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The Document Assistant project is designed to assist users in efficiently managing and retrieving information from various sources using advanced natural language processing techniques. This documentation outlines the architecture, components, and functionalities of the project, including web scraping, data ingestion, retrieval, and the Streamlit-based user interface. Components Web Scraping Module

File Name: download_docs.py Description: This module is responsible for extracting data from websites. It uses BeautifulSoup and requests libraries to parse HTML content and fetch structured data. Scraped data is prepared for further processing and ingestion into the vector database. Ingestion to Vector Database

File Name: ingestion.py Description: Handles the ingestion of structured data into a vector database. This module involves: Loading documents or data chunks. Generating embeddings using a specified embedding model. Storing embeddings in the vector database (e.g., FAISS) for efficient retrieval. Backend for Retrieval and RAG Operations

File Name: core.py Description: Implements the retrieval-augmented generation (RAG) operations. Key functionalities include: Retrieving relevant information from the vector database based on user queries. Combining retrieved information with user queries to generate contextually relevant responses using models like GPT (e.g., AzureOpenAI). Integration of retrieval and generation to ensure accurate and fluent responses. Main File for Streamlit UI

File Name: main.py Description: Implements the Streamlit-based user interface for interacting with the Documentation Assistant. Features include: Input forms for users to enter queries or select options. Display of retrieved information and generated responses. Integration with the backend for seamless interaction between the user interface and underlying functionalities. Architecture The project follows a modular architecture to ensure scalability and maintainability:

Web Scraping: Acquires fresh data from designated websites. Ingestion: Prepares and stores data in a vector database for efficient retrieval. Backend: Manages the retrieval and RAG operations using advanced NLP models. User Interface: Provides an intuitive front-end powered by Streamlit for user interaction. Usage Setup and Installation:

Clone the repository and install dependencies listed in requirements.txt. Ensure necessary libraries for web scraping, vector database (e.g., FAISS), and NLP models (e.g., AzureOpenAI) are installed and configured. Execution:

Run download_docs.py to fetch and preprocess data from target websites. Execute ingestion.py to generate embeddings and store them in the vector database. Start rag_backend.py to handle retrieval and RAG operations. Launch main.py to initiate the Streamlit UI for user interaction. User Interface:

Access the Streamlit UI via a web browser. Input queries or select options to retrieve relevant information. View generated responses and interact with the system seamlessly. Dependencies Python 3.x BeautifulSoup requests FAISS (or other vector database) Streamlit AzureOpenAI (or other NLP models) Future Enhancements Implement more robust error handling and logging mechanisms. Enhance scalability to handle larger datasets and more complex queries. Integrate additional NLP models or improve existing ones for better response generation. Conclusion The Documentation Assistant project showcases the integration of web scraping, data ingestion, retrieval, and advanced NLP techniques