README

README.md

@@ -1,160 +1,3 @@
-# 🎓 RAG Pedagogical Demo
-
-A pedagogical web application demonstrating Retrieval Augmented Generation (RAG) systems for students and learners.
-
-## 🌟 Features
-
-- **Bilingual Interface** (English/French)
-- **Document Processing**: Upload PDF documents or use default corpus
-- **Configurable Retrieval**:
-  - Choose embedding models
-  - Adjust chunk size and overlap
-  - Set top-k and similarity thresholds
-- **Configurable Generation**:
-  - Select different LLMs
-  - Adjust temperature and max tokens
-- **Educational Visualization**:
-  - View retrieved chunks with similarity scores
-  - See the exact prompt sent to the LLM
-  - Understand each step of the RAG pipeline
-
-## 🚀 Quick Start
-
-### Local Installation
-
-```bash
-# Clone the repository
-git clone <your-repo-url>
-cd RAG_pedago
-
-# Install dependencies
-pip install -r requirements.txt
-
-# Run the application
-python app.py
-```
-
-### HuggingFace Spaces
-
-This application is designed to run on HuggingFace Spaces with ZeroGPU support.
-
-1. Create a new Space on HuggingFace
-2. Select "Gradio" as the SDK
-3. Enable ZeroGPU in Space settings
-4. Upload all files from this repository
-5. The app will automatically deploy
-
-## 📚 Usage
-
-### 1. Corpus Management
-- Upload your own PDF document or use the included default corpus about RAG
-- Configure chunk size (100-1000 characters) and overlap (0-200 characters)
-- Process the corpus to create embeddings
-
-### 2. Retrieval Configuration
-- Choose an embedding model:
-  - `all-MiniLM-L6-v2`: Fast, lightweight
-  - `all-mpnet-base-v2`: Better quality, slower
-  - `paraphrase-multilingual-MiniLM-L12-v2`: Multilingual support
-- Set top-k (1-10): Number of chunks to retrieve
-- Set similarity threshold (0.0-1.0): Minimum similarity score
-
-### 3. Generation Configuration
-- Select a language model:
-  - `zephyr-7b-beta`: Fast, good quality
-  - `Mistral-7B-Instruct-v0.2`: High quality
-  - `Llama-2-7b-chat-hf`: Alternative option
-- Adjust temperature (0.0-2.0): Controls creativity
-- Set max tokens (50-1000): Response length
-
-### 4. Query & Results
-- Enter your question
-- Use example questions to get started
-- View the generated answer
-- Examine retrieved chunks with similarity scores
-- Inspect the prompt sent to the LLM
-
-## 🏗️ Architecture
-
-```
-┌─────────────────┐
-│  PDF Document   │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│  Text Chunking  │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│   Embeddings    │◄──── Embedding Model
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│  FAISS Index    │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│  User Query     │
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│   Retrieval     │──► Top-K Chunks
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│   Generation    │◄──── Language Model
-└────────┬────────┘
-         │
-         ▼
-┌─────────────────┐
-│     Answer      │
-└─────────────────┘
-```
-
-## 🛠️ Technical Stack
-
-- **Framework**: Gradio 4.44.0
-- **Embeddings**: Sentence Transformers
-- **Vector Store**: FAISS
-- **LLMs**: HuggingFace Inference API
-- **GPU**: HuggingFace ZeroGPU
-- **PDF Processing**: PyPDF2
-
-## 📝 Files Structure
-
-```
-RAG_pedago/
-├── app.py                 # Main Gradio interface
-├── rag_system.py         # Core RAG logic
-├── i18n.py               # Internationalization
-├── requirements.txt      # Python dependencies
-├── default_corpus.pdf    # Default corpus about RAG
-├── default_corpus.txt    # Source text for default corpus
-└── README.md            # This file
-```
-
-## 🎯 Educational Goals
-
-This application helps students understand:
-
-1. **Document Processing**: How text is split into chunks
-2. **Embeddings**: How text is converted to vectors
-3. **Similarity Search**: How relevant information is retrieved
-4. **Prompt Engineering**: How context is provided to LLMs
-5. **Generation**: How LLMs produce answers based on retrieved context
-6. **Parameter Impact**: How different settings affect results
-
-## 🔧 Configuration for HuggingFace Spaces
-
-Create a `README.md` in your Space with this header:
-
-```yaml
 ---
 title: RAG Pedagogical Demo
 emoji: 🎓
@@ -166,28 +9,52 @@ app_file: app.py
 pinned: false
 license: mit
 ---
-```
 
-## 🤝 Contributing
+# 🎓 RAG Pedagogical Demo
+
+An interactive educational application to learn about Retrieval Augmented Generation (RAG) systems.
+
+## What is RAG?
+
+Retrieval Augmented Generation (RAG) combines information retrieval with language generation to create more accurate and grounded AI responses. Instead of relying solely on a language model's training data, RAG systems:
+
+1. **Retrieve** relevant information from a document corpus
+2. **Augment** the query with this retrieved context
+3. **Generate** an answer based on both the query and the retrieved information
+
+## Features
+
+- 📚 **Upload your own PDFs** or use the default corpus
+- 🔧 **Configure retrieval parameters**: embedding models, chunk size, top-k, similarity threshold
+- 🤖 **Configure generation parameters**: LLM selection, temperature, max tokens
+- 📊 **Visualize the process**: see retrieved chunks, similarity scores, and prompts
+- 🌍 **Bilingual interface**: English and French
 
-Contributions are welcome! Feel free to:
-- Add more embedding models
-- Include additional LLMs
-- Improve the interface
-- Add more visualizations
-- Enhance documentation
+## How to Use
 
-## 📄 License
+1. **Corpus Tab**: Upload a PDF or use the default corpus about RAG
+2. **Retrieval Tab**: Choose embedding model and retrieval parameters
+3. **Generation Tab**: Select language model and generation settings
+4. **Query Tab**: Ask questions and see how RAG works!
 
-MIT License - Feel free to use this for educational purposes.
+## Educational Value
 
-## 🙏 Acknowledgments
+This demo helps you understand:
+- How documents are processed and chunked
+- How semantic search retrieves relevant information
+- How context is provided to language models
+- How different parameters affect the results
 
-- HuggingFace for the Spaces platform and ZeroGPU
-- Sentence Transformers for embeddings
-- FAISS for efficient similarity search
-- Gradio for the interface framework
+Perfect for students, educators, and anyone curious about modern AI systems!
 
-## 📧 Contact
+## Technology
+
+- **Framework**: Gradio
+- **Embeddings**: Sentence Transformers
+- **Vector Store**: FAISS
+- **LLMs**: HuggingFace Inference API
+- **Infrastructure**: HuggingFace ZeroGPU
+
+---
 
-For questions or feedback, please open an issue on GitHub.
+*Note: This application runs on ZeroGPU. Initial requests may take longer as models are loaded.*

README_old.md

@@ -0,0 +1,193 @@
+# 🎓 RAG Pedagogical Demo
+
+A pedagogical web application demonstrating Retrieval Augmented Generation (RAG) systems for students and learners.
+
+## 🌟 Features
+
+- **Bilingual Interface** (English/French)
+- **Document Processing**: Upload PDF documents or use default corpus
+- **Configurable Retrieval**:
+  - Choose embedding models
+  - Adjust chunk size and overlap
+  - Set top-k and similarity thresholds
+- **Configurable Generation**:
+  - Select different LLMs
+  - Adjust temperature and max tokens
+- **Educational Visualization**:
+  - View retrieved chunks with similarity scores
+  - See the exact prompt sent to the LLM
+  - Understand each step of the RAG pipeline
+
+## 🚀 Quick Start
+
+### Local Installation
+
+```bash
+# Clone the repository
+git clone <your-repo-url>
+cd RAG_pedago
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the application
+python app.py
+```
+
+### HuggingFace Spaces
+
+This application is designed to run on HuggingFace Spaces with ZeroGPU support.
+
+1. Create a new Space on HuggingFace
+2. Select "Gradio" as the SDK
+3. Enable ZeroGPU in Space settings
+4. Upload all files from this repository
+5. The app will automatically deploy
+
+## 📚 Usage
+
+### 1. Corpus Management
+- Upload your own PDF document or use the included default corpus about RAG
+- Configure chunk size (100-1000 characters) and overlap (0-200 characters)
+- Process the corpus to create embeddings
+
+### 2. Retrieval Configuration
+- Choose an embedding model:
+  - `all-MiniLM-L6-v2`: Fast, lightweight
+  - `all-mpnet-base-v2`: Better quality, slower
+  - `paraphrase-multilingual-MiniLM-L12-v2`: Multilingual support
+- Set top-k (1-10): Number of chunks to retrieve
+- Set similarity threshold (0.0-1.0): Minimum similarity score
+
+### 3. Generation Configuration
+- Select a language model:
+  - `zephyr-7b-beta`: Fast, good quality
+  - `Mistral-7B-Instruct-v0.2`: High quality
+  - `Llama-2-7b-chat-hf`: Alternative option
+- Adjust temperature (0.0-2.0): Controls creativity
+- Set max tokens (50-1000): Response length
+
+### 4. Query & Results
+- Enter your question
+- Use example questions to get started
+- View the generated answer
+- Examine retrieved chunks with similarity scores
+- Inspect the prompt sent to the LLM
+
+## 🏗️ Architecture
+
+```
+┌─────────────────┐
+│  PDF Document   │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│  Text Chunking  │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   Embeddings    │◄──── Embedding Model
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│  FAISS Index    │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│  User Query     │
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   Retrieval     │──► Top-K Chunks
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│   Generation    │◄──── Language Model
+└────────┬────────┘
+         │
+         ▼
+┌─────────────────┐
+│     Answer      │
+└─────────────────┘
+```
+
+## 🛠️ Technical Stack
+
+- **Framework**: Gradio 4.44.0
+- **Embeddings**: Sentence Transformers
+- **Vector Store**: FAISS
+- **LLMs**: HuggingFace Inference API
+- **GPU**: HuggingFace ZeroGPU
+- **PDF Processing**: PyPDF2
+
+## 📝 Files Structure
+
+```
+RAG_pedago/
+├── app.py                 # Main Gradio interface
+├── rag_system.py         # Core RAG logic
+├── i18n.py               # Internationalization
+├── requirements.txt      # Python dependencies
+├── default_corpus.pdf    # Default corpus about RAG
+├── default_corpus.txt    # Source text for default corpus
+└── README.md            # This file
+```
+
+## 🎯 Educational Goals
+
+This application helps students understand:
+
+1. **Document Processing**: How text is split into chunks
+2. **Embeddings**: How text is converted to vectors
+3. **Similarity Search**: How relevant information is retrieved
+4. **Prompt Engineering**: How context is provided to LLMs
+5. **Generation**: How LLMs produce answers based on retrieved context
+6. **Parameter Impact**: How different settings affect results
+
+## 🔧 Configuration for HuggingFace Spaces
+
+Create a `README.md` in your Space with this header:
+
+```yaml
+---
+title: RAG Pedagogical Demo
+emoji: 🎓
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: mit
+---
+```
+
+## 🤝 Contributing
+
+Contributions are welcome! Feel free to:
+- Add more embedding models
+- Include additional LLMs
+- Improve the interface
+- Add more visualizations
+- Enhance documentation
+
+## 📄 License
+
+MIT License - Feel free to use this for educational purposes.
+
+## 🙏 Acknowledgments
+
+- HuggingFace for the Spaces platform and ZeroGPU
+- Sentence Transformers for embeddings
+- FAISS for efficient similarity search
+- Gradio for the interface framework
+
+## 📧 Contact
+
+For questions or feedback, please open an issue on GitHub.

SPACE_README.md

@@ -1,60 +0,0 @@
----
-title: RAG Pedagogical Demo
-emoji: 🎓
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 4.44.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 🎓 RAG Pedagogical Demo
-
-An interactive educational application to learn about Retrieval Augmented Generation (RAG) systems.
-
-## What is RAG?
-
-Retrieval Augmented Generation (RAG) combines information retrieval with language generation to create more accurate and grounded AI responses. Instead of relying solely on a language model's training data, RAG systems:
-
-1. **Retrieve** relevant information from a document corpus
-2. **Augment** the query with this retrieved context
-3. **Generate** an answer based on both the query and the retrieved information
-
-## Features
-
-- 📚 **Upload your own PDFs** or use the default corpus
-- 🔧 **Configure retrieval parameters**: embedding models, chunk size, top-k, similarity threshold
-- 🤖 **Configure generation parameters**: LLM selection, temperature, max tokens
-- 📊 **Visualize the process**: see retrieved chunks, similarity scores, and prompts
-- 🌍 **Bilingual interface**: English and French
-
-## How to Use
-
-1. **Corpus Tab**: Upload a PDF or use the default corpus about RAG
-2. **Retrieval Tab**: Choose embedding model and retrieval parameters
-3. **Generation Tab**: Select language model and generation settings
-4. **Query Tab**: Ask questions and see how RAG works!
-
-## Educational Value
-
-This demo helps you understand:
-- How documents are processed and chunked
-- How semantic search retrieves relevant information
-- How context is provided to language models
-- How different parameters affect the results
-
-Perfect for students, educators, and anyone curious about modern AI systems!
-
-## Technology
-
-- **Framework**: Gradio
-- **Embeddings**: Sentence Transformers
-- **Vector Store**: FAISS
-- **LLMs**: HuggingFace Inference API
-- **Infrastructure**: HuggingFace ZeroGPU
-
----
-
-*Note: This application runs on ZeroGPU. Initial requests may take longer as models are loaded.*