Create a virtual environment and activate it (python -m venv venv &&source venv/bin/activate)
Run poetry install to install the packages
If you are a dev and have a data/ folder with you, skip this step.
Run training.py (for the first time/to update the model). This takes care of basically everything.
Install Ollama (https://ollama.com/) and download the models ollama pull llama3
Run ./start_local.sh to start all the services, and navigate to http://localhost:8501 to access the Streamlit frontend.
WAIT for a few minutes for the services to start. The terminal should show "sending first query to avoid cold start". Things will only work after this message.
Do not open the uvicorn server directly, as it will not work.
To stop the services, run ./stop_local.sh from a different terminal.
Enjoy :)
CLI access to the API
We all are lazy sometimes and don't want to use the interface sometimes. Or just want to test out different parts of the API without any hassle. To that end, you can either test out the individual components like so:
Note that the %20 are spaces in the URL.
Ollama
This is the server that runs an Ollama server (This is basically an optimized version of a local LLM. It does not do anything of itself but runs as a background service so you can use the LLM).
You can start it by running cd ollama && ./get_ollama.sh &
LLM Service
This component is the one that runs the query processing using LLMs module. It uses the Ollama server, runs queries and processes them.
You can start it by running cd llm_service && uvicorn llm_service:app --host 0.0.0.0 --port 8081 &
Curl Example : curl http://0.0.0.0:8081/llmquery/find%20me%20a%20mushroom%20dataset%20with%20less%20than%203000%20classes
Backend
This component runs the RAG pipeline. It returns a JSON with dataset ids of the OpenML datasets that match the query.
You can start it by running cd backend && uvicorn backend:app --host 0.0.0.0 --port 8000 &
Curl Example : curl http://0.0.0.0:8000/dataset/find%20me%20a%20mushroom%20dataset
Frontend
This component runs the Streamlit frontend. It is the UI that you see when you navigate to http://localhost:8501.
You can start it by running cd frontend && streamlit run ui.py &
Features
RAG
Multi-threaded downloading of OpenML datasets
Combining all information about a dataset to enable searching
Chunking of the data to prevent OOM errors
Hashed entries by document content to prevent duplicate entries to the database
RAG pipeline for OpenML datasets/flows
Specify config["training"] = True/False to switch between training and inference mode
Option to modify the prompt before querying the database (Example: HTML string replacements)
Results are returned in either JSON
Easily customizable pipeline
Easily run multiple queries and aggregate the results
LLM based query processing to enable "smart" filters
Streamlit frontend for now
Enhancements
One config file to rule them all
GUI search interface using FastAPI with extra search bar for the results
