This repository contains a Large Language Model project focused on building a Question-Answering System tailored for querying a specific website. The aim of this project is to develop an intelligent system that can understand user queries and provide relevant answers based on the content available on the website.
The primary objective of this project is to leverage the power of large language models to create a robust Question-Answering System. By utilizing advanced techniques in natural language processing and machine learning, we aim to enable users to ask questions and obtain meaningful responses directly from the website.
To achieve our goal, we have broken down the project into the following key steps:
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Web Scraping: We will scrape the website's content, extracting webpages and relevant information from the target website.
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Embeddings Generation: The extracted webpages will undergo an embeddings generation process, where numerical representations capturing the semantic meaning of the text will be created. These embeddings will allow us to measure the similarity between user queries and the website content.
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Query Processing: When a user submits a query, we will generate embeddings for the query text. This step ensures that both the query and the website content are represented in a compatible format for comparison.
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Comparison and Retrieval: By comparing the embeddings of the user query with the embeddings of the website content, we can identify the most relevant webpages based on their semantic similarity. This process will facilitate the retrieval of the most suitable information to answer the user's query.
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Context Injection: Once we identify the most relevant webpage(s) based on the query, we will inject the content of those webpages as context into the prompt for the large language model. This contextual information will enable the model to generate accurate and meaningful responses.
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Response Generation: With the context injected, the large language model will process the user query along with the relevant webpage content to generate a response. The response will provide the most relevant and informative answer to the user's query.
We believe that this project will greatly enhance the accessibility and usability of the website's information by providing users with an intuitive and efficient question-answering system. We invite you to explore the repository, contribute to its development, and witness the power of large language models in action.
├── README.md <- The top-level README for developers using this project. It contains the project overview, setup instructions, and other necessary information.
├── .github <- Contains the templates for issues and pull requests. It streamlines and standardizes the process of contributing to the project.
├── docs <- Contains detailed information about the project, how to use it, FAQs, and other documentation.
├── notebooks <- Google Colab notebooks. Contains interactive code and tutorials for understanding and using the QA app.
├── src <- Source code for use in this project. This could include scripts, algorithms, or other code logic.
├── config <- Configuration files for the project. These might include setup scripts, environment variables, or other configuration parameters.
├── test <- Contains test code, scripts, and resources. This folder is crucial for validating the functionality and correctness of your project.
├── .gitignore <- Specifies intentionally untracked files that Git should ignore. Helps keep the repository clean from backup files, log files, build outputs, etc.
We welcome and appreciate all contributions. If you're interested in contributing, please start by reading our CONTRIBUTING.md guide.
If you're new to the project or open source in general, we recommend starting with issues labeled as "Good First Issue". These issues are typically more straightforward and a great way to get started with the project.
Thank you for considering contributing to our project. We look forward to working with you!