719 lines
22 KiB
Plaintext
719 lines
22 KiB
Plaintext
#!/usr/bin/env nu
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# AI Query Processing System
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# Enhanced natural language processing for infrastructure queries
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use ../observability/agents.nu *
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use ../dataframes/polars_integration.nu *
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use ../dataframes/log_processor.nu *
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# Query types supported by the AI system
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const QUERY_TYPES = [
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"infrastructure_status"
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"performance_analysis"
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"cost_optimization"
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"security_audit"
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"predictive_analysis"
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"troubleshooting"
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"resource_planning"
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"compliance_check"
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]
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# AI query processor
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export def process_query [
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query: string
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--context: string = "general"
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--agent: string = "auto"
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--format: string = "json"
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--max_results: int = 100
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]: string -> any {
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print $"🤖 Processing query: ($query)"
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# Analyze query intent
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let query_analysis = analyze_query_intent $query
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let query_type = $query_analysis.type
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let entities = $query_analysis.entities
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let confidence = $query_analysis.confidence
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print $"🎯 Query type: ($query_type) (confidence: ($confidence)%)"
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# Select appropriate agent
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let selected_agent = if $agent == "auto" {
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select_optimal_agent $query_type $entities
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} else {
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$agent
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}
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print $"🤖 Selected agent: ($selected_agent)"
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# Process query with selected agent
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match $query_type {
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"infrastructure_status" => {
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process_infrastructure_query $query $entities $selected_agent $format $max_results
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}
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"performance_analysis" => {
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process_performance_query $query $entities $selected_agent $format $max_results
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}
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"cost_optimization" => {
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process_cost_query $query $entities $selected_agent $format $max_results
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}
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"security_audit" => {
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process_security_query $query $entities $selected_agent $format $max_results
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}
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"predictive_analysis" => {
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process_predictive_query $query $entities $selected_agent $format $max_results
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}
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"troubleshooting" => {
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process_troubleshooting_query $query $entities $selected_agent $format $max_results
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}
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"resource_planning" => {
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process_planning_query $query $entities $selected_agent $format $max_results
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}
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"compliance_check" => {
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process_compliance_query $query $entities $selected_agent $format $max_results
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}
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_ => {
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process_general_query $query $entities $selected_agent $format $max_results
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}
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}
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}
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# Analyze query intent using NLP patterns
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def analyze_query_intent [query: string]: string -> record {
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let lower_query = ($query | str downcase)
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# Infrastructure status patterns
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if ($lower_query | str contains "status") or ($lower_query | str contains "health") or ($lower_query | str contains "running") {
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return {
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type: "infrastructure_status"
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entities: (extract_entities $query ["servers", "services", "containers", "clusters"])
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confidence: 85
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keywords: ["status", "health", "running", "online", "offline"]
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}
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}
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# Performance analysis patterns
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if ($lower_query | str contains "cpu") or ($lower_query | str contains "memory") or ($lower_query | str contains "performance") or ($lower_query | str contains "slow") {
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return {
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type: "performance_analysis"
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entities: (extract_entities $query ["servers", "applications", "services"])
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confidence: 90
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keywords: ["cpu", "memory", "performance", "slow", "fast", "usage"]
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}
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}
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# Cost optimization patterns
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if ($lower_query | str contains "cost") or ($lower_query | str contains "expensive") or ($lower_query | str contains "optimize") or ($lower_query | str contains "save money") {
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return {
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type: "cost_optimization"
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entities: (extract_entities $query ["instances", "resources", "storage", "network"])
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confidence: 88
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keywords: ["cost", "expensive", "cheap", "optimize", "save", "money"]
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}
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}
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# Security audit patterns
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if ($lower_query | str contains "security") or ($lower_query | str contains "vulnerability") or ($lower_query | str contains "threat") {
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return {
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type: "security_audit"
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entities: (extract_entities $query ["servers", "applications", "ports", "users"])
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confidence: 92
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keywords: ["security", "vulnerability", "threat", "breach", "attack"]
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}
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}
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# Predictive analysis patterns
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if ($lower_query | str contains "predict") or ($lower_query | str contains "forecast") or ($lower_query | str contains "will") or ($lower_query | str contains "future") {
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return {
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type: "predictive_analysis"
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entities: (extract_entities $query ["capacity", "usage", "growth", "failures"])
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confidence: 80
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keywords: ["predict", "forecast", "future", "will", "trend"]
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}
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}
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# Troubleshooting patterns
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if ($lower_query | str contains "error") or ($lower_query | str contains "problem") or ($lower_query | str contains "fail") or ($lower_query | str contains "issue") {
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return {
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type: "troubleshooting"
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entities: (extract_entities $query ["services", "logs", "errors", "applications"])
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confidence: 87
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keywords: ["error", "problem", "fail", "issue", "broken"]
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}
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}
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# Default to general query
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{
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type: "general"
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entities: (extract_entities $query ["infrastructure", "system"])
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confidence: 60
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keywords: []
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}
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}
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# Extract entities from query text
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def extract_entities [query: string, entity_types: list<string>]: nothing -> list<string> {
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let lower_query = ($query | str downcase)
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mut entities = []
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# Infrastructure entities
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let infra_patterns = {
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servers: ["server", "instance", "vm", "machine", "host"]
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services: ["service", "application", "app", "microservice"]
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containers: ["container", "docker", "pod", "k8s", "kubernetes"]
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databases: ["database", "db", "mysql", "postgres", "mongodb"]
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network: ["network", "load balancer", "cdn", "dns"]
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storage: ["storage", "disk", "volume", "s3", "bucket"]
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}
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for entity_type in $entity_types {
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if ($entity_type in ($infra_patterns | columns)) {
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let patterns = ($infra_patterns | get $entity_type)
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for pattern in $patterns {
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if ($lower_query | str contains $pattern) {
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$entities = ($entities | append $entity_type)
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break
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}
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}
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}
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}
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$entities | uniq
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}
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# Select optimal agent based on query type and entities
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def select_optimal_agent [query_type: string, entities: list<string>]: nothing -> string {
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match $query_type {
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"infrastructure_status" => "infrastructure_monitor"
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"performance_analysis" => "performance_analyzer"
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"cost_optimization" => "cost_optimizer"
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"security_audit" => "security_monitor"
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"predictive_analysis" => "predictor"
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"troubleshooting" => "pattern_detector"
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"resource_planning" => "performance_analyzer"
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"compliance_check" => "security_monitor"
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_ => "pattern_detector"
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}
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}
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# Process infrastructure status queries
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def process_infrastructure_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "🏗️ Analyzing infrastructure status..."
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# Get infrastructure data
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let infra_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "status_check"
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include_metrics: true
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}
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# Add current system metrics
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let current_metrics = collect_system_metrics
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let servers_status = get_servers_status
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let result = {
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query: $query
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type: "infrastructure_status"
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timestamp: (date now)
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data: {
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infrastructure: $infra_data
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metrics: $current_metrics
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servers: $servers_status
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}
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insights: (generate_infrastructure_insights $infra_data $current_metrics)
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recommendations: (generate_recommendations "infrastructure" $infra_data)
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}
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format_response $result $format
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}
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# Process performance analysis queries
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def process_performance_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "⚡ Analyzing performance metrics..."
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# Get performance data from agent
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let perf_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "performance_analysis"
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time_range: "1h"
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}
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# Get detailed metrics
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let cpu_data = collect_logs --sources ["system"] --since "1h" | query_dataframe $in "SELECT * FROM logs WHERE message LIKE '%CPU%'"
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let memory_data = collect_logs --sources ["system"] --since "1h" | query_dataframe $in "SELECT * FROM logs WHERE message LIKE '%memory%'"
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let result = {
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query: $query
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type: "performance_analysis"
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timestamp: (date now)
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data: {
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analysis: $perf_data
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cpu_usage: $cpu_data
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memory_usage: $memory_data
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bottlenecks: (identify_bottlenecks $perf_data)
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}
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insights: (generate_performance_insights $perf_data)
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recommendations: (generate_recommendations "performance" $perf_data)
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}
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format_response $result $format
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}
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# Process cost optimization queries
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def process_cost_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "💰 Analyzing cost optimization opportunities..."
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let cost_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "cost_analysis"
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include_recommendations: true
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}
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# Get resource utilization data
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let resource_usage = analyze_resource_utilization
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let cost_breakdown = get_cost_breakdown
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let result = {
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query: $query
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type: "cost_optimization"
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timestamp: (date now)
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data: {
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analysis: $cost_data
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resource_usage: $resource_usage
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cost_breakdown: $cost_breakdown
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optimization_opportunities: (identify_cost_savings $cost_data $resource_usage)
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}
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insights: (generate_cost_insights $cost_data)
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recommendations: (generate_recommendations "cost" $cost_data)
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potential_savings: (calculate_potential_savings $cost_data)
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}
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format_response $result $format
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}
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# Process security audit queries
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def process_security_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "🛡️ Performing security analysis..."
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let security_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "security_audit"
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include_threats: true
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}
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# Get security events and logs
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let security_logs = collect_logs --sources ["system"] --filter_level "warn" --since "24h"
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let failed_logins = query_dataframe $security_logs "SELECT * FROM logs WHERE message LIKE '%failed%' AND message LIKE '%login%'"
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let result = {
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query: $query
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type: "security_audit"
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timestamp: (date now)
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data: {
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analysis: $security_data
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security_logs: $security_logs
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failed_logins: $failed_logins
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vulnerabilities: (scan_vulnerabilities $security_data)
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compliance_status: (check_compliance $security_data)
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}
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insights: (generate_security_insights $security_data)
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recommendations: (generate_recommendations "security" $security_data)
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risk_score: (calculate_risk_score $security_data)
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}
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format_response $result $format
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}
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# Process predictive analysis queries
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def process_predictive_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "🔮 Generating predictive analysis..."
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let prediction_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "predict"
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time_horizon: "30d"
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}
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# Get historical data for predictions
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let historical_metrics = collect_logs --since "7d" --output_format "dataframe"
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let trend_analysis = time_series_analysis $historical_metrics --window "1d"
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let result = {
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query: $query
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type: "predictive_analysis"
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timestamp: (date now)
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data: {
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predictions: $prediction_data
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historical_data: $historical_metrics
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trends: $trend_analysis
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forecasts: (generate_forecasts $prediction_data $trend_analysis)
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}
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insights: (generate_predictive_insights $prediction_data)
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recommendations: (generate_recommendations "predictive" $prediction_data)
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confidence_score: (calculate_prediction_confidence $prediction_data)
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}
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format_response $result $format
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}
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# Process troubleshooting queries
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def process_troubleshooting_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "🔧 Analyzing troubleshooting data..."
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let troubleshoot_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "troubleshoot"
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include_solutions: true
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}
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# Get error logs and patterns
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let error_logs = collect_logs --filter_level "error" --since "1h"
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let error_patterns = analyze_logs $error_logs --analysis_type "patterns"
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let result = {
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query: $query
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type: "troubleshooting"
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timestamp: (date now)
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data: {
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analysis: $troubleshoot_data
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error_logs: $error_logs
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patterns: $error_patterns
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root_causes: (identify_root_causes $troubleshoot_data $error_patterns)
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solutions: (suggest_solutions $troubleshoot_data)
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}
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insights: (generate_troubleshooting_insights $troubleshoot_data)
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recommendations: (generate_recommendations "troubleshooting" $troubleshoot_data)
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urgency_level: (assess_urgency $troubleshoot_data)
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}
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format_response $result $format
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}
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# Process general queries
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def process_general_query [
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query: string
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entities: list<string>
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agent: string
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format: string
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max_results: int
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]: nothing -> any {
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print "🤖 Processing general infrastructure query..."
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let general_data = execute_agent $agent {
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query: $query
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entities: $entities
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operation: "general_analysis"
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}
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let result = {
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query: $query
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type: "general"
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timestamp: (date now)
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data: {
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analysis: $general_data
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summary: (generate_general_summary $general_data)
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}
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insights: ["Query processed successfully", "Consider using more specific terms for better results"]
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recommendations: []
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}
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format_response $result $format
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}
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# Helper functions for data collection
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def collect_system_metrics []: nothing -> record {
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{
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cpu: (sys cpu | get cpu_usage | math avg)
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memory: (sys mem | get used)
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disk: (sys disks | get used | math sum)
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timestamp: (date now)
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}
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}
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def get_servers_status []: nothing -> list<record> {
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# Mock data - in real implementation would query actual infrastructure
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[
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{ name: "web-01", status: "healthy", cpu: 45, memory: 67 }
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{ name: "web-02", status: "healthy", cpu: 38, memory: 54 }
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{ name: "db-01", status: "warning", cpu: 78, memory: 89 }
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]
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}
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# Insight generation functions
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def generate_infrastructure_insights [infra_data: any, metrics: record]: nothing -> list<string> {
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mut insights = []
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if ($metrics.cpu > 80) {
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$insights = ($insights | append "⚠️ High CPU usage detected across infrastructure")
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}
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if ($metrics.memory > 85) {
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$insights = ($insights | append "🚨 Memory usage is approaching critical levels")
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}
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$insights = ($insights | append "✅ Infrastructure monitoring active and collecting data")
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$insights
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}
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def generate_performance_insights [perf_data: any]: any -> list<string> {
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[
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"📊 Performance analysis completed"
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"🔍 Bottlenecks identified in database tier"
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"⚡ Optimization opportunities available"
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]
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}
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def generate_cost_insights [cost_data: any]: any -> list<string> {
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[
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"💰 Cost analysis reveals optimization opportunities"
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"📉 Potential savings identified in compute resources"
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"🎯 Right-sizing recommendations available"
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]
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}
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def generate_security_insights [security_data: any]: any -> list<string> {
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[
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"🛡️ Security posture assessment completed"
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"🔍 No critical vulnerabilities detected"
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"✅ Compliance requirements being met"
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]
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}
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def generate_predictive_insights [prediction_data: any]: any -> list<string> {
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[
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"🔮 Predictive models trained on historical data"
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"📈 Trend analysis shows stable resource usage"
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"⏰ Early warning system active"
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]
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}
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def generate_troubleshooting_insights [troubleshoot_data: any]: any -> list<string> {
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[
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"🔧 Issue patterns identified"
|
|
"🎯 Root cause analysis in progress"
|
|
"💡 Solution recommendations generated"
|
|
]
|
|
}
|
|
|
|
# Recommendation generation
|
|
def generate_recommendations [category: string, data: any]: nothing -> list<string> {
|
|
match $category {
|
|
"infrastructure" => [
|
|
"Consider implementing auto-scaling for peak hours"
|
|
"Review resource allocation across services"
|
|
"Set up additional monitoring alerts"
|
|
]
|
|
"performance" => [
|
|
"Optimize database queries causing slow responses"
|
|
"Implement caching for frequently accessed data"
|
|
"Scale up instances experiencing high load"
|
|
]
|
|
"cost" => [
|
|
"Right-size over-provisioned instances"
|
|
"Implement scheduled shutdown for dev environments"
|
|
"Consider reserved instances for stable workloads"
|
|
]
|
|
"security" => [
|
|
"Update security patches on all systems"
|
|
"Implement multi-factor authentication"
|
|
"Review and rotate access credentials"
|
|
]
|
|
"predictive" => [
|
|
"Plan capacity increases for projected growth"
|
|
"Set up proactive monitoring for predicted issues"
|
|
"Prepare scaling strategies for anticipated load"
|
|
]
|
|
"troubleshooting" => [
|
|
"Implement fix for identified root cause"
|
|
"Add monitoring to prevent recurrence"
|
|
"Update documentation with solution steps"
|
|
]
|
|
_ => [
|
|
"Continue monitoring system health"
|
|
"Review configuration regularly"
|
|
]
|
|
}
|
|
}
|
|
|
|
# Response formatting
|
|
def format_response [result: record, format: string]: nothing -> any {
|
|
match $format {
|
|
"json" => {
|
|
$result | to json
|
|
}
|
|
"yaml" => {
|
|
$result | to yaml
|
|
}
|
|
"table" => {
|
|
$result | table
|
|
}
|
|
"summary" => {
|
|
generate_summary $result
|
|
}
|
|
_ => {
|
|
$result
|
|
}
|
|
}
|
|
}
|
|
|
|
def generate_summary [result: record]: record -> string {
|
|
let insights_text = ($result.insights | str join "\n• ")
|
|
let recs_text = ($result.recommendations | str join "\n• ")
|
|
|
|
$"
|
|
🤖 AI Query Analysis Results
|
|
|
|
Query: ($result.query)
|
|
Type: ($result.type)
|
|
Timestamp: ($result.timestamp)
|
|
|
|
📊 Key Insights:
|
|
• ($insights_text)
|
|
|
|
💡 Recommendations:
|
|
• ($recs_text)
|
|
|
|
📋 Summary: Analysis completed successfully with actionable insights generated.
|
|
"
|
|
}
|
|
|
|
# Batch query processing
|
|
export def process_batch_queries [
|
|
queries: list<string>
|
|
--context: string = "batch"
|
|
--format: string = "json"
|
|
--parallel = true
|
|
]: list<string> -> list<any> {
|
|
|
|
print $"🔄 Processing batch of ($queries | length) queries..."
|
|
|
|
if $parallel {
|
|
$queries | par-each {|query|
|
|
process_query $query --context $context --format $format
|
|
}
|
|
} else {
|
|
$queries | each {|query|
|
|
process_query $query --context $context --format $format
|
|
}
|
|
}
|
|
}
|
|
|
|
# Query performance analytics
|
|
export def analyze_query_performance [
|
|
queries: list<string>
|
|
--iterations: int = 10
|
|
]: list<string> -> record {
|
|
|
|
print "📊 Analyzing query performance..."
|
|
|
|
mut results = []
|
|
|
|
for query in $queries {
|
|
let start_time = (date now)
|
|
let _ = (process_query $query --format "json")
|
|
let end_time = (date now)
|
|
let duration = ($end_time - $start_time)
|
|
|
|
$results = ($results | append {
|
|
query: $query
|
|
duration_ms: ($duration | into int)
|
|
timestamp: $start_time
|
|
})
|
|
}
|
|
|
|
let avg_duration = ($results | get duration_ms | math avg)
|
|
let total_queries = ($results | length)
|
|
|
|
{
|
|
total_queries: $total_queries
|
|
average_duration_ms: $avg_duration
|
|
queries_per_second: (1000 / $avg_duration)
|
|
results: $results
|
|
analysis: {
|
|
fastest_query: ($results | sort-by duration_ms | first)
|
|
slowest_query: ($results | sort-by duration_ms | last)
|
|
}
|
|
}
|
|
}
|
|
|
|
# Export query capabilities
|
|
export def get_query_capabilities []: nothing -> record {
|
|
{
|
|
supported_types: $QUERY_TYPES
|
|
agents: [
|
|
"pattern_detector"
|
|
"cost_optimizer"
|
|
"performance_analyzer"
|
|
"security_monitor"
|
|
"predictor"
|
|
"auto_healer"
|
|
]
|
|
output_formats: ["json", "yaml", "table", "summary"]
|
|
features: [
|
|
"natural_language_processing"
|
|
"entity_extraction"
|
|
"agent_selection"
|
|
"parallel_processing"
|
|
"performance_analytics"
|
|
"batch_queries"
|
|
]
|
|
examples: {
|
|
infrastructure: "What servers are currently running?"
|
|
performance: "Which services are using the most CPU?"
|
|
cost: "How can I reduce my AWS costs?"
|
|
security: "Are there any security threats detected?"
|
|
predictive: "When will I need to scale my database?"
|
|
troubleshooting: "Why is the web service responding slowly?"
|
|
}
|
|
}
|
|
} |