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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.

Learning More about Enterprise QnA

Enterprise QnA is built on a pattern the AI community calls "Retrieval-Augmented Generation" (RAG). In addition to this repository having a reference architecture on how to implement this pattern on Azure, here are resources to familiarize yourself with the concepts in RAG, and samples to learn each underlying product's APIs:

IMPORTANT NOTE (OpenAI generated)

We have made some changes to the data format in the latest update of this repo. <br>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.

Use the Repo with Chat based deployment (gpt-35-turbo or gpt-4-32k or gpt-4)

By default, the repo uses an Instruction based model (like text-davinci-003) for QnA and Chat experience.
If you want to use a Chat based deployment (gpt-35-turbo or gpt-4-32k or gpt-4), please change the environment variables as described here

Running this repo

You have multiple options to run the code:

• Deploy on Azure (WebApp + Batch Processing) with Azure Cognitive Search
• Deploy on Azure (WebApp + Azure Cache for Redis + Batch Processing)
• Deploy on Azure/Azure China (WebApp + Redis Stack + Batch Processing)
• Deploy on Azure/Azure China (WebApp + Azure PostgreSQL + 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 + Batch Processing) with Azure Cognitive Search

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)

Signing up for Vector Search Private Preview in Azure Cognitive Search

Azure Cognitive Search supports searching using pure vectors, pure text, or in hybrid mode where both are combined. For the vector-based cases, you'll need to sign up for Vector Search Private Preview. To sign up, please fill in this form: https://aka.ms/VectorSearchSignUp.

Preview functionality is provided under Supplemental Terms of Use, without a service level agreement, and isn't recommended for production workloads.

Deploy on Azure (WebApp + Azure Cache for Redis Enterprise + Batch Processing)

Click on the Deploy to Azure button to automatically deploy a template on Azure by with the resources needed to run this example. This option will provision an instance of Azure Cache for Redis with RediSearch installed to store vectors and perform the similiarity search.

Please be aware that you still 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
• an existing Translator Resource
• Azure marketplace access. (Azure Cache for Redis Enterprise uses the marketplace for IP billing)

You will add the endpoint and access key information for these resources when deploying the template.

Deploy on Azure/Azure China (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)

Deploy on Azure/Azure China (WebApp + Azure PostgreSQL + Batch Processing)

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

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

Use the QnA API from the backend

You can use a QnA API on your data exposed by the Azure Function for Batch Processing.

POST https://YOUR_BATCH_PROCESS_AZURE_FUNCTION_URL/api/apiQnA
Body:
    question: str
    history: (str,str) -- OPTIONAL
    custom_prompt: str -- OPTIONAL
    custom_temperature: float --OPTIONAL

Return:
{'context': 'Introduction to Azure Cognitive Search - Azure Cognitive Search '
            '(formerly known as "Azure Search") is a cloud search service that '
            'gives developers infrastructure, APIs, and tools for building a '
            'rich search experience over private, heterogeneous content in '
            'web, mobile, and enterprise applications...'
            '...'
            '...',

 'question': 'What is ACS?',

 'response': 'ACS stands for Azure Cognitive Search, which is a cloud search service'
             'that provides infrastructure, APIs, and tools for building a rich search experience'
             'over private, heterogeneous content in web, mobile, and enterprise applications...'
             '...'
             '...',
             
 'sources': '[https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)'}

Call the API with no history for QnA mode

import requests

r = requests.post('http://http://YOUR_BATCH_PROCESS_AZURE_FUNCTION_URL/api/apiQnA', json={
    'question': 'What is the capital of Italy?'
    })

Call the API with history for Chat mode

r = requests.post('http://YOUR_BATCH_PROCESS_AZURE_FUNCTION_URL/api/apiQnA', json={
    'question': 'can I use python SDK?',
    'history': [
        ("what's ACS?", 
        'ACS stands for Azure Cognitive Search, which is a cloud search service that provides infrastructure, APIs, and tools for building a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. It includes a search engine for full-text search, rich indexing with lexical analysis and AI enrichment for content extraction and transformation, rich query syntax for text search, fuzzy search, autocomplete, geo-search, and more. ACS can be created, loaded, and queried using the portal, REST API, .NET SDK, or another SDK. It also includes data integration at the indexing layer, AI and machine learning integration with Azure Cognitive Services, and security integration with Azure Active Directory and Azure Private Link integration.'
        )
        ]
    })

Environment variables

Here is the explanation of the parameters:

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

DISCLAIMER

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