An implementation of a Retrieval-Augment-Generation (RAG) system where an LLM gives answers to a question based from a given database.
Live Website: https://lena-rag.vercel.app/
Techstack:
- LLM: Cohere Command-R
- Embedding Model: Cohere Embeddings
- LangChain
- LangGraph
- FastAPI
- NextJS
Database/Corpus The corpus covers 2 topics. Topic 1: Lang Yang Lamu Symbiosis
- Study of the symbiosis of three fictitious creatures
- Lang. Mythic wolf. Hunts down Yang. Urine nutritionally enhances Lamu Plant.
- Yang. Mythic sheep. Herbivore. When eating enhanced Lamu plant, its feces becomes great fertilizer
- Lamu. Miracle bloom. When eaten by Yangs, they gain a poisonous property lethal to Langs.
Topic 2: Side effects of time travelling
- Temporal disorientation/displacement
- Symptoms include “chrono-cultural shock”, stress and anxiety, and identity crises
- Coping mechanisms include journaling, meditation, etc.
- Dr. Alexander Hayes
- Pioneered time travel together with his team.
- First to experience the side effects of time travelling
Running the application
npm run dev
uvicorn api.index:app --reload
Once running, go to address: localhost:3000
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience