{"id":15442723,"url":"https://github.com/abeed04/rag-based-chat-with-pdf-using-llama3","last_synced_at":"2026-04-09T12:10:21.655Z","repository":{"id":248716664,"uuid":"829500861","full_name":"abeed04/RAG-based-chat-with-pdf-using-llama3","owner":"abeed04","description":"Turn your PDFs into a conversation with Llama3's RAG-powered chat.","archived":false,"fork":false,"pushed_at":"2024-07-20T06:30:29.000Z","size":26,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-10-07T18:43:57.198Z","etag":null,"topics":["chunking","faiss-vector-database","googlegenerativeai","groq","langchain","llama3","pycharm-community","python-3","rag","streamlit"],"latest_commit_sha":null,"homepage":"https://rag-based-chat-with-pdf-using-llama3.streamlit.app/","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/abeed04.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":"2024-07-16T14:54:34.000Z","updated_at":"2024-07-20T06:30:32.000Z","dependencies_parsed_at":"2024-07-20T07:52:34.725Z","dependency_job_id":null,"html_url":"https://github.com/abeed04/RAG-based-chat-with-pdf-using-llama3","commit_stats":{"total_commits":12,"total_committers":1,"mean_commits":12.0,"dds":0.0,"last_synced_commit":"292a4b0728a3da0ce0419f07dd3d9b8c2322b055"},"previous_names":["abeed04/rag-based-chat-with-pdf-using-llama3"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/abeed04/RAG-based-chat-with-pdf-using-llama3","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeed04%2FRAG-based-chat-with-pdf-using-llama3","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeed04%2FRAG-based-chat-with-pdf-using-llama3/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeed04%2FRAG-based-chat-with-pdf-using-llama3/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeed04%2FRAG-based-chat-with-pdf-using-llama3/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/abeed04","download_url":"https://codeload.github.com/abeed04/RAG-based-chat-with-pdf-using-llama3/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/abeed04%2FRAG-based-chat-with-pdf-using-llama3/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269747854,"owners_count":24469100,"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":"2025-08-10T02:00:08.965Z","response_time":71,"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":["chunking","faiss-vector-database","googlegenerativeai","groq","langchain","llama3","pycharm-community","python-3","rag","streamlit"],"created_at":"2024-10-01T19:29:40.381Z","updated_at":"2025-12-30T21:48:10.524Z","avatar_url":"https://github.com/abeed04.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\"\u003eChat with PDF using Llama3\u003c/h1\u003e\n\u003ch2 align=\"center\"\u003eIntroduction\u003c/h2\u003e\nA simple Streamlit app interface that answers questions about an uploaded PDF document via Llama3.\n\n[![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://rag-based-chat-with-pdf-using-llama.streamlit.app/)\n\n\u003cimg  align=\"right\" height=290 width=510 src=\"https://docs.aws.amazon.com/images/sagemaker/latest/dg/images/jumpstart/jumpstart-fm-rag.jpg\" /\u003e\n\n This project implements a chat interface that allows users to ask questions about uploaded PDF documents. It leverages a Retrieval-Augmented Generation (RAG) approach, combining:\n\n- Information Retrieval: Efficiently searching relevant passages in the PDF content using FAISS and text embeddings.\n- Generative Language Model (LLM): Answering user questions in a comprehensive and informative way using Llama3, a large language model from Google AI for embeddings.\n\n\u003ch2 align=\"center\"\u003eFeatures\u003c/h2\u003e\n\n- Upload multiple PDF documents.\n- Ask questions about the content of the uploaded PDFs.\n- Get answers generated by Llama3 based on the retrieved information.\n\n\u003ch2 align=\"center\"\u003eRequirements\u003c/h2\u003e\n\n- Python 3.x\n- Streamlit\n- PyPDF2\n- langchain\n- langchain_community\n- langchain-groq (Groq API access)\n- langchain-google-genai (Google Generative AI access)\n- dotenv (for environment variables)\n\n\u003ch2 align=\"center\"\u003eInstallation \u003c/h2\u003e\n\n1. Install the requirements\n\n   ```\n   $ pip install -r requirements.txt\n   ```\n\n2.  Set up your Groq API key and Google Cloud project credentials\n    ```\n    GROQ_API_KEY=your_groq_api_key\n    GOOGLE_API_KEY=your_google_api_key\n    ```\n    \n3.  Run the app\n\n    ```\n    $ streamlit run streamlit_app.py\n    ```\n\n4.  Upload your PDF files in the sidebar.\n5.  Click the \"Submit \u0026 Process\" button.\n6.  Once processing is complete, enter your question in the text box.\n7.  Click \"Enter\" to receive an answer generated by Llama3 based on the uploaded PDFs.    \n    \n\u003ch2 align=\"center\"\u003eUse Cases \u003c/h2\u003e\nResearch and Document Summarization:\n\n- Students and researchers can upload research papers, articles, or reports and ask specific questions about the content.\n- The chat interface can summarize key findings, identify relevant sections based on the question, or provide supporting evidence from the document.\n  \nLegal Document Analysis\n\n- Lawyers or legal professionals can upload contracts, agreements, or court documents and ask clarifying questions about terms, clauses, or procedures.\n- The chat can highlight relevant sections, identify potential ambiguities, or offer preliminary interpretations based on the document's content.\n  \nTechnical Documentation Exploration\n\n- Software developers, system administrators, or technical support personnel can upload user manuals, technical specifications, or troubleshooting guides.\n- The chat can answer questions about specific features, configuration options, or troubleshooting steps based on the information within the documents.\n  \nCustomer Service Chatbots\n\n- Companies can leverage this technology to build chatbots that answer customer questions about product manuals, FAQs, or service policies.\n- Users can upload relevant documents and get immediate, context-aware responses based on the retrieved information.\n\n Educational Materials and Assistive Technologies\n\n- Students with learning disabilities can upload educational materials and ask questions about specific concepts or passages.\n- The chat can provide paraphrased explanations, offer alternative learning resources, or highlight key points from the document.\n    \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeed04%2Frag-based-chat-with-pdf-using-llama3","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fabeed04%2Frag-based-chat-with-pdf-using-llama3","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fabeed04%2Frag-based-chat-with-pdf-using-llama3/lists"}