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title Intelligent Pathfinding Guide
description Semantic pathfinding finds paths that are not just shortest, but most relevant to your query and graph semantics.
category how-to
tags
tutorial
documentation
reference
visionclaw
updated-date 2025-12-18
difficulty-level intermediate

Intelligent Pathfinding Guide

Overview

Semantic pathfinding finds paths that are not just shortest, but most relevant to your query and graph semantics.

Algorithms

1. Semantic Path (Enhanced A*)

Finds shortest path weighted by:

  • Edge weights
  • Node type compatibility
  • Query relevance
POST /api/pathfinding/semantic-path
Content-Type: application/json

{
  "startId": 123,
  "endId": 456,
  "query": "machine learning projects"
}

Response:

{
  "path": [123, 234, 345, 456],
  "cost": 3.2,
  "relevance": 0.87,
  "explanation": "Found path with 3 hops"
}

2. Query-Guided Traversal

Explores graph prioritizing nodes matching your query:

POST /api/pathfinding/query-traversal
Content-Type: application/json

{
  "startId": 123,
  "query": "artificial intelligence",
  "maxNodes": 50
}

Returns most relevant nodes, sorted by query match.

3. Chunk Traversal

Explores local neighborhood without query context:

POST /api/pathfinding/chunk-traversal
Content-Type: application/json

{
  "startId": 123,
  "maxNodes": 50
}

Finds similar nodes based on:

  • Node type similarity
  • Local structure
  • Attribute similarity

Configuration

Pathfinding Parameters

{
  "maxLength": 10,
  "maxExplored": 1000,
  "edgeWeightFactor": 0.4,
  "semanticWeightFactor": 0.4,
  "typeWeightFactor": 0.2
}
  • edgeWeightFactor: How much edge weights matter (0.0-1.0)
  • semanticWeightFactor: How much query relevance matters
  • typeWeightFactor: How much type compatibility matters

Use Cases

Research Navigation

Find research papers related to query:

query: "neural networks"
→ Traverses to ML papers, AI researchers, related projects

Dependency Analysis

Find critical dependencies:

query: "authentication security"
→ Paths weighted by security relevance

Knowledge Discovery

Explore related concepts:

query: "quantum computing"
→ Discovers related papers, researchers, applications

Performance

  • Semantic Path: O(V log V) with semantic weighting
  • Query Traversal: O(V + E) with relevance pruning
  • Chunk Traversal: O(k * degree) for k nodes

Typical performance:

  • 10K nodes: <100ms
  • 100K nodes: <1s
  • 1M nodes: <5s (with limits)

Best Practices

  1. Set appropriate limits: maxLength and maxExplored prevent long searches
  2. Use query context: More specific queries = better results
  3. Choose right algorithm:
    • Semantic Path: When you know start and end
    • Query Traversal: When exploring by topic
    • Chunk Traversal: When exploring local structure
  4. Combine with filters: Use schema to filter node types first
  5. Cache results: Common paths can be cached

Related Documentation

Examples

See API documentation for complete examples and frontend integration.