Update Chunking Configuration

Update the configuration of a specific chunking node within a flow in your GraphorLM project. This endpoint allows you to modify chunking parameters like embedding models, splitter types, chunk sizes, and overlap settings, automatically marking the node for reprocessing.

Overview

The Update Chunking Configuration endpoint allows you to modify the configuration of chunking nodes within your flows. Chunking nodes are responsible for splitting documents into smaller pieces and generating embeddings, making this endpoint crucial for optimizing your RAG pipeline performance.

Method: PATCH
URL: https://{flow_name}.flows.graphorlm.com/chunking/{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 chunking node
`node_id`	string	Yes	The unique identifier of the chunking 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 chunking node
`config.embeddingModel`	string	No	Embedding model to use (e.g., “text-embedding-3-small”, “colqwen”)
`config.chunkingSplitter`	string	Yes	Type of splitter: “character”, “token”, “semantic”, “title”, or “element”
`config.chunkSize`	integer	No	Size of each chunk in characters or tokens
`config.chunkOverlap`	integer	No	Number of characters/tokens that overlap between chunks (default: 0)
`config.chunkSeparator`	string	No	Text separator used for splitting (default: “\n\n”)
`config.splitLevel`	integer	No	Split level for hierarchical splitters (default: 0)
`config.elementsToRemove`	array	No	List of document elements to remove during processing

Example Request

PATCH https://my-rag-pipeline.flows.graphorlm.com/chunking/chunking-1748287628685
Authorization: Bearer YOUR_API_TOKEN
Content-Type: application/json

{
  "config": {
    "embeddingModel": "text-embedding-3-small",
    "chunkingSplitter": "character",
    "chunkSize": 1000,
    "chunkOverlap": 200,
    "chunkSeparator": "\n\n",
    "splitLevel": 0,
    "elementsToRemove": ["Header", "Footer"]
  }
}

Response Format

Success Response (200 OK)

The response contains confirmation of the successful update:

{
  "success": true,
  "message": "Chunking node 'chunking-1748287628685' updated successfully",
  "node_id": "chunking-1748287628685"
}

Response Fields

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

Code Examples

JavaScript/Node.js

async function updateChunkingNode(flowName, nodeId, config, apiToken) {
  const response = await fetch(`https://${flowName}.flows.graphorlm.com/chunking/${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
const chunkingConfigs = {
  // High-performance chunking for large documents
  largeDocuments: {
    embeddingModel: "text-embedding-3-large",
    chunkingSplitter: "semantic",
    chunkSize: 1500,
    chunkOverlap: 300,
    chunkSeparator: "\n\n",
    elementsToRemove: ["Header", "Footer", "PageNumber"]
  },

  // Fast processing for smaller documents
  quickProcessing: {
    embeddingModel: "text-embedding-3-small",
    chunkingSplitter: "character",
    chunkSize: 800,
    chunkOverlap: 100,
    chunkSeparator: "\n",
    elementsToRemove: ["Header", "Footer"]
  },

  // Specialized configuration for code documentation
  codeDocumentation: {
    embeddingModel: "text-embedding-3-small",
    chunkingSplitter: "element",
    chunkSize: 2000,
    chunkOverlap: 0,
    splitLevel: 1,
    elementsToRemove: ["NarrativeText"]
  }
};

// Update chunking node with optimized configuration
updateChunkingNode(
  'my-rag-pipeline',
  'chunking-1748287628685',
  chunkingConfigs.largeDocuments,
  'YOUR_API_TOKEN'
)
  .then(result => {
    console.log('Chunking configuration updated:', result);
    console.log(`Node ${result.node_id} updated successfully`);
    console.log(`Message: ${result.message}`);
  })
  .catch(error => console.error('Error:', error));

Python

import requests
import json

def update_chunking_node(flow_name, node_id, config, api_token):
    url = f"https://{flow_name}.flows.graphorlm.com/chunking/{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_chunking_configuration(flow_name, node_id, api_token, optimization_type="balanced"):
    """
    Apply optimized chunking configurations based on use case
    """
    
    # Predefined optimization configurations
    optimizations = {
        "balanced": {
            "embeddingModel": "text-embedding-3-small",
            "chunkingSplitter": "character",
            "chunkSize": 1000,
            "chunkOverlap": 150,
            "chunkSeparator": "\n\n",
            "elementsToRemove": ["Header", "Footer"]
        },
        "high_quality": {
            "embeddingModel": "text-embedding-3-large",
            "chunkingSplitter": "semantic", 
            "chunkSize": 1200,
            "chunkOverlap": 200,
            "chunkSeparator": "\n\n",
            "elementsToRemove": ["Header", "Footer", "PageNumber"]
        },
        "fast_processing": {
            "embeddingModel": "text-embedding-3-small",
            "chunkingSplitter": "character",
            "chunkSize": 800,
            "chunkOverlap": 80,
            "chunkSeparator": "\n",
            "elementsToRemove": ["Header", "Footer"]
        },
        "academic_papers": {
            "embeddingModel": "text-embedding-3-large",
            "chunkingSplitter": "semantic",
            "chunkSize": 1500,
            "chunkOverlap": 300,
            "chunkSeparator": "\n\n",
            "splitLevel": 1,
            "elementsToRemove": ["Header", "Footer", "PageNumber", "Reference"]
        },
        "code_docs": {
            "embeddingModel": "text-embedding-3-small",
            "chunkingSplitter": "element",
            "chunkSize": 2000,
            "chunkOverlap": 0,
            "splitLevel": 2,
            "elementsToRemove": ["NarrativeText"]
        }
    }
    
    if optimization_type not in optimizations:
        raise ValueError(f"Unknown optimization type: {optimization_type}")
    
    config = optimizations[optimization_type]
    
    print(f"🔧 Applying {optimization_type} optimization to chunking node {node_id}")
    print(f"Configuration: {json.dumps(config, indent=2)}")
    
    try:
        result = update_chunking_node(flow_name, node_id, config, api_token)
        
        print("✅ Chunking configuration updated successfully!")
        print(f"Success: {result['success']}")
        print(f"Message: {result['message']}")
        print(f"Updated Node ID: {result['node_id']}")
        
        # Display applied configuration
        print(f"\n📊 Applied Configuration:")
        print(f"   Embedding Model: {config['embeddingModel']}")
        print(f"   Splitter Type: {config['chunkingSplitter']}")
        print(f"   Chunk Size: {config['chunkSize']}")
        print(f"   Chunk Overlap: {config['chunkOverlap']}")
        print(f"   Elements to Remove: {', '.join(config.get('elementsToRemove', []))}")
        
        return result
        
    except requests.exceptions.HTTPError as e:
        print(f"❌ Update failed: {e}")
        if e.response.status_code == 404:
            print("Flow or chunking node not found")
        elif e.response.status_code == 400:
            print("Invalid configuration parameters")
        raise

def batch_update_chunking_configurations(flow_name, api_token, node_configs):
    """
    Update multiple chunking nodes with different configurations
    """
    results = {
        "successful_updates": [],
        "failed_updates": [],
        "total_nodes": len(node_configs)
    }
    
    for node_id, config in node_configs.items():
        try:
            print(f"\n🔄 Updating node {node_id}...")
            result = update_chunking_node(flow_name, node_id, config, api_token)
            results["successful_updates"].append({
                "node_id": node_id,
                "config": config,
                "result": result
            })
            print(f"✅ Success: {result['message']}")
            
        except Exception as e:
            error_info = {
                "node_id": node_id,
                "config": config,
                "error": str(e)
            }
            results["failed_updates"].append(error_info)
            print(f"❌ Failed: {e}")
    
    return results

# Usage examples
try:
    # Single node optimization
    result = optimize_chunking_configuration(
        flow_name="my-rag-pipeline",
        node_id="chunking-1748287628685",
        api_token="YOUR_API_TOKEN",
        optimization_type="high_quality"
    )
    
    # Batch configuration update
    node_configs = {
        "chunking-node-1": {
            "embeddingModel": "text-embedding-3-small",
            "chunkingSplitter": "character",
            "chunkSize": 1000,
            "chunkOverlap": 150
        },
        "chunking-node-2": {
            "embeddingModel": "text-embedding-3-large",
            "chunkingSplitter": "semantic",
            "chunkSize": 1200,
            "chunkOverlap": 200
        }
    }
    
    batch_results = batch_update_chunking_configurations(
        "my-rag-pipeline",
        "YOUR_API_TOKEN", 
        node_configs
    )
    
    print(f"\n📈 Batch Update Summary:")
    print(f"Successful: {len(batch_results['successful_updates'])}")
    print(f"Failed: {len(batch_results['failed_updates'])}")
    
except Exception as e:
    print(f"Error: {e}")

cURL

# Basic chunking configuration update
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/chunking/chunking-1748287628685 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "embeddingModel": "text-embedding-3-small",
      "chunkingSplitter": "character",
      "chunkSize": 1000,
      "chunkOverlap": 200,
      "chunkSeparator": "\n\n",
      "elementsToRemove": ["Header", "Footer"]
    }
  }'

# High-performance configuration for large documents
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/chunking/chunking-1748287628685 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "embeddingModel": "text-embedding-3-large",
      "chunkingSplitter": "semantic",
      "chunkSize": 1500,
      "chunkOverlap": 300,
      "splitLevel": 1,
      "elementsToRemove": ["Header", "Footer", "PageNumber"]
    }
  }'

# Fast processing configuration
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/chunking/chunking-1748287628685 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "embeddingModel": "text-embedding-3-small",
      "chunkingSplitter": "character",
      "chunkSize": 800,
      "chunkOverlap": 80,
      "chunkSeparator": "\n"
    }
  }'

# Specialized configuration for code documentation
curl -X PATCH https://my-rag-pipeline.flows.graphorlm.com/chunking/chunking-1748287628685 \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "config": {
      "embeddingModel": "text-embedding-3-small",
      "chunkingSplitter": "element",
      "chunkSize": 2000,
      "chunkOverlap": 0,
      "splitLevel": 2,
      "elementsToRemove": ["NarrativeText"]
    }
  }'

PHP

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

function getOptimizedChunkingConfig($optimizationType) {
    $optimizations = [
        'balanced' => [
            'embeddingModel' => 'text-embedding-3-small',
            'chunkingSplitter' => 'character',
            'chunkSize' => 1000,
            'chunkOverlap' => 150,
            'chunkSeparator' => "\n\n",
            'elementsToRemove' => ['Header', 'Footer']
        ],
        'high_quality' => [
            'embeddingModel' => 'text-embedding-3-large',
            'chunkingSplitter' => 'semantic',
            'chunkSize' => 1200,
            'chunkOverlap' => 200,
            'chunkSeparator' => "\n\n",
            'elementsToRemove' => ['Header', 'Footer', 'PageNumber']
        ],
        'fast_processing' => [
            'embeddingModel' => 'text-embedding-3-small',
            'chunkingSplitter' => 'character',
            'chunkSize' => 800,
            'chunkOverlap' => 80,
            'chunkSeparator' => "\n",
            'elementsToRemove' => ['Header', 'Footer']
        ],
        'academic_papers' => [
            'embeddingModel' => 'text-embedding-3-large',
            'chunkingSplitter' => 'semantic',
            'chunkSize' => 1500,
            'chunkOverlap' => 300,
            'splitLevel' => 1,
            'elementsToRemove' => ['Header', 'Footer', 'PageNumber', 'Reference']
        ]
    ];
    
    if (!isset($optimizations[$optimizationType])) {
        throw new Exception("Unknown optimization type: {$optimizationType}");
    }
    
    return $optimizations[$optimizationType];
}

function optimizeChunkingConfiguration($flowName, $nodeId, $optimizationType, $apiToken) {
    echo "🔧 Applying {$optimizationType} optimization to chunking node {$nodeId}\n";
    
    $config = getOptimizedChunkingConfig($optimizationType);
    
    echo "Configuration:\n";
    echo "  Embedding Model: {$config['embeddingModel']}\n";
    echo "  Splitter Type: {$config['chunkingSplitter']}\n"; 
    echo "  Chunk Size: {$config['chunkSize']}\n";
    echo "  Chunk Overlap: {$config['chunkOverlap']}\n";
    
    if (isset($config['elementsToRemove'])) {
        echo "  Elements to Remove: " . implode(', ', $config['elementsToRemove']) . "\n";
    }
    
    try {
        $result = updateChunkingNode($flowName, $nodeId, $config, $apiToken);
        
        echo "✅ Chunking configuration updated successfully!\n";
        echo "Success: " . ($result['success'] ? 'true' : 'false') . "\n";
        echo "Message: {$result['message']}\n";
        echo "Updated Node ID: {$result['node_id']}\n";
        
        return $result;
        
    } catch (Exception $e) {
        echo "❌ Failed: " . $e->getMessage() . "\n";
        throw $e;
    }
}

function batchUpdateChunkingNodes($flowName, $nodeConfigs, $apiToken) {
    $results = [
        'successful_updates' => [],
        'failed_updates' => [],
        'total_nodes' => count($nodeConfigs)
    ];
    
    foreach ($nodeConfigs as $nodeId => $config) {
        echo "\n🔄 Updating node {$nodeId}...\n";
        
        try {
            $result = updateChunkingNode($flowName, $nodeId, $config, $apiToken);
            $results['successful_updates'][] = [
                'node_id' => $nodeId,
                'config' => $config,
                'result' => $result
            ];
            echo "✅ Success: {$result['message']}\n";
            
        } catch (Exception $e) {
            $results['failed_updates'][] = [
                'node_id' => $nodeId,
                'config' => $config,
                'error' => $e->getMessage()
            ];
            echo "❌ Failed: " . $e->getMessage() . "\n";
        }
        
        // Small delay between updates
        sleep(1);
    }
    
    return $results;
}

// Usage examples
try {
    // Single node optimization
    $result = optimizeChunkingConfiguration(
        'my-rag-pipeline',
        'chunking-1748287628685',
        'high_quality',
        'YOUR_API_TOKEN'
    );
    
    // Batch update example
    $nodeConfigs = [
        'chunking-node-1' => [
            'embeddingModel' => 'text-embedding-3-small',
            'chunkingSplitter' => 'character',
            'chunkSize' => 1000,
            'chunkOverlap' => 150
        ],
        'chunking-node-2' => [
            'embeddingModel' => 'text-embedding-3-large',
            'chunkingSplitter' => 'semantic',
            'chunkSize' => 1200,
            'chunkOverlap' => 200
        ]
    ];
    
    $batchResults = batchUpdateChunkingNodes(
        'my-rag-pipeline',
        $nodeConfigs,
        'YOUR_API_TOKEN'
    );
    
    echo "\n📈 Batch Update Summary:\n";
    echo "Successful: " . count($batchResults['successful_updates']) . "\n";
    echo "Failed: " . count($batchResults['failed_updates']) . "\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": "Chunk size must be greater than zero for character splitter"}`
401	Unauthorized - Invalid or missing API token	`{"detail": "Invalid authentication credentials"}`
404	Not Found - Flow or chunking node not found	`{"detail": "Chunking node with id 'invalid-node' not found in flow 'my-flow'"}`
500	Internal Server Error - Server error	`{"detail": "Failed to update chunking node"}`

Error Response Format

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

Example Error Responses

Invalid Chunk Size

{
  "detail": "Chunk size must be greater than zero for character splitter"
}

Invalid Splitter Type

{
  "detail": "Invalid splitter type: invalid_splitter"
}

Chunking Node Not Found

{
  "detail": "Chunking node with id 'chunking-invalid' not found in flow 'my-rag-pipeline'"
}

Flow Not Found

{
  "detail": "Flow with name 'nonexistent-flow' not found"
}

Invalid Configuration

{
  "detail": "Chunk overlap cannot be greater than or equal to chunk size"
}

Update Behavior

Node Status Changes

When you update a chunking node:

Configuration Updated: The node’s chunking parameters are replaced with the new configuration
Status Reset: The node is marked as "updated": false to indicate it needs reprocessing
Successor Nodes: All downstream nodes in the flow are also marked as needing updates
Flow State: The flow maintains its deployed status but requires redeployment to apply changes

Configuration Validation

The endpoint validates that:

Chunk size is appropriate for the selected splitter type
Chunk overlap is less than chunk size
Splitter type is one of: “character”, “token”, “semantic”, “title”, “element”
Split level is non-negative when specified
Embedding model is supported

Integration Examples

Chunking Configuration Manager

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

  async getCurrentNodes() {
    const response = await fetch(`${this.baseUrl}/chunking`, {
      headers: { 'Authorization': `Bearer ${this.apiToken}` }
    });

    if (!response.ok) {
      throw new Error(`Failed to get chunking nodes: ${response.status}`);
    }

    return await response.json();
  }

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

    if (!response.ok) {
      const error = await response.json();
      throw new Error(`Update failed: ${error.detail}`);
    }

    return await response.json();
  }

  async optimizeForDocumentType(nodeId, documentType) {
    const optimizations = {
      'academic_papers': {
        embeddingModel: 'text-embedding-3-large',
        chunkingSplitter: 'semantic',
        chunkSize: 1500,
        chunkOverlap: 300,
        chunkSeparator: '\n\n',
        elementsToRemove: ['Header', 'Footer', 'PageNumber', 'Reference']
      },
      'technical_docs': {
        embeddingModel: 'text-embedding-3-small',
        chunkingSplitter: 'element',
        chunkSize: 2000,
        chunkOverlap: 100,
        splitLevel: 1,
        elementsToRemove: ['Header', 'Footer']
      },
      'general_content': {
        embeddingModel: 'text-embedding-3-small',
        chunkingSplitter: 'character',
        chunkSize: 1000,
        chunkOverlap: 150,
        chunkSeparator: '\n\n',
        elementsToRemove: ['Header', 'Footer']
      },
      'code_documentation': {
        embeddingModel: 'text-embedding-3-small',
        chunkingSplitter: 'element',
        chunkSize: 2000,
        chunkOverlap: 0,
        splitLevel: 2,
        elementsToRemove: ['NarrativeText']
      }
    };

    if (!optimizations[documentType]) {
      throw new Error(`Unknown document type: ${documentType}`);
    }

    console.log(`Optimizing node ${nodeId} for ${documentType}`);
    return await this.updateNode(nodeId, optimizations[documentType]);
  }

  async adjustChunkSize(nodeId, sizeAdjustment) {
    const nodes = await this.getCurrentNodes();
    const targetNode = nodes.find(node => node.id === nodeId);
    
    if (!targetNode) {
      throw new Error(`Chunking node ${nodeId} not found`);
    }

    const currentConfig = targetNode.data.config;
    const currentSize = currentConfig.chunkSize || 1000;
    const newSize = Math.max(100, currentSize + sizeAdjustment); // Minimum 100

    console.log(`Adjusting chunk size from ${currentSize} to ${newSize}`);

    const updatedConfig = {
      ...currentConfig,
      chunkSize: newSize,
      chunkOverlap: Math.min(currentConfig.chunkOverlap || 0, Math.floor(newSize * 0.3))
    };

    return await this.updateNode(nodeId, updatedConfig);
  }

  async benchmarkConfigurations(nodeId, configurations) {
    const results = [];
    
    for (const [name, config] of Object.entries(configurations)) {
      console.log(`Testing configuration: ${name}`);
      
      try {
        const startTime = Date.now();
        const result = await this.updateNode(nodeId, config);
        const updateTime = Date.now() - startTime;
        
        results.push({
          name,
          config,
          success: true,
          updateTime,
          result
        });
        
        console.log(`✅ ${name} - Updated in ${updateTime}ms`);
        
        // Wait between tests
        await new Promise(resolve => setTimeout(resolve, 2000));
        
      } catch (error) {
        results.push({
          name,
          config,
          success: false,
          error: error.message
        });
        
        console.log(`❌ ${name} - Failed: ${error.message}`);
      }
    }
    
    return results;
  }
}

// Usage
const manager = new ChunkingConfigManager('my-rag-pipeline', 'YOUR_API_TOKEN');

// Optimize for different document types
manager.optimizeForDocumentType('chunking-node-1', 'academic_papers')
  .then(result => console.log('Optimized for academic papers:', result))
  .catch(console.error);

// Adjust chunk size
manager.adjustChunkSize('chunking-node-1', 200) // Increase by 200
  .then(result => console.log('Chunk size adjusted:', result))
  .catch(console.error);

Performance Testing Tool

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

class ChunkingPerformanceTester:
    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"
        
    def update_chunking_node(self, node_id: str, config: Dict[str, Any]) -> Dict[str, Any]:
        """Update a single chunking node configuration"""
        response = requests.patch(
            f"{self.base_url}/chunking/{node_id}",
            headers={
                "Authorization": f"Bearer {self.api_token}",
                "Content-Type": "application/json"
            },
            json={"config": config}
        )
        response.raise_for_status()
        return response.json()
    
    def test_configuration_performance(self, node_id: str, configurations: Dict[str, Dict[str, Any]]) -> Dict[str, Any]:
        """
        Test different chunking configurations for performance
        """
        results = {
            "node_id": node_id,
            "tests": [],
            "best_config": None,
            "fastest_update": None
        }
        
        fastest_time = float('inf')
        
        for config_name, config in configurations.items():
            print(f"🔄 Testing configuration: {config_name}")
            
            try:
                start_time = time.time()
                result = self.update_chunking_node(node_id, config)
                update_time = time.time() - start_time
                
                test_result = {
                    "config_name": config_name,
                    "config": config,
                    "success": True,
                    "update_time": update_time,
                    "result": result
                }
                
                results["tests"].append(test_result)
                
                if update_time < fastest_time:
                    fastest_time = update_time
                    results["fastest_update"] = test_result
                
                print(f"✅ {config_name} - Updated in {update_time:.2f}s")
                
                # Wait between tests to avoid rate limiting
                time.sleep(1)
                
            except Exception as e:
                test_result = {
                    "config_name": config_name,
                    "config": config,
                    "success": False,
                    "error": str(e)
                }
                
                results["tests"].append(test_result)
                print(f"❌ {config_name} - Failed: {e}")
        
        return results
    
    def generate_performance_report(self, test_results: Dict[str, Any]) -> str:
        """Generate a comprehensive performance report"""
        report = []
        report.append("📊 Chunking Configuration Performance Report")
        report.append("=" * 50)
        report.append(f"Node ID: {test_results['node_id']}")
        report.append(f"Total Tests: {len(test_results['tests'])}")
        
        successful_tests = [t for t in test_results['tests'] if t['success']]
        failed_tests = [t for t in test_results['tests'] if not t['success']]
        
        report.append(f"Successful: {len(successful_tests)}")
        report.append(f"Failed: {len(failed_tests)}")
        
        if test_results['fastest_update']:
            fastest = test_results['fastest_update']
            report.append(f"\n⚡ Fastest Configuration: {fastest['config_name']}")
            report.append(f"Update Time: {fastest['update_time']:.2f}s")
        
        report.append(f"\n📋 Test Details:")
        report.append("-" * 30)
        
        for test in test_results['tests']:
            if test['success']:
                report.append(f"✅ {test['config_name']}: {test['update_time']:.2f}s")
                config = test['config']
                report.append(f"   Model: {config.get('embeddingModel', 'N/A')}")
                report.append(f"   Splitter: {config.get('chunkingSplitter', 'N/A')}")
                report.append(f"   Size: {config.get('chunkSize', 'N/A')}")
                report.append(f"   Overlap: {config.get('chunkOverlap', 'N/A')}")
            else:
                report.append(f"❌ {test['config_name']}: {test['error']}")
            report.append("")
        
        return "\n".join(report)

# Usage
tester = ChunkingPerformanceTester("my-rag-pipeline", "YOUR_API_TOKEN")

# Define test configurations
test_configurations = {
    "small_fast": {
        "embeddingModel": "text-embedding-3-small",
        "chunkingSplitter": "character",
        "chunkSize": 500,
        "chunkOverlap": 50
    },
    "medium_balanced": {
        "embeddingModel": "text-embedding-3-small",
        "chunkingSplitter": "character",
        "chunkSize": 1000,
        "chunkOverlap": 150
    },
    "large_semantic": {
        "embeddingModel": "text-embedding-3-large",
        "chunkingSplitter": "semantic",
        "chunkSize": 1500,
        "chunkOverlap": 300
    }
}

try:
    results = tester.test_configuration_performance(
        "chunking-1748287628685",
        test_configurations
    )
    
    report = tester.generate_performance_report(results)
    print(report)
    
except Exception as e:
    print(f"Performance testing failed: {e}")

Best Practices

Configuration Optimization

Chunk Size Selection: Choose 500-2000 characters for most use cases; larger chunks for context-heavy tasks
Overlap Strategy: Use 10-20% overlap to maintain context; higher overlap for critical applications
Embedding Model Choice: Balance quality vs. speed; use larger models for better accuracy
Splitter Selection: Use “semantic” for better content preservation, “character” for speed

Performance Tuning

Document Type Matching: Tailor configurations to your specific document types
Resource Monitoring: Monitor processing time and adjust parameters accordingly
Batch Processing: Consider processing implications when setting chunk sizes
Memory Usage: Larger chunks and overlaps increase memory requirements

Configuration Management

Version Control: Track configuration changes for rollback capabilities
A/B Testing: Test different configurations to find optimal settings
Monitoring: Regularly check processing performance and adjust as needed
Documentation: Document configuration choices and their rationale

Troubleshooting

Invalid Chunk Size Error

Chunk Overlap Issues

Chunking Node Not Found

Unsupported Embedding Model

Configuration Not Applied

Next Steps

After updating chunking configurations, you might want to:

List Chunking Nodes

View updated chunking node configurations and status

Deploy Flow

Deploy your flow to apply configuration changes

Run Flow

Execute your flow with updated chunking configurations

Flow Overview

Learn about all available flow management endpoints

Get Started

Sources

Flows

​Overview

​Authentication

​Request Format

​Headers

​URL Parameters

​Request Body

​Example Request

​Response Format

​Success Response (200 OK)

​Response Fields

​Code Examples

​JavaScript/Node.js

​Python

​cURL

​PHP

​Error Responses

​Common Error Codes

​Error Response Format

​Example Error Responses

​Invalid Chunk Size

​Invalid Splitter Type

​Chunking Node Not Found

​Flow Not Found

​Invalid Configuration

​Update Behavior

​Node Status Changes

​Configuration Validation

​Integration Examples

​Chunking Configuration Manager

​Performance Testing Tool

​Best Practices

​Configuration Optimization

​Performance Tuning

​Configuration Management

​Troubleshooting

​Next Steps

List Chunking Nodes

Deploy Flow

Run Flow

Flow Overview

Overview

Authentication

Request Format

Headers

URL Parameters

Request Body

Example Request

Response Format

Success Response (200 OK)

Response Fields

Code Examples

JavaScript/Node.js

Python

cURL

PHP

Error Responses

Common Error Codes

Error Response Format

Example Error Responses

Invalid Chunk Size

Invalid Splitter Type

Chunking Node Not Found

Flow Not Found

Invalid Configuration

Update Behavior

Node Status Changes

Configuration Validation

Integration Examples

Chunking Configuration Manager

Performance Testing Tool

Best Practices

Configuration Optimization

Performance Tuning

Configuration Management

Troubleshooting

Next Steps