Smart RAG Endpoints Overview

Smart RAG nodes are intelligent RAG (Retrieval-Augmented Generation) components that combine document processing, chunking, and retrieval operations into a single, optimized unit. This comprehensive guide covers all aspects of managing smart RAG nodes, from configuration to advanced optimization strategies.

What are Smart RAG Nodes?

Smart RAG nodes are all-in-one RAG components that automatically handle:

Document Processing: Intelligent document ingestion and preparation
Chunking: Automated text segmentation with optimal chunk sizes
Embedding Generation: Vector embeddings for semantic search
Retrieval: Query-based document retrieval with configurable Top K
Quality Metrics: Built-in evaluation and performance tracking

Key Benefits

Simplified Workflow: Single node handles multiple RAG operations
Optimized Performance: Integrated processing reduces overhead
Intelligent Defaults: Pre-configured settings for common use cases
Scalable Architecture: Efficient handling of large document collections
Quality Monitoring: Built-in metrics for retrieval effectiveness

Available Endpoints

Smart RAG nodes are managed through two primary endpoints:

Endpoint	Method	Purpose	Documentation
List Smart RAG Nodes	`GET`	Retrieve all smart RAG nodes from a flow	📄 List Documentation
Update Smart RAG Configuration	`PATCH`	Modify smart RAG node settings	🔧 Update Documentation

Base URL Structure

https://{flow_name}.flows.graphorlm.com/smart-rag

: Your flow identifier
All endpoints require API token authentication
Content-Type: application/json for update operations

Core Concepts

Node Structure

Smart RAG nodes follow the standard GraphorLM node structure:

{
  "id": "smart-rag-1748287628685",
  "type": "smart-rag",
  "position": {"x": 500, "y": 300},
  "style": {"height": 200, "width": 320},
  "data": {
    "name": "Intelligent Document Processing",
    "config": {
      "topK": 10
    },
    "result": {
      "updated": true,
      "processing": false,
      "waiting": false,
      "has_error": false,
      "updatedMetrics": true,
      "total_processed": 1250,
      "total_chunks": 420,
      "total_retrieved": 85
    }
  }
}

Configuration Parameters

Smart RAG nodes have a focused configuration approach:

Parameter	Type	Default	Description
`topK`	integer \| null	5	Number of top results to retrieve. Set to `null` for unlimited processing

Result Metrics

Smart RAG nodes provide comprehensive result tracking:

Field	Type	Description
`updated`	boolean	Node processing status with current configuration
`processing`	boolean	Whether the node is currently processing
`waiting`	boolean	Whether the node is waiting for dependencies
`has_error`	boolean	Error status indicator
`updatedMetrics`	boolean	Whether evaluation metrics have been computed
`total_processed`	integer	Total number of documents processed
`total_chunks`	integer	Number of chunks generated during processing
`total_retrieved`	integer	Number of documents retrieved in recent queries

Smart RAG Strategies

Smart RAG nodes support multiple operational strategies based on the Top K configuration:

High-Precision Strategy

Configuration: topK: 3-5

{
  "config": {
    "topK": 5
  }
}

Characteristics:

Focus: Maximum relevance per result
Resource Usage: Low system overhead
Processing Speed: Fastest retrieval times
Quality Profile: High precision, moderate recall

Best For:

Critical decision support systems
Expert knowledge applications
Factual question answering
High-stakes information retrieval

Trade-offs:

Strengths: Fast, resource-efficient, highly relevant results
Limitations: May miss edge cases or broader context

Balanced Strategy

Configuration: topK: 8-12

{
  "config": {
    "topK": 10
  }
}

Characteristics:

Focus: Optimal balance of precision and coverage
Resource Usage: Moderate system consumption
Processing Speed: Good retrieval performance
Quality Profile: Balanced precision and recall

Best For:

General-purpose knowledge management
Customer support applications
Educational content retrieval
Business intelligence queries

Trade-offs:

Strengths: Good overall performance, versatile application
Limitations: Not specialized for extreme precision or coverage

Comprehensive Strategy

Configuration: topK: 15-25

{
  "config": {
    "topK": 20
  }
}

Characteristics:

Focus: Thorough coverage and exploration
Resource Usage: Higher system consumption
Processing Speed: Moderate retrieval times
Quality Profile: High recall, good precision

Best For:

Research and discovery applications
Content exploration systems
Comprehensive analysis tools
Academic research platforms

Trade-offs:

Strengths: Comprehensive coverage, discovery potential
Limitations: Higher resource requirements, longer processing

Unlimited Strategy

Configuration: topK: null

{
  "config": {
    "topK": null
  }
}

Characteristics:

Focus: Complete document analysis
Resource Usage: Maximum system consumption
Processing Speed: Longest processing times
Quality Profile: Maximum recall, comprehensive coverage

Best For:

Exhaustive research projects
Complete document analysis
Investigation and audit systems
Resource-unlimited scenarios

Trade-offs:

Strengths: Complete coverage, no missed information
Limitations: Highest resource usage, longest processing times

Strategy Selection Matrix

Use Case	Precision Need	Coverage Need	Resource Constraints	Recommended Strategy
Critical Decisions	High	Low	High	High-Precision (3-5)
General Q&A	Medium	Medium	Medium	Balanced (8-12)
Research	Medium	High	Low	Comprehensive (15-25)
Exhaustive Analysis	Low	Maximum	None	Unlimited (null)
Real-time Systems	High	Low	High	High-Precision (3-5)
Content Discovery	Low	High	Medium	Comprehensive (15-25)

Common Workflows

Basic Smart RAG Workflow

Configuration Optimization Workflow

Multi-Strategy Implementation

Authentication & Authorization

All smart RAG endpoints require API token authentication:

Authorization: Bearer YOUR_API_TOKEN

Token Requirements

Scope: Must have flow management permissions
Expiry: Check token validity before operations
Security: Use HTTPS for all API communications

Generate API tokens through the API Tokens guide.

Response Formats

Standard Success Response

{
  "success": true,
  "message": "Operation completed successfully",
  "node_id": "smart-rag-1748287628685"
}

Error Response Format

{
  "detail": "Descriptive error message"
}

Common Status Codes

Code	Meaning	Action Required
200	Success	Continue with result
400	Bad Request	Check request format
401	Unauthorized	Verify API token
404	Not Found	Check flow/node existence
422	Validation Error	Review configuration parameters
500	Server Error	Contact support if persistent

Integration Examples

Smart RAG Performance Monitor

import requests
import time
from typing import Dict, List, Any, Optional

class SmartRagPerformanceMonitor:
    def __init__(self, flow_name: str, api_token: str):
        self.flow_name = flow_name
        self.api_token = api_token
        self.base_url = f"https://{flow_name}.flows.graphorlm.com"
        self.headers = {"Authorization": f"Bearer {api_token}"}
    
    def get_all_nodes(self) -> List[Dict[str, Any]]:
        """Retrieve all smart RAG nodes"""
        response = requests.get(f"{self.base_url}/smart-rag", headers=self.headers)
        response.raise_for_status()
        return response.json()
    
    def analyze_performance(self) -> Dict[str, Any]:
        """Comprehensive performance analysis"""
        nodes = self.get_all_nodes()
        
        analysis = {
            "summary": {
                "total_nodes": len(nodes),
                "active_nodes": 0,
                "processing_nodes": 0,
                "error_nodes": 0,
                "nodes_with_metrics": 0
            },
            "resource_usage": {
                "low": 0,
                "medium": 0,
                "high": 0,
                "unlimited": 0
            },
            "performance_distribution": {},
            "throughput_metrics": {
                "total_processed": 0,
                "total_chunks": 0,
                "total_retrieved": 0,
                "processing_efficiency": 0
            },
            "quality_indicators": {
                "metrics_enabled": 0,
                "avg_retrieval_rate": 0
            },
            "recommendations": []
        }
        
        total_top_k = 0
        nodes_with_top_k = 0
        
        for node in nodes:
            config = node.get("data", {}).get("config", {})
            result = node.get("data", {}).get("result", {})
            
            # Node status analysis
            if result.get("processing"):
                analysis["summary"]["processing_nodes"] += 1
            elif result.get("has_error"):
                analysis["summary"]["error_nodes"] += 1
            elif result.get("updated"):
                analysis["summary"]["active_nodes"] += 1
            
            # Metrics tracking
            if result.get("updatedMetrics"):
                analysis["summary"]["nodes_with_metrics"] += 1
                analysis["quality_indicators"]["metrics_enabled"] += 1
            
            # Resource usage categorization
            top_k = config.get("topK")
            if top_k is None:
                analysis["resource_usage"]["unlimited"] += 1
            elif top_k <= 5:
                analysis["resource_usage"]["low"] += 1
            elif top_k <= 15:
                analysis["resource_usage"]["medium"] += 1
            else:
                analysis["resource_usage"]["high"] += 1
            
            # Performance distribution
            if top_k is not None:
                performance_key = f"topK_{top_k}"
                analysis["performance_distribution"][performance_key] = (
                    analysis["performance_distribution"].get(performance_key, 0) + 1
                )
                total_top_k += top_k
                nodes_with_top_k += 1
            
            # Throughput metrics
            if result.get("total_processed"):
                analysis["throughput_metrics"]["total_processed"] += result["total_processed"]
            if result.get("total_chunks"):
                analysis["throughput_metrics"]["total_chunks"] += result["total_chunks"]
            if result.get("total_retrieved"):
                analysis["throughput_metrics"]["total_retrieved"] += result["total_retrieved"]
        
        # Calculate averages and efficiency
        if nodes_with_top_k > 0:
            avg_top_k = total_top_k / nodes_with_top_k
            analysis["performance_distribution"]["average_top_k"] = round(avg_top_k, 1)
        
        # Processing efficiency calculation
        total_processed = analysis["throughput_metrics"]["total_processed"]
        total_chunks = analysis["throughput_metrics"]["total_chunks"]
        if total_processed > 0:
            analysis["throughput_metrics"]["processing_efficiency"] = round(
                total_chunks / total_processed, 2
            )
        
        # Retrieval rate calculation
        total_retrieved = analysis["throughput_metrics"]["total_retrieved"]
        if total_chunks > 0:
            analysis["quality_indicators"]["avg_retrieval_rate"] = round(
                (total_retrieved / total_chunks) * 100, 1
            )
        
        # Generate recommendations
        analysis["recommendations"] = self._generate_recommendations(analysis)
        
        return analysis
    
    def _generate_recommendations(self, analysis: Dict[str, Any]) -> List[str]:
        """Generate optimization recommendations"""
        recommendations = []
        summary = analysis["summary"]
        resource_usage = analysis["resource_usage"]
        quality = analysis["quality_indicators"]
        
        # Node health recommendations
        if summary["error_nodes"] > 0:
            recommendations.append(
                f"Address {summary['error_nodes']} nodes with errors to improve system reliability"
            )
        
        if summary["processing_nodes"] > summary["total_nodes"] * 0.3:
            recommendations.append(
                "High percentage of nodes are processing - consider resource allocation review"
            )
        
        # Resource optimization recommendations
        if resource_usage["unlimited"] > 0:
            recommendations.append(
                f"Review {resource_usage['unlimited']} unlimited nodes for resource optimization opportunities"
            )
        
        if resource_usage["high"] > resource_usage["low"] + resource_usage["medium"]:
            recommendations.append(
                "Consider balancing resource usage - many nodes use high Top K values"
            )
        
        # Quality improvement recommendations
        metrics_coverage = quality["metrics_enabled"] / summary["total_nodes"] * 100
        if metrics_coverage < 80:
            recommendations.append(
                f"Enable metrics on more nodes - currently only {metrics_coverage:.1f}% have metrics enabled"
            )
        
        # Performance recommendations
        avg_retrieval_rate = quality["avg_retrieval_rate"]
        if avg_retrieval_rate > 0:
            if avg_retrieval_rate < 10:
                recommendations.append(
                    "Low retrieval rate detected - consider increasing Top K values for better coverage"
                )
            elif avg_retrieval_rate > 50:
                recommendations.append(
                    "High retrieval rate - consider optimizing Top K values for better efficiency"
                )
        
        return recommendations
    
    def print_performance_report(self, analysis: Dict[str, Any]):
        """Print formatted performance report"""
        print("🤖 Smart RAG Performance Analysis Report")
        print("=" * 50)
        print(f"Flow: {self.flow_name}")
        print(f"Analysis Date: {time.strftime('%Y-%m-%d %H:%M:%S')}")
        
        # Summary section
        summary = analysis["summary"]
        print(f"\n📊 Node Summary:")
        print(f"  Total Nodes: {summary['total_nodes']}")
        print(f"  Active Nodes: {summary['active_nodes']}")
        print(f"  Processing Nodes: {summary['processing_nodes']}")
        print(f"  Error Nodes: {summary['error_nodes']}")
        print(f"  Nodes with Metrics: {summary['nodes_with_metrics']}")
        
        # Resource usage section
        resource = analysis["resource_usage"]
        print(f"\n⚡ Resource Usage Distribution:")
        print(f"  Low Usage (≤5): {resource['low']} nodes")
        print(f"  Medium Usage (6-15): {resource['medium']} nodes")
        print(f"  High Usage (>15): {resource['high']} nodes")
        print(f"  Unlimited Usage: {resource['unlimited']} nodes")
        
        # Performance metrics
        perf = analysis["performance_distribution"]
        if "average_top_k" in perf:
            print(f"\n🎯 Performance Metrics:")
            print(f"  Average Top K: {perf['average_top_k']}")
        
        # Throughput analysis
        throughput = analysis["throughput_metrics"]
        print(f"\n📈 Throughput Analysis:")
        print(f"  Total Documents Processed: {throughput['total_processed']:,}")
        print(f"  Total Chunks Generated: {throughput['total_chunks']:,}")
        print(f"  Total Documents Retrieved: {throughput['total_retrieved']:,}")
        print(f"  Processing Efficiency: {throughput['processing_efficiency']} chunks/doc")
        
        # Quality indicators
        quality = analysis["quality_indicators"]
        print(f"\n🎯 Quality Indicators:")
        print(f"  Metrics Coverage: {(quality['metrics_enabled']/summary['total_nodes']*100):.1f}%")
        if quality["avg_retrieval_rate"] > 0:
            print(f"  Average Retrieval Rate: {quality['avg_retrieval_rate']}%")
        
        # Recommendations
        if analysis["recommendations"]:
            print(f"\n💡 Optimization Recommendations:")
            for i, rec in enumerate(analysis["recommendations"], 1):
                print(f"  {i}. {rec}")
        
        print("\n" + "=" * 50)

# Usage example
monitor = SmartRagPerformanceMonitor("my-rag-pipeline", "YOUR_API_TOKEN")
analysis = monitor.analyze_performance()
monitor.print_performance_report(analysis)

Smart RAG Configuration Optimizer

class SmartRagConfigurationOptimizer {
  constructor(flowName, apiToken) {
    this.flowName = flowName;
    this.apiToken = apiToken;
    this.baseUrl = `https://${flowName}.flows.graphorlm.com`;
  }

  async getAllNodes() {
    const response = await fetch(`${this.baseUrl}/smart-rag`, {
      headers: { 'Authorization': `Bearer ${this.apiToken}` }
    });
    
    if (!response.ok) {
      throw new Error(`Failed to fetch nodes: ${response.statusText}`);
    }
    
    return await response.json();
  }

  async optimizeConfiguration(nodeId, targetProfile = 'balanced') {
    const profiles = {
      'precision': { topK: 5, description: 'High-precision, low resource usage' },
      'balanced': { topK: 10, description: 'Balanced precision and coverage' },
      'comprehensive': { topK: 20, description: 'High coverage, thorough results' },
      'unlimited': { topK: null, description: 'Complete analysis, maximum resources' }
    };

    const profile = profiles[targetProfile];
    if (!profile) {
      throw new Error(`Invalid profile: ${targetProfile}. Available: ${Object.keys(profiles).join(', ')}`);
    }

    console.log(`🎯 Optimizing node ${nodeId} for ${targetProfile} profile`);
    console.log(`   Configuration: Top K = ${profile.topK || 'unlimited'}`);
    console.log(`   Description: ${profile.description}`);

    const response = await fetch(`${this.baseUrl}/smart-rag/${nodeId}`, {
      method: 'PATCH',
      headers: {
        'Authorization': `Bearer ${this.apiToken}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({ config: { topK: profile.topK } })
    });

    if (!response.ok) {
      throw new Error(`Optimization failed: ${response.statusText}`);
    }

    const result = await response.json();
    console.log(`✅ ${result.message}`);
    return result;
  }

  async batchOptimization(optimizations) {
    console.log(`🔄 Starting batch optimization for ${optimizations.length} nodes`);
    const results = [];

    for (const { nodeId, profile } of optimizations) {
      try {
        const result = await this.optimizeConfiguration(nodeId, profile);
        results.push({ nodeId, profile, success: true, result });
        
        // Small delay to avoid overwhelming the API
        await new Promise(resolve => setTimeout(resolve, 500));
      } catch (error) {
        console.error(`❌ Failed to optimize ${nodeId}: ${error.message}`);
        results.push({ nodeId, profile, success: false, error: error.message });
      }
    }

    this.generateBatchReport(results);
    return results;
  }

  generateBatchReport(results) {
    console.log('\n📋 Batch Optimization Report');
    console.log('============================');
    
    const successful = results.filter(r => r.success);
    const failed = results.filter(r => !r.success);
    
    console.log(`Total Operations: ${results.length}`);
    console.log(`Successful: ${successful.length}`);
    console.log(`Failed: ${failed.length}`);
    
    if (successful.length > 0) {
      console.log('\n✅ Successful Optimizations:');
      successful.forEach(({ nodeId, profile }) => {
        console.log(`   ${nodeId} → ${profile} profile`);
      });
    }
    
    if (failed.length > 0) {
      console.log('\n❌ Failed Optimizations:');
      failed.forEach(({ nodeId, profile, error }) => {
        console.log(`   ${nodeId} (${profile}): ${error}`);
      });
    }
  }

  async recommendOptimizations() {
    const nodes = await this.getAllNodes();
    const recommendations = [];

    console.log('🔍 Analyzing nodes for optimization opportunities...\n');

    for (const node of nodes) {
      const config = node.data.config || {};
      const result = node.data.result || {};
      const topK = config.topK;
      const nodeName = node.data.name;

      let recommendation = null;

      // Analysis logic
      if (result.has_error) {
        recommendation = {
          nodeId: node.id,
          nodeName,
          currentTopK: topK,
          issue: 'Node has errors',
          suggestedProfile: 'precision',
          reason: 'Reduce resource usage to address stability issues',
          priority: 'high'
        };
      } else if (result.processing && topK > 20) {
        recommendation = {
          nodeId: node.id,
          nodeName,
          currentTopK: topK,
          issue: 'Long processing time with high Top K',
          suggestedProfile: 'balanced',
          reason: 'Balance performance and quality',
          priority: 'medium'
        };
      } else if (topK === null && !result.updatedMetrics) {
        recommendation = {
          nodeId: node.id,
          nodeName,
          currentTopK: topK,
          issue: 'Unlimited processing without metrics',
          suggestedProfile: 'comprehensive',
          reason: 'Limit resource usage and enable monitoring',
          priority: 'medium'
        };
      } else if (topK && topK < 3) {
        recommendation = {
          nodeId: node.id,
          nodeName,
          currentTopK: topK,
          issue: 'Very low Top K may limit coverage',
          suggestedProfile: 'balanced',
          reason: 'Improve result coverage while maintaining efficiency',
          priority: 'low'
        };
      }

      if (recommendation) {
        recommendations.push(recommendation);
      }
    }

    this.printRecommendations(recommendations);
    return recommendations;
  }

  printRecommendations(recommendations) {
    if (recommendations.length === 0) {
      console.log('✅ No optimization recommendations - all nodes appear well-configured');
      return;
    }

    console.log(`💡 Found ${recommendations.length} optimization opportunities:\n`);

    const byPriority = {
      high: recommendations.filter(r => r.priority === 'high'),
      medium: recommendations.filter(r => r.priority === 'medium'),
      low: recommendations.filter(r => r.priority === 'low')
    };

    ['high', 'medium', 'low'].forEach(priority => {
      if (byPriority[priority].length > 0) {
        const icon = priority === 'high' ? '🔴' : priority === 'medium' ? '🟡' : '🟢';
        console.log(`${icon} ${priority.toUpperCase()} Priority (${byPriority[priority].length} items):`);
        
        byPriority[priority].forEach(rec => {
          console.log(`   📍 ${rec.nodeName} (${rec.nodeId})`);
          console.log(`      Issue: ${rec.issue}`);
          console.log(`      Current Top K: ${rec.currentTopK || 'unlimited'}`);
          console.log(`      Suggested: ${rec.suggestedProfile} profile`);
          console.log(`      Reason: ${rec.reason}\n`);
        });
      }
    });
  }
}

// Usage examples
const optimizer = new SmartRagConfigurationOptimizer('my-rag-pipeline', 'YOUR_API_TOKEN');

// Single node optimization
optimizer.optimizeConfiguration('smart-rag-123', 'balanced').catch(console.error);

// Batch optimization
const batchOps = [
  { nodeId: 'smart-rag-123', profile: 'precision' },
  { nodeId: 'smart-rag-456', profile: 'balanced' },
  { nodeId: 'smart-rag-789', profile: 'comprehensive' }
];
optimizer.batchOptimization(batchOps).catch(console.error);

// Get recommendations
optimizer.recommendOptimizations().catch(console.error);

Best Practices

Configuration Management

Start with Defaults: Begin with balanced configuration (Top K = 10) and adjust based on results
Incremental Changes: Make small adjustments and measure impact before larger changes
Environment-Specific Settings: Use different configurations for development, staging, and production
Documentation: Maintain clear records of configuration decisions and their rationale

Performance Optimization

Resource Management

Monitor System Impact: Track resource usage across different Top K settings
Balance Quality vs Efficiency: Higher Top K improves coverage but increases resource consumption
Scaling Considerations: Plan resource allocation for peak query volumes
Graceful Degradation: Implement fallback strategies for resource constraints

Processing Efficiency

Batch Operations: Process multiple queries efficiently through smart batching
Caching Strategies: Implement intelligent caching for frequently accessed results
Load Distribution: Balance processing load across multiple smart RAG nodes
Asynchronous Processing: Use background processing for non-critical operations

Quality Assurance

Metrics and Monitoring

Enable Metrics: Activate evaluation metrics on all production nodes
Regular Assessment: Schedule periodic quality reviews and optimizations
User Feedback Integration: Incorporate user feedback into quality measurements
Comparative Analysis: Compare performance across different configurations

Continuous Improvement

A/B Testing: Test different configurations with real user queries
Performance Baselines: Establish and maintain performance benchmarks
Quality Regression Detection: Monitor for quality degradation over time
Optimization Cycles: Implement regular optimization review cycles

Operational Excellence

Monitoring and Alerting

Health Checks: Implement comprehensive node health monitoring
Performance Alerts: Set up alerts for performance degradation
Resource Monitoring: Track system resource consumption patterns
Error Tracking: Monitor and respond to processing errors promptly

Maintenance and Updates

Regular Reviews: Schedule monthly configuration reviews
Update Coordination: Coordinate smart RAG updates with system changes
Version Control: Maintain configuration version history
Rollback Procedures: Have tested rollback procedures for configuration changes

Optimization Cycles

Monthly Reviews: Assess smart RAG performance and resource usage monthly
Quarterly Optimization: Implement major optimization changes quarterly
Annual Strategy Review: Review overall smart RAG strategy annually
Continuous Monitoring: Monitor key metrics continuously

Troubleshooting

High Resource Usage

Poor Result Quality

Slow Processing Performance

Inconsistent Results

Configuration Changes Not Applied

Advanced Features

Intelligent Resource Management

Dynamic Top K: Adjust Top K based on query complexity and available resources
Performance Prediction: Predict processing resource needs before execution
Resource Alerts: Automatic alerts when approaching resource thresholds
Usage Analytics: Detailed analysis of smart RAG usage patterns

Quality Optimization

A/B Testing Framework: Built-in support for comparing smart RAG strategies
Quality Regression Detection: Automatic detection of quality degradation
User Feedback Integration: Incorporate user ratings into optimization
Contextual Adaptation: Adjust behavior based on query context

Integration Capabilities

Multi-Flow Coordination: Coordinate smart RAG nodes across multiple flows
External System Integration: Connect with external knowledge bases
Real-time Updates: Dynamic configuration updates without downtime
API Ecosystem: Rich API ecosystem for custom integrations

Use Cases by Industry

Technology & Software

Documentation Search: Technical documentation and API reference
Code Knowledge: Code repository search and analysis
Support Systems: Technical support and troubleshooting
Decision: Precision-focused strategy (Top K = 3-5)

Healthcare & Life Sciences

Medical Research: Research paper and clinical trial analysis
Knowledge Management: Medical knowledge base queries
Diagnostic Support: Symptom and diagnosis information
Decision: Comprehensive strategy (Top K = 15-20)

Financial Services

Regulatory Compliance: Regulation and compliance document search
Market Research: Financial analysis and market intelligence
Risk Assessment: Risk evaluation and compliance checking
Decision: Balanced strategy (Top K = 8-12)

Education & Research

Academic Research: Research paper and literature review
Educational Content: Learning material and course content
Knowledge Discovery: Exploratory research and analysis
Decision: Unlimited strategy (Top K = null) for comprehensive coverage

Legal Services

Case Law Research: Legal precedent and case analysis
Document Review: Contract and legal document analysis
Compliance Checking: Regulatory compliance verification
Decision: Comprehensive strategy (Top K = 15-25)

Migration and Maintenance

From Traditional RAG

Assessment: Evaluate current RAG pipeline components
Mapping: Map existing functionality to smart RAG features
Configuration: Set up equivalent smart RAG configurations
Testing: Validate performance and quality equivalence
Gradual Migration: Phase migration to minimize disruption

Version Management

Configuration Versioning: Track configuration changes over time
Rollback Procedures: Quick rollback to previous configurations
Change Documentation: Document all configuration changes
Impact Assessment: Evaluate impact of changes before implementation

Capacity Planning

Growth Projection: Plan for increased query volumes
Resource Scaling: Scale resources based on usage patterns
Performance Benchmarking: Maintain performance benchmarks
Optimization Scheduling: Schedule regular optimization reviews

Relationship with Other Endpoints

Smart RAG nodes integrate with the broader GraphorLM ecosystem:

Dataset Integration

Input Source: Dataset nodes provide document input to smart RAG nodes
File Management: Smart RAG automatically processes files from connected datasets
Update Coordination: Dataset changes trigger smart RAG reprocessing

Flow Management

Flow Orchestration: Smart RAG nodes participate in larger flow orchestrations
Dependency Management: Proper handling of node dependencies and execution order
State Management: Coordinate processing state across flow components

Metrics and Evaluation

Quality Metrics: Integration with evaluation and metrics systems
Performance Tracking: Built-in performance and resource usage tracking
Reporting: Comprehensive reporting integration with GraphorLM analytics

Next Steps

Explore related documentation to maximize your smart RAG implementation:

List Smart RAG Nodes

Learn how to retrieve and analyze smart RAG node configurations

Update Smart RAG Configuration

Master smart RAG configuration management and optimization

Dataset Management

Understand how datasets integrate with smart RAG nodes

Flow Orchestration

Learn about comprehensive flow management

Get Started

Sources

Flows

​What are Smart RAG Nodes?

​Key Benefits

​Available Endpoints

​Base URL Structure

​Core Concepts

​Node Structure

​Configuration Parameters

​Result Metrics

​Smart RAG Strategies

​High-Precision Strategy

​Balanced Strategy

​Comprehensive Strategy

​Unlimited Strategy

​Strategy Selection Matrix

​Common Workflows

​Basic Smart RAG Workflow

​Configuration Optimization Workflow

​Multi-Strategy Implementation

​Authentication & Authorization

​Token Requirements

​Response Formats

​Standard Success Response

​Error Response Format

​Common Status Codes

​Integration Examples

​Smart RAG Performance Monitor

​Smart RAG Configuration Optimizer

​Best Practices

​Configuration Management

​Performance Optimization

​Resource Management

​Processing Efficiency

​Quality Assurance

​Metrics and Monitoring

​Continuous Improvement

​Operational Excellence

​Monitoring and Alerting

​Maintenance and Updates

​Optimization Cycles

​Troubleshooting

​Advanced Features

​Intelligent Resource Management

​Quality Optimization

​Integration Capabilities

​Use Cases by Industry

​Technology & Software

​Healthcare & Life Sciences

​Financial Services

​Education & Research

​Legal Services

​Migration and Maintenance

​From Traditional RAG

​Version Management

​Capacity Planning

​Relationship with Other Endpoints

​Dataset Integration

​Flow Management

​Metrics and Evaluation

​Next Steps

List Smart RAG Nodes

Update Smart RAG Configuration

Dataset Management

Flow Orchestration

What are Smart RAG Nodes?

Key Benefits

Available Endpoints

Base URL Structure

Core Concepts

Node Structure

Configuration Parameters

Result Metrics

Smart RAG Strategies

High-Precision Strategy

Balanced Strategy

Comprehensive Strategy

Unlimited Strategy

Strategy Selection Matrix

Common Workflows

Basic Smart RAG Workflow

Configuration Optimization Workflow

Multi-Strategy Implementation

Authentication & Authorization

Token Requirements

Response Formats

Standard Success Response

Error Response Format

Common Status Codes

Integration Examples

Smart RAG Performance Monitor

Smart RAG Configuration Optimizer

Best Practices

Configuration Management

Performance Optimization

Resource Management

Processing Efficiency

Quality Assurance

Metrics and Monitoring

Continuous Improvement

Operational Excellence

Monitoring and Alerting

Maintenance and Updates

Optimization Cycles

Troubleshooting

Advanced Features

Intelligent Resource Management

Quality Optimization

Integration Capabilities

Use Cases by Industry

Technology & Software

Healthcare & Life Sciences

Financial Services

Education & Research

Legal Services

Migration and Maintenance

From Traditional RAG

Version Management

Capacity Planning

Relationship with Other Endpoints

Dataset Integration

Flow Management

Metrics and Evaluation

Next Steps