update documentation

Author: Md-Emon-Hasan

README.md

@@ -1,20 +1,20 @@
-# 🧠 AutoDocThinker: Agentic RAG System with Intelligent Search Engine
+# AutoDocThinker: Agentic RAG System with Intelligent Search Engine
 
-The Agentic RAG System is a full-stack, AI-powered multi-agent document intelligence platform that extracts insights from PDFs, DOCX, TXT files, and web URLs through natural language queries. Built with Python, Flask, LangChain, ChromaDB, and HuggingFace embeddings, it orchestrates modular agents for document ingestion, chunking, vector storage, context retrieval, LLM reasoning, and Wikipedia fallback.
+[![AutoDocThinker](https://github.com/user-attachments/assets/8d5c8a4c-cdc8-4569-8ade-af06b8318db9)](https://github.com/user-attachments/assets/8d5c8a4c-cdc8-4569-8ade-af06b8318db9)
 
-The system achieves ~95% context retrieval coverage, generates answers with ~70% accuracy and F1 ~68%, and triggers fallback in only 5% of queries, demonstrating robust reliability. Intelligent agent routing improves query efficiency by ~40%, and the system scales to handle 50+ documents simultaneously. With a responsive HTML/CSS/Bootstrap UI, secure file handling, modular backend, logging, and Docker deployment, the platform delivers measurable business impact through fast, accurate, and scalable document intelligence.
+## **Project Overview**
 
-[![AutoDocThinker](https://github.com/user-attachments/assets/8d5c8a4c-cdc8-4569-8ade-af06b8318db9)](https://github.com/user-attachments/assets/8d5c8a4c-cdc8-4569-8ade-af06b8318db9)
+The Agentic RAG System is an AI-powered document intelligence platform that enables users to extract insights from uploaded files (PDFs, Word docs, text) or web URLs through natural language queries. Built with Python/Flask and LangChain, the system uses a multi-agent workflow to intelligently process documents, retrieve relevant information from a vector database (ChromaDB), and generate human-like answers—seamlessly falling back to Wikipedia when needed. The responsive web interface (HTML/CSS/Bootstrap) allows users to ask questions conversationally, while the modular backend demonstrates robust error handling, logging, and secure file processing.
 
 ---
 
-## 🚀 **Live Demo**
+## **Live Demo**
 
-🖥️ **Try it now**: [AutoDocThinker: Agentic RAG System with Intelligent Search Engine](https://autodocthinker.onrender.com/)
+**Try it now**: [AutoDocThinker: Agentic RAG System with Intelligent Search Engine](https://autodocthinker.onrender.com/)
 
 ---
 
-## ⚙️ **Features & Functionalities**
+## **Features & Functionalities**
 
 | #  | Module               | Technology Stack             | Your Implementation Details              |
 |----|----------------------|------------------------------|------------------------------------------|
@@ -34,7 +34,7 @@ The system achieves ~95% context retrieval coverage, generates answers with ~70%
 
 ---
 
-## 🧱 **Project Structure**
+## **Project Structure**
 
 ```
 AutoDocThinker/
@@ -85,7 +85,7 @@ AutoDocThinker/
 
 ---
 
-## 🧱 **System Architecture**
+## **System Architecture**
 
 ```mermaid
 %% Agentic RAG System Architecture - Colorful Version
@@ -140,7 +140,7 @@ graph TD
 
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-### 🌍 **Real-World Applications**
+### **Real-World Applications**
 
   1. **Corporate HR Automation**
   2. **Legal Document Review**
@@ -155,7 +155,7 @@ graph TD
 
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-## 📥 Installation
+## Installation
 
 ```bash
 # 1. Clone the repository
@@ -178,7 +178,7 @@ docker run -p 8501:8501 auto-doc-thinker
 
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-## 🔁 GitHub Actions CI/CD
+## GitHub Actions CI/CD
 
 **.github/workflows/main.yml**
 
@@ -212,24 +212,29 @@ jobs:
 
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-## 📝 Future Enhancements
+## Future Enhancements
 
-* ✅ Multilingual document ingestion
-* ✅ Audio document ingestion + whisper
-* ⏳ Long-term memory + history viewer
-* ⏳ MongoDB/FAISS alternative for Chroma
-* ✅ More tools (WolframAlpha, SerpAPI)
-* ⏳ Model selection dropdown (Gemini, LLaMA, GPT-4)
+* Multilingual document ingestion
+* Audio document ingestion + whisper
+* Long-term memory + history viewer
+* MongoDB/FAISS alternative for Chroma
+* More tools (WolframAlpha, SerpAPI)
+* Model selection dropdown (Gemini, LLaMA, GPT-4)
 
 ---
 
-## 👨‍💻 Author
+## Author
 
 **Md Emon Hasan**
-📧 Email: [email](mailto:iconicemon01@gmail.com)
-🔗 LinkedIn: [md-emon-hasan](https://www.linkedin.com/in/md-emon-hasan-695483237/)
-🔗 GitHub: [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
-🔗 Facebook: [mdemon.hasan2001/](https://www.facebook.com/mdemon.hasan2001/)
-🔗 WhatsApp: [8801834363533](https://wa.me/8801834363533)
 
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+Email: [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
+
+LinkedIn: [md-emon-hasan](https://www.linkedin.com/in/md-emon-hasan-695483237/)
+
+GitHub: [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
+
+Facebook: [mdemon.hasan2001/](https://www.facebook.com/mdemon.hasan2001/)
+
+WhatsApp: [8801834363533](https://wa.me/8801834363533)
+
+---