{"id":27888821,"url":"https://github.com/torchstack-ai/rag-summarization-pdf","last_synced_at":"2025-05-05T09:28:52.456Z","repository":{"id":280528755,"uuid":"942309073","full_name":"torchstack-ai/rag-summarization-pdf","owner":"torchstack-ai","description":null,"archived":false,"fork":false,"pushed_at":"2025-03-03T22:53:44.000Z","size":4496,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-05T09:28:37.536Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/torchstack-ai.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}},"created_at":"2025-03-03T22:51:40.000Z","updated_at":"2025-03-03T22:55:13.000Z","dependencies_parsed_at":"2025-03-03T23:39:19.294Z","dependency_job_id":null,"html_url":"https://github.com/torchstack-ai/rag-summarization-pdf","commit_stats":null,"previous_names":["scottcampit/rag-inboundsquare"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torchstack-ai%2Frag-summarization-pdf","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torchstack-ai%2Frag-summarization-pdf/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torchstack-ai%2Frag-summarization-pdf/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/torchstack-ai%2Frag-summarization-pdf/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/torchstack-ai","download_url":"https://codeload.github.com/torchstack-ai/rag-summarization-pdf/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252470893,"owners_count":21753068,"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":[],"created_at":"2025-05-05T09:28:51.681Z","updated_at":"2025-05-05T09:28:52.433Z","avatar_url":"https://github.com/torchstack-ai.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RAG Paper Summarizer\n\nA Python application that implements Retrieval Augmented Generation (RAG) to download and summarize academic papers. Currently configured to process the ReAct paper from arXiv.\n\n## Features\n\n- Automatic paper download from arXiv\n- PDF processing and text chunking\n- Vector store creation using Chroma\n- RAG-based summarization using OpenAI's GPT-4 and LangChain\n\n## Prerequisites\n\n- Python 3.x\n- OpenAI API key\n\n## Installation\n\n1. Clone the repository:\n```bash\ngit clone \u003crepository-url\u003e\ncd rag-inboundsquare\n```\n\n2. Install the required dependencies:\n```bash\npip install -r requirements.txt\n```\n\n3. Create a `.env` file in the root directory and add your OpenAI API key:\n```bash\nOPENAI_API_KEY=your_api_key_here\n```\n\n## Usage\n\nRun the script using:\n\n```bash\npython rag.py\n```\n\nThe script will:\n1. Download the ReAct paper if not already present\n2. Process the PDF and split it into chunks\n3. Create a vector store using Chroma\n4. Generate a comprehensive summary using RAG\n\n## Dependencies\n\n- langchain\n- openai\n- chromadb\n- arxiv\n- python-dotenv\n- requests\n\n## Note\n\nThe current implementation is configured to summarize the ReAct paper (arXiv:2210.03629). You can modify the `process_pdf` function to work with other papers or PDF documents.\n\n## License\n\n[Add your license here] ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorchstack-ai%2Frag-summarization-pdf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorchstack-ai%2Frag-summarization-pdf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorchstack-ai%2Frag-summarization-pdf/lists"}