Update Retrieval Configuration

Update the configuration of a specific retrieval node within a flow in your GraphorLM project. This endpoint allows you to modify search parameters, threshold settings, and retrieval strategies to optimize the performance and accuracy of your RAG system.

Overview

The Update Retrieval Configuration endpoint allows you to modify the configuration of retrieval nodes within your flows. Retrieval nodes are critical components that perform similarity search and document retrieval operations, and updating them is essential for optimizing search quality, performance, and relevance in your RAG pipelines.

Method: PATCH
URL: https://{flow_name}.flows.graphorlm.com/retrieval/{node_id}
Authentication: Required (API Token)

Authentication

All requests must include a valid API token in the Authorization header:

Authorization: Bearer YOUR_API_TOKEN

Learn how to generate API tokens in the API Tokens guide.

Request Format

Headers

Header	Value	Required
`Authorization`	`Bearer YOUR_API_TOKEN`	Yes
`Content-Type`	`application/json`	Yes

URL Parameters

Parameter	Type	Required	Description
`flow_name`	string	Yes	The name of the flow containing the retrieval node
`node_id`	string	Yes	The unique identifier of the retrieval node to update

Request Body

The request body should be a JSON object with the following structure:

Field	Type	Required	Description
`config`	object	Yes	The new configuration for the retrieval node

Config Object Structure

Field	Type	Required	Description
`searchType`	string	Yes	Type of search: “similarity”, “hybrid”, “keyword”, or “semantic”
`topK`	integer	No	Maximum number of documents to retrieve (default: 10)
`scoreThreshold`	float	No	Minimum similarity score threshold (0.0-1.0, default: 0.0)
`retrievalQuery`	string	No	Custom query template for specialized retrieval patterns

Example Request

PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-1748287628686
Authorization: Bearer YOUR_API_TOKEN
Content-Type: application/json

{
  "config": {
    "searchType": "hybrid",
    "topK": 15,
    "scoreThreshold": 0.75,
    "retrievalQuery": "Find documents related to: {query}"
  }
}

Response Format

Success Response (200 OK)

{
  "success": true,
  "message": "Retrieval node 'retrieval-1748287628686' updated successfully",
  "node_id": "retrieval-1748287628686"
}

Response Structure

Field	Type	Description
`success`	boolean	Whether the update was successful
`message`	string	Descriptive message about the update result
`node_id`	string	The ID of the updated retrieval node

Code Examples

JavaScript/Node.js

async function updateRetrievalNode(flowName, nodeId, config, apiToken) {
  const response = await fetch(`https://${flowName}.flows.graphorlm.com/retrieval/${nodeId}`, {
    method: 'PATCH',
    headers: {
      'Authorization': `Bearer ${apiToken}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      config: config
    })
  });

  if (!response.ok) {
    throw new Error(`HTTP error! status: ${response.status}`);
  }

  return await response.json();
}

// Usage examples

// 1. Update search type and Top K for better recall
const highRecallConfig = {
  searchType: "hybrid",
  topK: 20,
  scoreThreshold: 0.6
};

updateRetrievalNode('my-rag-pipeline', 'retrieval-node-123', highRecallConfig, 'YOUR_API_TOKEN')
  .then(result => {
    console.log('✅ Retrieval configuration updated for high recall');
    console.log(`Message: ${result.message}`);
  })
  .catch(error => console.error('❌ Update failed:', error));

// 2. Configure for high precision with strict threshold
const highPrecisionConfig = {
  searchType: "similarity",
  topK: 5,
  scoreThreshold: 0.85
};

updateRetrievalNode('my-rag-pipeline', 'retrieval-node-123', highPrecisionConfig, 'YOUR_API_TOKEN')
  .then(result => {
    console.log('✅ Retrieval configured for high precision');
    console.log(`Updated node: ${result.node_id}`);
  })
  .catch(error => console.error('❌ Update failed:', error));

// 3. Set custom retrieval query for domain-specific search
const customQueryConfig = {
  searchType: "semantic",
  topK: 12,
  scoreThreshold: 0.7,
  retrievalQuery: "Search for technical documentation about: {query}"
};

updateRetrievalNode('my-rag-pipeline', 'retrieval-node-123', customQueryConfig, 'YOUR_API_TOKEN')
  .then(result => {
    console.log('✅ Custom retrieval query configured');
    console.log(`Success: ${result.success}`);
  })
  .catch(error => console.error('❌ Update failed:', error));

Python

import requests
import json

def update_retrieval_node(flow_name, node_id, config, api_token):
    url = f"https://{flow_name}.flows.graphorlm.com/retrieval/{node_id}"
    
    headers = {
        "Authorization": f"Bearer {api_token}",
        "Content-Type": "application/json"
    }
    
    payload = {
        "config": config
    }
    
    response = requests.patch(url, headers=headers, json=payload)
    response.raise_for_status()
    
    return response.json()

def optimize_retrieval_performance(flow_name, node_id, api_token, optimization_type="balanced"):
    """
    Apply different retrieval optimization strategies
    """
    
    optimization_configs = {
        "high_recall": {
            "searchType": "hybrid",
            "topK": 25,
            "scoreThreshold": 0.5,
            "retrievalQuery": None
        },
        "high_precision": {
            "searchType": "similarity", 
            "topK": 8,
            "scoreThreshold": 0.85,
            "retrievalQuery": None
        },
        "balanced": {
            "searchType": "semantic",
            "topK": 15,
            "scoreThreshold": 0.7,
            "retrievalQuery": None
        },
        "fast_search": {
            "searchType": "keyword",
            "topK": 10,
            "scoreThreshold": 0.6,
            "retrievalQuery": None
        },
        "domain_specific": {
            "searchType": "semantic",
            "topK": 12,
            "scoreThreshold": 0.75,
            "retrievalQuery": "Find relevant information about: {query}"
        }
    }
    
    if optimization_type not in optimization_configs:
        raise ValueError(f"Unknown optimization type: {optimization_type}")
    
    config = optimization_configs[optimization_type]
    
    try {
        result = update_retrieval_node(flow_name, node_id, config, api_token)
        
        print(f"🎯 Applied {optimization_type} optimization")
        print(f"   Search Type: {config['searchType']}")
        print(f"   Top K: {config['topK']}")
        print(f"   Score Threshold: {config['scoreThreshold']}")
        if config['retrievalQuery']:
            print(f"   Custom Query: {config['retrievalQuery']}")
        print(f"   Result: {result['message']}")
        
        return result
        
    except requests.exceptions.HTTPError as e:
        print(f"❌ Optimization failed: {e}")
        if e.response.status_code == 404:
            print("   Node or flow not found")
        elif e.response.status_code == 400:
            print("   Invalid configuration parameters")
        raise

def batch_update_retrieval_configs(flow_name, node_configs, api_token):
    """
    Update multiple retrieval nodes with different configurations
    """
    results = []
    
    print(f"🔄 Updating {len(node_configs)} retrieval nodes...")
    
    for i, (node_id, config) in enumerate(node_configs.items(), 1):
        try:
            print(f"\n[{i}/{len(node_configs)}] Updating {node_id}...")
            
            result = update_retrieval_node(flow_name, node_id, config, api_token)
            
            print(f"   ✅ Success: {result['message']}")
            results.append({
                "node_id": node_id,
                "success": True,
                "result": result
            })
            
        except Exception as e:
            print(f"   ❌ Failed: {str(e)}")
            results.append({
                "node_id": node_id, 
                "success": False,
                "error": str(e)
            })
    
    # Summary
    successful = sum(1 for r in results if r["success"])
    failed = len(results) - successful
    
    print(f"\n📊 Batch Update Summary:")
    print(f"   ✅ Successful: {successful}")
    print(f"   ❌ Failed: {failed}")
    
    return results

# Usage examples

try:
    # 1. Simple configuration update
    simple_config = {
        "searchType": "similarity",
        "topK": 10,
        "scoreThreshold": 0.8
    }
    
    result = update_retrieval_node(
        "my-rag-pipeline", 
        "retrieval-node-123", 
        simple_config, 
        "YOUR_API_TOKEN"
    )
    print(f"Update result: {result}")
    
    # 2. Apply optimization strategy
    optimize_retrieval_performance(
        "my-rag-pipeline",
        "retrieval-node-123", 
        "YOUR_API_TOKEN",
        optimization_type="high_precision"
    )
    
    # 3. Batch update multiple nodes
    batch_configs = {
        "retrieval-primary": {
            "searchType": "hybrid",
            "topK": 15,
            "scoreThreshold": 0.75
        },
        "retrieval-fallback": {
            "searchType": "keyword", 
            "topK": 20,
            "scoreThreshold": 0.6
        },
        "retrieval-specialized": {
            "searchType": "semantic",
            "topK": 8,
            "scoreThreshold": 0.85,
            "retrievalQuery": "Technical documentation search: {query}"
        }
    }
    
    batch_results = batch_update_retrieval_configs(
        "my-rag-pipeline",
        batch_configs,
        "YOUR_API_TOKEN"
    )

except Exception as e:
    print(f"Error: {e}")

cURL

# Basic retrieval configuration update
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-node-123 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "searchType": "similarity",
      "topK": 10,
      "scoreThreshold": 0.8
    }
  }'

# High recall configuration for comprehensive search
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-node-123 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "searchType": "hybrid",
      "topK": 25,
      "scoreThreshold": 0.5
    }
  }'

# High precision configuration with custom query
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-node-123 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "searchType": "semantic",
      "topK": 8,
      "scoreThreshold": 0.9,
      "retrievalQuery": "Find expert-level content about: {query}"
    }
  }'

# Fast keyword-based search for quick responses
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-node-123 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "searchType": "keyword",
      "topK": 12,
      "scoreThreshold": 0.6
    }
  }'

# Domain-specific retrieval with custom template
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/retrieval/retrieval-node-123 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "searchType": "semantic",
      "topK": 15,
      "scoreThreshold": 0.75,
      "retrievalQuery": "Search technical documentation for: {query}"
    }
  }'

PHP

<?php
function updateRetrievalNode($flowName, $nodeId, $config, $apiToken) {
    $url = "https://{$flowName}.flows.graphorlm.com/retrieval/{$nodeId}";
    
    $payload = json_encode([
        'config' => $config
    ]);
    
    $options = [
        'http' => [
            'header' => [
                "Authorization: Bearer {$apiToken}",
                "Content-Type: application/json"
            ],
            'method' => 'PATCH',
            'content' => $payload
        ]
    ];
    
    $context = stream_context_create($options);
    $result = file_get_contents($url, false, $context);
    
    if ($result === FALSE) {
        throw new Exception('Failed to update retrieval node');
    }
    
    return json_decode($result, true);
}

function applyRetrievalOptimization($flowName, $nodeId, $apiToken, $strategy = 'balanced') {
    $strategies = [
        'high_recall' => [
            'searchType' => 'hybrid',
            'topK' => 25,
            'scoreThreshold' => 0.5
        ],
        'high_precision' => [
            'searchType' => 'similarity',
            'topK' => 8, 
            'scoreThreshold' => 0.85
        ],
        'balanced' => [
            'searchType' => 'semantic',
            'topK' => 15,
            'scoreThreshold' => 0.7
        ],
        'fast_search' => [
            'searchType' => 'keyword',
            'topK' => 10,
            'scoreThreshold' => 0.6
        ]
    ];
    
    if (!isset($strategies[$strategy])) {
        throw new Exception("Unknown strategy: {$strategy}");
    }
    
    $config = $strategies[$strategy];
    
    try {
        $result = updateRetrievalNode($flowName, $nodeId, $config, $apiToken);
        
        echo "🎯 Applied {$strategy} optimization\n";
        echo "   Search Type: {$config['searchType']}\n";
        echo "   Top K: {$config['topK']}\n";
        echo "   Score Threshold: {$config['scoreThreshold']}\n";
        echo "   Result: {$result['message']}\n";
        
        return $result;
        
    } catch (Exception $e) {
        echo "❌ Optimization failed: " . $e->getMessage() . "\n";
        throw $e;
    }
}

function validateRetrievalConfig($config) {
    $errors = [];
    
    // Validate search type
    $validSearchTypes = ['similarity', 'hybrid', 'keyword', 'semantic'];
    if (!isset($config['searchType']) || !in_array($config['searchType'], $validSearchTypes)) {
        $errors[] = 'searchType must be one of: ' . implode(', ', $validSearchTypes);
    }
    
    // Validate Top K
    if (isset($config['topK'])) {
        if (!is_int($config['topK']) || $config['topK'] < 1 || $config['topK'] > 100) {
            $errors[] = 'topK must be an integer between 1 and 100';
        }
    }
    
    // Validate score threshold
    if (isset($config['scoreThreshold'])) {
        if (!is_numeric($config['scoreThreshold']) || $config['scoreThreshold'] < 0 || $config['scoreThreshold'] > 1) {
            $errors[] = 'scoreThreshold must be a number between 0.0 and 1.0';
        }
    }
    
    // Validate custom query
    if (isset($config['retrievalQuery']) && !is_string($config['retrievalQuery'])) {
        $errors[] = 'retrievalQuery must be a string';
    }
    
    return $errors;
}

// Usage examples
try {
    // 1. Simple update with validation
    $config = [
        'searchType' => 'similarity',
        'topK' => 15,
        'scoreThreshold' => 0.8
    ];
    
    $errors = validateRetrievalConfig($config);
    if (!empty($errors)) {
        echo "❌ Configuration errors:\n";
        foreach ($errors as $error) {
            echo "   - {$error}\n";
        }
        exit(1);
    }
    
    $result = updateRetrievalNode('my-rag-pipeline', 'retrieval-node-123', $config, 'YOUR_API_TOKEN');
    echo "✅ Configuration updated: " . $result['message'] . "\n";
    
    // 2. Apply optimization strategy
    applyRetrievalOptimization('my-rag-pipeline', 'retrieval-node-123', 'YOUR_API_TOKEN', 'high_precision');
    
    // 3. Custom domain-specific configuration
    $domainConfig = [
        'searchType' => 'semantic',
        'topK' => 12,
        'scoreThreshold' => 0.75,
        'retrievalQuery' => 'Find technical documentation about: {query}'
    ];
    
    $result = updateRetrievalNode('my-rag-pipeline', 'retrieval-node-123', $domainConfig, 'YOUR_API_TOKEN');
    echo "✅ Domain-specific configuration applied: " . $result['message'] . "\n";
    
} catch (Exception $e) {
    echo "Error: " . $e->getMessage() . "\n";
}
?>

Error Responses

Common Error Codes

Status Code	Description	Example Response
400	Bad Request - Invalid configuration parameters	`{"detail": "Invalid scoreThreshold value. Must be between 0.0 and 1.0"}`
401	Unauthorized - Invalid or missing API token	`{"detail": "Invalid authentication credentials"}`
404	Not Found - Flow or node not found	`{"detail": "Retrieval node not found"}`
422	Unprocessable Entity - Validation error	`{"detail": "searchType is required"}`
500	Internal Server Error - Server error	`{"detail": "Failed to update retrieval node"}`

Error Response Format

{
  "detail": "Error message describing what went wrong"
}

Example Error Responses

Invalid Configuration

{
  "detail": "Invalid scoreThreshold value. Must be between 0.0 and 1.0"
}

Missing Required Field

{
  "detail": "searchType is required"
}

Node Not Found

{
  "detail": "Retrieval node not found"
}

Invalid Search Type

{
  "detail": "Invalid searchType. Must be one of: similarity, hybrid, keyword, semantic"
}

Configuration Guidelines

Search Type Selection

Similarity Search

Best for: General purpose document retrieval
Characteristics: Vector-based similarity matching
Recommended threshold: 0.7-0.9
Typical Top K: 10-20

{
  "searchType": "similarity",
  "topK": 15,
  "scoreThreshold": 0.8
}

Hybrid Search

Best for: Combining semantic and keyword matching
Characteristics: Balanced precision and recall
Recommended threshold: 0.6-0.8
Typical Top K: 15-25

{
  "searchType": "hybrid",
  "topK": 20,
  "scoreThreshold": 0.7
}

Keyword Search

Best for: Fast exact term matching
Characteristics: Traditional keyword-based search
Recommended threshold: 0.5-0.7
Typical Top K: 10-15

{
  "searchType": "keyword",
  "topK": 12,
  "scoreThreshold": 0.6
}

Semantic Search

Best for: Conceptual and contextual matching
Characteristics: Deep understanding of query intent
Recommended threshold: 0.7-0.9
Typical Top K: 8-15

{
  "searchType": "semantic",
  "topK": 10,
  "scoreThreshold": 0.8
}

Parameter Optimization

Top K Guidelines

Low volume (1-5): High precision, narrow results
Medium volume (10-20): Balanced approach for most use cases
High volume (25+): High recall, comprehensive results

Score Threshold Guidelines

Low threshold (0.5-0.6): More results, lower precision
Medium threshold (0.7-0.8): Balanced quality and quantity
High threshold (0.9+): Fewer but highly relevant results

Custom Query Templates

Use retrievalQuery for domain-specific optimization:

{
  "retrievalQuery": "Find technical documentation about: {query}"
}

Common templates:

"Search for {query} in user manuals"
"Find FAQ entries related to: {query}"
"Locate product information about {query}"
"Technical support content for: {query}"

Update Behavior

Immediate Effects

Configuration changes are applied instantly
Node status changes to updated: false indicating reprocessing needed
Previous results remain available until reprocessing

Reprocessing Requirements

After configuration updates, you should:

Re-run the flow to apply new retrieval settings
Monitor performance to validate configuration changes
Adjust parameters based on result quality

Best Practices for Updates

Test changes incrementally rather than making large adjustments
Monitor metrics before and after configuration changes
Document configuration rationale for future reference
Use A/B testing to compare retrieval strategies

Integration Examples

Automated Performance Tuning

class RetrievalAutoTuner {
  constructor(flowName, apiToken) {
    this.flowName = flowName;
    this.apiToken = apiToken;
    this.performanceHistory = [];
  }

  async optimizeRetrieval(nodeId, targetMetric = 'balanced') {
    const strategies = {
      'precision': { searchType: 'similarity', topK: 8, scoreThreshold: 0.85 },
      'recall': { searchType: 'hybrid', topK: 25, scoreThreshold: 0.5 },
      'balanced': { searchType: 'semantic', topK: 15, scoreThreshold: 0.7 },
      'speed': { searchType: 'keyword', topK: 10, scoreThreshold: 0.6 }
    };

    const config = strategies[targetMetric] || strategies['balanced'];
    
    try {
      const result = await this.updateConfiguration(nodeId, config);
      console.log(`🎯 Applied ${targetMetric} optimization strategy`);
      
      // Log performance for tracking
      this.performanceHistory.push({
        timestamp: new Date(),
        nodeId,
        strategy: targetMetric,
        config,
        result
      });
      
      return result;
    } catch (error) {
      console.error(`❌ Optimization failed: ${error.message}`);
      throw error;
    }
  }

  async updateConfiguration(nodeId, config) {
    const response = await fetch(`https://${this.flowName}.flows.graphorlm.com/retrieval/${nodeId}`, {
      method: 'PATCH',
      headers: {
        'Authorization': `Bearer ${this.apiToken}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({ config })
    });

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

    return await response.json();
  }

  getPerformanceHistory() {
    return this.performanceHistory;
  }
}

// Usage
const tuner = new RetrievalAutoTuner('my-rag-pipeline', 'YOUR_API_TOKEN');
tuner.optimizeRetrieval('retrieval-node-123', 'precision').catch(console.error);

Configuration Validation Service

class RetrievalConfigValidator:
    def __init__(self, flow_name: str, api_token: str):
        self.flow_name = flow_name
        self.api_token = api_token
    
    def validate_config(self, config: dict) -> dict:
        """Validate retrieval configuration and provide recommendations"""
        validation_result = {
            "is_valid": True,
            "errors": [],
            "warnings": [],
            "recommendations": []
        }
        
        # Validate search type
        valid_search_types = ['similarity', 'hybrid', 'keyword', 'semantic']
        search_type = config.get('searchType')
        
        if not search_type:
            validation_result["errors"].append("searchType is required")
            validation_result["is_valid"] = False
        elif search_type not in valid_search_types:
            validation_result["errors"].append(f"Invalid searchType. Must be one of: {', '.join(valid_search_types)}")
            validation_result["is_valid"] = False
        
        # Validate Top K
        top_k = config.get('topK')
        if top_k is not None:
            if not isinstance(top_k, int) or top_k < 1:
                validation_result["errors"].append("topK must be a positive integer")
                validation_result["is_valid"] = False
            elif top_k > 50:
                validation_result["warnings"].append("topK > 50 may impact performance")
            elif top_k < 5:
                validation_result["warnings"].append("topK < 5 may limit result diversity")
        
        # Validate score threshold
        threshold = config.get('scoreThreshold')
        if threshold is not None:
            if not isinstance(threshold, (int, float)) or threshold < 0 or threshold > 1:
                validation_result["errors"].append("scoreThreshold must be between 0.0 and 1.0")
                validation_result["is_valid"] = False
            elif threshold > 0.95:
                validation_result["warnings"].append("scoreThreshold > 0.95 may be too restrictive")
            elif threshold < 0.3:
                validation_result["warnings"].append("scoreThreshold < 0.3 may include low-quality results")
        
        # Configuration-specific recommendations
        if search_type == 'similarity' and top_k and top_k > 20:
            validation_result["recommendations"].append("For similarity search, consider topK <= 20 for better precision")
        
        if search_type == 'hybrid' and threshold and threshold > 0.8:
            validation_result["recommendations"].append("Hybrid search works well with moderate thresholds (0.6-0.8)")
        
        if search_type == 'keyword' and threshold and threshold > 0.7:
            validation_result["recommendations"].append("Keyword search typically uses lower thresholds (0.5-0.7)")
        
        return validation_result
    
    async def update_with_validation(self, node_id: str, config: dict) -> dict:
        """Update retrieval configuration after validation"""
        
        # Validate configuration first
        validation = self.validate_config(config)
        
        if not validation["is_valid"]:
            raise ValueError(f"Invalid configuration: {'; '.join(validation['errors'])}")
        
        # Show warnings
        for warning in validation["warnings"]:
            print(f"⚠️  Warning: {warning}")
        
        # Show recommendations
        for rec in validation["recommendations"]:
            print(f"💡 Recommendation: {rec}")
        
        # Proceed with update
        try:
            result = await self.update_retrieval_node(node_id, config)
            print(f"✅ Configuration updated successfully")
            return result
        except Exception as e:
            print(f"❌ Update failed: {e}")
            raise

# Usage
validator = RetrievalConfigValidator("my-rag-pipeline", "YOUR_API_TOKEN")

config = {
    "searchType": "semantic",
    "topK": 15,
    "scoreThreshold": 0.75
}

validation = validator.validate_config(config)
if validation["is_valid"]:
    print("✅ Configuration is valid")
else:
    print("❌ Configuration errors:", validation["errors"])

Best Practices

Configuration Management

Start Conservative: Begin with moderate settings and adjust based on performance
Test Incrementally: Make small changes and measure impact
Document Changes: Keep track of configuration rationale and performance impact
Use Validation: Always validate configurations before applying

Performance Optimization

Monitor Quality: Track retrieval relevance and adjust thresholds accordingly
Balance Speed vs Accuracy: Choose search types based on response time requirements
Consider Content Volume: Adjust Top K based on available content and user needs
Regular Tuning: Periodically review and optimize configurations

Search Strategy Selection

Similarity: General purpose, good balance of speed and accuracy
Hybrid: Best overall performance for most applications
Keyword: Fast responses for exact term matching
Semantic: Best understanding of user intent and context

Threshold Management

Quality vs Quantity: Higher thresholds for quality, lower for comprehensiveness
Content Dependent: Adjust based on content quality and embedding model performance
User Feedback: Use retrieval feedback to optimize threshold settings

Troubleshooting

Invalid Configuration Parameters

Poor Retrieval Quality

Slow Retrieval Performance

Configuration Not Taking Effect

Custom Query Not Working

Next Steps

After updating retrieval node configuration, you might want to:

List Retrieval Nodes

View updated retrieval node configurations and status

Run Flow

Execute your flow with the new retrieval configuration

Update Chunking Nodes

Optimize chunking settings to improve retrieval quality

Flow Overview

Learn about all available flow management endpoints

Get Started

Sources

Flows

​Overview

​Authentication

​Request Format

​Headers

​URL Parameters

​Request Body

​Config Object Structure

​Example Request

​Response Format

​Success Response (200 OK)

​Response Structure

​Code Examples

​JavaScript/Node.js

​Python

​cURL

​PHP

​Error Responses

​Common Error Codes

​Error Response Format

​Example Error Responses

​Invalid Configuration

​Missing Required Field

​Node Not Found

​Invalid Search Type

​Configuration Guidelines

​Search Type Selection

​Similarity Search

​Hybrid Search

​Keyword Search

​Semantic Search

​Parameter Optimization

​Top K Guidelines

​Score Threshold Guidelines

​Custom Query Templates

​Update Behavior

​Immediate Effects

​Reprocessing Requirements

​Best Practices for Updates

​Integration Examples

​Automated Performance Tuning

​Configuration Validation Service

​Best Practices

​Configuration Management

​Performance Optimization

​Search Strategy Selection

​Threshold Management

​Troubleshooting

​Next Steps

List Retrieval Nodes

Run Flow

Update Chunking Nodes

Flow Overview

Overview

Authentication

Request Format

Headers

URL Parameters

Request Body

Config Object Structure

Example Request

Response Format

Success Response (200 OK)

Response Structure

Code Examples

JavaScript/Node.js

Python

cURL

PHP

Error Responses

Common Error Codes

Error Response Format

Example Error Responses

Invalid Configuration

Missing Required Field

Node Not Found

Invalid Search Type

Configuration Guidelines

Search Type Selection

Similarity Search

Hybrid Search

Keyword Search

Semantic Search

Parameter Optimization

Top K Guidelines

Score Threshold Guidelines

Custom Query Templates

Update Behavior

Immediate Effects

Reprocessing Requirements

Best Practices for Updates

Integration Examples

Automated Performance Tuning

Configuration Validation Service

Best Practices

Configuration Management

Performance Optimization

Search Strategy Selection

Threshold Management

Troubleshooting

Next Steps