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README.md

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+# RAG-based Local Chat Box
+
+Author: Md Mutasim Billah Abu Noman Akanda, Machine Learning Engineer  
+Updated on: 1 October, 2024
+
+## Project Description
+
+RAG-based Local Chat Box is a Streamlit-based web application designed to integrate a Local Language Model (LLM) with Retrieval-Augmented Generation (RAG) capabilities. This project allows users to index documents, create embeddings, and interact with their data through an intuitive chat interface powered by state-of-the-art language models. Ideal for researchers and developers, `rag-based-local-chat-box` facilitates efficient data retrieval and conversational interactions within a local environment.
+
+## Features
+
+- **Model Selection:** Choose from a variety of local LLM models (`Mistral`, `llama`) to suit your needs.
+- **Document Indexing:** Easily load and index documents from a specified directory.
+- **Embedding Creation:** Utilize the `nomic-embed-text` model to create meaningful embeddings for your documents.
+- **Interactive Chat Interface:** Engage with your indexed data through a seamless chat interface.
+- **Session Management:** Maintains chat history and model selections across user sessions.
+- **Real-time Responses:** Receive streaming responses from the LLM for a dynamic interaction experience.
+
+## Project Structure
+
+```
+rag-based-local-chat-box/
+├── ui.py                              # Main Streamlit application script
+├── document_loader.py                 # Handles loading and indexing of documents
+├── models.py                          # Retrieves and manages the list of available LLM models
+├── llm.py                             # Manages interactions with the Language Model, including streaming responses
+├── requirements.txt                   # Python package dependencies
+├── README.md                          # Project documentation
+└── Research/                          # Default directory for storing and indexing documents
+```
+
+## Prerequisites
+
+Before setting up the project, ensure you have the following prerequisites installed:
+
+- **Python:** Version 3.8 or higher
+- **Streamlit:** For running the web application
+- **Ollama:** A local LLM framework
+- **Git:** For cloning the repository
+- **Pip:** Python package installer
+
+## Setup Instructions
+
+### 1. Clone the Repository
+
+Begin by cloning the `rag-based-local-chat-box` repository to your local machine:
+
+```bash
+git clone https://github.com/noman024/rag-based-local-chat-box.git
+cd rag-based-local-chat-box
+```
+
+### 2. Set Up a Virtual Environment
+
+It's recommended to use a virtual environment to manage dependencies:
+
+```bash
+sudo chmod 777 setup_env.sh
+./setup_env.sh
+```
+
+### 3. Prepare Your Documents
+
+Place the documents you wish to index in the `Research/` directory or specify a different folder path via the application's sidebar.
+
+## Usage
+
+### Running the Application
+
+Start the Streamlit application by navigating to the project directory and executing:
+
+```bash
+streamlit run ui.py
+```
+
+This command will launch the RAG-based Local Chat Box web interface in your default web browser.
+
+### Using RAG-based Local Chat Box
+
+#### 1. Select a Model:
+
+- Navigate to the sidebar.
+- Use the dropdown menu to select your desired local LLM model from the available options.
+
+#### 2. Specify Folder Path:
+
+- Enter the path to the folder containing the documents you wish to index.
+- he default path is set to `Research`, but you can modify this as needed.
+
+#### 3. Index Documents:
+
+- Click the `Index Documents` button.
+- The application will validate the directory and begin creating embeddings.
+- Once indexing is complete, a confirmation message will appear.
+
+#### 4. Interact via Chat:
+
+- Use the chat input at the bottom of the interface to ask questions.
+- The assistant will provide answers based on the indexed documents.
+- Chat history is maintained throughout your session for continuity.
+
+## Dependencies
+
+RAG-based Local Chat Box relies on several Python packages and external tools:
+
+- **Streamlit:** For building the web interface.
+- **LangChain Community:** Provides utilities for working with language models.
+- **Ollama:** Facilitates interactions with local LLMs.
+- **nomic-embed-text:** Embedding model for creating document embeddings.
+
+For the full list, see the `requirements.txt` file.
+
+## Contact
+
+If you have any questions or issues, please open an issue on the GitHub repository.