Refine README.md for clarity and accuracy in feature descriptions, enhance multilingual support details, and update project structure information.

README.md

@@ -15,41 +15,45 @@ An intelligent chatbot based on Hugging Face and LangChain for legal consultatio
 
 ## 📝 Description
 
-Status Law Assistant is a smart chatbot that answers user questions about Status Law company's legal services. The bot uses RAG (Retrieval-Augmented Generation) technology to find relevant information in a knowledge base created from the official website content and generates responses using a language model.
+Status Law Assistant is a smart chatbot that answers user questions about Status Law company's legal services. The bot uses RAG (Retrieval-Augmented Generation) technology to find relevant information in a knowledge base created from the official website content.
 
-## ✨ Features
+## ✨ Key Features
 
 - Automatic creation and updating of knowledge base from status.law website content
-- Relevant information search for user queries
+- Intelligent information retrieval for query responses
 - Context-aware response generation
-- Multi-language query support (responds in the language of the question)
-- Customizable text generation parameters (temperature, token count, etc.)
-- Model switching with fallback mechanism
+- Advanced multilingual support:
+  - Automatic language detection
+  - Native language response generation
+  - Built-in translation system with fallback mechanism
+  - Support for 30+ languages
+- Customizable text generation parameters
+- Model switching system with automatic fallback
 - Fine-tuning capabilities based on chat history
 - Multiple model support:
-  - Llama 2 7B Chat: Meta's general-purpose model optimized for chat
-  - Zephyr 7B: State-of-the-art model with strong performance
-  - Mistral 7B Instruct v0.2: Advanced model with superior multilingual capabilities
-  - XGLM 7.5B: Specialized model for cross-lingual generation with 30+ language support
+  - Llama 2 7B Chat (primary): Optimized for dialogues
+  - Zephyr 7B: Enhanced performance and response quality
+  - Mistral 7B Instruct v0.2: Superior multilingual capabilities
+  - XGLM 7.5B: Specialized cross-lingual generation model (requires paid API access)
 
 ## 🚀 Technologies
 
-- **LangChain**: For query processing chains and knowledge base management
-- **Hugging Face**: For language model access and application hosting
-- **FAISS**: For efficient vector search
-- **Gradio**: For user interface creation
-- **BeautifulSoup**: For web page information extraction
-- **PEFT**: For efficient fine-tuning using LoRA
-- **SentencePiece**: For tokenization
+- **LangChain**: Query processing chains and knowledge base management
+- **Hugging Face**: Language model access and hosting
+- **FAISS**: Efficient vector search
+- **Gradio**: User interface creation
+- **BeautifulSoup**: Web page information extraction
+- **PEFT**: Efficient fine-tuning using LoRA
+- **SentencePiece**: Tokenization
 
 ## 🏗️ Project Structure
 
 ```
 status-law-gbot/
-├── app.py                 # Main application file with interface and request handling logic
+├── app.py                 # Main application file
 ├── requirements.txt       # Project dependencies
 ├── config/               # Configuration files
-│   ├── settings.py       # Application settings and model configurations
+│   ├── settings.py       # Application and model settings
 │   └── constants.py      # Constants and default values
 ├── src/                  # Source code
 │   ├── analytics/        # Analytics module
@@ -57,26 +61,24 @@ status-law-gbot/
 │   ├── knowledge_base/   # Knowledge base management
 │   │   ├── loader.py
 │   │   └── vector_store.py
-│   ├── training/         # Model training module
-│   │   ├── fine_tuner.py  # LoRA fine-tuning implementation
-│   │   └── model_manager.py  # Model switching and management
-│   └── models/          # Model storage
-│       └── fine_tuned/  # Fine-tuned model storage
-├── web/                 # Web interface components
-│   └── training_interface.py
+│   └── training/         # Training module
+│       ├── fine_tuner.py
+│       └── model_manager.py
 └── data/               # Data storage
     ├── vector_store/   # FAISS vector storage
     │   ├── index.faiss
     │   └── index.pkl
-    └── chat_history/   # Conversation logs
-        └── logs.json
+    ├── chat_history/   # Conversation logs
+    │   └── logs.json
+    └── fine_tuned_models/  # Fine-tuned model storage
+        └── model_registry.json
 ```
 
 ## 💾 Data Storage
 
 ### Vector Store
 - `data/vector_store/index.faiss`: FAISS vector store for document embeddings
-- `data/vector_store/index.pkl`: Metadata and configuration for the vector store
+- `data/vector_store/index.pkl`: Metadata and configuration for vector store
 
 ### Chat History
 - `data/chat_history/logs.json`: JSON file containing chat history and metadata
@@ -85,21 +87,11 @@ status-law-gbot/
 - `src/models/fine_tuned/`: Directory for storing fine-tuned models
 - `src/models/registry.json`: Model registry and configuration
 
-## 🚀 Usage
-
-The Status Law Assistant chatbot uses this structure to:
-1. Store and retrieve document embeddings for context-aware responses
-2. Maintain chat history for conversation continuity
-3. Track user interactions and improve response quality
-4. Fine-tune models based on conversation history
-5. Provide automatic model fallback in case of API errors
-6. Support multiple language models with easy switching
-
 ## 🛠️ Setup
 
 1. Clone the repository:
 ```bash
-git clone https://github.com/yourusername/status-law-gbot.git
+git clone https://github.com/PtOlga/status-law-gbot.git
 cd status-law-gbot
 ```
 
@@ -136,23 +128,24 @@ The fine-tuning process uses LoRA (Low-Rank Adaptation) for efficient training w
 
 The application supports multiple models with automatic fallback:
 
-- Llama 2 7B Chat (default)
-- Zephyr 7B
-- Custom fine-tuned versions
+- Llama 2 7B Chat (default): Optimized for dialogues
+- Zephyr 7B: Enhanced performance and response quality
+- Mistral 7B Instruct v0.2: Superior multilingual capabilities
+- XGLM 7.5B: Specialized cross-lingual generation model (requires paid API access)
 
 Models can be switched dynamically through the interface or programmatically:
 
 ```python
 from src.training.model_manager import switch_to_model
 
-switch_to_model("llama-7b")  # or "zephyr-7b"
+switch_to_model("llama-7b")  # or "zephyr-7b", "mistral-7b", "xglm-7b"
 ```
 
 ## 🔗 Related Links
 
 - [Status Law Website](https://status.law)
-- [Status Law Assistant on Hugging Face](https://huggingface.co/spaces/Rulga/status-law-assistant)
+- [Status Law Assistant on Hugging Face](https://huggingface.co/spaces/Rulga/status-law-gbot)
 
 ## 📝 License
 
-Private repository for Status Law Assistant usage only.
+Public repository for Status Law Assistant.