{"id":29854971,"url":"https://github.com/2000pawan/rag_based_llm_model","last_synced_at":"2025-07-29T22:21:27.367Z","repository":{"id":303467499,"uuid":"1015571181","full_name":"2000pawan/Rag_Based_LLM_Model","owner":"2000pawan","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-18T06:43:12.000Z","size":32,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-18T10:43:55.570Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/2000pawan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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,"zenodo":null}},"created_at":"2025-07-07T17:47:52.000Z","updated_at":"2025-07-18T06:43:15.000Z","dependencies_parsed_at":"2025-07-07T20:59:59.119Z","dependency_job_id":"d26b4cfa-a93c-4ca0-b13c-6c536f58b9de","html_url":"https://github.com/2000pawan/Rag_Based_LLM_Model","commit_stats":null,"previous_names":["2000pawan/rag_based_llm_model"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/2000pawan/Rag_Based_LLM_Model","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/2000pawan%2FRag_Based_LLM_Model","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/2000pawan%2FRag_Based_LLM_Model/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/2000pawan%2FRag_Based_LLM_Model/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/2000pawan%2FRag_Based_LLM_Model/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/2000pawan","download_url":"https://codeload.github.com/2000pawan/Rag_Based_LLM_Model/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/2000pawan%2FRag_Based_LLM_Model/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267771941,"owners_count":24142081,"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","status":"online","status_checked_at":"2025-07-29T02:00:12.549Z","response_time":2574,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2025-07-29T22:21:25.214Z","updated_at":"2025-07-29T22:21:27.348Z","avatar_url":"https://github.com/2000pawan.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"### RAG-Based LLM Model with FastAPI Frontend\n\nThis project implements a Retrieval-Augmented Generation (RAG) model using a FastAPI backend and a frontend built with HTML, CSS, and JavaScript. It allows users to upload PDF documents and ask questions based on the content of those documents.\n\n## Overview\n\nThe project consists of the following main components:\n\n* **FastAPI Backend:** Handles PDF uploads, processes the PDF content, builds a vector index, and creates an agent for question answering. It exposes API endpoints for uploading files and asking questions.\n* **RAG Engine:** Responsible for processing the uploaded PDF, building a vector index for efficient retrieval, and using an agent to answer user questions based on the retrieved information.\n* **Frontend:** A web interface built with HTML, CSS, and JavaScript that allows users to upload PDF files and submit questions to the backend.\n\n## Setup Instructions\n\nFollow these steps to set up and run the project:\n\n1.  **Clone the repository:** (If you haven't already)\n    ```bash\n    git clone https://github.com/2000pawan/Rag_Based_LLM_Model.git\n    ```\n\n2.  **Navigate to the `frontend` directory and then back to the root directory:** The FastAPI application assumes the frontend files are in a directory named `frontend` in the same directory as the main Python file. Uploads\\Project\\LLM\\Rag_Based_LLM_Model\\frontend`. Ensure you have a `frontend` directory with your HTML, CSS, and JS files at that location or adjust the `app.mount` path accordingly.\n\n3.  **Install backend dependencies:**\n    ```bash\n    pip install -r requirements.txt  # You might need to create this file with required libraries\n    ```\n    *(Note: You will need to create a `requirements.txt` file listing the dependencies for your backend. This would typically include `fastapi`, `uvicorn`, `pydantic`, and any libraries used in your `rag_engine.py` file such as those for PDF processing, vector databases, and LLM integration.)*\n\n## Running the Application\n\n1.  **Run the FastAPI backend:**\n    ```bash\n    https://rag-llm-pawan.onrender.com/  # In this case, the file content provided is the main file\n    ```\n\n\n## How to Use\n\n1.  **Upload a PDF:** Use the \"Choose File\" button on the frontend to select a PDF document from your local machine.\n2.  **Process the PDF:** Click the \"Upload\" button to send the PDF to the backend for processing. You should see a \"PDF processed successfully!\" message upon completion.\n3.  **Ask a Question:** In the provided text area, type your question related to the content of the uploaded PDF.\n4.  **Get the Answer:** Click the \"Ask\" button. The backend will process your question using the RAG engine and display the answer on the frontend.\n\n## Project Structure\n\nyour_project_directory/\n\n        ├── backend/              (Optional: You might organize backend code in a subfolder)\n        │   └── main.py           (Your FastAPI application code)\n        │   └── rag_engine.py     (Your RAG engine logic)\n        │   └── ...\n        ├── frontend/\n        │   ├── index.html        (Frontend HTML)\n        │   ├── styles.css        (Frontend CSS)\n        │   ├── script.js         (Frontend JavaScript)\n        │   └── ...\n        ├── uploads/              (Directory to store uploaded PDF files)\n        ├── requirements.txt      (Backend dependencies)\n        └── README.md\n\n*(Adjust the directory structure above to match your actual project organization.)*\n\n## Technologies Used\n\n* **Backend:** FastAPI, Python\n* **Frontend:** HTML, CSS, JavaScript\n* **RAG Engine:** *(List the specific libraries you used for PDF processing, vector database, and LLM integration, e.g., PyPDF2, ChromaDB, Langchain, etc.)*\n\n## Further Improvements\n\n* Implement error handling for file uploads and API requests.\n* Add loading states to the frontend to improve user experience.\n* Allow users to upload multiple PDF files.\n* Implement user authentication and authorization if needed.\n* Deploy the application to a cloud platform for wider access.\n\n### 👤 Contact\n\nName: Pawan Yadav\n\nEmail: yaduvanshi2000pawan@gmail.com\n\n### 📄 License\n\nThis project is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2000pawan%2Frag_based_llm_model","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F2000pawan%2Frag_based_llm_model","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F2000pawan%2Frag_based_llm_model/lists"}