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.

IMPORTANT NOTE (OpenAI generated)

We have made some changes to the data format in the latest update of this repo.
The new format is more efficient and compatible with the latest standards and libraries. However, we understand that some of you may have existing applications that rely on the previous format and may not be able to migrate to the new one immediately.

Therefore, we have provided a way for you to continue using the previous format in a running application. All you need to do is to set your web application tag to fruocco/oai-embeddings:2023-03-27_25. This will ensure that your application will use the data format that was available on March 27, 2023. We strongly recommend that you update your applications to use the new format as soon as possible.

If you want to move to the new format, please go to:

• "Add Document" -> "Add documents in Batch" and click on "Convert all files and add embeddings" to reprocess your documents.

Running this repo

You have multiple options to run the code:

• Deploy on Azure (WebApp + Redis Stack + Batch Processing)
• Run everything locally in Docker (WebApp + Redis Stack + Batch Processing)
• 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 + Batch Processing)

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 Azure OpenAI resource with models deployments (instruction models e.g. text-davinci-003, and embeddings models e.g. text-embedding-ada-002)
• 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 + Batch Processing)

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 three Docker containers:

• The WebApp itself
• Redis Stack for storing the embeddings
• Batch Processing Azure Function

NOTE: Please note that the Batch Processing Azure Function uses an Azure Storage Account for queuing the documents to process. Please create a Queue named "doc-processing" in the account used for the "AzureWebJobsStorage" env setting.

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

You can run a local Batch Processing Azure Function:

 docker run -p 7071:80 fruocco/oai-batch: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

You can run a local Batch Processing Azure Function:

 docker run -p 7071:80 fruocco/oai-batch: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 --env-file .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 . -f Dockerfile -t your_docker_registry/your_docker_image:your_tag
docker run --env-file .env -p 8080:80 your_docker_registry/your_docker_image:your_tag

Note: You can use

• WebApp.Dockerfile to build the Web Application
• BatchProcess.Dockerfile to build the Azure Function for Batch Processing

Environment variables

Here is the explanation of the parameters:

App Setting	Value	Note
OPENAI_ENGINE	text-davinci-003	Instruction engine deployed in your Azure OpenAI resource
OPENAI_EMBEDDINGS_ENGINE_DOC	text-embedding-ada-002	Embedding engine for documents deployed in your Azure OpenAI resource
OPENAI_EMBEDDINGS_ENGINE_QUERY	text-embedding-ada-002	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_PORT	6379	Port for Redis
REDIS_PASSWORD	redis-stack-password	OPTIONAL - Password for your Redis Stack
REDIS_ARGS	--requirepass redis-stack-password	OPTIONAL - Password for your Redis Stack
REDIS_PROTOCOL	redis://
CHUNK_SIZE	500	OPTIONAL: Chunk size for splitting long documents in multiple subdocs. Default value: 500
CHUNK_OVERLAP	100	OPTIONAL: Overlap between chunks for document splitting. Default: 100
CONVERT_ADD_EMBEDDINGS_URL	http://batch/api/BatchStartProcessing	URL for Batch processing Function: "http://batch/api/BatchStartProcessing" for docker compose
AzureWebJobsStorage	AZURE_BLOB_STORAGE_CONNECTION_STRING FOR_AZURE_FUNCTION_EXECUTION	Azure Blob Storage Connection string for Azure Function - Batch Processing

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
PAGES_PER_EMBEDDINGS	Number of pages for embeddings creation. Keep in mind you should have less than 3K token for each embedding.	Default: A new embedding is created every 2 pages.
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

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