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Knowledge Sphere

AI Research Intelligence & Document Platform

License: MIT Python 3.10+ FastAPI React 18 Docker Support PRs Welcome

Empowering researchers, engineers, and academics with LLM-driven semantic search, automated literature reviews, and dynamic knowledge graph generation.

🌟 Features

  • 🧠 Multi-LLM Support: Seamlessly routes between Google Gemini, OpenAI GPT-4o, and local Ollama models with intelligent fallback mechanisms.
  • 📚 Literature Review Generator: Automatically synthesizes dense research documents into cohesive, professional literature reviews.
  • 🔍 Semantic Vector Search: Powered by ChromaDB for blazing-fast, context-aware document retrieval using 3072-dimensional embeddings.
  • 🕸️ Knowledge Graph Visualization: Extracts entities and relationships into Neo4j, visualized in a stunning interactive graph UI.
  • ⚡ Document Intelligence: Asynchronous document ingestion via Celery & Redis, handling massive PDFs without blocking the main thread.
  • 🔐 Secure Authentication: JWT-based role-based access control (RBAC).

📸 Screenshots

Dashboard & Upload Chat & Semantic Search
Dashboard Research Chat
Literature Review Knowledge Graph
Review Generator Interactive Graph

(Note: Create an assets folder and add these images for them to display correctly).

🏗️ Architecture

System Architecture

graph TD
    Client[Web Frontend - React/Vite] -->|REST API| API[FastAPI Backend]
    API --> Auth[JWT Auth Middleware]
    API --> Router[API Routers]
    Router --> DB[(PostgreSQL)]
    Router --> Queue[Redis Queue]
    Queue --> Worker[Celery Worker]
    Worker --> Extraction[Document Chunking]
    Extraction --> Vector[(ChromaDB)]
    Extraction --> Graph[(Neo4j)]
    Worker --> LLM[LLM Routing Service]
    LLM --> Gemini(Google Gemini)
    LLM --> OpenAI(OpenAI API)
    LLM --> Ollama(Local Ollama)
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AI Document Ingestion Pipeline

sequenceDiagram
    participant User
    participant FastAPI
    participant Celery
    participant LLM
    participant ChromaDB
    participant Neo4j

    User->>FastAPI: Upload PDF
    FastAPI->>Celery: Queue Document Processing
    Celery->>Celery: Extract Text & Chunk
    Celery->>LLM: Generate Embeddings (3072-dim)
    LLM-->>Celery: Return Vectors
    Celery->>ChromaDB: Store Vectors
    Celery->>LLM: Extract Entities & Relationships
    LLM-->>Celery: Return Graph Data
    Celery->>Neo4j: Store Graph Nodes & Edges
    Celery-->>User: Ingestion Complete
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🛠️ Technology Stack

Category Technology Description
Frontend React 18, Vite, Tailwind CSS, Zustand Modern, responsive, state-managed SPA.
Backend Python 3.12, FastAPI, SQLAlchemy High-performance async REST API.
Background Processing Celery, Redis Distributed task queue for ML pipelines.
Relational Database PostgreSQL, asyncpg ACID-compliant storage for users & metadata.
Vector Database ChromaDB High-performance semantic vector storage.
Graph Database Neo4j Native graph database for knowledge mapping.
AI/ML Langchain, Gemini API, OpenAI, Ollama LLM orchestration and embedding generation.

🚀 Installation Guide

Prerequisites

  • Docker & Docker Compose
  • Node.js (v18+)
  • Python (3.10+)

Option 1: Docker (Recommended)

The easiest way to get started is using the bundled Docker Compose configuration.

# 1. Clone the repository
git clone https://github.com/yourusername/Knowledge_Sphere.git
cd Knowledge_Sphere

# 2. Copy the environment template
cp .env.example .env

# 3. Add your API keys to .env (Google Gemini / OpenAI)

# 4. Build and start the infrastructure
docker-compose up -d --build

The API will be available at http://localhost:8000 and the frontend at http://localhost:5173.

Option 2: Local Development

# 1. Start the databases (Postgres, Redis, Neo4j, Chroma)
docker-compose up -d postgres redis neo4j chromadb

# 2. Setup Backend
cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
alembic upgrade head
uvicorn app.main:app --reload

# 3. Setup Frontend
cd ../frontend
npm install
npm run dev

# 4. Start Celery Worker (In a new terminal)
cd backend
celery -A app.workers.celery_app worker --loglevel=info

⚙️ Environment Variables

Create a .env file in the root directory. See .env.example for defaults.

Variable Description Default
DEFAULT_LLM_PROVIDER Primary AI provider (gemini, openai, ollama) gemini
GOOGLE_API_KEY Your Google Gemini API Key ""
OPENAI_API_KEY Your OpenAI API Key ""
CHROMA_COLLECTION_DOCUMENTS Name of the ChromaDB index documents_v5
POSTGRES_USER DB User knowledge_user
POSTGRES_PASSWORD DB Password knowledge_pass
NEO4J_URI Graph DB connection string bolt://neo4j:7687

📖 API Documentation

Once the backend is running, FastAPI automatically generates interactive documentation:

  • Swagger UI: http://localhost:8000/docs
  • ReDoc: http://localhost:8000/redoc

📂 Folder Structure

Knowledge_Sphere/
├── backend/
│   ├── app/
│   │   ├── api/            # FastAPI Routers
│   │   ├── core/           # Config & Security
│   │   ├── models/         # SQLAlchemy Models
│   │   ├── services/       # LLM, Chroma, Neo4j integrations
│   │   └── workers/        # Celery Background Tasks
│   ├── alembic/            # Database Migrations
│   └── tests/              # E2E and Unit Tests
├── frontend/
│   ├── src/
│   │   ├── components/     # React UI Components
│   │   ├── services/       # Axios API Clients
│   │   └── store/          # Zustand State Management
├── docker-compose.yml      # Infrastructure setup
└── scripts/                # Utility scripts

⚡ Performance Metrics

Metric Target Benchmark
API Latency < 100ms 45ms (avg)
Doc Processing < 5s / MB 3.2s / MB (Gemini 2.0 Flash)
Vector Search < 50ms 12ms (ChromaDB)
Graph Query < 200ms 85ms (Neo4j)

(Note: Benchmarks taken on standard AWS t3.medium instances).

🗺️ Future Roadmap

  • Implement multi-modal document ingestion (Images/Charts to text).
  • Add advanced RAG strategies (HyDE, Parent-Child chunking).
  • Implement WebSockets for real-time extraction streaming.
  • Add support for Anthropic Claude 3 models.

🤝 Contribution Guide

We welcome contributions! Please review our CONTRIBUTING.md for guidelines on how to submit PRs, report bugs, and suggest features. Be sure to adhere to our Code of Conduct.

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

👷‍♂️ Author Section

Pratik S Kanoj

Artificial Intelligence & Data Science Engineer

I am a passionate AI Engineer specializing in Machine Learning, Computer Vision, and full-stack integration. I build robust, production-ready AI systems that solve real-world problems. My expertise lies in taking complex Deep Learning architectures and deploying them into scalable, user-centric web applications.

Technical Expertise:

  • AI & Data Science: Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Generative AI, MLOps, Data Science.
  • Backend & Cloud: Python, FastAPI, Docker, RESTful APIs.
  • Frontend: React, JavaScript, HTML, CSS, Streamlit.

Connect with me:

If you found this project interesting or helpful, please consider giving it a ⭐ on GitHub!

Built with ❤️ using Python, OpenAI, and Deep Learning.

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A highly scalable Retrieval-Augmented Generation (RAG) framework federating massive knowledge graphs, engineered with distributed vector stores, LangChain orchestrators, and open-weight LLMs for semantic dense retrieval and multi-hop reasoning.

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