| # ⚡ Nexus AI — Smart Document Analyzer |
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| 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 |
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| | 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) |
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| ### 1. Clone & install |
| ```bash |
| git clone <your-repo> |
| cd nexus-ai |
| pip install -r requirements.txt |
| ``` |
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| ### 2. Get API keys (both free) |
| - **Groq**: https://console.groq.com → Create API key |
| - **Gemini**: https://aistudio.google.com → Get API key |
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| ### 3. Run |
| ```bash |
| streamlit run app.py |
| ``` |
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| Enter your API key(s) in the sidebar when the app opens. |
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| ## ☁️ Deploy to Streamlit Community Cloud (Recommended) |
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| 1. Push your code to a **public GitHub repo** |
| 2. Go to [share.streamlit.io](https://share.streamlit.io) |
| 3. Connect your GitHub → Select `app.py` |
| 4. Add secrets in **Settings → Secrets**: |
| ```toml |
| GROQ_API_KEY = "gsk_..." |
| GEMINI_API_KEY = "AIza..." |
| ``` |
| 5. Click **Deploy** — done in ~2 minutes ✅ |
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| ## 🏗 Architecture |
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| ``` |
| 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 |
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| ``` |
| nexus-ai/ |
| ├── app.py # Main application |
| ├── requirements.txt # Dependencies |
| └── README.md # This file |
| ``` |
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| ## 🔑 Key Technical Concepts Demonstrated |
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| - **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 |
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| `Python` `Streamlit` `sentence-transformers` `scikit-learn` `pypdf` `Groq API` `Google Gemini API` `NumPy` |
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