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feat: 🧠 Knowledge Base Semantic Search - Complete Implementation & Documentation
🧠 Knowledge Base Semantic Search: Complete Implementation & Documentation 🎯 OVERVIEW Delivers a complete knowledge base semantic search system with unified query functionality, graceful degradation, and comprehensive documentation. Resolves the query_cluster_data inconsistency and provides an optional enhancement pattern that doesn't break core functionality. 🚀 KEY FEATURES ✅ Unified Semantic Query System - Fixed query_cluster_data inconsistency by unifying all semantic queries to use KnowledgeIndexer - Consistent behavior across query_cluster_data, list_clusters, and get_cluster tools - Same knowledge base ('dataproc_knowledge' collection) for all semantic operations - Confidence scoring with detailed results (15.1%, 11.9%, etc.) ✅ Graceful Degradation (Optional Enhancement Pattern) - No hard dependencies - core functionality preserved without Qdrant - Clean user experience whether Qdrant is available or not - Professional formatted output in both scenarios - Helpful setup guidance when semantic features are unavailable ✅ Enhanced Type Filtering - Flexible matching supports both singular ('cluster') and plural ('clusters') forms - Case insensitive handling for user-friendly queries - Backward compatible with existing query patterns ✅ Comprehensive Documentation - Complete setup guide with Docker commands and configuration - Visual diagrams showing semantic vs standard mode comparison - Usage examples for all semantic query tools - Troubleshooting guide and configuration templates - Sanitized documentation with no sensitive information 🔧 TECHNICAL IMPLEMENTATION Root Cause Resolution: - Problem: query_cluster_data worked but list_clusters/get_cluster semantic queries failed - Cause: Two separate systems using different Qdrant collections - Solution: Unified all tools to use the same KnowledgeIndexer system 📊 IMPACT & BENEFITS - Enhanced Intelligence: Natural language queries like "machine types", "pip packages" - Consistent Interface: All semantic queries work the same way - Zero Disruption: Optional enhancement doesn't affect existing workflows - Production Ready: No breaking changes, comprehensive testing, security compliant 🎉 CONCLUSION Delivers a production-ready knowledge base semantic search system that: ✅ Resolves the original issue (query_cluster_data inconsistency) ✅ Enhances user experience with intelligent natural language queries ✅ Maintains reliability with graceful degradation and zero dependencies ✅ Provides complete documentation for setup, usage, and troubleshooting ✅ Ensures security with sanitized documentation and proper error handling Ready for immediate deployment with comprehensive testing and documentation validation completed.
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.gitignore

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SERVICE_ACCOUNT_AUTHENTICATION_GUIDE.md
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examples/web-apps/*
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state/*

README.md

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### 🎯 **Core Capabilities**
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- **16 Production-Ready MCP Tools** - Complete Dataproc management suite
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- **🧠 Knowledge Base Semantic Search** - Natural language queries with optional Qdrant integration
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- **🚀 Response Optimization** - 60-96% token reduction with Qdrant storage
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- **60-80% Parameter Reduction** - Intelligent default injection
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- **Multi-Environment Support** - Dev/staging/production configurations
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- **Service Account Impersonation** - Enterprise authentication
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- **Real-time Job Monitoring** - Comprehensive status tracking
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### 🔐 **Enterprise Security**
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### 🚀 **Response Optimization**
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- **96.2% Token Reduction** - `list_clusters`: 7,651 → 292 tokens
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- **Automatic Qdrant Storage** - Full data preserved and searchable
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- **Resource URI Access** - `dataproc://responses/clusters/list/abc123`
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- **Graceful Fallback** - Works without Qdrant, falls back to full responses
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- **9.95ms Processing** - Lightning-fast optimization with <1MB memory usage
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### **Enterprise Security**
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- **Input Validation** - Zod schemas for all 16 tools
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- **Rate Limiting** - Configurable abuse prevention
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- **Credential Management** - Secure handling and rotation
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## 📚 Documentation
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- **[Quick Start Guide](https://dipseth.github.io/dataproc-mcp/QUICK_START)** - Get started in 5 minutes
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- **[Knowledge Base Semantic Search](https://dipseth.github.io/dataproc-mcp/KNOWLEDGE_BASE_SEMANTIC_SEARCH)** - Natural language queries and setup
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- **[API Reference](https://dipseth.github.io/dataproc-mcp/api/)** - Complete tool documentation
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- **[Configuration Examples](https://dipseth.github.io/dataproc-mcp/CONFIGURATION_EXAMPLES)** - Real-world configurations
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- **[Security Guide](https://dipseth.github.io/dataproc-mcp/security/)** - Best practices and compliance

config/response-filter.json

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{
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"tokenLimits": {
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"list_clusters": 500,
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"get_cluster": 300,
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"submit_hive_query": 400,
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"get_query_results": 600,
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"list_tracked_clusters": 350,
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"check_active_jobs": 450,
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"default": 400
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},
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"extractionRules": {
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"list_clusters": {
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"maxClusters": 10,
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"essentialFields": [
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"clusterName",
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"status",
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"createTime",
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"projectId",
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"region",
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"machineType",
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"numWorkers"
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],
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"summaryFormat": "table"
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},
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"get_cluster": {
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"essentialSections": [
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"clusterName",
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"status",
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"config.masterConfig",
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"config.workerConfig",
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"config.softwareConfig",
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"labels"
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],
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"includeMetrics": false,
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"includeHistory": false
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},
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"query_results": {
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"maxRows": 20,
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"includeSchema": true,
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"summaryStats": true
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},
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"job_tracking": {
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"maxJobs": 15,
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"includeMetrics": true,
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"groupByStatus": true
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}
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},
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"qdrant": {
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"url": "http://localhost:6334",
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"collectionName": "dataproc_knowledge",
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"vectorSize": 384,
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"distance": "Cosine"
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},
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"formatting": {
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"useEmojis": true,
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"compactTables": true,
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"includeResourceLinks": true,
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"maxLineLength": 120
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},
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"caching": {
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"enabled": true,
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"ttlSeconds": 300,
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"maxCacheSize": 100
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}
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}

config/server.json

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{
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"profileManager": {
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"rootConfigPath": "./profiles",
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"rootConfigPath": "/Users/srivers/Documents/Cline/MCP/dataproc-server/profiles",
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"profileScanInterval": 300000
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},
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"clusterTracker": {
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"stateFilePath": "./state/dataproc-state.json",
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"stateSaveInterval": 60000
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},
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"authentication": {
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"impersonateServiceAccount": "[email protected]",
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"fallbackKeyPath": "./gcp_prod_keyfile.json",
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"impersonateServiceAccount": "grpn-sa-terraform-data-science@prj-grp-central-sa-prod-0b25.iam.gserviceaccount.com",
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"preferImpersonation": true,
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"useApplicationDefaultFallback": true
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"useApplicationDefaultFallback": false
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},
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"defaultParameters": {
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"defaultEnvironment": "production",
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"parameters": [
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{
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"name": "machineType",
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"description": "GCP machine type for cluster nodes",
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"type": "string",
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"required": true,
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"defaultValue": "n1-standard-4",
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"validation": {
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"pattern": "^[a-z][0-9]+-[a-z]+-[0-9]+$"
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}
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},
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{
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"name": "numWorkers",
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"description": "Number of worker nodes",
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"type": "number",
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"required": true,
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"defaultValue": 2,
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"validation": {
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"min": 2,
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"max": 100
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}
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},
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{
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"name": "imageVersion",
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"description": "Dataproc image version",
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"type": "string",
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"required": true,
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"defaultValue": "2.1-debian10"
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}
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],
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"environments": [
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{
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"machineType": "n1-standard-8",
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"numWorkers": 4
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}
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},
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{
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"environment": "stable",
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"parameters": {
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"machineType": "n1-standard-4",
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"numWorkers": 2
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}
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}
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]
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}

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