https://github.com/logan-markewich/llama_index_starter_pack
This repository provides very basic flask, streamlit, and docker examples for the llama_index (fka gpt_index) package
https://github.com/logan-markewich/llama_index_starter_pack
Last synced: 2 days ago
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This repository provides very basic flask, streamlit, and docker examples for the llama_index (fka gpt_index) package
- Host: GitHub
- URL: https://github.com/logan-markewich/llama_index_starter_pack
- Owner: logan-markewich
- License: mit
- Created: 2023-03-04T17:33:02.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-08-28T03:19:20.000Z (8 months ago)
- Last Synced: 2025-04-03T18:15:43.836Z (11 days ago)
- Language: Python
- Size: 122 MB
- Stars: 618
- Watchers: 11
- Forks: 179
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# llama_index_starter_pack
This repository provides very basic flask, [Streamlit](https://llama-index.streamlit.app/), and docker examples for the [llama_index](https://github.com/run-llama/llama_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`.
The basic demo includes the classic "Paul Graham Essay" from the original llama_index repo. Some good starting questions are
- What did the author do growing up?
- Tell me more about interleaf
## Local Setup
```
conda create --name llama_index python=3.11
pip install -r requirements.txt
```
## What is included?
There are two main example folders
- flask_react (runs three services on localhost:5601/5602/3000)
- `sh launch_app.sh `
- creates a simple api that loads the text from the documents folder (if any), also launches the react frontend
- the "/query" endpoint accepts requests that contain a "text" parameter, which is used to query the index
- the "/upload" endpoint is a POST endpoint that inserts an attached text file into the index
- the index is managed by a seperate server using locks, since inserting a document is a mutable operation and flask is multithreaded
- I strongly recommend using a tool like [Postman](https://www.postman.com/downloads/) to test the api - there are example screenshots using postman in the `postman_examples` folder
- react_frontend
- A basic frontend using react, which calls the flask_api to insert, view, and query a VectorStoreIndex
- The scope for a frontend is basically limitless - this is intended to give a small taste of what's possible for users less familiar with python
- streamlit_vector (runs on localhost:8501)
- `streamlit run streamlit_demo.py`
- creates a simple UI using streamlit and VectorStoreIndex
- 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
- streamlit_sql_sandbox (runs on localhost:8501)
- `streamlit run streamlit_demo.py`
- creates a streamlit app using a local SQL database about restaurant inspections in San Francisco ([data sample](https://docs.google.com/spreadsheets/d/1Ag5DBIviYiuRrt2yr3nXmbPFV-FOg5fDH5SM3ZEDnpw/edit#gid=780513932))
- The "Setup" tab allows you to configure various LLM and LLama Index settings
- The ""Single-Shot Query" tab demos some basic Text2SQL capabilities using only Llama Index
- The ""Agent + Chat History" tab uses a llamaindex agent, and uses the SQL index from Llama Index as a tool during conversations.
- streamlit_term_definition (runs on localhost:8501)
- `streamlit run streamlit_demo.py`
- creates a small app that allows users to extract terms/definitions from documents and query against the extracted information
- pre-loaded with information from the NYC Wikipedia page
- supports reading text from image uploads
- allows users to configure LLM settings
- users can build their own knowledge base of terms/definitions
- query against these terms as they are added
## Docker
Each example contains a `Dockerfile`. You can run `docker build -t my_tag_name .` to build a python3.11-slim docker image inside your desired folder. It ends up being about 600MB-900MB depending on the example.
Inside the `Dockerfile`, certain ports are exposed based on which ports the examples need.
When running the image, be sure to include the -p option to access the proper ports (8501, 5601, or 3000).
## Contributing
I welcome any suggestions or PRs, or more examples!