ClairvoyAI Technical Architecture

ClairvoyAI’s technical architecture is designed to provide modularity, scalability, and performance. It uses a decoupled, microservices-based architecture to ensure that each component operates independently, enabling seamless integration, high availability, and extensibility. This section outlines the detailed architecture, core components, data flow, and optimization strategies that form the backbone of ClairvoyAI.

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System Design

ClairvoyAI’s architecture is divided into three primary layers, each responsible for a distinct part of the system’s functionality:

Frontend Layer

• Description: The user-facing interface is built using modern web frameworks like React and Vue.js.

• Responsibilities:

  • Rendering dynamic UIs, capturing user inputs, and managing real-time interactions.

  • Communicating with backend services via RESTful and WebSocket protocols.

• Features:

  • Responsive design for cross-platform compatibility.

  • Context preservation for multi-turn conversations.

  • Customizable query configurations for advanced users.

Backend Layer

• Description: API-driven backend that acts as the orchestrator of all processes.

• Built On: FastAPI and Express.js for lightweight and high-performance operations.

• Responsibilities:

  • Query preprocessing and semantic enrichment.

  • Routing queries to appropriate pipelines (e.g., Focus Modes).

  • Aggregating and formatting results for frontend consumption.

• Security:

  • Implements OAuth 2.0 for authentication.

  • Role-based access control (RBAC) for managing permissions.

Model Orchestration Layer

• Description: Dynamically invokes and manages pre-trained and custom models based on query requirements.

• Features:

  • Embedding-based ranking and similarity scoring.

  • Real-time load balancing across GPUs and CPUs.

  • Integration with external model APIs (e.g., DeepSeek, OpenAI, Hugging Face).

• Tools Used: Kubernetes, Docker Swarm for deployment and scaling.

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Core Components

Frontend

• Built with modern JavaScript frameworks for high performance and interactivity.

• Features real-time updates using WebSockets for collaboration (e.g., Spaces).

• User-configurable options for Focus Modes, query parameters, and result visualizations.

Backend APIs

• Stateless microservices architecture ensuring scalability and fault isolation.

• Key Microservices:

  • Query Processing Service:

    • Tokenizes and preprocesses user queries.

    • Enriches queries with semantic metadata and user context.

  • Data Retrieval Service:

    • Integrates with external APIs and metasearch engines (e.g., SearxNG, Reddit, YouTube).

    • Performs deduplication and contextual filtering.

  • Result Aggregation Service:

    • Aggregates results from multiple sources.

    • Applies ranking algorithms to ensure relevance.

Model Orchestration

• Orchestrates models like DeepSeek, GPT-4, Claude, and Llama.

• Supports:

  • Task-specific pipelines for Focus Modes and custom models.

  • Runtime model selection based on task type, latency, and user preferences.

• Technologies Used:

  • ONNX Runtime for efficient inference.

  • TensorRT for GPU-accelerated computations.

  • Hugging Face Transformers for pre-trained models.

Data Storage

• Distributed Storage System: Optimized for scalability and fault tolerance.

• Components:

  • Object Storage:

    • Stores uploaded files and documents (e.g., PDFs, spreadsheets).

    • Backends include Amazon S3, Google Cloud Storage, and MinIO.

  • Indexing Engine:

    • Embedding-based indexing using Elasticsearch and Pinecone.

    • Supports fast and semantic retrieval for context-aware searches.

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Data Flow

The end-to-end data flow in ClairvoyAI is structured for efficiency and clarity:

1. Query Input:

   • Captured by the frontend and sent to the backend via REST or WebSocket protocols.

   • Includes metadata like query context, Focus Mode selection, and user preferences.

2. Preprocessing:

   • Tokenization and embedding generation using models like BERT.

   • Context enrichment through session history and semantic augmentation.

3. Pipeline Routing:

   • The Query Processing Service identifies the task type and routes the query to:

     • Data Retrieval Service for fetching external results.

     • Model Orchestration Layer for LLM-based processing.

4. Data Retrieval and Model Inference:

   • Real-time retrieval from external APIs and internal indices.

   • Parallel inference from one or more AI models.

   • Outputs are normalized into a unified response schema.

5. Result Aggregation and Ranking:

   • Aggregates outputs from multiple sources or models.

   • Applies ranking algorithms using similarity metrics (e.g., cosine similarity, Euclidean distance).

6. Response Delivery:

   • Results are sent back to the frontend for rendering.

   • Updates are synchronized in real-time across users for collaborative workflows.

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Performance Optimization

• Load Balancing:

  • Implements load balancers (e.g., NGINX, HAProxy) for distributing requests across backend services.

  • Ensures even distribution of GPU workloads for model inference.

• Caching Mechanisms:

  • Uses Redis or Memcached for caching frequently accessed data and query results.

  • Reduces redundant API calls and inference requests.

• Asynchronous Processing:

  • Asynchronous task queues (e.g., Celery and RabbitMQ) handle non-blocking tasks like file processing and API queries.

• Horizontal Scalability:

  • Microservices can scale independently to handle spikes in traffic.

  • Kubernetes manages scaling based on metrics like CPU and memory usage.

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Security and Privacy

• Data Security:

  • All user data is encrypted at rest using AES-256 and in transit using TLS.

  • Role-based access control (RBAC) ensures secure user permissions.

• Privacy Compliance:

  • Complies with GDPR and CCPA standards for data handling.

  • Users can delete stored session data or models upon request.