{"id":25998769,"url":"https://github.com/parvvaresh/rag-application","last_synced_at":"2025-03-05T17:37:41.411Z","repository":{"id":279922014,"uuid":"939569832","full_name":"parvvaresh/RAG-Application","owner":"parvvaresh","description":"This repository contains a Retrieval-Augmented Generation (RAG) application, which combines the power of retrieval-based and generative models to provide accurate and contextually relevant responses.","archived":false,"fork":false,"pushed_at":"2025-02-28T08:15:54.000Z","size":17757,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-28T14:35:15.556Z","etag":null,"topics":["faiss","llm","rag"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/parvvaresh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-26T18:48:49.000Z","updated_at":"2025-02-28T08:15:57.000Z","dependencies_parsed_at":"2025-02-28T14:46:24.922Z","dependency_job_id":null,"html_url":"https://github.com/parvvaresh/RAG-Application","commit_stats":null,"previous_names":["parvvaresh/rag-application"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/parvvaresh%2FRAG-Application","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/parvvaresh%2FRAG-Application/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/parvvaresh%2FRAG-Application/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/parvvaresh%2FRAG-Application/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/parvvaresh","download_url":"https://codeload.github.com/parvvaresh/RAG-Application/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242070661,"owners_count":20067297,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["faiss","llm","rag"],"created_at":"2025-03-05T17:37:40.806Z","updated_at":"2025-03-05T17:37:41.405Z","avatar_url":"https://github.com/parvvaresh.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RAG Application\n\nThis repository contains a Retrieval-Augmented Generation (RAG) application, which combines the power of retrieval-based and generative models to provide accurate and contextually relevant responses. The application is designed to enhance question-answering systems by leveraging external knowledge sources and advanced natural language processing techniques.\n![Alt Text](/assets/rag_pipeline.png)\n\n\n## Table of Contents\n\n- [Introduction](#introduction)\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\nRetrieval-Augmented Generation (RAG) is a hybrid approach that integrates the strengths of both retrieval-based and generative models. The retrieval component fetches relevant documents or passages from a knowledge base, while the generative component synthesizes the information to produce a coherent and contextually appropriate response.\n\n\n## Features\n\n- **Retrieval Component**: Efficiently retrieves relevant documents or passages from a knowledge base.\n- **Generative Component**: Generates coherent and contextually appropriate responses based on retrieved information.\n- **Customizable Knowledge Base**: Easily integrate your own knowledge base or dataset.\n- **Scalable**: Designed to handle large-scale datasets and high query volumes.\n- **User-Friendly Interface**: Simple and intuitive API for easy integration into existing systems.\n\n## Installation\n\nTo get started with the RAG application, follow these steps:\n\n1. **Clone the repository**:\n   ```bash\n   git clone https://github.com/parvvaresh/RAG-Application.git\n   cd RAG-Application\n   ```\n\n2. **Install dependencies**:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. **Set up the knowledge base**:\n   - Place your documents or passages in the `knowledge_base/` directory.\n   - Update the configuration file to point to your knowledge base.\n\n4. **Run the application**:\n   ```bash\n   python app.py\n   ```\n\n## Example Usage\n\n![Alt Text](/assets/result.png)\n\n\n\n\n\n## Contributing\n\nWe welcome contributions from the community! If you'd like to contribute, please follow these steps:\n\n1. Fork the repository.\n2. Create a new branch for your feature or bugfix.\n3. Commit your changes and push to your fork.\n4. Submit a pull request with a detailed description of your changes.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n\n---\n\nFor any questions or issues, please open an issue on the [GitHub repository](https://github.com/parvvaresh/RAG-Application/issues).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparvvaresh%2Frag-application","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fparvvaresh%2Frag-application","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fparvvaresh%2Frag-application/lists"}