V2 — Academic RAG Chatbot for Media Literacy

Built by Asvix

V2 is an AI-driven academic assistant designed to help teachers and students understand and teach Media Literacy more effectively. Powered by a hybrid architecture—open-source LLMs, RAG pipelines, Neo4j knowledge graphs, and multimodal capabilities—V2 provides deeply contextual responses, pedagogically structured explanations, and exam-oriented learning tools.

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Vision

To make Media Literacy education accessible, accurate, and easily teachable through an AI-powered companion that supports lesson planning, conceptual understanding, and interactive learning.

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Key Features

Dual User Modes

1. Teacher Mode (Primary Focus)

• Structured responses in curated sections:

  • Methodology
  • Conceptual Understanding
  • Classroom Flow
  • Assessment Ideas

• Uses the knowledge graph to surface prerequisite topics and related concepts.

• Designed to support lesson preparation and pedagogical clarity.

2. Student Mode

• Conversational QA experience.
• Clear, concise academic explanations.
• Multilingual output support.

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Core Architecture

V2 is built on a multi-layered academic retrieval system consisting of:

1. Open-Source LLM

• Scalable, cost-effective, and less dependent on deprecated APIs.
• Candidates: LLaMA, Mistral, Qwen, etc.
• Served through an inference engine (vLLM/TGI).

2. Vector Database

Used for semantic search across media-literacy-related documents.

• Option: Qdrant or Pinecone
• Stores embeddings with detailed metadata.

3. Knowledge Graph (Neo4j)

Neo4j powers contextual understanding by structuring relationships between:

• Subjects
• Topics & Subtopics
• Media literacy concepts
• Resources
• Question banks
• Prerequisite relationships

This enhances teaching guidance and interconnected explanations.

4. Multi-source Dataset

Composed of:

• PDF-based unstructured academic text
• SQL database containing structured academic content, question banks, and metadata

Both datasets are unified into a RAG-ready searchable format.

5. RAG Pipeline

• Query → Embedding → Vector Search
• Expand context using Neo4j for concept relationships
• Incorporate SQL-derived structured insights
• Prompt templates vary based on Teacher/Student mode

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Premium Features (Planned)

Preparation Mode (Exam-Focused)

• Study plans
• Topic progression
• Dynamic quiz generation
• Expanded academic dataset ingestion

Image-Based Questions

• Support for diagrams, charts, textbook screenshots
• Visual question understanding

Speech-to-Speech (Multilingual)

• Audio-based interaction
• ASR → RAG → TTS pipeline

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Project Roadmap

Phase 1 – Data Engineering

• PDF ingestion, cleaning, chunking
• SQL → structured text conversion
• Initial embedding + vector DB population
• Neo4j schema + first KG build

Phase 2 – Backend (RAG + Dual Mode Behaviour)

• Retrieval fusion: Vector DB + Neo4j + SQL
• Teacher Mode vs Student Mode response engine
• FastAPI backend endpoints

Phase 3 – Streamlit MVP

• Role-based UI
• Interactive chat interface
• Teacher-optimized response formatting

Phase 4 – Premium Layer

• Preparation mode
• Speech & image support
• User progress tracking

Phase 5 – Full Frontend Deployment

• Custom React/Next.js frontend
• Supabase for auth, user roles, subscription logic
• Cloud-hosted backend infrastructure

Phase 6 – Iteration

• Logging, evaluations
• KG refinement
• Dataset growth
• Performance tuning

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Contributing

We are working with the Asvix intern team. Internal contributors should follow:

• Branch naming conventions
• PR-based review workflows
• Documentation updates with each new module

External contributions will be opened in future phases.