ClairvoyAI
  • Executive Summary
  • Introduction
  • Core Components And Features
    • Multi-Model Support
    • Custom Model Integration
    • Context-Aware Search
    • Real-Time Data Retrieval
    • Spaces for Collaboration
    • Pro Search Tools
    • Focus Modes
    • Crypto Research and Analytics
  • Architectural Framework
    • ClairvoyAI Technical Architecture
    • AI Model Selection and Integration
    • ClairvoyAI’s Functional Landscape
  • ClairvoyAI Roadmap
  • Tokenomics
  • official links
    • Website
    • X
    • Telegram
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  • Architecture of Spaces
  • Core Features of Spaces
  • Backend Workflow
  • Access and Security
  • Use Cases of Spaces
  • Technical Benefits
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  1. Core Components And Features

Spaces for Collaboration

ClairvoyAI introduces Spaces, a collaborative feature that enables users to organize, share, and analyze information within dedicated environments. These workspaces are designed to facilitate teamwork, enhance productivity, and support real-time collaboration while maintaining robust access control and data security.


Architecture of Spaces

Spaces operate as modular, cloud-based environments, allowing users to integrate and manage their data collaboratively. The design includes:

Distributed Storage

  • Utilizes object storage systems (e.g., Amazon S3, Azure Blob Storage) for scalability.

  • Files are partitioned and indexed for efficient retrieval and processing.

Access Control Mechanism

  • Implements role-based access control (RBAC) to manage permissions.

  • Supports granular roles, such as Owner, Editor, and Viewer.

Event-Driven Architecture

  • Real-time updates within Spaces are powered by event-driven protocols using WebSockets or long-polling techniques.


Core Features of Spaces

File Upload and Processing

  • Users can upload diverse file formats, including PDFs, Word documents, spreadsheets, and JSON data.

  • Files are preprocessed for:

    • Text extraction using OCR for scanned documents.

    • Metadata generation, such as file type, creation date, and content summary.

  • Indexed content is stored in an embedding-based search engine (e.g., Elasticsearch or Pinecone) for contextual queries.

Multi-User Collaboration

  • Real-time collaboration allows multiple users to interact with the same Space.

  • Updates, annotations, and query results are synchronized across all participants.

Contextual Querying

  • Queries performed within a Space are scoped to its content.

  • Example: A Space containing research papers on renewable energy can filter queries like "recent advancements in solar panels" to relevant documents within the Space.

Annotations and Notes

  • Users can highlight text, add comments, and attach notes to specific documents.

  • Annotations are indexed and searchable, enhancing traceability.

Activity Logs

  • Every action in a Space is logged, providing an auditable trail.

  • Logs include metadata like the user, timestamp, and type of activity (e.g., file upload, query execution).


Backend Workflow

Space Creation

  • A unique identifier (UUID) is assigned to each Space, along with metadata like owner information, creation date, and access permissions.

  • A distributed document store is initialized to manage files and metadata.

File Processing Pipeline

  • Uploaded files are processed through a pipeline comprising:

    • File Ingestion: Raw files are uploaded and temporarily cached.

    • Data Extraction: Text, tables, and images are parsed using libraries like Apache Tika or Tesseract for OCR.

    • Indexing: Extracted data is embedded and indexed for semantic retrieval.

Collaborative Query Execution

  • Queries initiated within a Space are processed by a dedicated instance of the ClairvoyAI retrieval engine.

  • Results are scoped to the Space’s indexed content and returned to all active participants.

Synchronization Engine

  • Changes in a Space are broadcasted in real-time using a message broker like RabbitMQ or Kafka.

  • WebSocket connections ensure instant updates for connected clients.


Access and Security

Role-Based Permissions

  • Owners can assign roles to collaborators, defining who can upload, edit, query, or view content.

  • Permissions are enforced at the API and database levels.

Data Encryption

  • Files and metadata are encrypted at rest using AES-256.

  • Data in transit is secured with TLS protocols.

Authentication and Authorization

  • Integrates with identity providers (e.g., OAuth2, SAML) for single sign-on (SSO) and multi-factor authentication (MFA).


Use Cases of Spaces

Research Teams

  • Enables teams to collaborate on large datasets or shared projects, such as scientific research or technical documentation.

Corporate Knowledge Management

  • Acts as a centralized repository for organizational knowledge, including manuals, policies, and reports.

Educational Collaboration

  • Supports educators and students in organizing course materials and conducting collaborative learning.

Legal Document Review

  • Allows legal teams to annotate, query, and analyze case files collectively.


Technical Benefits

Scalability

  • Designed to handle large file volumes and high query traffic using distributed storage and processing.

Modularity

  • Spaces operate as independent modules, allowing for seamless integration into larger systems or workflows.

Real-Time Interactivity

  • Low-latency updates ensure that collaborators can see changes as they happen, enhancing the user experience.

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Last updated 4 months ago