This repository provides very basic flask, Streamlit, and docker examples for the llama_index (FKA gpt_index) package.
If you need to quickly create a POC to impress your boss, start here!
If you are having trouble with dependencies, I dump my entire env into requirements_full.txt, but otherwise, use the base requirements.txt.
conda create --name llama_index python=3.11
pip install -r requirements.txt
There are two main example files
-
flask_demo.py (localhost:5601)
python ./flask_demo.py- creates a simple api that loads the text from the documents folder
- The "/query" endpoint accepts requests that contain a "text" parameter, which is used to query the index
- resturns string response containing the query answer
-
streamlit_demo.py (localhost:8501)
streamlit run streamlit_demo.py- creates a simple UI using streamlit
- loads text from the documents folder (using
st.cache_resource, so it only loads once) - provides an input text-box and a button to run the query
- The string response is displayed after it finishes
- Want to see this example in action? Check it out here
Using the local Dockerfile, you can run docker build -t my_tag_name . to build a python3.11-slim docker image. It ends up being about 980MB.
Inside the Dockerfile, you can comment the app you want to run and the port you want to expose, based on if you want streamlit or flask.
When running the image, be sure to include the -p option to access the proper ports (8501, or 5601).
I welcome any suggestions or PRs! If we start adding more examples, it might be good to refactor to have a folder per example type