Food QnA Chatbot : Help Answer Food Related Questions from Your Own Cookbook

Set Up Local Virtual Environment

• Step 1: Install Miniconda on MacOS, see instruction here.
• Step 2: Create a default conda env with Python 3.9: conda create --name food_qna_app python=3.9 -y
• Step 3: Activate the conda env created above: conda activate food_qna_app
• Step 4: Install first set of required libraries in the conda env:

pip3 install -r requirements.txt

• Step 5: Deactivate the conda env when you are done (note: you need activate this virtual env to work on the app)

conda deactivate 

Set Up Open AI Secrete for Local Dev

OPENAI_API_KEY will be needed when calling the OpenAI API endpoint for generating embeddings for the documents, so do recommend exporting OPENAI_API_KEY as an enviroment variable on your local dev machine; also, we will need create a secrets.toml file and add the OPENAI_API_KEY there, so the streamlit app can pick up the API key when send requests to OpenAI endpoint during runtime.

• Step 1: Export OPENAI_API_KEY as an enviroment variable on your local dev machine
• Step 2: Create .streamlit directory under the root repo

cd food_qna_chatbot_demo 
mkdir .streamlit

• Step 3: Create secrets.toml file under .streamlit directory and add your OPENAI_API_KEY there

OPENAI_API_KEY=<YOUR_OPENAI_API_KEY_HERE>

• Step 4: To reduce the risk of accidentally committing your secrets to your repo, add .streamlit to your .gitignore file.

echo ".streamlit/" >> .gitignore

Set up the Redis Database on your local dev machine

• Step 1: Install Docker Desktop on Mac, and start the docker desktop.
• Step 2: We're going to use Redis as our database for both document contents and the vector embeddings. You will need the full Redis Stack to enable use of Redisearch, which is the module that allows semantic search - more detail is in the docs for Redis Stack. Run the following command in your terminal to start the docker container:

docker run -d --name redis-stack -p 127.0.0.1:6379:6379 -p 8001:8001 redis/redis-stack:latest

• Step 3: Initiate a Redis connection and create a Hierarchical Navigable Small World (HNSW) index for semantic search using a recipe book which can be found under the directory cook_book_data

python build_knowledge_base.py

If the recipe book is indexed succefully into the DB, you should expect the following info printed out in the console:

===
Number of documents indexed in the DB: 144

Run the APP

In your terminal, run the App by

streamlit run food_qna_app.py

You can start asking questions related to food preparation and cooking, and also some follow up questions. See screenshot below.

Original Content	Chat History - first turn	Chat History - follow up