# Storage & Data Management - Business Requirements

**Document Version**: 1.0
**Date**: January 2025
**Status**: Business Requirements Specification
**Module**: Storage & Data Management (`pkg/storage/`, `internal/actionhistory/`, `internal/database/`)

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## 1. Purpose & Scope

### 1.1 Business Purpose
The Storage & Data Management layer provides comprehensive data persistence, retrieval, and management capabilities supporting AI-driven decision making, pattern recognition, historical analysis, and operational intelligence through traditional data storage, caching systems, and future vector database capabilities.

### 1.2 Scope
- **v1**: Core data persistence (PostgreSQL) for action history and operational data
- **v1**: High-performance caching for improved system responsiveness
- **v1**: Comprehensive tracking of all remediation actions
- **v2**: Vector Database capabilities (pending evaluation) - similarity search and embeddings management
- **Database Operations**: Core database connectivity and operations management

---

## 2. Vector Database (v2 - Pending Evaluation)

**Status**: Deferred to Version 2 pending business value evaluation and technical assessment

### 2.1 Business Capabilities (v2)

#### 2.1.1 Vector Storage & Retrieval (v2)
- **BR-VDB-001**: MUST store high-dimensional vector embeddings with associated metadata (v2)
- **BR-VDB-002**: MUST support efficient similarity search with configurable algorithms (v2)
- **BR-VDB-003**: MUST provide vector upsert operations (insert or update) (v2)
- **BR-VDB-004**: MUST support vector deletion and cleanup operations (v2)
- **BR-VDB-005**: MUST maintain vector-metadata consistency and integrity (v2)

#### 2.1.2 Embedding Services (v2)
- **BR-VDB-006**: MUST generate embeddings from text using multiple embedding models (v2)
- **BR-VDB-007**: MUST support caching of embeddings to reduce computation costs (v2)
- **BR-VDB-008**: MUST provide embedding quality validation and scoring (v2)
- **BR-VDB-009**: MUST support batch embedding generation for efficiency (v2)
- **BR-VDB-010**: MUST implement embedding versioning for model upgrades (v2)

#### 2.1.3 Pattern Extraction
- **BR-VDB-011**: MUST extract meaningful patterns from structured and unstructured data
- **BR-VDB-012**: MUST identify similar patterns across historical remediation actions
- **BR-VDB-013**: MUST support multi-dimensional pattern analysis
- **BR-VDB-014**: MUST provide pattern confidence scoring and validation
- **BR-VDB-015**: MUST maintain pattern evolution tracking over time

#### 2.1.4 Search & Matching
- **BR-VDB-016**: MUST support k-nearest neighbor (KNN) search with configurable distance metrics
- **BR-VDB-017**: MUST provide approximate nearest neighbor (ANN) search for large datasets
- **BR-VDB-018**: MUST support filtered search with metadata constraints
- **BR-VDB-019**: MUST implement range queries for similarity thresholds
- **BR-VDB-020**: MUST provide search result ranking and relevance scoring

### 2.2 Database Implementations
- **BR-VDB-021**: MUST support PostgreSQL with pgvector extension for production use
- **BR-VDB-022**: MUST provide in-memory vector database for development and testing
- **BR-VDB-023**: MUST implement connection pooling for optimal performance
- **BR-VDB-024**: MUST support database sharding for horizontal scalability
- **BR-VDB-025**: MUST provide database migration and upgrade capabilities

### 2.3 Performance Optimization (v2)
- **BR-VDB-026**: MUST implement vector indexing for fast similarity search (v2)
- **BR-VDB-027**: MUST support query optimization and execution planning (v2)
- **BR-VDB-028**: MUST provide compression for efficient vector storage (v2)
- **BR-VDB-029**: MUST implement incremental indexing for real-time updates (v2)
- **BR-VDB-030**: MUST support parallel processing for batch operations (v2)

**Note**: All vector database requirements (BR-VDB-001 to BR-VDB-030) are deferred to Version 2 pending:
- Business value assessment of vector similarity search capabilities
- Technical evaluation of external vector database providers (Pinecone, Weaviate)
- Cost-benefit analysis of embedding services vs. current HolmesGPT capabilities
- Integration complexity assessment with existing PostgreSQL infrastructure

---

## 3. Cache Management

### 3.1 Business Capabilities

#### 3.1.1 Multi-Level Caching
- **BR-CACHE-001**: MUST implement in-memory caching for frequently accessed data
- **BR-CACHE-002**: MUST support distributed caching with Redis for scalability
- **BR-CACHE-003**: MUST provide cache hierarchy with configurable eviction policies
- **BR-CACHE-004**: MUST implement cache warming strategies for critical data
- **BR-CACHE-005**: MUST support cache partitioning for isolation and performance

#### 3.1.2 Cache Operations
- **BR-CACHE-006**: MUST support atomic cache operations (get, set, delete)
- **BR-CACHE-007**: MUST implement batch cache operations for efficiency
- **BR-CACHE-008**: MUST provide cache expiration with TTL (Time To Live)
- **BR-CACHE-009**: MUST support conditional cache updates (compare-and-swap)
- **BR-CACHE-010**: MUST implement cache versioning for consistency

#### 3.1.3 Cache Strategies
- **BR-CACHE-011**: MUST implement Least Recently Used (LRU) eviction policy
- **BR-CACHE-012**: MUST support Least Frequently Used (LFU) eviction policy
- **BR-CACHE-013**: MUST provide cache-aside pattern for application caching
- **BR-CACHE-014**: MUST implement write-through caching for critical data
- **BR-CACHE-015**: MUST support write-behind caching for performance optimization

#### 3.1.4 Cache Monitoring
- **BR-CACHE-016**: MUST track cache hit rates and miss ratios
- **BR-CACHE-017**: MUST monitor cache memory usage and performance
- **BR-CACHE-018**: MUST provide cache health checks and diagnostics
- **BR-CACHE-019**: MUST implement cache analytics and optimization recommendations
- **BR-CACHE-020**: MUST support cache warming and preloading strategies

---

## 4. Action History Management

### 4.1 Business Capabilities

#### 4.1.1 Action Tracking
- **BR-HIST-001**: MUST record comprehensive history of all remediation actions
- **BR-HIST-002**: MUST capture action context including alert details and cluster state
  - Store alert tracking ID from Remediation Processor (BR-SP-021) for end-to-end correlation
  - Capture complete alert lifecycle state transitions and timestamps
  - **Enhanced for Post-Mortem**: Store AI decision rationale, confidence scores, and context data used
  - **Enhanced for Post-Mortem**: Record performance metrics, error conditions, and recovery actions
  - **Enhanced for Post-Mortem**: Capture human interventions, manual overrides, and operator decisions
  - **Enhanced for Post-Mortem**: Store business impact assessment and affected resource inventory
  - **Enhanced for Post-Mortem**: Record resolution validation results and effectiveness metrics
  - **Enhanced for Post-Mortem**: Maintain timeline correlation between all events and decisions
  - Maintain correlation between gateway receipt, processor tracking, and action execution
  - Support audit trail queries linking alerts to all subsequent actions taken
  - **Enhanced for Post-Mortem**: Enable comprehensive incident reconstruction for analysis
- **BR-HIST-003**: MUST track action outcomes and effectiveness measurements
- **BR-HIST-004**: MUST maintain temporal relationships between related actions
- **BR-HIST-005**: MUST support action correlation and pattern analysis

#### 4.1.2 Data Repository
- **BR-HIST-006**: MUST provide PostgreSQL-based persistent storage for action history
- **BR-HIST-007**: MUST implement data partitioning for efficient querying
- **BR-HIST-008**: MUST support data indexing for fast retrieval and analysis
- **BR-HIST-009**: MUST provide data compression for storage optimization
- **BR-HIST-010**: MUST implement data archival and retention policies

#### 4.1.3 Query & Analysis
- **BR-HIST-011**: MUST support complex queries for historical analysis
- **BR-HIST-012**: MUST provide time-based filtering and range queries
- **BR-HIST-013**: MUST support aggregation queries for trend analysis
- **BR-HIST-014**: MUST implement full-text search for action descriptions
- **BR-HIST-015**: MUST provide API access for external analysis tools

#### 4.1.4 Effectiveness Assessment
- **BR-HIST-016**: MUST track action effectiveness scores over time
- **BR-HIST-017**: MUST correlate actions with system health improvements
- **BR-HIST-018**: MUST identify successful action patterns and trends
- **BR-HIST-019**: MUST detect unsuccessful actions for learning purposes
- **BR-HIST-020**: MUST provide effectiveness reporting and visualization

---

## 5. Database Operations

### 5.1 Business Capabilities

#### 5.1.1 Connection Management
- **BR-DB-001**: MUST support PostgreSQL database connections with SSL/TLS
- **BR-DB-002**: MUST implement connection pooling for optimal resource usage
- **BR-DB-003**: MUST provide connection health monitoring and recovery
- **BR-DB-004**: MUST support multiple database instances for high availability
- **BR-DB-005**: MUST implement connection failover and load balancing

#### 5.1.2 Transaction Management
- **BR-DB-006**: MUST support ACID transactions for data consistency
- **BR-DB-007**: MUST implement transaction isolation levels as appropriate
- **BR-DB-008**: MUST provide transaction retry mechanisms for transient failures
- **BR-DB-009**: MUST support distributed transactions where necessary
- **BR-DB-010**: MUST implement deadlock detection and resolution

#### 5.1.3 Schema Management
- **BR-DB-011**: MUST support database schema migrations and versioning
- **BR-DB-012**: MUST implement schema validation and integrity checks
- **BR-DB-013**: MUST provide schema backup and recovery capabilities
- **BR-DB-014**: MUST support schema evolution without data loss
- **BR-DB-015**: MUST implement stored procedures for complex operations

#### 5.1.4 Data Operations
- **BR-DB-016**: MUST provide standard CRUD operations (Create, Read, Update, Delete)
- **BR-DB-017**: MUST support bulk operations for large datasets
- **BR-DB-018**: MUST implement data validation and constraint enforcement
- **BR-DB-019**: MUST provide data export and import capabilities
- **BR-DB-020**: MUST support data synchronization between instances

---

## 6. Performance Requirements

### 6.1 Vector Database Performance
- **BR-PERF-001**: Vector similarity searches MUST complete within 100ms for <10K vectors
- **BR-PERF-002**: Embedding generation MUST process 1000 texts within 30 seconds
- **BR-PERF-003**: Vector insertion MUST handle 1000 vectors per second
- **BR-PERF-004**: Pattern extraction MUST complete within 5 seconds for standard datasets
- **BR-PERF-005**: Vector database MUST support 1M+ vectors with sub-second search

### 6.2 Cache Performance
- **BR-PERF-006**: Cache operations MUST complete within 1ms for in-memory cache
- **BR-PERF-007**: Distributed cache operations MUST complete within 5ms
- **BR-PERF-008**: Cache hit rate MUST exceed 80% for frequently accessed data
- **BR-PERF-009**: Cache MUST support 10,000 operations per second
- **BR-PERF-010**: Cache warming MUST complete within 2 minutes for critical data

### 6.3 Database Performance
- **BR-PERF-011**: Database queries MUST respond within 100ms for simple operations
- **BR-PERF-012**: Complex analytical queries MUST complete within 5 seconds
- **BR-PERF-013**: Database MUST handle 1000 concurrent connections
- **BR-PERF-014**: Bulk operations MUST process 10,000 records per minute
- **BR-PERF-015**: Database recovery MUST complete within 15 minutes

### 6.4 Scalability
- **BR-PERF-016**: Storage systems MUST scale horizontally to handle growth
- **BR-PERF-017**: MUST support terabyte-scale data storage and processing
- **BR-PERF-018**: MUST maintain performance with 100x data growth
- **BR-PERF-019**: MUST support geographic distribution for global deployments
- **BR-PERF-020**: MUST implement auto-scaling based on demand patterns

---

## 7. Reliability & Availability Requirements

### 7.1 High Availability
- **BR-REL-001**: Storage systems MUST maintain 99.9% uptime
- **BR-REL-002**: MUST support active-passive failover for critical databases
- **BR-REL-003**: MUST implement data replication across multiple zones
- **BR-REL-004**: MUST provide automated backup and recovery procedures
- **BR-REL-005**: MUST support zero-downtime maintenance operations

### 7.2 Data Durability
- **BR-REL-006**: MUST guarantee data durability with 99.999999999% (11 9's) reliability
- **BR-REL-007**: MUST implement point-in-time recovery capabilities
- **BR-REL-008**: MUST provide data integrity validation and corruption detection
- **BR-REL-009**: MUST support cross-region data replication for disaster recovery
- **BR-REL-010**: MUST maintain data consistency across all replicas

### 7.3 Fault Tolerance
- **BR-REL-011**: MUST handle node failures without data loss
- **BR-REL-012**: MUST recover from network partitions gracefully
- **BR-REL-013**: MUST implement automatic repair and resynchronization
- **BR-REL-014**: MUST provide graceful degradation during partial outages
- **BR-REL-015**: MUST support emergency read-only mode for critical scenarios

---

## 8. Security Requirements

### 8.1 Data Protection
- **BR-SEC-001**: MUST encrypt data at rest using AES-256 encryption
- **BR-SEC-002**: MUST encrypt data in transit using TLS 1.3+
- **BR-SEC-003**: MUST implement secure key management and rotation
- **BR-SEC-004**: MUST support data masking for non-production environments
- **BR-SEC-005**: MUST provide data anonymization capabilities

### 8.2 Access Control
- **BR-SEC-006**: MUST implement role-based access control (RBAC) for all data operations
- **BR-SEC-007**: MUST support database-level authentication and authorization
- **BR-SEC-008**: MUST implement API-level security for data access
- **BR-SEC-009**: MUST support audit logging for all data access operations
- **BR-SEC-010**: MUST provide data access monitoring and alerting

### 8.3 Compliance
- **BR-SEC-011**: MUST comply with data protection regulations (GDPR, CCPA)
- **BR-SEC-012**: MUST support data retention and deletion policies
- **BR-SEC-013**: MUST provide data lineage tracking for compliance reporting
- **BR-SEC-014**: MUST implement data sovereignty controls
- **BR-SEC-015**: MUST support compliance monitoring and reporting

---

## 9. Data Quality & Governance

### 9.1 Data Quality
- **BR-QUAL-001**: MUST implement data validation at ingestion points
- **BR-QUAL-002**: MUST detect and handle data anomalies and outliers
- **BR-QUAL-003**: MUST provide data quality scoring and monitoring
- **BR-QUAL-004**: MUST implement data cleansing and normalization
- **BR-QUAL-005**: MUST support data quality reporting and alerting

### 9.2 Data Governance
- **BR-QUAL-006**: MUST maintain comprehensive data cataloging and metadata
- **BR-QUAL-007**: MUST implement data classification and sensitivity labeling
- **BR-QUAL-008**: MUST support data ownership and stewardship
- **BR-QUAL-009**: MUST provide data usage tracking and analytics
- **BR-QUAL-010**: MUST implement data governance policies and enforcement

### 9.3 Data Lifecycle
- **BR-QUAL-011**: MUST implement automated data archival based on policies
- **BR-QUAL-012**: MUST support data purging and deletion procedures
- **BR-QUAL-013**: MUST provide data migration capabilities
- **BR-QUAL-014**: MUST implement data versioning and change tracking
- **BR-QUAL-015**: MUST support data retention compliance and validation

---

## 10. Integration Requirements

### 10.1 Internal Integration
- **BR-INT-001**: MUST integrate with AI components for vector operations and embeddings
- **BR-INT-002**: MUST support workflow engine data persistence and retrieval
- **BR-INT-003**: MUST provide platform layer with action history and metrics
- **BR-INT-004**: MUST integrate with intelligence components for pattern storage
- **BR-INT-005**: MUST coordinate with monitoring systems for performance data

### 10.2 External Integration
- **BR-INT-006**: MUST support integration with external databases (MySQL, MongoDB)
- **BR-INT-007**: MUST integrate with cloud storage services (S3, GCS, Azure Blob)
- **BR-INT-008**: MUST support data lake integration for large-scale analytics
- **BR-INT-009**: MUST integrate with business intelligence and analytics tools
- **BR-INT-010**: MUST support API integration for external data sources

---

## 11. Monitoring & Observability

### 11.1 Performance Monitoring
- **BR-MON-001**: MUST track database query performance and optimization opportunities
- **BR-MON-002**: MUST monitor cache hit rates and efficiency metrics
- **BR-MON-003**: MUST provide vector search performance analytics
- **BR-MON-004**: MUST monitor storage utilization and capacity planning
- **BR-MON-005**: MUST track data ingestion rates and processing times

### 11.2 Health Monitoring
- **BR-MON-006**: MUST provide real-time health checks for all storage components
- **BR-MON-007**: MUST monitor data integrity and consistency across systems
- **BR-MON-008**: MUST track replication lag and synchronization status
- **BR-MON-009**: MUST monitor backup success rates and recovery capabilities
- **BR-MON-010**: MUST provide storage capacity and utilization alerting

### 11.3 Business Metrics
- **BR-MON-011**: MUST track data quality metrics and improvement trends
- **BR-MON-012**: MUST monitor data access patterns and usage analytics
- **BR-MON-013**: MUST provide cost optimization metrics and recommendations
- **BR-MON-014**: MUST track compliance adherence and violation reporting
- **BR-MON-015**: MUST measure business value derived from stored data

---

## 12. Backup & Recovery

### 12.1 Backup Strategy
- **BR-BKP-001**: MUST implement automated daily backups with retention policies
- **BR-BKP-002**: MUST support incremental and differential backup strategies
- **BR-BKP-003**: MUST provide cross-region backup replication
- **BR-BKP-004**: MUST implement backup encryption and secure storage
- **BR-BKP-005**: MUST support backup compression for storage efficiency

### 12.2 Recovery Capabilities
- **BR-BKP-006**: MUST support point-in-time recovery with 15-minute granularity
- **BR-BKP-007**: MUST provide database restoration within 30 minutes for <100GB
- **BR-BKP-008**: MUST support selective data recovery for specific components
- **BR-BKP-009**: MUST implement automated recovery testing and validation
- **BR-BKP-010**: MUST provide disaster recovery with <4 hour RTO

### 12.3 Business Continuity
- **BR-BKP-011**: MUST support hot standby systems for critical components
- **BR-BKP-012**: MUST implement data synchronization across availability zones
- **BR-BKP-013**: MUST provide runbook procedures for recovery scenarios
- **BR-BKP-014**: MUST support emergency read-only operations during outages
- **BR-BKP-015**: MUST maintain service continuity during backup operations

---

## 13. Cost Optimization

### 13.1 Storage Optimization
- **BR-COST-001**: MUST implement intelligent data tiering based on access patterns
- **BR-COST-002**: MUST provide compression algorithms to reduce storage costs
- **BR-COST-003**: MUST support data deduplication for similar content
- **BR-COST-004**: MUST implement lifecycle policies for automated cost management
- **BR-COST-005**: MUST provide cost analysis and optimization recommendations

### 13.2 Compute Optimization
- **BR-COST-006**: MUST optimize query execution plans for cost efficiency
- **BR-COST-007**: MUST implement auto-scaling to match demand with resources
- **BR-COST-008**: MUST support scheduled scaling for predictable workloads
- **BR-COST-009**: MUST provide resource utilization optimization
- **BR-COST-010**: MUST implement cost budgeting and alerting

---

## 14. Success Criteria

### 14.1 Functional Success
- Vector database provides accurate similarity search with >95% relevance
- Cache systems achieve >80% hit rates for frequently accessed data
- Action history system maintains complete audit trails with 100% reliability
- Database operations support all business requirements with full ACID compliance
- Data quality meets defined standards with <1% error rate

### 14.2 Performance Success
- All storage operations meet defined latency requirements under normal load
- System scales to handle 10x data growth without performance degradation
- High availability targets are achieved with <0.1% downtime
- Backup and recovery operations complete within defined RTO/RPO objectives
- Cost optimization reduces storage expenses by 25% through intelligent tiering

### 14.3 Business Success
- Storage systems enable effective AI-driven decision making
- Historical data analysis provides valuable business insights
- Data governance ensures compliance with all regulatory requirements
- User satisfaction with data access and performance exceeds 90%
- Storage infrastructure demonstrates clear ROI through efficiency gains

---

## 15. Local Vector Operations (V1 Enhancement)

### 15.1 Local Embedding Generation

#### **BR-VECTOR-V1-001: Local Embedding Generation**
**Business Requirement**: The system MUST provide comprehensive local embedding generation capabilities using multiple embedding techniques to support similarity search and pattern matching without external dependencies.

**Functional Requirements**:
1. **Multi-Technique Embedding** - MUST support multiple embedding generation techniques (TF-IDF, Word2Vec, sentence transformers)
2. **Local Processing** - MUST generate embeddings locally without external API dependencies
3. **Embedding Optimization** - MUST optimize embedding generation for performance and accuracy
4. **Embedding Storage** - MUST efficiently store and manage generated embeddings

**Success Criteria**:
- Support for 3+ embedding generation techniques
- Local embedding generation with no external API dependencies
- 384-dimensional embeddings with normalized magnitude
- <1 second embedding generation time for typical text inputs

**Business Value**: Local embedding generation eliminates external dependencies and reduces operational costs

#### **BR-VECTOR-V1-002: Similarity Search and Pattern Matching**
**Business Requirement**: The system MUST provide high-performance similarity search and pattern matching capabilities using local vector operations to support intelligent pattern discovery and analysis.

**Functional Requirements**:
1. **Similarity Search** - MUST implement efficient similarity search algorithms for vector data
2. **Pattern Matching** - MUST provide pattern matching capabilities based on vector similarity
3. **Relevance Scoring** - MUST implement relevance scoring for search results
4. **Performance Optimization** - MUST optimize search performance for large vector datasets

**Success Criteria**:
- >90% relevance accuracy in similarity search results
- <100ms search response time for datasets with 10,000+ patterns
- Support for cosine similarity, euclidean distance, and dot product metrics
- Scalable search performance with linear complexity

**Business Value**: High-performance pattern matching enables intelligent analysis and decision making

#### **BR-VECTOR-V1-003: Memory and PostgreSQL Integration**
**Business Requirement**: The system MUST provide seamless integration between in-memory vector operations and PostgreSQL persistence with automatic failover and data consistency.

**Functional Requirements**:
1. **Dual Storage** - MUST support both in-memory and PostgreSQL vector storage
2. **Automatic Failover** - MUST provide automatic failover between memory and PostgreSQL storage
3. **Data Consistency** - MUST ensure data consistency between memory and persistent storage
4. **Performance Optimization** - MUST optimize performance for both storage types

**Success Criteria**:
- <1 second failover time between memory and PostgreSQL storage
- 100% data consistency between storage types
- 90% performance retention during failover scenarios
- Automatic recovery and synchronization capabilities

**Business Value**: Reliable vector operations with high availability and data consistency

---

## 16. External Vector Database Integration (V2 Advanced)

### 16.1 Multi-Provider Vector Database Support

#### **BR-EXTERNAL-VECTOR-001: Multi-Provider Vector Database Integration**
**Business Requirement**: The system MUST provide comprehensive integration with external vector database providers (Pinecone, Weaviate, Chroma) to support enterprise-scale vector operations and advanced similarity search capabilities.

**Functional Requirements**:
1. **Multi-Provider Support** - MUST support integration with multiple external vector database providers
2. **Provider Abstraction** - MUST provide abstraction layer for seamless provider switching
3. **Failover Management** - MUST implement automatic failover between providers
4. **Performance Optimization** - MUST optimize performance for each provider's capabilities

**Success Criteria**:
- Support for 3+ external vector database providers
- <1 second failover time between providers
- 99.9% reliability for external vector operations
- Provider-specific performance optimization

**Business Value**: Enterprise-scale vector operations with provider flexibility and reliability

#### **BR-EXTERNAL-VECTOR-002: Advanced Embedding Models**
**Business Requirement**: The system MUST integrate with advanced embedding models (OpenAI, Cohere, HuggingFace) to provide state-of-the-art embedding quality and domain-specific optimization.

**Functional Requirements**:
1. **Model Integration** - MUST integrate with multiple advanced embedding model providers
2. **Quality Optimization** - MUST optimize embedding quality for specific use cases
3. **Model Management** - MUST provide comprehensive model lifecycle management
4. **Performance Monitoring** - MUST monitor embedding model performance and quality

**Success Criteria**:
- 20% improvement in embedding quality compared to local models
- Support for 5+ advanced embedding models
- Complete model lifecycle management with versioning
- Real-time performance monitoring and quality assessment

**Business Value**: Superior embedding quality enables more accurate pattern recognition and analysis

#### **BR-EXTERNAL-VECTOR-003: Enterprise Scalability**
**Business Requirement**: The system MUST provide enterprise-scale vector operations supporting millions of vectors with high-performance search capabilities and enterprise reliability requirements.

**Functional Requirements**:
1. **Scale Support** - MUST support 10M+ vectors with consistent performance
2. **High-Performance Search** - MUST maintain <100ms search times at enterprise scale
3. **Enterprise Reliability** - MUST provide 99.9% reliability for enterprise operations
4. **Resource Management** - MUST efficiently manage resources at enterprise scale

**Success Criteria**:
- Support for 10M+ vectors with linear scalability
- <100ms search response time at enterprise scale
- 99.9% reliability for all vector operations
- Efficient resource utilization with cost optimization

**Business Value**: Enterprise-scale capabilities enable large-scale pattern recognition and analysis

---

## 17. Workflow Catalog Enhancements (V1.1 Candidates)

### 17.1 Hybrid Scoring Improvements

#### **BR-STORAGE-050: True Score Normalization (V1.1 Candidate)**
**Business Requirement**: The system MAY provide true score normalization for hybrid workflow scoring, scaling all scores to a 0-1 range based on the maximum possible score rather than simple capping.

**Status**: 🟡 **V1.1 CANDIDATE** - Pending user feedback on V1.0 capping behavior

**Current V1.0 Behavior**:
- Hybrid scoring uses: `final_score = min(base_similarity + label_boost - label_penalty, 1.0)`
- Scores are **capped** at 1.0 (100%), not normalized
- Label boost: +0.10 per matching optional label
- Label penalty: -0.10 per conflicting optional label

**Proposed V1.1 Enhancement**:
1. **True Normalization** - MAY scale all scores to 0-1 range based on max possible score
2. **Dynamic Range** - MAY adjust normalization based on candidate set
3. **Confidence Calibration** - MAY provide calibrated confidence scores for downstream decisions

**Evaluation Criteria** (for V1.1 decision):
- User feedback on whether capping at 1.0 is sufficient for ranking purposes
- Impact on workflow selection quality
- Complexity vs. benefit analysis
- Performance implications of dynamic normalization

**Reference**: DD-WORKFLOW-004 v1.1, QUESTIONS_FOR_DATA_STORAGE_TEAM.md A4

**Business Value**: Improved confidence calibration enables better downstream decision-making in approval policies

---

*This document serves as the definitive specification for business requirements of Kubernaut's Storage & Data Management components. All implementation and testing should align with these requirements to ensure reliable, performant, and secure data operations supporting intelligent remediation capabilities.*
