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.
π 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.
π Available Intelligence Features
Our search system provides sophisticated capabilities for knowledge exploration and analysis:
- π Enhanced Semantic Search - AI-powered similarity search with intelligent query understanding
- ποΈ Hierarchy-Aware Navigation - Structure-aware search with document relationships
- π Intelligent Attachment Search - Specialized search for files with content analysis
- π€ Cross-Document Intelligence - Relationship analysis, conflict detection, and content clustering
Core Search Tools Available
- Semantic Search - AI-powered similarity search across all documents
- Hierarchy Search - Structure-aware search with document relationships
- Attachment Search - Specialized search for files and documents
π₯ Cross-Document Intelligence Features
- Document Relationship Analysis - Comprehensive relationship analysis (Available)
- Document Similarity Detection - Find similar and related documents (Available)
- Conflict Detection - Identify contradictions across documents (Available)
- Complementary Content Discovery - Find related and supporting content (Available)
- Document Clustering - Group documents by similarity and relationships (Available)
π Enhanced Semantic Search
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?"
π 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?"
π 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"
π 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"]
}
}
ποΈ Enhanced Hierarchy Search
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:
π API Documentation (Root)
βββ π Getting Started (3 children)
βββ π Authentication (5 children)
β βββ π JWT Implementation
β βββ π OAuth Setup
β βββ π API Keys
βββ π Endpoints (8 children)
β βββ π User Management
β βββ π Data Operations
βββ π Examples (4 children)
π‘ 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
}
}
}
π Enhanced Attachment Search
Intelligent File and Document Search
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. π system-architecture-v3.pdf (2.3 MB)
π§ Content Analysis: "API gateway, authentication services, encrypted databases"
ποΈ Components: Security controls, data encryption, access management
π Security Coverage: 85% - Comprehensive security architecture
2. πΌοΈ security-flow-diagram.png (1.1 MB)
π§ OCR Analysis: "User authentication flow with multi-factor authentication"
ποΈ Components: MFA, token validation, secure sessions
π Security Coverage: 92% - Detailed security implementation
Code and Configuration Files
Query: "deployment configuration scripts"
Content Analysis Results:
1. π deploy-production.yml (45 KB)
π Parent: Deployment Documentation
π§ Content: "Production deployment configuration with security settings"
β οΈ Risk Assessment: Medium - contains sensitive configuration
2. π§ setup-environment.sh (12 KB)
π Parent: Environment Setup Guide
π§ Content: "Environment initialization and dependency installation"
β
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"
}
}
}
π€ 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:
π Document Network Analysis:
βββ Central Documents: 3 high-connectivity hubs
βββ Related Clusters: 4 topic-based groups
βββ Cross-References: 12 external links
βββ Dependency Chain: 5-level hierarchy
π 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)
π‘ 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)
π Overlap: Same concepts, different implementation approach
π Relationship: Alternative implementation strategy
2. "API Throttling Configuration" (Similarity: 0.87)
π Overlap: Similar technical solution, different focus
π Relationship: Configuration vs. implementation guide
3. "API Performance Optimization" (Similarity: 0.75)
π Overlap: Rate limiting as part of broader strategy
π 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:
π¨ Conflict 1: Authentication Token Expiration
βββ Document A: "API Security Guidelines" β 1 hour expiration
βββ Document B: "Mobile App Configuration" β 24 hour expiration
βββ π‘ Suggestion: Standardize token expiration policies
π¨ Conflict 2: Rate Limiting Configuration
βββ Document A: "Production Setup Guide" β 100 requests/minute
βββ Document B: "API Documentation" β 500 requests/minute
βββ π‘ Suggestion: Update documentation to match production
π 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)
π Reason: Provides implementation details for the API specification
π― Strategy: Requirements β Implementation relationship
2. Security Testing Procedures (Relevance: 0.85)
π Reason: Covers security validation for authentication systems
π― Strategy: Specification β Validation relationship
3. Authentication Troubleshooting Guide (Relevance: 0.82)
π Reason: Addresses common issues with authentication
π― Strategy: Implementation β Support relationship
4. User Database Schema (Relevance: 0.78)
π Reason: Defines data structure supporting authentication
π― 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:
π Cluster 1: Authentication & Security (5 documents)
βββ Coherence Score: 0.91
βββ Shared Topics: authentication, security, tokens
βββ Documents: JWT Guide, OAuth Setup, Security Policy, etc.
π Cluster 2: API Implementation (7 documents)
βββ Coherence Score: 0.87
βββ Shared Topics: endpoints, implementation, code
βββ Documents: API Reference, Code Examples, Integration Guide, etc.
π Cluster 3: Troubleshooting & Support (4 documents)
βββ Coherence Score: 0.83
βββ Shared Topics: errors, debugging, troubleshooting
βββ Documents: Error Guide, FAQ, Common Issues, etc.
π‘ Insights:
- Well-organized documentation with clear topic separation
- Strong coherence within each cluster
- Good coverage across implementation lifecycle
π― 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
π§ 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
- Optimize Search Parameters
- Use appropriate
limit
values for your needs - Filter by
source_types
orproject_ids
when possible -
Use specific search tools for targeted queries
-
Cross-Document Intelligence Tuning
- Limit analysis scope with appropriate
limit
parameters - Use specific queries to reduce processing overhead
- Filter by project or source type for focused analysis
π 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: - β Query Processing: Fast semantic understanding and result ranking - β Document Analysis: Efficient relationship and similarity detection - β Content Intelligence: Comprehensive file and attachment analysis - β Scalability: Handles large document collections effectively
π 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! π§
With these search capabilities, you're not just finding documentsβyou'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! π