📘 KnowBase

AI-Powered Knowledge Base for Courses, Campuses, Faculty & Modules

KnowBase is an AI-driven academic search engine that makes it effortless to explore and connect knowledge across campuses, courses, faculty, and modules. It transforms structured data into semantic embeddings, enabling natural language search and intelligent retrieval of information.

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✨ Features

• 🔍 Semantic Search – Ask natural questions, get meaningful results
• 🏫 Campus-wide Indexing – Organizes content by campus, courses, faculty, and modules
• 🤖 AI Embeddings – Powered by vector search for context-aware retrieval
• 📚 Scalable Knowledge Base – Handles growing datasets seamlessly
• ⚡ Fast Retrieval – Optimized batching and vector indexing

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🏗️ Tech Stack

• Python – Core data processing
• Pandas – Data handling & transformation
• LangChain – AI pipelines (embeddings, query processing)
• Chroma / Pinecone – Vector database for indexing and search
• OpenAI Embeddings – Semantic representation of course data

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📂 Dataset Structure

KnowBase expects a JSON dataset in the following format:

{
  "content": [
    {
      "campus": "Campus Name",
      "campus_description": "Description of the campus",
      "courses": [
        {
          "course": "Course Name",
          "course_description": "Description of the course",
          "faculty": [
            {
              "faculty_name": "Faculty Name",
              "faculty_bio": "Short bio"
            }
          ],
          "modules": [
            {
              "module": "Module Name",
              "module_description": "Description of the module"
            }
          ]
        }
      ]
    }
  ]
}

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⚙️ Installation

Clone the repository:

git clone https://github.com/your-username/KnowBase.git
cd KnowBase

Create a virtual environment & install dependencies:

python -m venv venv
source venv/bin/activate   # Mac/Linux
venv\Scripts\activate      # Windows

pip install -r requirements.txt

Set up environment variables in .env:

OPENAI_API_KEY=your_openai_api_key

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🚀 Usage

1. Prepare the dataset (course.json).

2. Run the embedding pipeline:

   python index_data.py

3. Start search queries:

   python query.py "Find all AI-related modules in Delhi campus"

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📊 Example Query

Input:

python query.py "Who teaches Data Structures in Bangalore campus?"

Output:

Campus: Bangalore
Course: Computer Science
Faculty: Dr. Meera Sharma
Module: Data Structures

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🛠️ Roadmap

• Add UI for search (React/Streamlit)
• Support for PDF/Doc ingestion
• Multi-language search support
• Deploy with Docker + Cloud DB

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🤝 Contributing

Contributions are welcome! Fork the repo, make changes, and submit a PR.

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📜 License

MIT License – feel free to use and adapt.