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README.md

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* [Features](#features)
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* [Getting started](#getting-started)
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* [GitHub Codespaces](#github-codespaces)
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* [VS Code Dev Containers](#vs-code-dev-containers)
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* [Local environment](#local-environment)
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* [GitHub Codespaces](#github-codespaces)
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* [VS Code Dev Containers](#vs-code-dev-containers)
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* [Local environment](#local-environment)
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* [Deployment](#deployment)
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* [Local development](#local-development)
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* [Costs](#costs)
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* OpenAI function calling to optionally convert user queries into query filter conditions, such as turning "Climbing gear cheaper than $30?" into "WHERE price < 30".
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* Conversion of user queries into vectors using the OpenAI embedding API.
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![Screenshot of chat app with question about climbing gear](docs/screenshot_chat.png)
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![Screenshot of chat app with question about climbing gear](docs/images/screenshot_chat.png)
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## Architecture diagram
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The deployed app uses a user-assigned managed identity to authenticate to Azure services, and stores logs in Log Analytics.
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![Architecture diagram: Azure Container Apps, Azure Container Registry, Managed Identity, Azure OpenAI, Azure Database for PostgreSQL](docs/images/architecture.png)
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## Getting started
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For GitHub Codespaces users, if the previous command fails, try:
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```shell
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azd auth login --use-device-code
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azd auth login --use-device-code
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```
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2. Create a new azd environment:
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3. (Optional) If you would like to customize the deployment to [use existing Azure resources](docs/deploy_existing.md), you can set the values now.
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3. Provision the resources and deploy the code:
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4. Provision the resources and deploy the code:
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```shell
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azd up
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Further documentation is available in the `docs/` folder:
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* [Understanding the RAG flow](docs/rag_flow.md)
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* [Customizing the data](docs/customize_data.md)
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* [Deploying with existing resources](docs/deploy_existing.md)
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* [Monitoring with Azure Monitor](docs/monitoring.md)
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## Resources
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* [RAGHack livestream: Building RAG with PostgreSQL](https://www.youtube.com/watch?v=Dk65oQjYAfo)
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* [RAG chat with Azure AI Search + Python](https://github.com/Azure-Samples/azure-search-openai-demo/)
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* [Develop Python apps that use Azure AI services](https://learn.microsoft.com/azure/developer/python/azure-ai-for-python-developers)

docs/customize_data.md

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# Customizing the data
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# RAG on PostgreSQL: Customizing the data
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This guide shows you how to bring in a table with a different schema than the sample table.
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## Add the seed data to the database
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Now that you have the new table schema and `seed_data.json` populated with embeddings, you can add the seed data to the database:
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```shell
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python src/backend/fastapi_app/setup_postgres_seeddata.py

docs/deploy_existing.md

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# Deploying with existing Azure resources
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# RAG on PostgreSQL: Deploying with existing Azure resources
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If you already have existing Azure resources, or if you want to specify the exact name of new Azure Resource, you can do so by setting `azd` environment values.
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You should set these values before running `azd up`. Once you've set them, return to the [deployment steps](../README.md#deployment).
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### Openai.com OpenAI account
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## Openai.com OpenAI account
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1. Run `azd env set DEPLOY_AZURE_OPENAI false`
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1. Run `azd env set OPENAI_CHAT_HOST openaicom`

docs/images/advanced_hybrid_flow.png

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docs/images/simple_hybrid_flow.png

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docs/images/simple_rag_thoughts.png

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