docs/architecture/data-flow.md

metadata

title: Data Flow
description: Data Flow patterns and information processing in KGraph-MCP

Data Flow Architecture

Comprehensive documentation of how data flows through KGraph-MCP's intelligent orchestration system, from user input to tool execution and knowledge graph updates.

🌊 Overview

KGraph-MCP implements a sophisticated data flow architecture that enables:

• Semantic Processing: Natural language understanding and goal extraction
• Knowledge Retrieval: Intelligent tool discovery through graph querying
• Execution Coordination: Multi-agent coordination for complex workflows
• Continuous Learning: Knowledge graph enhancement from execution feedback

🔄 Primary Data Flow Patterns

1. User Request Processing Flow

flowchart TD
    Start([User Input]) --> Validate{Input Validation}
    Validate -->|Valid| Parse[Parse Natural Language]
    Validate -->|Invalid| Error[Return Error]
    
    Parse --> Extract[Extract Goals & Context]
    Extract --> Analyze[Analyze Requirements]
    Analyze --> Plan[Generate Execution Plan]
    
    Plan --> QueryKG[Query Knowledge Graph]
    QueryKG --> RankTools[Rank Available Tools]
    RankTools --> SelectTools[Select Best Tools]
    
    SelectTools --> Execute[Execute Tool Chain]
    Execute --> Monitor[Monitor Execution]
    Monitor --> Validate_Results{Validate Results}
    
    Validate_Results -->|Success| Format[Format Response]
    Validate_Results -->|Failure| Retry{Retry Logic}
    
    Retry -->|Yes| Execute
    Retry -->|No| Fallback[Fallback Strategy]
    
    Format --> UpdateKG[Update Knowledge Graph]
    UpdateKG --> Return([Return to User])
    
    Fallback --> Return
    Error --> Return
    
    style Start fill:#e3f2fd
    style Parse fill:#e8f5e8
    style QueryKG fill:#f3e5f5
    style Execute fill:#fff3e0
    style Return fill:#e1f5fe

2. Knowledge Graph Information Flow

flowchart LR
    subgraph "📥 Data Ingestion Pipeline"
        Discovery[MCP Server Discovery]
        Extraction[Metadata Extraction]
        Validation[Schema Validation]
        Transformation[Data Transformation]
    end
    
    subgraph "🧠 Processing Pipeline"
        Embedding[Vector Embedding]
        Analysis[Capability Analysis]
        Relation[Relationship Detection]
        Classification[Tool Classification]
    end
    
    subgraph "💾 Storage Pipeline"
        GraphDB[(Graph Database<br/>Neo4j)]
        VectorDB[(Vector Database<br/>Qdrant)]
        MetadataDB[(Metadata Store<br/>JSON)]
        CacheLayer[(Redis Cache)]
    end
    
    subgraph "🔍 Query Pipeline"
        SemanticSearch[Semantic Search]
        GraphQuery[Graph Traversal]
        HybridRetrieval[Hybrid Retrieval]
        RankingEngine[Ranking Engine]
    end
    
    Discovery --> Extraction
    Extraction --> Validation
    Validation --> Transformation
    
    Transformation --> Embedding
    Transformation --> Analysis
    Transformation --> Relation
    Transformation --> Classification
    
    Embedding --> VectorDB
    Analysis --> GraphDB
    Relation --> GraphDB
    Classification --> MetadataDB
    
    VectorDB --> SemanticSearch
    GraphDB --> GraphQuery
    MetadataDB --> HybridRetrieval
    CacheLayer --> RankingEngine
    
    SemanticSearch --> RankingEngine
    GraphQuery --> RankingEngine
    HybridRetrieval --> RankingEngine
    
    style Discovery fill:#e1f5fe
    style Embedding fill:#e8f5e8
    style GraphDB fill:#f3e5f5
    style SemanticSearch fill:#fff3e0

3. Agent Coordination Data Flow

sequenceDiagram
    participant User
    participant API as FastAPI
    participant Planner as Planner Agent
    participant KG as Knowledge Graph
    participant Selector as Selector Agent
    participant Executor as Executor Agent
    participant MCP as MCP Server
    participant Supervisor as Supervisor Agent
    
    User->>API: Submit Request
    API->>Planner: Forward Request
    
    Note over Planner: Goal Analysis & Decomposition
    Planner->>Planner: Parse Natural Language
    Planner->>Planner: Extract Objectives
    Planner->>Planner: Generate Sub-tasks
    
    Planner->>Selector: Request Tool Selection
    Note over Selector,KG: Tool Discovery Process
    Selector->>KG: Query Available Tools
    KG-->>Selector: Return Tool Metadata
    Selector->>KG: Query Tool Relationships
    KG-->>Selector: Return Capability Graph
    
    Note over Selector: Tool Ranking & Selection
    Selector->>Selector: Calculate Similarity Scores
    Selector->>Selector: Apply Business Rules
    Selector-->>Planner: Return Ranked Tools
    
    Planner->>Executor: Execute Plan
    Note over Executor,MCP: Tool Execution Loop
    loop For Each Tool in Plan
        Executor->>MCP: Invoke Tool
        MCP-->>Executor: Stream Response
        Executor->>Supervisor: Validate Response
        Note over Supervisor: Quality Check
        Supervisor-->>Executor: Validation Result
    end
    
    Executor->>Planner: Execution Complete
    Note over Planner: Result Aggregation
    Planner->>KG: Update Usage Statistics
    Planner->>API: Return Final Results
    API->>User: Response with Results
    
    Note over KG: Learning Update
    KG->>KG: Update Tool Rankings
    KG->>KG: Enhance Relationships

📊 Data Processing Layers

Layer 1: Input Processing & Validation

graph TD
    subgraph "🔍 Input Analysis"
        RawInput[Raw User Input]
        NLP[Natural Language Processing]
        Intent[Intent Recognition]
        Context[Context Extraction]
    end
    
    subgraph "✅ Validation Layer"
        Schema[Schema Validation]
        Security[Security Checks]
        Rate[Rate Limiting]
        Auth[Authentication]
    end
    
    subgraph "🎯 Goal Processing"
        Parse[Goal Parsing]
        Decompose[Task Decomposition]
        Priority[Priority Assignment]
        Dependencies[Dependency Analysis]
    end
    
    RawInput --> NLP
    NLP --> Intent
    Intent --> Context
    
    Context --> Schema
    Schema --> Security
    Security --> Rate
    Rate --> Auth
    
    Auth --> Parse
    Parse --> Decompose
    Decompose --> Priority
    Priority --> Dependencies
    
    style RawInput fill:#e3f2fd
    style Schema fill:#e8f5e8
    style Parse fill:#f3e5f5

Layer 2: Knowledge Graph Operations

flowchart TB
    subgraph "🔍 Query Processing"
        QueryInput[Query Input]
        QueryParse[Query Parsing]
        QueryOpt[Query Optimization]
        QueryExec[Query Execution]
    end
    
    subgraph "📊 Graph Operations"
        Traversal[Graph Traversal]
        Aggregation[Data Aggregation]
        Filtering[Result Filtering]
        Ranking[Result Ranking]
    end
    
    subgraph "🧮 Vector Operations"
        Similarity[Similarity Search]
        Clustering[Tool Clustering]
        Embedding[Embedding Lookup]
        Indexing[Index Operations]
    end
    
    subgraph "🔄 Update Operations"
        Insert[Data Insertion]
        Update[Data Updates]
        Delete[Data Deletion]
        Optimize[Index Optimization]
    end
    
    QueryInput --> QueryParse
    QueryParse --> QueryOpt
    QueryOpt --> QueryExec
    
    QueryExec --> Traversal
    QueryExec --> Similarity
    
    Traversal --> Aggregation
    Aggregation --> Filtering
    Filtering --> Ranking
    
    Similarity --> Clustering
    Clustering --> Embedding
    Embedding --> Indexing
    
    Ranking --> Insert
    Insert --> Update
    Update --> Delete
    Delete --> Optimize
    
    style QueryInput fill:#e1f5fe
    style Traversal fill:#e8f5e8
    style Similarity fill:#f3e5f5
    style Insert fill:#fff3e0

Layer 3: Tool Execution & Monitoring

graph LR
    subgraph "🚀 Execution Pipeline"
        Prepare[Prepare Execution]
        Invoke[Tool Invocation]
        Monitor[Real-time Monitoring]
        Collect[Result Collection]
    end
    
    subgraph "🔄 State Management"
        StateInit[Initialize State]
        StateUpdate[Update State]
        StateSync[Synchronize State]
        StateCleanup[Cleanup State]
    end
    
    subgraph "📊 Performance Tracking"
        Metrics[Collect Metrics]
        Timing[Execution Timing]
        Resource[Resource Usage]
        Quality[Quality Metrics]
    end
    
    subgraph "🛡️ Error Handling"
        Detect[Error Detection]
        Classify[Error Classification]
        Recover[Recovery Strategy]
        Fallback[Fallback Options]
    end
    
    Prepare --> StateInit
    StateInit --> Invoke
    Invoke --> Monitor
    Monitor --> StateUpdate
    
    Monitor --> Metrics
    Metrics --> Timing
    Timing --> Resource
    Resource --> Quality
    
    Monitor --> Detect
    Detect --> Classify
    Classify --> Recover
    Recover --> Fallback
    
    Collect --> StateSync
    StateSync --> StateCleanup
    
    style Prepare fill:#e3f2fd
    style StateInit fill:#e8f5e8
    style Metrics fill:#f3e5f5
    style Detect fill:#ffebee

🔄 Feedback and Learning Loops

Continuous Knowledge Enhancement

flowchart TD
    ExecutionData[Execution Results] --> Analyze[Analyze Performance]
    Analyze --> Patterns[Identify Patterns]
    Patterns --> Insights[Extract Insights]
    
    Insights --> UpdateRankings[Update Tool Rankings]
    UpdateRankings --> UpdateRelations[Update Relationships]
    UpdateRelations --> UpdateEmbeddings[Update Embeddings]
    
    UpdateEmbeddings --> ValidateChanges{Validate Changes}
    ValidateChanges -->|Valid| ApplyChanges[Apply to Knowledge Graph]
    ValidateChanges -->|Invalid| RevertChanges[Revert Changes]
    
    ApplyChanges --> MonitorImpact[Monitor Impact]
    MonitorImpact --> MeasureImprovement[Measure Improvement]
    MeasureImprovement --> FeedbackLoop[Feedback to Analysis]
    FeedbackLoop --> Analyze
    
    RevertChanges --> LogError[Log Error]
    LogError --> Analyze
    
    subgraph "📈 Learning Metrics"
        MonitorImpact
        MeasureImprovement
        FeedbackLoop
    end
    
    style ExecutionData fill:#e3f2fd
    style Insights fill:#e8f5e8
    style ApplyChanges fill:#f3e5f5
    style MonitorImpact fill:#fff3e0

📈 Performance Optimization Patterns

Caching Strategy

graph TB
    subgraph "🔄 Cache Layers"
        L1[L1: In-Memory Cache<br/>Agent State & Results]
        L2[L2: Redis Cache<br/>Query Results & Embeddings]
        L3[L3: Persistent Cache<br/>Tool Metadata & Rankings]
    end
    
    subgraph "📊 Cache Operations"
        Read[Cache Read]
        Write[Cache Write]
        Invalidate[Cache Invalidation]
        Refresh[Cache Refresh]
    end
    
    subgraph "🎯 Cache Policies"
        TTL[Time-to-Live Policy]
        LRU[Least Recently Used]
        Priority[Priority-based Eviction]
        Dependency[Dependency Invalidation]
    end
    
    Request[Incoming Request] --> Read
    Read --> Hit{Cache Hit?}
    Hit -->|Yes| Return[Return Cached Result]
    Hit -->|No| Compute[Compute Result]
    
    Compute --> Write
    Write --> L1
    Write --> L2
    Write --> L3
    
    L1 --> TTL
    L2 --> LRU
    L3 --> Priority
    
    Update[Data Update] --> Invalidate
    Invalidate --> Dependency
    Dependency --> Refresh
    
    style Request fill:#e3f2fd
    style Hit fill:#e8f5e8
    style L1 fill:#f3e5f5
    style TTL fill:#fff3e0

Stream Processing Architecture

flowchart LR
    subgraph "📥 Input Streams"
        UserStream[User Requests]
        ExecutionStream[Tool Execution Events]
        FeedbackStream[Learning Feedback]
        SystemStream[System Metrics]
    end
    
    subgraph "🔄 Processing Pipeline"
        Router[Event Router]
        Transform[Data Transformation]
        Enrich[Context Enrichment]
        Aggregate[Event Aggregation]
    end
    
    subgraph "📊 Output Channels"
        UIChannel[UI Updates]
        APIChannel[API Responses]
        KGChannel[Knowledge Graph Updates]
        LogChannel[Logging & Metrics]
    end
    
    UserStream --> Router
    ExecutionStream --> Router
    FeedbackStream --> Router
    SystemStream --> Router
    
    Router --> Transform
    Transform --> Enrich
    Enrich --> Aggregate
    
    Aggregate --> UIChannel
    Aggregate --> APIChannel
    Aggregate --> KGChannel
    Aggregate --> LogChannel
    
    style UserStream fill:#e1f5fe
    style Router fill:#e8f5e8
    style Transform fill:#f3e5f5
    style UIChannel fill:#fff3e0

🔗 Data Flow Integration Points

External System Integration

graph TB
    subgraph "🌐 External Systems"
        MCPServers[MCP Servers]
        APIs[External APIs]
        Databases[External Databases]
        Services[Cloud Services]
    end
    
    subgraph "🔌 Integration Layer"
        Adapters[Protocol Adapters]
        Connectors[Service Connectors]
        Transformers[Data Transformers]
        Validators[Response Validators]
    end
    
    subgraph "🛡️ Reliability Layer"
        Retry[Retry Logic]
        Circuit[Circuit Breakers]
        Timeout[Timeout Handling]
        Fallback[Fallback Mechanisms]
    end
    
    subgraph "📊 Monitoring Layer"
        Health[Health Checks]
        Metrics[Performance Metrics]
        Alerts[Alert Management]
        Logs[Audit Logging]
    end
    
    MCPServers --> Adapters
    APIs --> Connectors
    Databases --> Transformers
    Services --> Validators
    
    Adapters --> Retry
    Connectors --> Circuit
    Transformers --> Timeout
    Validators --> Fallback
    
    Retry --> Health
    Circuit --> Metrics
    Timeout --> Alerts
    Fallback --> Logs
    
    style MCPServers fill:#e1f5fe
    style Adapters fill:#e8f5e8
    style Retry fill:#f3e5f5
    style Health fill:#fff3e0

📋 Data Quality & Governance

Data Quality Pipeline

flowchart TD
    Input[Incoming Data] --> Validate[Data Validation]
    Validate --> Cleanse[Data Cleansing]
    Cleanse --> Enrich[Data Enrichment]
    Enrich --> Quality{Quality Check}
    
    Quality -->|Pass| Store[Store Data]
    Quality -->|Fail| Quarantine[Quarantine Data]
    
    Store --> Index[Update Indices]
    Index --> Monitor[Monitor Quality]
    
    Quarantine --> Review[Manual Review]
    Review --> Fix[Fix Data Issues]
    Fix --> Validate
    
    Monitor --> Report[Quality Reports]
    Report --> Improve[Process Improvement]
    Improve --> Validate
    
    style Input fill:#e3f2fd
    style Validate fill:#e8f5e8
    style Quality fill:#fff3e0
    style Store fill:#f3e5f5
    style Quarantine fill:#ffebee

---

🔗 Related Documentation

• System Architecture Overview - High-level system design
• Agent Architecture - Agent coordination patterns
• Knowledge Graph Architecture - Graph data structures
• API Documentation - API endpoints and data formats

This data flow documentation provides detailed insights into how information moves through KGraph-MCP's intelligent orchestration system, enabling efficient and reliable MCP tool coordination.