# 🤖 RAGent Chatbot — Intelligent RAG + ReAct Assistant > 💡 **Blog Guide**: [How This Chatbot is Built](https://shafiqulai.github.io/blogs/blog_6.html) The **RAGent Chatbot** is a smart, modular, and explainable AI assistant that combines **Retrieval-Augmented Generation (RAG)** with **ReAct-style agent reasoning**. It goes beyond document Q&A — deciding when to read, when to think, and when to act using tools like web search, Wikipedia, calculator, and more.

## 🧠 What Can RAGent Do?

📄 Answer questions from uploaded documents (PDF, DOCX, PPTX, XLSX, TXT, CSV, JSON)
🛠️ Use external tools dynamically — web search, weather, Wikipedia, calculator, etc.
🔍 Combine semantic and keyword search using hybrid retrieval
🧠 Think step-by-step using the ReAct agent framework
💬 Maintain chat context using memory for natural multi-turn conversations

## 🧱 Architecture Highlights

🤖 ReAct Agent: Reasoning + tool calling in a loop: Thought → Action → Observation → Final Answer
📚 RAG Pipeline: Retrieves relevant chunks from Qdrant DB and answers with Gemini LLM
🛠️ Custom Tooling: Extensible tool framework (BaseTool) with weather, web, wiki, calculator, LLM, etc.
💾 Memory: Per-session chat history powered by MemoryManager
🖥️ Gradio UI: Intuitive chat + upload interface

## 🛠 Tech Stack | Layer | Technology | |--------------|--------------------------------------| | 🧠 LLM | Google Gemini via LangChain | | 📦 Embeddings | BAAI/bge-base-en-v1.5 | | 🔍 Vector DB | Qdrant (supports hybrid search) | | 💬 UI | Gradio | | 🧰 Tools

Agents

ragent-chatbot