysisInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to analyze"`
+ TimeRangeMinutes int `json:"timeRangeMinutes"
jsonschema:"required,description=Time range in minutes"`
+}
+
+type LatencyBottleneck struct {
+ DownstreamService string `json:"downstreamService"`
+ LatencyAvgMillis int `json:"latencyAvgMillis"`
+ ContributionPercentage float64 `json:"contributionPercentage"`
+}
+
+type TraceLatencyAnalysisOutput struct {
+ ToolName string `json:"toolName"`
+ TotalLatencyAvgMillis int `json:"totalLatencyAvgMillis"`
+ Bottlenecks []LatencyBottleneck `json:"bottlenecks"`
+ Summary string `json:"summary"`
+}
+
+func (o TraceLatencyAnalysisOutput) Tool() string {
+ return o.ToolName
+}
+
+func traceLatencyAnalysis(ctx *ai.ToolContext, input
TraceLatencyAnalysisInput) (TraceLatencyAnalysisOutput, error) {
+ log.Printf("Tool 'trace_latency_analysis' called for service: %s",
input.ServiceName)
+
+ return TraceLatencyAnalysisOutput{
+ ToolName: "trace_latency_analysis",
+ TotalLatencyAvgMillis: 3200,
+ Bottlenecks: []LatencyBottleneck{
+ {
+ DownstreamService: "mysql-orders-db",
+ LatencyAvgMillis: 3050,
+ ContributionPercentage: 95.3,
+ },
+ {
+ DownstreamService: "user-service",
+ LatencyAvgMillis: 150,
+ ContributionPercentage: 4.7,
+ },
+ },
+ Summary: "平均总延迟为 3200ms。瓶颈已定位，95.3% 的延迟来自对下游 'mysql-orders-db'
的调用",
+ }, nil
+}
+
+// ================================================
+// Database Connection Pool Analysis Tool
+// ================================================
+
+type DatabaseConnectionPoolAnalysisInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service to analyze"`
+}
+
+type DatabaseConnectionPoolAnalysisOutput struct {
+ ToolName string `json:"toolName"`
+ MaxConnections int `json:"maxConnections"`
+ ActiveConnections int `json:"activeConnections"`
+ IdleConnections int `json:"idleConnections"`
+ PendingRequests int `json:"pendingRequests"`
+ Summary string `json:"summary"`
+}
+
+func (o DatabaseConnectionPoolAnalysisOutput) Tool() string {
+ return o.ToolName
+}
+
+func databaseConnectionPoolAnalysis(ctx *ai.ToolContext, input
DatabaseConnectionPoolAnalysisInput) (DatabaseConnectionPoolAnalysisOutput,
error) {
+ log.Printf("Tool 'database_connection_pool_analysis' called for
service: %s", input.ServiceName)
+
+ return DatabaseConnectionPoolAnalysisOutput{
+ ToolName: "database_connection_pool_analysis",
+ MaxConnections: 100,
+ ActiveConnections: 100,
+ IdleConnections: 0,
+ PendingRequests: 58,
+ Summary: "数据库连接池已完全耗尽 (100/100)，当前有 58 个请求正在排队等待连接",
+ }, nil
+}
+
+// ================================================
+// Kubernetes Get Pod Resources Tool
+// ================================================
+
+type KubernetesGetPodResourcesInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service"`
+ Namespace string `json:"namespace"
jsonschema:"required,description=The namespace of the service"`
+}
+
+type PodResource struct {
+ PodName string `json:"podName"`
+ CPUUsageCores float64 `json:"cpuUsageCores"`
+ CPURequestCores float64 `json:"cpuRequestCores"`
+ CPULimitCores float64 `json:"cpuLimitCores"`
+ MemoryUsageMi int `json:"memoryUsageMi"`
+ MemoryRequestMi int `json:"memoryRequestMi"`
+ MemoryLimitMi int `json:"memoryLimitMi"`
+}
+
+type KubernetesGetPodResourcesOutput struct {
+ ToolName string `json:"toolName"`
+ Pods []PodResource `json:"pods"`
+ Summary string `json:"summary"`
+}
+
+func (o KubernetesGetPodResourcesOutput) Tool() string {
+ return o.ToolName
+}
+
+func kubernetesGetPodResources(ctx *ai.ToolContext, input
KubernetesGetPodResourcesInput) (KubernetesGetPodResourcesOutput, error) {
+ log.Printf("Tool 'kubernetes_get_pod_resources' called for service: %s
in namespace: %s", input.ServiceName, input.Namespace)
+
+ return KubernetesGetPodResourcesOutput{
+ ToolName: "kubernetes_get_pod_resources",
+ Pods: []PodResource{
+ {
+ PodName: "order-service-pod-1",
+ CPUUsageCores: 0.8,
+ CPURequestCores: 0.5,
+ CPULimitCores: 1.0,
+ MemoryUsageMi: 1800,
+ MemoryRequestMi: 1024,
+ MemoryLimitMi: 2048,
+ },
+ {
+ PodName: "order-service-pod-2",
+ CPUUsageCores: 0.9,
+ CPURequestCores: 0.5,
+ CPULimitCores: 1.0,
+ MemoryUsageMi: 1950,
+ MemoryRequestMi: 1024,
+ MemoryLimitMi: 2048,
+ },
+ },
+ Summary: "2 out of 2 pods are near their memory limits",
+ }, nil
+}
+
+// ================================================
+// Dubbo Service Status Tool
+// ================================================
+
+type DubboServiceStatusInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the Dubbo service"`
+}
+
+type DubboProvider struct {
+ IP string `json:"ip"`
+ Port int `json:"port"`
+ Status string `json:"status"`
+}
+
+type DubboConsumer struct {
+ IP string `json:"ip"`
+ Application string `json:"application"`
+ Status string `json:"status"`
+}
+
+type DubboServiceStatusOutput struct {
+ ToolName string `json:"toolName"`
+ Providers []DubboProvider `json:"providers"`
+ Consumers []DubboConsumer `json:"consumers"`
+}
+
+func (o DubboServiceStatusOutput) Tool() string {
+ return o.ToolName
+}
+
+func dubboServiceStatus(ctx *ai.ToolContext, input DubboServiceStatusInput)
(DubboServiceStatusOutput, error) {
+ log.Printf("Tool 'dubbo_service_status' called for service: %s",
input.ServiceName)
+
+ return DubboServiceStatusOutput{
+ ToolName: "dubbo_service_status",
+ Providers: []DubboProvider{
+ {IP: "192.168.1.10", Port: 20880, Status: "healthy"},
+ {IP: "192.168.1.11", Port: 20880, Status: "healthy"},
+ },
+ Consumers: []DubboConsumer{
+ {IP: "192.168.1.20", Application: "web-frontend",
Status: "connected"},
+ {IP: "192.168.1.21", Application: "api-gateway",
Status: "connected"},
+ },
+ }, nil
+}
+
+// ================================================
+// Query Log Database Tool
+// ================================================
+
+type QueryLogDatabaseInput struct {
+ ServiceName string `json:"serviceName"
jsonschema:"required,description=The name of the service"`
+ Keyword string `json:"keyword"
jsonschema:"required,description=Keyword to search for"`
+ TimeRangeMinutes int `json:"timeRangeMinutes"
jsonschema:"required,description=Time range in minutes"`
+}
+
+type LogEntry struct {
+ Timestamp string `json:"timestamp"`
+ Level string `json:"level"`
+ Message string `json:"message"`
+}
+
+type QueryLogDatabaseOutput struct {
+ ToolName string `json:"toolName"`
+ TotalHits int `json:"totalHits"`
+ Logs []LogEntry `json:"logs"`
+ Summary string `json:"summary"`
+}
+
+func (o QueryLogDatabaseOutput) Tool() string {
+ return o.ToolName
+}
+
+func queryLogDatabase(ctx *ai.ToolContext, input QueryLogDatabaseInput)
(QueryLogDatabaseOutput, error) {
+ log.Printf("Tool 'query_log_database' called for service: %s, keyword:
%s", input.ServiceName, input.Keyword)
+
+ return QueryLogDatabaseOutput{
+ ToolName: "query_log_database",
+ TotalHits: 152,
+ Logs: []LogEntry{
+ {
+ Timestamp: "2025-08-16T15:32:05Z",
+ Level: "WARN",
+ Message: "Timeout waiting for idle object in
database connection pool.",
+ },
+ {
+ Timestamp: "2025-08-16T15:32:08Z",
+ Level: "WARN",
+ Message: "Timeout waiting for idle object in
database connection pool.",
+ },
+ },
+ Summary: fmt.Sprintf("在过去%d分钟内，发现 152 条关于 '%s' 的日志条目",
input.TimeRangeMinutes, input.Keyword),
+ }, nil
+}
+
+// ================================================
+// Search Archived Logs Tool
+// ================================================
+
+type SearchArchivedLogsInput struct {
+ FilePathPattern string `json:"filePathPattern"
jsonschema:"required,description=File path pattern to search"`
+ GrepKeyword string `json:"grepKeyword"
jsonschema:"required,description=Keyword to grep for"`
+}
+
+type MatchingLine struct {
+ FilePath string `json:"filePath"`
+ LineNumber int `json:"lineNumber"`
+ LineContent string `json:"lineContent"`
+}
+
+type SearchArchivedLogsOutput struct {
+ ToolName string `json:"toolName"`
+ FilesSearched int `json:"filesSearched"`
+ MatchingLines []MatchingLine `json:"matchingLines"`
+ Summary string `json:"summary"`
+}
+
+func (o SearchArchivedLogsOutput) Tool() string {
+ return o.ToolName
+}
+
+func searchArchivedLogs(ctx *ai.ToolContext, input SearchArchivedLogsInput)
(SearchArchivedLogsOutput, error) {
+ log.Printf("Tool 'search_archived_logs' called with pattern: %s,
keyword: %s", input.FilePathPattern, input.GrepKeyword)
+
+ return SearchArchivedLogsOutput{
+ ToolName: "search_archived_logs",
+ FilesSearched: 5,
+ MatchingLines: []MatchingLine{
+ {
+ FilePath:
"/logs/mysql-orders-db/slow-query-2025-08-16.log.gz",
+ LineNumber: 1024,
+ LineContent: "Query_time: 25.3s | SELECT
COUNT(id), SUM(price) FROM orders WHERE user_id = 'VIP_USER_123';",
+ },
+ {
+ FilePath:
"/logs/mysql-orders-db/slow-query-2025-08-16.log.gz",
+ LineNumber: 1029,
+ LineContent: "Query_time: 28.1s | SELECT
COUNT(id), SUM(price) FROM orders WHERE user_id = 'VIP_USER_456';",
+ },
+ },
+ Summary: "在归档的日志文件中，发现了多条查询，搜索了 5 个文件，找到了 2 条匹配行",
+ }, nil
+}
+
+// ================================================
+// Query Knowledge Base Tool
+// ================================================
+
+type QueryKnowledgeBaseInput struct {
+ QueryText string `json:"queryText"
jsonschema:"required,description=Query text to search for"`
+}
+
+type KnowledgeDocument struct {
+ Source string `json:"source"`
+ ContentSnippet string `json:"contentSnippet"`
+ SimilarityScore float64 `json:"similarityScore"`
+}
+
+type QueryKnowledgeBaseOutput struct {
+ ToolName string `json:"toolName"`
+ Documents []KnowledgeDocument `json:"documents"`
+}
+
+func (o QueryKnowledgeBaseOutput) Tool() string {
+ return o.ToolName
+}
+
+func queryKnowledgeBase(ctx *ai.ToolContext, input QueryKnowledgeBaseInput)
(QueryKnowledgeBaseOutput, error) {
+ log.Printf("Tool 'query_knowledge_base' called with query: %s",
input.QueryText)
+
+ return QueryKnowledgeBaseOutput{
+ ToolName: "query_knowledge_base",
+ Documents: []KnowledgeDocument{
+ {
+ Source:
"Project-VIP-Feature-Design-Doc.md",
+ ContentSnippet: "The 'Lifetime Achievement'
badge requires calculating total user spending. Note: This may cause slow
queries on the orders table if the user_id column is not properly indexed.",
+ SimilarityScore: 0.92,
+ },
+ },
+ }, nil
+}
+
+// ================================================
+// Tool Registration Function
+// ================================================
+
+var registeredTools []ai.Tool = nil
Review Comment:
[nitpick] Explicit initialization to nil is unnecessary for slice types.
Simply declare as `var registeredTools []ai.Tool` for cleaner code.
```suggestion
var registeredTools []ai.Tool
```
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