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| title: Documind | |
| emoji: 📚 | |
| colorFrom: yellow | |
| colorTo: gray | |
| sdk: docker | |
| pinned: false | |
| license: apache-2.0 | |
| # 🤖 Portfolio-AI — The Backend Brain of My Portfolio | |
| > A RAG-powered AI that knows everything I've built — and can talk about it. | |
| **Live Site → [aaravkumarranjan.netlify.app](https://aaravkumarranjan.netlify.app)** | |
| --- | |
| ## What Is This? | |
| This is the backend powering the AI chat feature on my personal portfolio. Instead of a static "About Me" page, visitors can actually *talk* to my portfolio — asking about my projects, my stack, how I learn, or anything else. | |
| Under the hood, it's a **Retrieval-Augmented Generation (RAG)** system built from scratch. The knowledge base is a PDF of my portfolio content. When someone asks a question, the system retrieves the most relevant chunks from that PDF and passes them to an LLM to generate a grounded, accurate answer. | |
| This backend is built on the same architecture as Documind, adapted specifically to power the AI chat feature on my personal portfolio. | |
| --- | |
| ## Architecture | |
| ``` | |
| portfolio.pdf → loader → chunker → embedder → vector store | |
| ↓ | |
| User Question → embed query → cosine similarity → top chunks | |
| ↓ | |
| LLM → Answer | |
| ``` | |
| | Module | Role | | |
| |---|---| | |
| | `loader.py` | Extracts text from `portfolio.pdf` | | |
| | `chunker.py` | Splits text into overlapping chunks | | |
| | `embedder.py` | Generates semantic embeddings via `sentence-transformers` | | |
| | `vector.py` | In-memory vector store for chunk embeddings | | |
| | `retriever.py` | Cosine similarity search — returns top-k relevant chunks | | |
| | `app.py` | FastAPI server that ties everything together | | |
| --- | |
| ## Why Build This Instead of Using a Library? | |
| Because I wanted to understand what's actually happening. LangChain and LlamaIndex are great tools, but they abstract away the parts I care most about — how chunking affects retrieval quality, how similarity thresholds prevent hallucination, how the pipeline actually flows end to end. | |
| This project is both a portfolio feature and a learning exercise. | |
| --- | |
| ## Tech Stack | |
| **Backend:** Python, FastAPI, Sentence Transformers, scikit-learn, NumPy, PyPDF2 | |
| **Deployment:** Render | |
| **Connected Frontend:** [aaravkumarranjan.netlify.app](https://aaravkumarranjan.netlify.app) | |
| --- | |
| ## Local Setup | |
| ```bash | |
| git clone https://huggingface.co/spaces/Aaravkumar/documind | |
| cd Portfolio-ai | |
| pip install -r requirements.txt | |
| uvicorn app:app --reload | |
| ``` | |
| Replace `portfolio.pdf` with your own PDF knowledge base to adapt this for your own portfolio. | |
| --- | |
| ## Author | |
| **Aarav Kumar Ranjan** | |
| [Portfolio](https://aaravkumarranjan.netlify.app) · [GitHub](https://github.com/akop-cyber) · [Kaggle](https://kaggle.com/aaravkumarranjan) | |