📝 AI RAG Navigator: Intelligent Study Companion

An enterprise-grade Retrieval-Augmented Generation (RAG) application designed to transform static study materials into interactive AI-driven conversations. Built for the modern LLM ecosystem in 2026.

🚀 Key Features

• Context-Aware Retrieval: Uses Modern LCEL (LangChain Expression Language) for stable and high-performance document interrogation.
• Multimodal Document Processing: High-accuracy PDF parsing and semantic chunking.
• Vector Intelligence: Seamlessly integrates with ChromaDB for local, privacy-centric vector storage.
• Dual-Model Inference: Leverages Google Gemini 1.5 Flash for high-speed, cost-effective reasoning.

🛠️ Technical Implementation

• Framework: LangChain v0.3 Core (History-aware retrievers).
• Embeddings: Google GoogleGenerativeAIEmbeddings (embedding-001).
• Database: SQLITE-optimized ChromaDB (pysqlite3 monkeypatched for cloud).
• Interface: Real-time Streamlit dashboard with conversational history.

🏃 Quick Start

1. Explore the Live App: AI RAG Navigator Demo
2. Local Setup:

   git clone https://github.com/AkkiKrsingh2005/ai-rag-navigator.git
   cd ai-rag-navigator
   pip install -r requirements.txt
   streamlit run app.py
   

3. Environment: Add your GOOGLE_API_KEY to the application sidebar or a .env file.

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Developed as part of an AI/ML Internship Portfolio 🧠

Developed by Ankit Kumar | Portfolio