---
title: Adda-Bot Interactive Agent
emoji: 🐈‍⬛
colorFrom: purple
colorTo: purple
sdk: gradio
sdk_version: 5.0.0
app_file: app.py
pinned: false
---

# Adda-Bot Interactive Agent 🐈‍⬛ 
🐈‍⬛ Meet Nova's human! Chat with Adda's AI portfolio agent.

## ✨ About Adda-Bot 🐈‍⬛
Welcome! I am an interactive AI agent designed to help you explore **Adda Weathers'** background, technical projects, and professional journey. 

Rather than just reading a static resume, you can ask me specific questions like:
- "What is Adda's experience with Python and AI?"
- "Tell me about her favorite projects."
- "What did she achieve in her current role?"

### 🛠️ Technical Stack
I'm not just a simple chatbot; I'm built using a modern **RAG (Retrieval-Augmented Generation)** architecture:
* **LLM:** Llama 3.2 3B (via Hugging Face Inference Providers)
* **Orchestration:** LangChain
* **Vector Database:** ChromaDB
* **UI:** Gradio 6.0
* **Data:** Custom Markdown-based knowledge base of Adda's portfolio.

### 🐾 Fun Fact
I'm named after my creator, Adda, but I take my "stealthy efficiency" cues from her cat, **Nova**.

## 🚀 Why This Matters for Recruiters

In a sea of PDF resumes, Adda-Bot demonstrates three key high-level competencies that are essential for modern AI and Software Engineering roles:

  **1. Practical RAG Implementation:** Most developers can prompt an AI, but building a Retrieval-Augmented Generation (RAG) pipeline requires understanding how to process data, manage vector embeddings, and handle context windows. This bot is a live proof-of-concept of my ability to build production-ready AI architectures.

  **2. Solving the "Information Overload" Problem:** Recruiters often have to hunt through pages of text to find a specific skill. This bot respects your time by allowing for natural language querying. Instead of scanning for "Python," you can simply ask, "How has Adda applied Python in a professional setting?" and get an instant, cited answer.

  **3. Full-Stack AI Thinking: This project showcases the full lifecycle of a feature:** from data engineering (Markdown parsing) to backend logic (LangChain & ChromaDB) and UI/UX design (Gradio with custom CSS). It proves I can take a concept from a blank page to a deployed, user-facing application.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference