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MultiMind Architecture

This document provides a comprehensive overview of the MultiMind SDK architecture, including its core components, interfaces, and data flow.

System Overview

graph TB
    subgraph "MultiMind SDK"
        Core[Core Components]
        Ensemble[Ensemble System]
        Pipeline[Pipeline System]
        Compliance[Compliance & Governance]
        Interfaces[Interfaces]
        RAG[RAG System]
        FineTuning[Fine-tuning System]
        MCP[Model Control Plane]
        Memory[Memory System]
        Orchestration[Orchestration Engine]
        Gateway[API Gateway]
        Router[Model Router]
        Tools[Tool System]
        Monitoring[Monitoring System]
    end

    subgraph "Model Providers"
        OpenAI[OpenAI]
        Claude[Claude]
        Mistral[Mistral]
        Ollama[Ollama]
        HuggingFace[HuggingFace]
        Groq[Groq]
        Local[Local Models]
    end

    subgraph "Core Components"
        Models[Model Wrappers]
        Agents[Agent System]
        Memory[Memory Management]
        Tools[Tool System]
        Config[Configuration]
    end

    subgraph "Ensemble System"
        Methods[Ensemble Methods]
        Providers[Model Providers]
        Voting[Voting System]
    end

    subgraph "Pipeline System"
        Tasks[Task Management]
        Chains[Prompt Chains]
        Workflows[Workflow Engine]
    end

    subgraph "Compliance & Governance"
        Privacy[Privacy Management]
        Audit[Audit System]
        Policy[Policy Engine]
        Training[Compliance Training]
        Risk[Risk Assessment]
    end

    subgraph "RAG System"
        Indexing[Document Indexing]
        Retrieval[Vector Retrieval]
        Augmentation[Context Augmentation]
        Storage[Vector Storage]
        Embedding[Embedding Models]
    end

    subgraph "Fine-tuning System"
        Training[Model Training]
        Evaluation[Model Evaluation]
        Versioning[Model Versioning]
        Deployment[Model Deployment]
        LoRA[LoRA Training]
        QLoRA[QLoRA Training]
    end

    subgraph "Model Control Plane"
        Monitoring[Model Monitoring]
        Scaling[Auto Scaling]
        Routing[Request Routing]
        LoadBalancing[Load Balancing]
        Executor[MCP Executor]
        Parser[MCP Parser]
    end

    subgraph "Memory System"
        ShortTerm[Short-term Memory]
        LongTerm[Long-term Memory]
        WorkingMemory[Working Memory]
        Episodic[Episodic Memory]
        Buffer[Memory Buffer]
    end

    subgraph "Orchestration Engine"
        TaskScheduler[Task Scheduler]
        ResourceManager[Resource Manager]
        StateManager[State Manager]
        EventBus[Event Bus]
        WorkflowEngine[Workflow Engine]
    end

    subgraph "API Gateway"
        Auth[Authentication]
        RateLimit[Rate Limiting]
        Routing[Request Routing]
        Metrics[Metrics Collection]
        Session[Session Management]
    end

    subgraph "Model Router"
        Strategy[Routing Strategy]
        Fallback[Fallback Handler]
        LoadBalancer[Load Balancer]
        Metrics[Performance Metrics]
    end

    subgraph "Tool System"
        Calculator[Calculator Tool]
        WebSearch[Web Search Tool]
        FileOps[File Operations]
        CustomTools[Custom Tools]
    end

    subgraph "Monitoring System"
        Usage[Usage Tracker]
        Trace[Trace Logger]
        Metrics[Metrics Collector]
        Alerts[Alert System]
        Reports[Report Generator]
    end

    subgraph "Interfaces"
        CLI[Command Line Interface]
        API[REST API]
        WS[WebSocket API]
    end

    Core --> Ensemble
    Core --> Pipeline
    Core --> Compliance
    Core --> Interfaces
    Core --> RAG
    Core --> FineTuning
    Core --> MCP
    Core --> Memory
    Core --> Orchestration
    Core --> Gateway
    Core --> Router
    Core --> Tools
    Core --> Monitoring

    Models --> Providers
    Agents --> Tools
    Memory --> Agents
    Tools --> Agents

    Methods --> Voting
    Providers --> Voting
    Voting --> Ensemble

    OpenAI --> Providers
    Claude --> Providers
    Mistral --> Providers
    Ollama --> Providers
    HuggingFace --> Providers
    Groq --> Providers
    Local --> Providers

    Tasks --> Chains
    Chains --> Workflows
    Workflows --> Pipeline

    Privacy --> Audit
    Policy --> Privacy
    Audit --> Compliance
    Training --> Compliance
    Risk --> Compliance

    Indexing --> Storage
    Retrieval --> Storage
    Augmentation --> Retrieval
    Embedding --> Indexing

    Training --> Evaluation
    Evaluation --> Versioning
    Versioning --> Deployment
    LoRA --> Training
    QLoRA --> Training

    Monitoring --> Scaling
    Scaling --> Routing
    Routing --> LoadBalancing
    Executor --> MCP
    Parser --> MCP

    ShortTerm --> WorkingMemory
    LongTerm --> WorkingMemory
    Episodic --> WorkingMemory
    Buffer --> WorkingMemory

    TaskScheduler --> ResourceManager
    ResourceManager --> StateManager
    StateManager --> EventBus
    WorkflowEngine --> TaskScheduler

    Auth --> Gateway
    RateLimit --> Gateway
    Routing --> Gateway
    Metrics --> Gateway
    Session --> Gateway

    Strategy --> Router
    Fallback --> Router
    LoadBalancer --> Router
    Metrics --> Router

    Calculator --> Tools
    WebSearch --> Tools
    FileOps --> Tools
    CustomTools --> Tools

    Usage --> Monitoring
    Trace --> Monitoring
    Metrics --> Monitoring
    Alerts --> Monitoring
    Reports --> Monitoring

    CLI --> Core
    API --> Core
    WS --> Core
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Model Providers

The MultiMind SDK supports the following model providers:

OpenAI

  • GPT-3.5 Turbo
  • GPT-4
  • GPT-4 Vision
  • Text Embedding Models

Claude

  • Claude 3 Opus
  • Claude 3 Sonnet
  • Claude 3 Haiku
  • Claude Instant

Mistral

  • Mistral 7B
  • Mixtral 8x7B
  • Mistral Small
  • Mistral Medium

Ollama

  • Local model hosting
  • Custom model support
  • Model management
  • Inference API

HuggingFace

  • Open source models
  • Custom model hosting
  • Model fine-tuning
  • Model deployment

Groq

  • Mixtral 8x7B
  • High-performance inference
  • Low latency
  • Cost-effective

Local Models

  • Custom model support
  • Local deployment
  • Offline capabilities
  • Resource optimization

Each provider is integrated through a standardized interface, allowing for:

  • Consistent API access
  • Unified error handling
  • Standardized response formats
  • Provider-specific optimizations

Component Details

Core Components

classDiagram
    class ModelWrapper {
        +query_model()
        +available_models()
        +load_environment()
    }
    
    class Agent {
        +model
        +memory
        +tools
        +system_prompt
        +run()
        +chat()
    }
    
    class AgentMemory {
        +max_history
        +add_message()
        +get_history()
        +clear()
    }
    
    class Tool {
        +name
        +description
        +execute()
    }
    
    ModelWrapper <|-- OpenAIModel
    ModelWrapper <|-- ClaudeModel
    ModelWrapper <|-- MistralModel
    Agent --> ModelWrapper
    Agent --> AgentMemory
    Agent --> Tool
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Ensemble System

classDiagram
    class Ensemble {
        +providers
        +method
        +weights
        +combine_results()
    }
    
    class EnsembleMethod {
        +weighted_voting()
        +confidence_cascade()
        +parallel_voting()
        +majority_voting()
        +rank_based()
    }
    
    class Provider {
        +name
        +weight
        +confidence
        +query()
    }
    
    Ensemble --> EnsembleMethod
    Ensemble --> Provider
    Provider <|-- OpenAIProvider
    Provider <|-- AnthropicProvider
    Provider <|-- OllamaProvider
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Pipeline System

classDiagram
    class Pipeline {
        +tasks
        +chains
        +workflows
        +run()
    }
    
    class Task {
        +name
        +type
        +config
        +execute()
    }
    
    class Chain {
        +steps
        +dependencies
        +run()
    }
    
    class Workflow {
        +name
        +tasks
        +schedule
        +execute()
    }
    
    Pipeline --> Task
    Pipeline --> Chain
    Pipeline --> Workflow
    Chain --> Task
    Workflow --> Task
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Compliance & Governance

classDiagram
    class Compliance {
        +privacy
        +audit
        +policy
        +check_compliance()
    }
    
    class PrivacyCompliance {
        +config
        +export_user_data()
        +erase_user_data()
        +request_model_approval()
    }
    
    class AuditSystem {
        +verify_log_chain()
        +track_changes()
        +generate_report()
    }
    
    class PolicyEngine {
        +publish_policy()
        +validate_compliance()
        +enforce_rules()
    }
    
    Compliance --> PrivacyCompliance
    Compliance --> AuditSystem
    Compliance --> PolicyEngine
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Interfaces

classDiagram
    class Interface {
        +cli
        +api
        +websocket
    }
    
    class CLI {
        +ensemble_commands()
        +model_commands()
        +compliance_commands()
    }
    
    class API {
        +rest_endpoints()
        +websocket_endpoints()
        +authentication()
    }
    
    class WebSocket {
        +connect()
        +subscribe()
        +publish()
    }
    
    Interface --> CLI
    Interface --> API
    Interface --> WebSocket
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RAG System

classDiagram
    class RAGSystem {
        +index_documents()
        +retrieve_context()
        +augment_prompt()
    }
    
    class DocumentIndexer {
        +chunk_documents()
        +create_embeddings()
        +store_vectors()
    }
    
    class VectorRetriever {
        +search_vectors()
        +rank_results()
        +filter_results()
    }
    
    class ContextAugmenter {
        +combine_context()
        +format_prompt()
        +validate_context()
    }
    
    RAGSystem --> DocumentIndexer
    RAGSystem --> VectorRetriever
    RAGSystem --> ContextAugmenter
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Fine-tuning System

classDiagram
    class FineTuningSystem {
        +prepare_data()
        +train_model()
        +evaluate_model()
        +deploy_model()
    }
    
    class ModelTrainer {
        +train()
        +validate()
        +save_checkpoint()
    }
    
    class ModelEvaluator {
        +evaluate()
        +compare_models()
        +generate_metrics()
    }
    
    class ModelDeployer {
        +deploy()
        +rollback()
        +monitor()
    }
    
    FineTuningSystem --> ModelTrainer
    FineTuningSystem --> ModelEvaluator
    FineTuningSystem --> ModelDeployer
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Model Control Plane

classDiagram
    class ModelControlPlane {
        +monitor_models()
        +scale_resources()
        +route_requests()
        +balance_load()
    }
    
    class ModelMonitor {
        +track_metrics()
        +detect_anomalies()
        +generate_alerts()
    }
    
    class ResourceScaler {
        +scale_up()
        +scale_down()
        +optimize_resources()
    }
    
    class RequestRouter {
        +route_request()
        +load_balance()
        +failover()
    }
    
    ModelControlPlane --> ModelMonitor
    ModelControlPlane --> ResourceScaler
    ModelControlPlane --> RequestRouter
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Memory System

classDiagram
    class MemorySystem {
        +store_memory()
        +retrieve_memory()
        +update_memory()
    }
    
    class ShortTermMemory {
        +buffer_size
        +add_to_buffer()
        +clear_buffer()
    }
    
    class LongTermMemory {
        +store_permanent()
        +retrieve_permanent()
        +update_permanent()
    }
    
    class WorkingMemory {
        +current_context
        +update_context()
        +clear_context()
    }
    
    class EpisodicMemory {
        +store_episode()
        +retrieve_episode()
        +link_episodes()
    }
    
    MemorySystem --> ShortTermMemory
    MemorySystem --> LongTermMemory
    MemorySystem --> WorkingMemory
    MemorySystem --> EpisodicMemory
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Orchestration Engine

classDiagram
    class OrchestrationEngine {
        +schedule_tasks()
        +manage_resources()
        +handle_events()
    }
    
    class TaskScheduler {
        +schedule()
        +prioritize()
        +reschedule()
    }
    
    class ResourceManager {
        +allocate()
        +deallocate()
        +optimize()
    }
    
    class StateManager {
        +track_state()
        +update_state()
        +recover_state()
    }
    
    class EventBus {
        +publish()
        +subscribe()
        +handle_event()
    }
    
    OrchestrationEngine --> TaskScheduler
    OrchestrationEngine --> ResourceManager
    OrchestrationEngine --> StateManager
    OrchestrationEngine --> EventBus
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Model Client Architecture and Routing

The MultiMind SDK features an extensible model client system for advanced and flexible model management:

  • ModelClient: The base class for all model clients (transformer and non-transformer). Subclass to implement custom models.
  • Prebuilt Clients: Includes LSTMModelClient, RNNModelClient, GRUModelClient, MoEModelClient (Mixture-of-Experts), DynamicMoEModelClient (runtime metrics-based routing), MultiModalClient (unified text, image, audio, video, code), and more.
  • Routing Logic: MoE and DynamicMoE clients route requests to the best expert model based on prompt or runtime metrics. FederatedRouter enables routing between local and cloud models based on context (input size, latency, privacy, etc.).
  • Extensibility: Easily add new model types or routing strategies by subclassing ModelClient or implementing custom routers.

This architecture enables dynamic model selection, multimodal workflows, and advanced routing for cost, latency, or quality optimization. See the Usage Guide for code examples.

Data Flow

sequenceDiagram
    participant User
    participant Interface
    participant Core
    participant RAG
    participant Ensemble
    participant MCP
    participant Provider
    
    User->>Interface: Request
    Interface->>Core: Process Request
    Core->>RAG: Get Context
    RAG-->>Core: Context
    Core->>Ensemble: Get Ensemble Result
    Ensemble->>MCP: Route Request
    MCP->>Provider: Query Providers
    Provider-->>MCP: Provider Results
    MCP-->>Ensemble: Routed Results
    Ensemble-->>Core: Combined Result
    Core-->>Interface: Processed Response
    Interface-->>User: Final Response
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Deployment Architecture

graph TB
    subgraph "Client Layer"
        CLI[CLI Client]
        API[API Client]
        WS[WebSocket Client]
    end
    
    subgraph "Application Layer"
        Server[MultiMind Server]
        Redis[(Redis Cache)]
        Chroma[(Chroma DB)]
    end
    
    subgraph "Model Layer"
        OpenAI[OpenAI API]
        Anthropic[Anthropic API]
        Ollama[Ollama Service]
        HF[HuggingFace API]
    end
    
    CLI --> Server
    API --> Server
    WS --> Server
    
    Server --> Redis
    Server --> Chroma
    
    Server --> OpenAI
    Server --> Anthropic
    Server --> Ollama
    Server --> HF
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Configuration

The architecture supports various configuration options through environment variables and configuration files:

graph LR
    subgraph "Configuration Sources"
        Env[Environment Variables]
        Config[Config Files]
        Secrets[Secret Management]
    end
    
    subgraph "Configuration Types"
        API[API Keys]
        Model[Model Settings]
        System[System Settings]
        Compliance[Compliance Rules]
    end
    
    Env --> API
    Config --> Model
    Config --> System
    Secrets --> API
    Config --> Compliance
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Security Architecture

graph TB
    subgraph "Security Layers"
        Auth[Authentication]
        Authz[Authorization]
        Audit[Audit Logging]
        Privacy[Privacy Controls]
    end
    
    subgraph "Security Features"
        API[API Key Management]
        RBAC[Role-Based Access]
        Logging[Secure Logging]
        Encryption[Data Encryption]
    end
    
    Auth --> API
    Authz --> RBAC
    Audit --> Logging
    Privacy --> Encryption
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This architecture documentation provides a comprehensive overview of the MultiMind SDK's structure and components. Each diagram illustrates different aspects of the system, from high-level overview to detailed component interactions.