Read Me

Execute below commands for:

Create Virtual Env

# deprecated pip and migrated to use uv
#python3 -m venv .venv
vu venv

Activate Virtual Env

source .venv/bin/activate

Install requirements

# deprecated pip and migrated to use uv
#python3 -m pip install -r requirements.txt
uv add -r requirements.txt
uv sync

NOTE: Please add .env file and add your own OpenAI API Key "OPENAI_API_KEY=<Key Here>"

Run UI Agent

streamlit run demo_app.py

Run CLI Agent (if you have it!)

python3 myagent_cli.py

Source Structure

Data Folder demo_data contains all the data files read to embed/tokenize the data and store into a Vector DB

Vector Database

In this small demo/poc, I am leveraging Chroma DB. Chroma DB is a free, simple to use open-source vector/embedding database.

Model

Use the OpenAI Models you're interested, obviously considering the cost and limitations.

Embedding Model

Here are list of OpenAI Embedding Models model that generate embeddings. An embedding is a vector (list) of floating point numbers. The distance between two vectors measures their relatedness. Small distances suggest high relatedness and large distances suggest low relatedness.