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Application_Report.md

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+# 🤖 RAG Chatbot — Application Report
+
+**Course:** Makers Lab (Term 3) | **Institute:** SPJIMR  
+**Date:** February 10, 2026
+
+---
+
+## 1. What Is This Application?
+
+This is an **AI-powered chatbot** that can answer questions by reading through your own documents. Instead of searching the internet, it looks through a personal "knowledge base" — a folder of text files you provide — and gives you accurate, sourced answers.
+
+Think of it like having a **personal assistant who has read all your documents** and can instantly recall information from them when you ask a question.
+
+---
+
+## 2. The Core Idea: RAG (Retrieval-Augmented Generation)
+
+**RAG** stands for **Retrieval-Augmented Generation**. In simple terms, it combines two steps:
+
+| Step | What Happens | Analogy |
+|------|-------------|---------|
+| **1. Retrieval** | The system searches your documents and finds the most relevant paragraphs related to your question | Like flipping through a textbook to find the right page |
+| **2. Generation** | An AI model reads those paragraphs and writes a clear, human-like answer | Like a student summarizing what they found in their own words |
+
+> [!IMPORTANT]
+> The AI **only answers from your documents** — it does not make things up or pull information from the internet. If the answer isn't in your files, it will tell you.
+
+---
+
+## 3. How It Works (Step by Step)
+
+```mermaid
+flowchart LR
+    A["📄 Your Documents"] --> B["✂️ Split into Chunks"]
+    B --> C["🔢 Convert to Numbers\n(Embeddings)"]
+    C --> D["🗄️ Store in FAISS\n(Vector Database)"]
+    E["❓ Your Question"] --> F["🔢 Convert to Numbers"]
+    F --> G["🔍 Find Similar Chunks"]
+    D --> G
+    G --> H["🤖 AI Generates Answer"]
+    H --> I["💬 Response Shown"]
+```
+
+### Breaking it down:
+
+1. **You add documents** — Place `.txt` files in the `knowledge_base` folder (e.g., company policies, notes, research papers)
+
+2. **Documents are split** — Large files are broken into smaller, manageable pieces called "chunks" (like cutting a book into individual pages)
+
+3. **Chunks become numbers** — Each chunk is converted into a list of numbers (called an "embedding") that captures its meaning. This is done by an **Embedding Model** running on HuggingFace's servers
+
+4. **Numbers are stored** — These numerical representations are saved in a **FAISS database** (a fast search engine for numbers)
+
+5. **You ask a question** — Your question is also converted into numbers the same way
+
+6. **Similar chunks are found** — The system compares your question's numbers with all the chunk numbers to find the closest matches (like finding the most relevant pages)
+
+7. **AI writes the answer** — The matching chunks are sent to a **Language Model (LLM)** which reads them and generates a clear, natural-language answer
+
+---
+
+## 4. Key Features
+
+### 📚 Custom Knowledge Base
+- Add any `.txt` files to the `knowledge_base` folder
+- Reload anytime using the sidebar button
+- Currently loaded with 6 documents (profile, experience, skills, projects, achievements, goals)
+
+### 🤖 Multiple AI Models
+The app lets you choose from different AI models:
+
+| Model | Best For |
+|-------|----------|
+| Mistral 7B Instruct | General-purpose, reliable |
+| Zephyr 7B | Conversational, friendly |
+| Phi-3 Mini | Fast, efficient |
+| Llama 3.2 3B | Meta's latest compact model |
+| Gemma 2 2B | Google's lightweight model |
+
+### 🔍 Configurable Retrieval
+- **Chunk Size** (500–2000): Controls how big each document piece is
+- **Number of Results** (1–5): How many relevant pieces to retrieve
+
+### 📄 Source Citations
+Every answer includes an expandable section showing exactly which document fragments were used — so you can verify the answer.
+
+### ⚡ 100% Free
+All processing happens via HuggingFace's free Inference API — no paid subscriptions or expensive GPU hardware needed.
+
+### 💬 Chat History
+The app remembers your conversation, so you can ask follow-up questions naturally.
+
+---
+
+## 5. Technology Stack
+
+| Component | Technology | Role |
+|-----------|-----------|------|
+| User Interface | **Streamlit** | Creates the web-based chat interface |
+| Document Loading | **LangChain** | Reads and processes text files |
+| Text Splitting | **RecursiveCharacterTextSplitter** | Breaks documents into chunks intelligently |
+| Embeddings | **HuggingFace API** (e.g., all-MiniLM-L6-v2) | Converts text into numerical representations |
+| Vector Database | **FAISS** (Facebook AI Similarity Search) | Stores and searches embeddings efficiently |
+| Answer Generation | **HuggingFace Inference API** | Runs the LLM to generate answers |
+| Environment Mgmt | **python-dotenv** | Manages configuration securely |
+
+---
+
+## 6. How to Use the Application
+
+### First-Time Setup
+1. **Get a free HuggingFace account** at [huggingface.co](https://huggingface.co/join)
+2. **Create a token** at [Settings → Tokens](https://huggingface.co/settings/tokens)
+   - Choose **"Fine-grained"** type
+   - Enable **"Make calls to Inference Providers"**
+3. **Install dependencies**: `pip install -r requirements.txt`
+4. **Add documents** to the `knowledge_base/` folder
+
+### Running the App
+```
+streamlit run app.py
+```
+Then open `http://localhost:8501` in your browser.
+
+### Asking Questions
+1. Paste your HuggingFace token in the sidebar
+2. Wait for the knowledge base to load (green ✅ confirmation)
+3. Type your question in the chat box
+4. View the AI-generated answer and optionally expand source documents
+
+---
+
+## 7. Project File Structure
+
+```
+ApplicationTest1/
+├── app.py                  ← Main application (320 lines)
+├── requirements.txt        ← Python package dependencies
+├── .env                    ← Stores your HuggingFace token
+├── README.md               ← Quick-start guide
+├── knowledge_base/         ← Your documents go here
+│   ├── profile.txt
+│   ├── experience.txt
+│   ├── skills.txt
+│   ├── projects.txt
+│   ├── achievements.txt
+│   └── goals.txt
+└── venv_rag/               ← Python virtual environment
+```
+
+---
+
+## 8. Error Handling
+
+The application includes user-friendly error handling:
+
+| Error | What It Means | Solution |
+|-------|--------------|----------|
+| **403 Forbidden** | Token doesn't have correct permissions | Recreate token with "Inference Providers" enabled |
+| **Model loading** | AI model is starting up on the server | Wait 20–30 seconds and retry |
+| **No documents found** | Knowledge base folder is empty | Add `.txt` files and reload |
+| **Embedding error** | Issue converting text to numbers | Check token and selected model |
+
+---
+
+## 9. Key Takeaways
+
+> [!TIP]
+> **Why RAG matters:** Traditional AI models can "hallucinate" — make up information. RAG solves this by grounding AI answers in your actual documents, making it far more reliable for business and academic use.
+
+- **RAG = Search + AI** — Combines document retrieval with AI generation
+- **Your data stays private** — Documents are processed in your session only
+- **Completely free** — No paid APIs, no GPU required
+- **Customizable** — Swap models, tune chunk sizes, change the knowledge base anytime
+- **Transparent** — Always shows which sources were used for each answer
+
+---
+
+*Report prepared for Makers Lab, SPJIMR — Term 3*

README.md

@@ -1,73 +1,73 @@
----
-title: RAG Chatbot
-emoji: 🤖
-colorFrom: blue
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.54.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# 🤖 RAG Chatbot
-
-An intelligent chatbot powered by Retrieval Augmented Generation (RAG) that answers questions based on your custom knowledge base.
-
-## ✨ Features
-
-- 📚 **Custom Knowledge Base**: Upload your own documents (.txt files)
-- 🔍 **Smart Retrieval**: Uses FAISS for efficient similarity search
-- 🤖 **Multiple LLM Options**: Choose from various open-source models
-- 💬 **Chat Interface**: Interactive conversation with context
-- 📄 **Source Citations**: See which documents were used for answers
-- 🚀 **100% Free**: Uses HuggingFace's free inference API
-
-## 🚀 How to Use
-
-1. **Get a HuggingFace Token** (free):
-   - Visit [HuggingFace Settings](https://huggingface.co/settings/tokens)
-   - Create a **Fine-grained** token
-   - ✅ Enable **'Make calls to Inference Providers'**
-   - Copy the token
-
-2. **Enter Your Token**:
-   - Paste it in the sidebar
-
-3. **Ask Questions**:
-   - The chatbot will answer based on the knowledge base documents
-   - View source documents for each answer
-
-## 📋 Knowledge Base
-
-The knowledge base contains documents that the chatbot uses to answer questions. You can customize it by:
-
-1. Adding .txt files to the `knowledge_base/` folder
-2. Clicking "🔄 Reload Knowledge Base" in the sidebar
-
-## 🛠️ Configuration Options
-
-- **Embedding Model**: Choose how text is converted to vectors
-- **LLM Model**: Select the language model for generating answers
-- **Chunk Size**: Control how documents are split
-- **Number of Results**: How many document chunks to retrieve
-
-## 🔒 Privacy
-
-- Your token is only used to call HuggingFace APIs
-- Conversations are not stored
-- Documents remain private to your session
-
-## 🐛 Troubleshooting
-
-**Model is loading**: Wait 20-30 seconds and try again  
-**Token error**: Make sure 'Make calls to Inference Providers' is enabled  
-**No documents found**: Add .txt files to the knowledge_base folder
-
-## 📝 License
-
-Apache 2.0
-
----
-
+---
+title: RAG Chatbot
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
+sdk: streamlit
+sdk_version: 1.28.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# 🤖 RAG Chatbot
+
+An intelligent chatbot powered by Retrieval Augmented Generation (RAG) that answers questions based on your custom knowledge base.
+
+## ✨ Features
+
+- 📚 **Custom Knowledge Base**: Upload your own documents (.txt files)
+- 🔍 **Smart Retrieval**: Uses FAISS for efficient similarity search
+- 🤖 **Multiple LLM Options**: Choose from various open-source models
+- 💬 **Chat Interface**: Interactive conversation with context
+- 📄 **Source Citations**: See which documents were used for answers
+- 🚀 **100% Free**: Uses HuggingFace's free inference API
+
+## 🚀 How to Use
+
+1. **Get a HuggingFace Token** (free):
+   - Visit [HuggingFace Settings](https://huggingface.co/settings/tokens)
+   - Create a **Fine-grained** token
+   - ✅ Enable **'Make calls to Inference Providers'**
+   - Copy the token
+
+2. **Enter Your Token**:
+   - Paste it in the sidebar
+
+3. **Ask Questions**:
+   - The chatbot will answer based on the knowledge base documents
+   - View source documents for each answer
+
+## 📋 Knowledge Base
+
+The knowledge base contains documents that the chatbot uses to answer questions. You can customize it by:
+
+1. Adding .txt files to the `knowledge_base/` folder
+2. Clicking "🔄 Reload Knowledge Base" in the sidebar
+
+## 🛠️ Configuration Options
+
+- **Embedding Model**: Choose how text is converted to vectors
+- **LLM Model**: Select the language model for generating answers
+- **Chunk Size**: Control how documents are split
+- **Number of Results**: How many document chunks to retrieve
+
+## 🔒 Privacy
+
+- Your token is only used to call HuggingFace APIs
+- Conversations are not stored
+- Documents remain private to your session
+
+## 🐛 Troubleshooting
+
+**Model is loading**: Wait 20-30 seconds and try again  
+**Token error**: Make sure 'Make calls to Inference Providers' is enabled  
+**No documents found**: Add .txt files to the knowledge_base folder
+
+## 📝 License
+
+Apache 2.0
+
+---
+
 Built with ❤️ using Streamlit, LangChain, and HuggingFace