{"id":26686160,"url":"https://github.com/harmeshgv/rag-experiments","last_synced_at":"2025-07-30T15:04:00.221Z","repository":{"id":276960002,"uuid":"929071763","full_name":"harmeshgv/RAG-Experiments","owner":"harmeshgv","description":"Showcasing diverse RAG techniques for enhanced natural language processing tasks.","archived":false,"fork":false,"pushed_at":"2025-03-18T11:05:05.000Z","size":3,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-18T11:38:16.518Z","etag":null,"topics":["gorq","retrival-augmented-generation"],"latest_commit_sha":null,"homepage":"","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/harmeshgv.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":"2025-02-07T18:53:07.000Z","updated_at":"2025-03-18T11:06:03.000Z","dependencies_parsed_at":"2025-02-11T11:45:14.323Z","dependency_job_id":null,"html_url":"https://github.com/harmeshgv/RAG-Experiments","commit_stats":null,"previous_names":["harmesh095/rag-experiments","harmeshgv/rag-experiments"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmeshgv%2FRAG-Experiments","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmeshgv%2FRAG-Experiments/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmeshgv%2FRAG-Experiments/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/harmeshgv%2FRAG-Experiments/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/harmeshgv","download_url":"https://codeload.github.com/harmeshgv/RAG-Experiments/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245641434,"owners_count":20648644,"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":["gorq","retrival-augmented-generation"],"created_at":"2025-03-26T11:17:03.730Z","updated_at":"2025-03-26T11:17:04.399Z","avatar_url":"https://github.com/harmeshgv.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Stat IQ - PDF Q\u0026A System\n\n## Overview\nStat IQ is a simple Retrieval-Augmented Generation (RAG) system that enables users to upload a PDF document and ask questions related to its content. The system extracts text from the uploaded PDF and leverages the Groq API to generate responses based on the document's content.\n\n## Features\n- Upload a PDF file for text extraction.\n- Ask questions related to the document.\n- AI-powered responses using a conversational history.\n- Handles API errors and rate limits efficiently.\n\n## How It Works\n1. User uploads a PDF file.\n2. The system extracts text from the document.\n3. User asks a question related to the PDF content.\n4. The Groq API processes the query with the extracted text as context and returns a response.\n5. The response is displayed to the user.\n\n## Future Enhancements\nThis repository will be updated with more advanced RAG techniques, including:\n- Chunk-based retrieval for better context handling.\n- Vector embeddings for semantic search.\n- Hybrid search using dense and sparse retrieval methods.\n- Multi-document querying with ranking algorithms.\n\nStay tuned for more updates on advanced RAG implementations!\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmeshgv%2Frag-experiments","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fharmeshgv%2Frag-experiments","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fharmeshgv%2Frag-experiments/lists"}