This project creates a web-based chat application with an API backend that can use OpenAI chat models to answer questions about the items in a PostgreSQL database table. The frontend is built with React and FluentUI, while the backend is written with Python and FastAPI.
This project is designed for deployment to Azure using the Azure Developer CLI, hosting the app on Azure Container Apps, the database in Azure PostgreSQL Flexible Server, and the models in Azure OpenAI.
This project provides the following features:
- Hybrid search on the PostgreSQL database table, using the pgvector extension for the vector search plus full text search, combining the results using RRF (Reciprocal Rank Fusion).
- OpenAI function calling to optionally convert user queries into query filter conditions, such as turning "Climbing gear cheaper than $30?" into "WHERE price < 30".
- Conversion of user queries into vectors using the OpenAI embedding API.
You have a few options for getting started with this template. The quickest way to get started is GitHub Codespaces, since it will setup all the tools for you, but you can also set it up locally.
You can run this template virtually by using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:
-
Open the template (this may take several minutes):
-
Open a terminal window
-
Continue with the deployment steps
A related option is VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:
-
Start Docker Desktop (install it if not already installed)
-
Open the project:
-
In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
-
Continue with the deployment steps
-
Make sure the following tools are installed:
-
Download the project code:
azd init -t rag-postgres-openai-python
-
Open the project folder
-
Install required Python packages and backend application:
pip install -r requirements-dev.txt pip install -e src/backend
-
Continue with the deployment steps
Once you've opened the project in Codespaces, Dev Containers, or locally, you can deploy it to Azure.
-
Sign in to your Azure account:
azd auth login
If you have any issues with that command, you may also want to try
azd auth login --use-device-code
. -
Create a new azd environment:
azd env new
This will create a folder under
.azure/
in your project to store the configuration for this deployment. You may have multiple azd environments if desired. -
(Optional) If you would like to customize the deployment to use existing Azure resources, you can set the values now.
-
Provision the resources and deploy the code:
azd up
You will be asked to select two locations, first a region for most of the resources (Container Apps, PostgreSQL), then a region specifically for the Azure OpenAI models. This project uses the gpt-3.5-turbo (version 0125) and text-embedding-ada-002 models which may not be available in all Azure regions. Check for up-to-date region availability and select a region accordingly.
Since the local app uses OpenAI models, you should first deploy it for the optimal experience.
-
Copy
.env.sample
into a.env
file. -
To use Azure OpenAI, set
OPENAI_CHAT_HOST
andOPENAI_EMBED_HOST
to "azure". Then fill in the values ofAZURE_OPENAI_ENDPOINT
andAZURE_OPENAI_CHAT_DEPLOYMENT
based on the deployed values. You can display the values using this command:azd env get-values
-
To use OpenAI.com OpenAI, set
OPENAI_CHAT_HOST
andOPENAI_EMBED_HOST
to "openai". Then fill in the value forOPENAICOM_KEY
. -
To use Ollama, set
OPENAI_CHAT_HOST
to "ollama". Then update the values forOLLAMA_ENDPOINT
andOLLAMA_CHAT_MODEL
to match your local setup and model. Note that most Ollama models are not compatible with the "Advanced flow", due to the need for function calling support, so you'll need to disable that in Developer Settings in the UI. In addition, the database rows are embedded using the default OpenAI embedding model, so you can't search them using an Ollama embedding model. You can either choose to setOPENAI_EMBED_HOST
to "azure" or "openai", or turn off vector search in Developer Settings.
-
Run these commands to install the web app as a local package (named
fastapi_app
), set up the local database, and seed it with test data:python3 -m pip install -e src/backend python ./src/backend/fastapi_app/setup_postgres_database.py python ./src/backend/fastapi_app/setup_postgres_seeddata.py
-
Build the frontend:
cd src/frontend npm install npm run build cd ../../
There must be an initial build of static assets before running the backend, since the backend serves static files from the
src/static
directory. -
Run the FastAPI backend (with hot reloading). This should be run from the root of the project:
python3 -m uvicorn fastapi_app:create_app --factory --reload
Or you can run "Backend" in the VS Code Run & Debug menu.
-
Run the frontend (with hot reloading):
cd src/frontend npm run dev
Or you can run "Frontend" or "Frontend & Backend" in the VS Code Run & Debug menu.
-
Open the browser at
http://localhost:5173/
and you will see the frontend.
Pricing may vary per region and usage. Exact costs cannot be estimated. You may try the Azure pricing calculator for the resources below:
- Azure Container Apps: Pay-as-you-go tier. Costs based on vCPU and memory used. Pricing
- Azure OpenAI: Standard tier, GPT and Ada models. Pricing per 1K tokens used, and at least 1K tokens are used per question. Pricing
- Azure PostgreSQL Flexible Server: Burstable Tier with 1 CPU core, 32GB storage. Pricing is hourly. Pricing
- Azure Monitor: Pay-as-you-go tier. Costs based on data ingested. Pricing
This template uses Managed Identity for authenticating to the Azure services used (Azure OpenAI, Azure PostgreSQL Flexible Server).
Additionally, we have added a GitHub Action that scans the infrastructure-as-code files and generates a report containing any detected issues. To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.
Further documentation is available in the docs/
folder:
Please post in the issue tracker with any questions or issues.