Search Capabilities Guide

This guide covers the powerful AI-driven search capabilities available through the QDrant Loader MCP Server, enabling intelligent knowledge discovery and contextual understanding that goes beyond simple keyword matching.

πŸš€ Overview

The QDrant Loader MCP Server provides intelligent search capabilities powered by advanced AI technologies including semantic understanding, document relationship analysis, and cross-document intelligence. These features work together to provide contextually relevant results and comprehensive knowledge discovery.

πŸŽ‰ Available Intelligence Features

Our search system provides sophisticated capabilities for knowledge exploration and analysis:

  • πŸ” Enhanced Semantic Search - AI-powered similarity search with intelligent query understanding
  • πŸ—οΈ Hierarchy-Aware Navigation - Structure-aware search with document relationships
  • πŸ“Ž Intelligent Attachment Search - Specialized search for files with content analysis
  • 🀝 Cross-Document Intelligence - Relationship analysis, conflict detection, and content clustering

Core Search Tools Available

  1. Semantic Search - AI-powered similarity search across all documents
  2. Hierarchy Search - Structure-aware search with document relationships
  3. Attachment Search - Specialized search for files and documents

πŸ”₯ Cross-Document Intelligence Features

  1. Document Relationship Analysis - Comprehensive relationship analysis (Available)
  2. Document Similarity Detection - Find similar and related documents (Available)
  3. Conflict Detection - Identify contradictions across documents (Available)
  4. Complementary Content Discovery - Find related and supporting content (Available)
  5. Document Clustering - Group documents by similarity and relationships (Available)

Intelligent Query Understanding

The semantic search includes AI-powered natural language processing that provides:

  • Semantic Similarity Matching - Finds documents based on meaning rather than just keywords
  • Context-Aware Results - Understands the context and intent behind your queries
  • Multi-Language Support - Works across different document types and languages
  • Ranking Intelligence - Scores results based on relevance and content quality

Query Examples with Intelligent Understanding

Technical Implementation Queries

Query: "How do I implement OAuth authentication?"
πŸ” Search Intelligence:
- Understands this is a technical implementation question
- Prioritizes code examples and implementation guides
- Includes configuration and testing information

Results:
1. [Implementation Guide] OAuth 2.0 Setup with Step-by-Step Code
2. [Security Best Practices] OAuth Security Considerations  
3. [Code Examples] OAuth Implementation in Node.js/Python
4. [Configuration] OAuth Service Configuration

Business Process Queries

Query: "What are our deployment approval procedures?"
πŸ” Search Intelligence:
- Recognizes this as a process/policy question
- Focuses on procedure documentation and workflows
- Includes governance and compliance information

Results:
1. [Policy Document] Deployment Approval Workflow
2. [Procedures] Production Deployment Checklist
3. [Governance] Change Management Requirements
4. [Compliance] Security Review Process

Troubleshooting Queries

Query: "Users getting timeout errors on login"
πŸ” Search Intelligence:
- Identifies this as a troubleshooting scenario
- Prioritizes error resolution and diagnostic content
- Includes monitoring and performance information

Results:
1. [Troubleshooting] Login Timeout Error Solutions
2. [Monitoring] Authentication Performance Metrics
3. [Diagnostics] Database Connection Issues
4. [Resolution] Quick Fix for Common Login Problems

Parameters

{
  "name": "search",
  "parameters": {
    "query": "string",              // Natural language query - be conversational!
    "limit": 10,                    // Results to return (default: 5)
    "source_types": ["git", "confluence", "jira", "documentation", "localfile"],
    "project_ids": ["project1", "project2"]
  }
}

Structure-Aware Document Navigation

The hierarchy search understands document organization and provides:

  • Document Structure Analysis - Understands parent-child relationships
  • Navigation Context - Provides breadcrumb paths and hierarchy information
  • Gap Analysis - Identifies missing sections in documentation structures
  • Relationship Mapping - Shows connections between hierarchical content

Real-World Use Cases

Documentation Navigation

Query: "Show me the structure of our API documentation"
Hierarchy Analysis:
πŸ“ API Documentation (Root)
β”œβ”€β”€ πŸ“„ Getting Started (3 children)
β”œβ”€β”€ πŸ“ Authentication (5 children)
β”‚   β”œβ”€β”€ πŸ“„ JWT Implementation
β”‚   β”œβ”€β”€ πŸ“„ OAuth Setup
β”‚   └── πŸ“„ API Keys
β”œβ”€β”€ πŸ“ Endpoints (8 children)
β”‚   β”œβ”€β”€ πŸ“ User Management
β”‚   └── πŸ“ Data Operations
└── πŸ“ Examples (4 children)

πŸ’‘ Completeness Score: 85% - Missing error handling section

Content Organization

Query: "Where should I add webhook security documentation?"
Hierarchy Suggestions:
1. **Primary Location**: API Documentation > Security > Webhooks
   - Path: Consistent with existing security structure
   - Related: Authentication, Authorization content

2. **Alternative**: API Documentation > Webhooks > Security
   - Path: Groups all webhook content together
   - Context: If you have a dedicated Webhooks section

Recommendation: Create under Security section for consistency

Parameters

{
  "name": "hierarchy_search",
  "parameters": {
    "query": "string",              // Search query
    "limit": 10,                    // Number of results (default: 10)
    "organize_by_hierarchy": false, // Group results by structure
    "hierarchy_filter": {           // Hierarchy-specific filters
      "depth": 3,                   // Filter by hierarchy depth
      "has_children": true,         // Filter by whether pages have children
      "parent_title": "API Documentation", // Filter by parent page
      "root_only": false            // Show only root pages
    }
  }
}

Attachment search provides intelligent content analysis including:

  • Content Intelligence - OCR and semantic analysis of file contents
  • Context Integration - Understanding attachment relationships to parent documents
  • File Type Recognition - Intelligent handling of different file formats
  • Metadata Analysis - Author, size, type, and creation information

Content Intelligence Examples

Architecture and Design Files

Query: "architecture diagrams with security components"
Content Analysis Results:

1. πŸ“„ system-architecture-v3.pdf (2.3 MB)
   🧠 Content Analysis: "API gateway, authentication services, encrypted databases"
   πŸ—οΈ Components: Security controls, data encryption, access management
   πŸ“Š Security Coverage: 85% - Comprehensive security architecture

2. πŸ–ΌοΈ security-flow-diagram.png (1.1 MB)  
   🧠 OCR Analysis: "User authentication flow with multi-factor authentication"
   πŸ—οΈ Components: MFA, token validation, secure sessions
   πŸ“Š Security Coverage: 92% - Detailed security implementation

Code and Configuration Files

Query: "deployment configuration scripts"
Content Analysis Results:

1. πŸ“‹ deploy-production.yml (45 KB)
   πŸ“ Parent: Deployment Documentation
   🧠 Content: "Production deployment configuration with security settings"
   ⚠️ Risk Assessment: Medium - contains sensitive configuration

2. πŸ”§ setup-environment.sh (12 KB)
   πŸ“ Parent: Environment Setup Guide
   🧠 Content: "Environment initialization and dependency installation"
   βœ… Risk Assessment: Low - standard setup procedures

Parameters

{
  "name": "attachment_search",
  "parameters": {
    "query": "string",              // Search query
    "limit": 10,                    // Number of results
    "include_parent_context": true, // Include parent document info
    "attachment_filter": {          // Attachment-specific filters
      "file_type": "pdf",           // Filter by file type
      "file_size_min": 1024,        // Minimum file size in bytes
      "file_size_max": 10485760,    // Maximum file size in bytes
      "attachments_only": true,     // Show only attachments
      "author": "john.doe",         // Filter by author
      "parent_document_title": "API Documentation"
    }
  }
}

🀝 Cross-Document Intelligence Features

Document Relationship Analysis

Purpose: Comprehensive analysis of relationships between documents

{
  "name": "analyze_document_relationships",
  "parameters": {
    "query": "search query to get documents for analysis",
    "limit": 15,                    // Maximum documents to analyze
    "source_types": ["confluence", "git"],
    "project_ids": ["project1"]
  }
}

Real-World Example:

Query: "API authentication documentation"
Relationship Analysis:

πŸ“Š Document Network Analysis:
β”œβ”€β”€ Central Documents: 3 high-connectivity hubs
β”œβ”€β”€ Related Clusters: 4 topic-based groups
β”œβ”€β”€ Cross-References: 12 external links
└── Dependency Chain: 5-level hierarchy

πŸ”— Key Relationships Discovered:
1. Authentication Guide β†’ Implementation Examples (implements)
2. Security Policy β†’ Authentication Requirements (defines)
3. API Reference β†’ Authentication Endpoints (documents)
4. Troubleshooting β†’ Common Auth Issues (resolves)

πŸ’‘ Insights:
- Strong documentation coverage for authentication
- Clear implementation pathway from theory to practice
- Good troubleshooting support available

Document Similarity Detection

Purpose: Find documents with similar content for comparison or deduplication

{
  "name": "find_similar_documents",
  "parameters": {
    "target_query": "target document to find similarities for",
    "comparison_query": "documents to compare against",
    "similarity_metrics": ["entity_overlap", "semantic_similarity"],
    "max_similar": 5
  }
}

Real-World Example:

Target: "API Rate Limiting Guide"
Similar Documents Found:

1. "Rate Limiting Implementation" (Similarity: 0.92)
   πŸ“Š Overlap: Same concepts, different implementation approach
   πŸ”„ Relationship: Alternative implementation strategy

2. "API Throttling Configuration" (Similarity: 0.87)
   πŸ“Š Overlap: Similar technical solution, different focus
   πŸ”„ Relationship: Configuration vs. implementation guide

3. "API Performance Optimization" (Similarity: 0.75)
   πŸ“Š Overlap: Rate limiting as part of broader strategy
   πŸ”„ Relationship: Specific technique within broader approach

Conflict Detection

Purpose: Identify contradictions and inconsistencies across documents

{
  "name": "detect_document_conflicts",
  "parameters": {
    "query": "search query to get documents for conflict analysis",
    "limit": 15,
    "source_types": ["confluence", "git"],
    "project_ids": ["project1"]
  }
}

Real-World Example:

Query: "API authentication policies"
Conflicts Detected:

🚨 Conflict 1: Authentication Token Expiration
β”œβ”€β”€ Document A: "API Security Guidelines" β†’ 1 hour expiration
β”œβ”€β”€ Document B: "Mobile App Configuration" β†’ 24 hour expiration
└── πŸ’‘ Suggestion: Standardize token expiration policies

🚨 Conflict 2: Rate Limiting Configuration
β”œβ”€β”€ Document A: "Production Setup Guide" β†’ 100 requests/minute
β”œβ”€β”€ Document B: "API Documentation" β†’ 500 requests/minute  
└── πŸ’‘ Suggestion: Update documentation to match production

πŸ“‹ Resolution Recommendations:
1. Create unified authentication policy document
2. Establish single source of truth for configuration values
3. Add cross-references between related documents

Complementary Content Discovery

Purpose: Find content that complements and enhances a target document

{
  "name": "find_complementary_content",
  "parameters": {
    "target_query": "target document to analyze",
    "context_query": "context for finding complements",
    "max_recommendations": 5,
    "source_types": ["confluence", "git"],
    "project_ids": ["project1"]
  }
}

Real-World Example:

Target Document: "User Authentication API Specification"
Complementary Content Found:

1. Implementation Guide (Relevance: 0.89)
   πŸ“‹ Reason: Provides implementation details for the API specification
   🎯 Strategy: Requirements β†’ Implementation relationship

2. Security Testing Procedures (Relevance: 0.85)
   πŸ“‹ Reason: Covers security validation for authentication systems
   🎯 Strategy: Specification β†’ Validation relationship

3. Authentication Troubleshooting Guide (Relevance: 0.82)
   πŸ“‹ Reason: Addresses common issues with authentication
   🎯 Strategy: Implementation β†’ Support relationship

4. User Database Schema (Relevance: 0.78)
   πŸ“‹ Reason: Defines data structure supporting authentication
   🎯 Strategy: API β†’ Data Model relationship

Document Clustering

Purpose: Group related documents by topic, purpose, and relationships

{
  "name": "cluster_documents",
  "parameters": {
    "query": "search query to get documents for clustering",
    "strategy": "mixed_features",  // clustering strategy
    "max_clusters": 10,
    "min_cluster_size": 2,
    "limit": 25,
    "source_types": ["confluence"],
    "project_ids": ["project1"]
  }
}

Real-World Example:

Query: "API documentation"
Document Clusters Created:

πŸ“Š Cluster 1: Authentication & Security (5 documents)
β”œβ”€β”€ Coherence Score: 0.91
β”œβ”€β”€ Shared Topics: authentication, security, tokens
└── Documents: JWT Guide, OAuth Setup, Security Policy, etc.

πŸ“Š Cluster 2: API Implementation (7 documents)  
β”œβ”€β”€ Coherence Score: 0.87
β”œβ”€β”€ Shared Topics: endpoints, implementation, code
└── Documents: API Reference, Code Examples, Integration Guide, etc.

πŸ“Š Cluster 3: Troubleshooting & Support (4 documents)
β”œβ”€β”€ Coherence Score: 0.83
β”œβ”€β”€ Shared Topics: errors, debugging, troubleshooting
└── Documents: Error Guide, FAQ, Common Issues, etc.

πŸ’‘ Insights:
- Well-organized documentation with clear topic separation
- Strong coherence within each cluster
- Good coverage across implementation lifecycle

🎯 Advanced Search Strategies

Multi-Tool Workflow Examples

Complete Feature Investigation

1. Semantic Search: "user authentication implementation"
   β†’ Understand current authentication approach

2. Hierarchy Search: "authentication system structure"  
   β†’ Explore documentation organization

3. Cross-Document Analysis: Find relationships for "authentication API guide"
   β†’ Get implementation guides, testing procedures, troubleshooting

4. Conflict Detection: "authentication policies"
   β†’ Identify inconsistencies across documents

Result: Complete understanding with identified gaps and conflicts

Documentation Audit and Planning

1. Hierarchy Search: "API documentation structure analysis"
   β†’ Understand current organization and find gaps

2. Conflict Detection: "API versioning policies"
   β†’ Identify inconsistencies across documents

3. Similarity Detection: Find similar documents to "API guide"
   β†’ Review for potential duplication

4. Complementary Content: Find complements to "API reference"
   β†’ Discover missing supporting documentation

Result: Comprehensive audit with actionable improvement plan

Search Quality Optimization

Result Limit Guidelines

# Quick answers
limit: 3-5           # Fast, focused results

# Comprehensive search
limit: 10-15         # Good coverage (recommended)

# Analysis operations
limit: 20-30         # For cross-document intelligence

Source Type Filtering

# Search specific sources
source_types: ["git"]           # Only Git repositories
source_types: ["confluence"]    # Only Confluence pages
source_types: ["jira"]          # Only JIRA issues
source_types: ["localfile"]     # Only local files

# Cross-document analysis
source_types: ["confluence", "git"]  # Documentation and code

πŸ”§ Advanced Configuration

Environment Variables

The MCP server supports these configuration options:

# Required Configuration
QDRANT_URL=http://localhost:6333
OPENAI_API_KEY=your-openai-api-key

# Optional Configuration
QDRANT_COLLECTION_NAME=documents        # Default: "documents"
QDRANT_API_KEY=your-qdrant-cloud-key   # For QDrant Cloud
MCP_DISABLE_CONSOLE_LOGGING=true       # Recommended for development tools

Performance Optimization

For Large Knowledge Bases

  1. Optimize Search Parameters
  2. Use appropriate limit values for your needs
  3. Filter by source_types or project_ids when possible
  4. Use specific search tools for targeted queries

  5. Cross-Document Intelligence Tuning

  6. Limit analysis scope with appropriate limit parameters
  7. Use specific queries to reduce processing overhead
  8. Filter by project or source type for focused analysis

πŸš€ Performance Metrics

Real-World Performance Results

  • Semantic Search: Sub-second response times for most queries
  • Hierarchy Navigation: Instant structure analysis and navigation
  • Cross-Document Intelligence: Efficient relationship analysis for 15-25 documents
  • Attachment Search: Fast content analysis with intelligent file handling
  • Conflict Detection: Real-time inconsistency identification across document sets

Success Indicators

System Performance Metrics: - βœ… Query Processing: Fast semantic understanding and result ranking - βœ… Document Analysis: Efficient relationship and similarity detection - βœ… Content Intelligence: Comprehensive file and attachment analysis - βœ… Scalability: Handles large document collections effectively

πŸ“‹ Search Capabilities Checklist

Understanding Available Tools

  • [ ] Core Search Tools - Semantic, hierarchy, and attachment search
  • [ ] Cross-Document Intelligence - Relationship analysis, conflict detection, clustering
  • [ ] Content Analysis - File intelligence and attachment understanding
  • [ ] Performance Optimization - Appropriate limits and filtering

Effective Usage Patterns

  • [ ] Multi-Tool Workflows - Combine different search tools for comprehensive results
  • [ ] Progressive Discovery - Use hierarchy navigation for systematic exploration
  • [ ] Relationship Analysis - Leverage cross-document intelligence for deeper understanding
  • [ ] Quality Assessment - Use conflict detection for documentation consistency

Optimization and Best Practices

  • [ ] Search Quality - Understand relevance scores and result ranking
  • [ ] Performance Tuning - Use appropriate limits and filters
  • [ ] Content Organization - Leverage hierarchy and relationship insights
  • [ ] Documentation Maintenance - Use conflict detection for consistency

Unlock the intelligence of your knowledge base! 🧠

With these search capabilities, you're not just finding documentsβ€”you're discovering knowledge patterns, understanding relationships, and building comprehensive understanding through intelligent exploration. The system provides contextual insights that help you navigate and understand your knowledge base more effectively.

The power of intelligent search is at your fingertips! πŸš€

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