Buckets:
| # CA18_memory_systems | |
| ## 📁 Project Structure | |
| ``` | |
| CA18_memory_systems/ | |
| ├── CA18.ipynb | |
| ├── COMPLETION_SUMMARY.md | |
| ├── Experimental_RAG_Memory_Systems.ipynb | |
| ├── README.md | |
| ├── README_ADVANCED.md | |
| ├── README_AI_ENHANCED.md | |
| ├── README_COMPLETE.md | |
| ├── __pycache__/ | |
| ├── advanced_memory_systems.py | |
| ├── advanced_memory_systems_langgraph.py | |
| ├── advanced_reporting.py | |
| ├── advanced_visualization.py | |
| ├── ai_benchmark.py | |
| ├── ai_memory_demo.py | |
| ├── ai_pipeline.py | |
| ├── ai_visualization.py | |
| ├── benchmark.py | |
| ├── benchmark_results/ | |
| ├── comprehensive_benchmark.py | |
| ├── config.py | |
| ├── experimental_rag_system.py | |
| ├── interactive_dashboard.py | |
| ├── langgraph_workflow.py | |
| ├── llm_memory_systems.py | |
| ├── logs/ | |
| ├── main.py | |
| ├── memory_systems.py | |
| ├── memory_systems_env/ | |
| ├── memory_systems_simple.py | |
| ├── project_summary.sh | |
| ├── requirements.txt | |
| ├── results/ | |
| ├── run.sh | |
| ├── run_ai_systems.sh | |
| ├── run_pipeline.sh | |
| ├── statistical_analysis.py | |
| ├── stress_testing.py | |
| ├── test_simple.sh | |
| ├── vector_store_comparison.py | |
| ├── visualization.py | |
| └── visualizations/ | |
| ``` | |
| ## 🚀 Running the Project | |
| ```bash | |
| chmod +x run.sh | |
| ./run.sh | |
| ``` | |
| ## 📦 Installing Dependencies | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## 🔧 Setup | |
| 1. Clone the repository | |
| 2. Install dependencies: `pip install -r requirements.txt` | |
| 3. Configure environment variables if needed | |
| 4. Run the project: `./run.sh` | |
| ## 📚 Documentation | |
| Detailed documentation is provided across `README_ADVANCED.md`, `README_AI_ENHANCED.md`, and `README_COMPLETE.md`, along with the primary notebooks. | |
| ## 🧪 Testing | |
| Run smoke checks using the provided scripts: | |
| ```bash | |
| chmod +x test_simple.sh | |
| ./test_simple.sh | |
| ``` | |
| ## 🎮 Demos | |
| Interactive demos are exposed through `interactive_dashboard.py` and `ai_memory_demo.py`. | |
| ## 📊 Results | |
| Results aggregate in the `results/` directory, benchmark artifacts in `benchmark_results/`, and figures in `visualizations/`. | |
| --- | |
| _This project is part of the System2_in_AI CA collection._ | |
Xet Storage Details
- Size:
- 2.19 kB
- Xet hash:
- 9ba568073ec00493bfe33688325f10a4ba77330db83fac7234fc083c195f20b1
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.