Introduction by ChatGpt
In the realm of AI innovation, Azure's integration with OpenAI models offers unparalleled potential for transformative solutions. By leveraging the power of OpenAI's cutting-edge technology, users can now bring their own data into the fold, empowering them to tailor AI models to their unique needs and objectives. One particularly promising tool in this arsenal is RAG (Retrieval-Augmented Generation), which marries the capabilities of retrieval-based and generative models to produce contextually rich and coherent outputs. Imagine a world where information retrieval seamlessly fuels the creative process, generating insightful, nuanced content with unmatched accuracy and relevance. Through this demonstration, we aim to showcase the boundless possibilities of this synergistic approach, illustrating how it can revolutionize industries ranging from content creation to customer service, ushering in a new era of AI-enabled innovation and productivity. Join us as we embark on a journey to unlock the full potential of AI, where imagination meets data-driven precision to shape a brighter future for all.
- deploy model : gpt4
- deploy model : text-embedding-ada-002
- using template : /azure-infra !! Manually for now !
- Navigate to Azure AI Studio > Chat > Add Data
- Using upload file option(data/data_en.pdf)
- Select index (pre-created)
- select text-embedding-ada-002, enable vectorization
spring.ai.azure.openai.api-key=<replace with key>
spring.ai.azure.openai.endpoint=https://xxxxx.openai.azure.com/
spring.ai.azure.openai.chat.model=gpt4
app.azure.cognitive.search.index=<replace with index name>
app.azure.cognitive.search.endpoint=https://xxxxx.search.windows.net
app.azure.cognitive.search.key=<replace with key>
app.azue.cognitive.search.user.provided.data.vectorizationSource=text-embedding-ada-002
by ChatGpt
Here's a step-by-step deployment guide for your multi-service application using Docker Compose:
- Docker installed on your machine.
- Docker Compose installed on your machine.
- Access to the Docker images specified in your Docker Compose file (demoworkeracr.azurecr.io/azure-openai-demo-frontend, demoworkeracr.azurecr.io/azure-openai-demo-backend, demoworkeracr.azurecr.io/azure-openai-demo-nginx).
Clone the repository containing your Docker Compose file and application source code if you haven't already done so.
Build Docker Images: Navigate to the root directory of your project containing the Docker Compose file.
cd your_project_directory
Then, run the following command to build the Docker images defined in the Docker Compose file:
docker-compose build
Run the Application: Once the images are built, you can start the application using Docker Compose:
docker-compose up -d
This command will start all the services defined in the Docker Compose file in detached mode.
- The frontend application will be accessible at http://localhost:3000.
- The backend application will be accessible at http://localhost:8080.
- The NGINX service will be accessible at http://localhost.
To monitor the logs of all services, you can use:
docker-compose logs -f
If there are any issues, you can troubleshoot by inspecting the logs for each service. Stopping the Application: To stop the application and shut down the services, run:
docker-compose down
If you need to customize the NGINX configuration, you can edit the nginx.conf file in the ./nginx directory before building the NGINX image.
- Ensure that your Docker images and dependencies are up to date to mitigate security vulnerabilities.
- Secure sensitive data such as credentials and API keys.
- Implement proper network security configurations.
You can scale individual services by running multiple containers of the same service using Docker Compose scale command.
- Introduce authentication
- Add new AzureOpenAI API capability
- Introduce more use cases
- Automate infrastructure provisioning
- Automate deployemnt using CI/CD