{"id":23908170,"url":"https://github.com/awrsha/llm-rag-research","last_synced_at":"2026-04-29T21:06:02.499Z","repository":{"id":271018956,"uuid":"912166595","full_name":"Awrsha/LLM-RAG-Research","owner":"Awrsha","description":"An integrated AI suite combining intelligent PDF analysis, automated research capabilities, and multi-agent academic paper generation, powered by both cloud and local language models to streamline research and document processing workflows.","archived":false,"fork":false,"pushed_at":"2025-01-04T19:55:32.000Z","size":0,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-04T20:38:24.960Z","etag":null,"topics":["crewai","docker","flask","groq","langchain","llm","multi-agent","open-webui","rag","tinyllama"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/Awrsha.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":"2025-01-04T19:41:10.000Z","updated_at":"2025-01-04T19:55:35.000Z","dependencies_parsed_at":"2025-01-04T20:38:29.405Z","dependency_job_id":"cb61bb2b-3300-4cbd-af47-70ce70b5cd28","html_url":"https://github.com/Awrsha/LLM-RAG-Research","commit_stats":null,"previous_names":["awrsha/llm-rag-research"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FLLM-RAG-Research","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FLLM-RAG-Research/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FLLM-RAG-Research/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Awrsha%2FLLM-RAG-Research/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Awrsha","download_url":"https://codeload.github.com/Awrsha/LLM-RAG-Research/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":234867681,"owners_count":18899230,"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":["crewai","docker","flask","groq","langchain","llm","multi-agent","open-webui","rag","tinyllama"],"created_at":"2025-01-05T04:25:07.539Z","updated_at":"2026-04-29T21:06:02.492Z","avatar_url":"https://github.com/Awrsha.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🤖 AI Research \u0026 Analysis Suite\n\n\u003cdiv align=\"center\"\u003e\n\n[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\n*A comprehensive suite of AI-powered research and analysis tools*\n\n[📚 Documentation](#documentation) •\n[🚀 Quick Start](#quick-start) •\n[🛠️ Components](#components) •\n[💻 Installation](#installation) •\n[📘 Usage](#usage)\n\n\u003c/div\u003e\n\n## 🎯 Suite Components\n\n### 1. [📑 AI PDF Assistant](./pdf-assistant)\n- Intelligent PDF document analysis\n- Question-answering system\n- Secure document handling\n\n### 2. [🔬 AI Research Assistant](./research-assistant)\n- Automated framework research\n- Web-based information gathering\n- Comprehensive report generation\n\n### 3. [🤖 Autonomous Research Framework](./autonomous-framework)\n- Multi-agent research system\n- Academic paper generation\n- Dual model support (Groq/TinyLlama)\n\n## 🚀 Quick Start\n\n```bash\n# Create virtual environment\npython -m venv venv\n\n# Activate virtual environment\nsource venv/bin/activate  # Linux/Mac\nvenv\\Scripts\\activate     # Windows\n\n# Install core dependencies\npip install -r requirements.txt\n```\n\n## 📦 Core Requirements\n\n```text\nflask\u003e=2.0.0\ngroq\u003e=0.9.0\nlangchain\u003e=0.1.0\nlangchain-groq\u003e=0.1.0\npython-dotenv\u003e=1.0.0\nmarkdown2\u003e=2.4.0\ntorch\u003e=2.0.0\ntransformers\u003e=4.35.0\ngradio\u003e=3.50.0\n```\n\n## ⚙️ Global Configuration\n\nCreate a `.env` file in the root directory:\n```env\nGROQ_API_KEY=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\n```\n\n## 🔧 System Requirements\n\n- Python 3.8+\n- 8GB+ RAM\n- 10GB+ Disk Space\n- NVIDIA GPU (optional)\n- Docker (for TinyLlama)\n- Internet Connection\n\n## 🔐 Security Features\n\n- API key management\n- Secure file handling\n- Rate limiting\n- Input validation\n- Temporary storage management\n\n## 🤝 Contributing\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## 📘 Documentation\n\nDetailed documentation for each component:\n- [PDF Assistant Documentation](./pdf-assistant/README.md)\n- [Research Assistant Documentation](./research-assistant/README.md)\n- [Autonomous Framework Documentation](./autonomous-framework/README.md)\n\n\n## 📊 Feature Comparison\n\n| Feature | PDF Assistant | Research Assistant | Autonomous Framework |\n|---------|--------------|-------------------|---------------------|\n| Input | PDF Documents | Research Topics | Multiple Sources |\n| Output | Q\u0026A Responses | Research Reports | Academic Papers |\n| Model | Groq | Groq | Groq/TinyLlama |\n| Interface | Web UI | Web UI | Open WebUI |\n| Agents | Single | Single | Multi-Agent |\n\n## 🌟 Use Cases\n\n- 📚 Academic Research\n- 📊 Market Analysis\n- 📝 Document Processing\n- 🔍 Literature Review\n- 📈 Trend Analysis\n- 🎓 Educational Support\n\n## 📜 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- [Groq](https://groq.com) for LLM services\n- [LangChain](https://langchain.com) for the framework\n- [CrewAI](https://github.com/crewai) for multi-agent capabilities\n- [TinyLlama](https://github.com/tinyllama) for local model support\n- [Flask](https://flask.palletsprojects.com/) for web framework\n\n---","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawrsha%2Fllm-rag-research","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fawrsha%2Fllm-rag-research","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fawrsha%2Fllm-rag-research/lists"}