⚡ Nexus AI — Smart Document Analyzer

A production-grade RAG (Retrieval-Augmented Generation) system for intelligent PDF Q&A with real semantic search, page-level citations, and multi-document support.

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🚀 Features

Feature	Description
Real Semantic Search	Uses sentence-transformers + cosine similarity — not keyword matching
Multi-PDF Support	Upload and query across multiple documents simultaneously
Page Citations	Every answer shows exact source page + relevance score
Auto Summary	Document is summarized automatically on upload
Dual API	Switch between Groq (Llama 3.1) and Google Gemini with one click
Conversation Memory	Follow-up questions retain context from previous turns
Export Chat	Download your full Q&A session as a text file

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🛠 Setup (Local)

1. Clone & install

git clone <your-repo>
cd nexus-ai
pip install -r requirements.txt

2. Get API keys (both free)

• Groq: https://console.groq.com → Create API key
• Gemini: https://aistudio.google.com → Get API key

3. Run

streamlit run app.py

Enter your API key(s) in the sidebar when the app opens.

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☁️ Deploy to Streamlit Community Cloud (Recommended)

1. Push your code to a public GitHub repo
2. Go to share.streamlit.io
3. Connect your GitHub → Select app.py
4. Add secrets in Settings → Secrets:

GROQ_API_KEY = "gsk_..."
GEMINI_API_KEY = "AIza..."

5. Click Deploy — done in ~2 minutes ✅

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🏗 Architecture

PDF Upload
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Page-by-Page Text Extraction (pypdf)
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Semantic Chunking (280 words, 55 overlap)
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Embedding Generation (all-MiniLM-L6-v2)
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In-memory Vector Store (numpy arrays)
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  Query
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Semantic Search (cosine similarity, top-5)
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Context + Citations → LLM (Groq / Gemini)
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Answer with Page Citations + Relevance Scores

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📁 Project Structure

nexus-ai/
├── app.py              # Main application
├── requirements.txt    # Dependencies
└── README.md           # This file

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🔑 Key Technical Concepts Demonstrated

• RAG Pipeline — Full retrieval-augmented generation from scratch
• Semantic Embeddings — Sentence transformers for meaning-based search
• Vector Similarity — Cosine similarity for chunk retrieval
• LLM Integration — Multi-provider API abstraction
• Context Window Management — Conversation history with truncation
• PDF Processing — Page-level text extraction and chunking

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🏷 Tech Stack

Python Streamlit sentence-transformers scikit-learn pypdf Groq API Google Gemini API NumPy