Azure OpenAI Embeddings QnA

A simple web application for a OpenAI-enabled document search. This repo uses Azure OpenAI Service for creating embeddings vectors from documents. For answering the question of a user, it retrieves the most relevant document and then uses GPT-3 to extract the matching answer for the question.

Running this repo

You have multiple options to run the code:

• Deploy on Azure (WebApp + Redis Stack)
• Run everything locally in Docker (WebApp + Redis Stack)
• Run everything locally in Python with Conda (WebApp only)
• Run everything locally in Python with venv
• Run WebApp locally in Docker against an existing Redis deployment

Deploy on Azure (WebApp + Redis Stack)

Click on the Deploy to Azure button and configure your settings in the Azure Portal as described in the Environment variables section.

Please be aware that you need:

• an existing OpenAI with deployed models (instruction models e.g. text-davinci-003, and embeddings models e.g. text-search-davinci-doc-001 and text-search-davinci-query-001)
• an existing Form Recognizer Resource (OPTIONAL - if you want to extract text out of documents)
• an existing Translator Resource (OPTIONAL - if you want to translate documents)

Run everything locally in Docker (WebApp + Redis Stack)

First, clone the repo:

git clone https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna
cd azure-open-ai-embeddings-qna

Next, configure your .env as described in Environment variables:

cp .env.template .env
vi .env # or use whatever you feel comfortable with

Finally run the application:

docker compose up

Open your browser at http://localhost:8080

This will spin up two Docker containers:

• The WebApp itself
• Redis Stack for storing the embeddings

Run everything locally in Python with Conda (WebApp only)

This requires Redis running somewhere and expects that you've setup .env as described above. In this case, point REDIS_ADDRESS to your Redis deployment.

You can run a local Redis instance via:

 docker run -p 6379:6379 redis/redis-stack-server:latest

Create conda environment for Python:

conda env create -f code/environment.yml
conda activate openai-qna-env

Configure your .env as described in as described in Environment variables

Run WebApp:

cd code
streamlit run OpenAI_Queries.py

Run everything locally in Python with venv

This requires Redis running somewhere and expects that you've setup .env as described above. In this case, point REDIS_ADDRESS to your Redis deployment.

You can run a local Redis instance via:

 docker run -p 6379:6379 redis/redis-stack-server:latest

Please ensure you have Python 3.9+ installed.

Create venv environment for Python:

python -m venv .venv
.venv\Scripts\activate

Install PIP Requirements

pip install -r code\requirements.txt

Configure your .env as described in as described in Environment variables

Run the WebApp

cd code
streamlit run OpenAI_Queries.py

Run WebApp locally in Docker against an existing Redis deployment

Option 1 - Run the prebuilt Docker image

Configure your .env as described in as described in Environment variables

Then run:

docker run -e .env -p 8080:80 fruocco/oai-embeddings:latest

Option 2 - Build the Docker image yourself

Configure your .env as described in as described in Environment variables

docker build . -t your_docker_registry/your_docker_image:your_tag
docker run -e .env -p 8080:80 your_docker_registry/your_docker_image:your_tag

Environment variables

Here is the explanation of the parameters:

App Setting	Value	Note
OPENAI_ENGINES	davinci-002,text-curie-001,text-babbage-001,text-ada-001	Instruction engines deployed in your Azure OpenAI resource
OPENAI_EMBEDDINGS_ENGINE_DOC	text-search-davinci-doc-001	Embedding engine for documents deployed in your Azure OpenAI resource
OPENAI_EMBEDDINGS_ENGINE_QUERY	text-search-davinci-query-001	Embedding engine for query deployed in your Azure OpenAI resource
OPENAI_API_BASE	https://YOUR_AZURE_OPENAI_RESOURCE.openai.azure.com/	Your Azure OpenAI Resource name. Get it in the Azure Portal
OPENAI_API_KEY	YOUR_AZURE_OPENAI_KEY	Your Azure OpenAI API Key. Get it in the Azure Portal
REDIS_ADDRESS	api	URL for Redis Stack: "api" for docker compose
REDIS_PASSWORD	redis-stack-password	OPTIONAL - Password for your Redis Stack
REDIS_ARGS	--requirepass redis-stack-password	OPTIONAL - Password for your Redis Stack

Optional parameters for additional features (e.g. document text extraction with OCR):

App Setting	Value	Note
BLOB_ACCOUNT_NAME	YOUR_AZURE_BLOB_STORAGE_ACCOUNT_NAME	OPTIONAL - Get it in the Azure Portal if you want to use the document extraction feature
BLOB_ACCOUNT_KEY	YOUR_AZURE_BLOB_STORAGE_ACCOUNT_KEY	OPTIONAL - Get it in the Azure Portalif you want to use document extraction feature
BLOB_CONTAINER_NAME	YOUR_AZURE_BLOB_STORAGE_CONTAINER_NAME	OPTIONAL - Get it in the Azure Portal if you want to use document extraction feature
FORM_RECOGNIZER_ENDPOINT	YOUR_AZURE_FORM_RECOGNIZER_ENDPOINT	OPTIONAL - Get it in the Azure Portal if you want to use document extraction feature
FORM_RECOGNIZER_KEY	YOUR_AZURE_FORM_RECOGNIZER_KEY	OPTIONAL - Get it in the Azure Portal if you want to use document extraction feature
TRANSLATE_ENDPOINT	YOUR_AZURE_TRANSLATE_ENDPOINT	OPTIONAL - Get it in the Azure Portal if you want to use translation feature
TRANSLATE_KEY	YOUR_TRANSLATE_KEY	OPTIONAL - Get it in the Azure Portal if you want to use translation feature
TRANSLATE_REGION	YOUR_TRANSLATE_REGION	OPTIONAL - Get it in the Azure Portal if you want to use translation feature