RAPTOR RAG Endpoints Overview

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Benefits of RAPTOR RAG Nodes

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Hierarchical Document Abstraction

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Advanced Tree Construction

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Multi-Level Retrieval Strategy

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Available Endpoints

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Core Concepts

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RAPTOR RAG Node Structure

{
  "id": "raptor-rag-1748287628685",
  "type": "raptor-rag",
  "data": {
    "name": "Hierarchical RAPTOR RAG",
    "config": {
      "topK": 25,
      "max_level": 4
    },
    "result": {
      "updated": true,
      "tree_levels": 4,
      "total_clusters": 65,
      "total_summaries": 45,
      "total_processed": 1850,
      "total_chunks": 520,
      "total_retrieved": 80
    }
  }
}

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Configuration Parameters

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Hierarchical Tree Metrics

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RAPTOR Tree Strategies

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1. Precision-Focused Strategy

{
  "config": {
    "topK": 10,
    "max_level": 3
  }
}

• Tree Depth: Standard 3-level hierarchy
• Retrieval Scope: Highly selective with 10 top results
• Processing Speed: Fast tree construction and traversal
• Memory Usage: Low (~240MB estimated)

• Legal document analysis with precise precedent matching
• Medical research requiring accurate diagnostic information
• Technical specification lookup with exact parameter matching

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2. Balanced Hierarchy Strategy

{
  "config": {
    "topK": 25,
    "max_level": 4
  }
}

• Tree Depth: Extended 4-level hierarchy for richer abstractions
• Retrieval Scope: Balanced coverage with 25 results
• Processing Speed: Moderate construction time with good traversal efficiency
• Memory Usage: Medium (~385MB estimated)

• Knowledge management systems with diverse content types
• Research paper analysis across multiple domains
• Documentation systems requiring hierarchical navigation

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3. Comprehensive Coverage Strategy

{
  "config": {
    "topK": 50,
    "max_level": 5
  }
}

• Tree Depth: Deep 5-level hierarchy with maximum abstraction layers
• Retrieval Scope: Extensive coverage with 50 results
• Processing Speed: Longer construction time with comprehensive traversal
• Memory Usage: High (~620MB estimated)

• Literature review systems requiring exhaustive topic coverage
• Discovery research with broad conceptual exploration
• Comprehensive content analysis across large corpora

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4. Unlimited Exploration Strategy

{
  "config": {
    "topK": null,
    "max_level": 6
  }
}

• Tree Depth: Maximum 6-level hierarchy with deepest abstractions
• Retrieval Scope: Unlimited results from complete tree traversal
• Processing Speed: Resource-intensive with comprehensive coverage
• Memory Usage: Very High (~1000MB+ estimated)

• Academic research requiring exhaustive literature analysis
• Comprehensive surveys across multiple research domains
• Advanced knowledge exploration systems

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Strategy Selection Matrix

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Basic Integration Example

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JavaScript RAPTOR RAG Configuration

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

  async updateRaptorConfiguration(nodeId, config) {
    const response = await fetch(`${this.baseUrl}/raptor-rag/${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();
  }

  async listRaptorNodes() {
    const response = await fetch(`${this.baseUrl}/raptor-rag`, {
      headers: { Authorization: `Bearer ${this.apiToken}` },
    });

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

    return await response.json();
  }
}

// Usage Example
const raptorManager = new RaptorRagManager("my-rag-pipeline", "YOUR_API_TOKEN");

// Configure RAPTOR RAG node
raptorManager
  .updateRaptorConfiguration("raptor-rag-1748287628685", {
    topK: 25,
    max_level: 4,
  })
  .then((result) => {
    console.log("✅ RAPTOR configuration updated successfully");
    console.log(`Node ID: ${result.node_id}`);
  })
  .catch((error) => console.error("❌ Configuration update failed:", error));

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Best Practices

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Hierarchical Tree Design

• Document Collection Analysis: Always analyze document characteristics before selecting RAPTOR configuration
• Strategy Selection: Choose strategies based on use case requirements, not arbitrary preferences
• Tree Depth Optimization: Balance abstraction richness with processing performance for optimal results
• Clustering Quality: Monitor clustering ratios to ensure effective semantic grouping across tree levels

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Performance Optimization

• Memory Management: Plan memory allocation for deep hierarchical trees, especially with large document collections
• Processing Efficiency: Use document-aware configuration to optimize tree construction time
• Retrieval Strategy: Balance Top K values with traversal efficiency for optimal query performance
• Resource Monitoring: Continuously monitor tree construction and retrieval performance metrics

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Configuration Management

• Dynamic Optimization: Adjust configurations based on actual performance metrics and user feedback
• Strategy Evolution: Evolve from precision-focused to comprehensive strategies as document collections grow
• Quality Assessment: Regularly evaluate clustering and summarization quality across tree levels
• Performance Tracking: Maintain historical performance data to identify optimization trends

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Troubleshooting

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Next Steps