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This sample shows how to build an AI chat experience with Retrieval-Augmented Generation (RAG) using LangChain4J and OpenAI language models. The application is hosted on Azure Static Web Apps and Azure Container Apps, with Qdrant as the vector database. You can use it as a starting point for building more complex AI applications.
Important
👉 Follow the full-length workshop to learn how we built this sample and how you can run and deploy it.
This sample uses Quarkus to create a Java service that leverage OpenAI SDK and LangChain4j to build a chatbot that will answer questions based on a corpus of documents, with a website to interact with the API.
This project is structured as monorepo, all packages source code is located under the src/ folder.
Here's the architecture of the application:
You can use GitHub Codespaces to work on this project directly from your browser:
You can also use Docker and the Dev Containers extension for VS Code to work locally using a ready-to-use dev environment:
If you prefer to install all the tools locally, you can follow these setup instructions.
Tip
You can run this sample entirely locally without any cost using Ollama. Follow the instructions above to setup the tools locally to get started.
- Azure account. If you're new to Azure, get an Azure account for free to get free Azure credits to get started. If you're a student, you can also get free credits with Azure for Students.
- Azure subscription with access enabled for the Azure OpenAI Service. You can request access with this form.
- Azure account permissions:
- Your Azure account must have
Microsoft.Authorization/roleAssignments/writepermissions, such as Role Based Access Control Administrator, User Access Administrator, or Owner. If you don't have subscription-level permissions, you must be granted RBAC for an existing resource group and deploy to that existing group. - Your Azure account also needs
Microsoft.Resources/deployments/writepermissions on the subscription level.
- Your Azure account must have
Open a terminal and run the following commands:
azd auth login
azd upThis commands will first ask you to log in into Azure. Then it will provison the Azure resources, package the services and deploy them to Azure.
To clean up all the Azure resources created by this sample:
- Run
azd down --purge - When asked if you are sure you want to continue, enter
y
The resource group and all the resources will be deleted.
This sample and workshop exists in different versions:
Here are some resources to learn more about the technologies used in this sample:
You can also find more Azure AI samples here.
This sample/workshop was based on the enterprise-ready sample ChatGPT + Enterprise data with Azure OpenAI and AI Search:
If you want to go further with more advanced use-cases, authentication, history and more, you should check it out!
This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.
When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.