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baabd413eb
* docs update WIP * getting started guide updated * update getting started guide * clarify github app creation * add webhook secret to getting started guide and gh-flow app * restructure Readme * fix the Organization assumption * add mermaid diagram of the event flow * devtunnel feature to devcontainer * throw all the exceptions and add the history to the prompt * Update github-flow.md * update readme
98 lines
4.5 KiB
Markdown
98 lines
4.5 KiB
Markdown
## Prerequisites
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- Access to gpt3.5-turbo or preferably gpt4 - [Get access here](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview#how-do-i-get-access-to-azure-openai)
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- [Setup a Github app](#how-do-i-setup-the-github-app)
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- [Install the Github app](https://docs.github.com/en/apps/using-github-apps/installing-your-own-github-app)
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- [Provision the azure resources](#how-do-I-deploy-the-azure-bits)
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- [Create labels for the dev team skills](#which-labels-should-i-create)
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### How do I setup the Github app?
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- [Register a Github app](https://docs.github.com/en/apps/creating-github-apps/registering-a-github-app/registering-a-github-app), with the options listed above
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- Setup the following permissions
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- Repository
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- Contents - read and write
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- Issues - read and write
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- Metadata - read only
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- Pull requests - read and write
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- Subscribe to the following events:
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- Issues
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- Issue comment
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- Allow this app to be installed by any user or organization
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- Add a dummy value for the webhook url, we'll come back to this setting
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- Enter a webhook secret, which you'll need later on when filling in the `WebhookSecret` property in the `appsettings.json` file
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- After the app is created, generate a private key, we'll use it later for authentication to Github from the app
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### Which labels should I create?
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In order for us to know which skill and persona we need to talk with, we are using Labels in Github Issues
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The default bunch of skills and personnas are as follows:
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- PM.Readme
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- Do.It
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- DevLead.Plan
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- Developer.Implement
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Once you start adding your own skills, just remember to add the corresponding Label!
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## How do I run this locally?
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Codespaces are preset for this repo.
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Start by creating a codespace
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and fill in the `appsettings.json` file, located in the `src\apps\gh-flow` folder.
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There is a `appsettings.local.template.json` which you can copy and fill in, containing comments on the different config values.
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In the Explorer tab in VS Code, find the Solution explorer, right click on the `gh-flow` project and click Debug -> Start new instance
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We'll need to expose the running application to the GH App webhooks, for example using [DevTunnels](https://learn.microsoft.com/en-us/azure/developer/dev-tunnels/overview), but any tool like ngrok can also work.
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The following commands will create a persistent tunnel, so we need to only do this once:
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```bash
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TUNNEL_NAME=_name_yout_tunnel_here_
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devtunnel user login
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devtunnel create -a $TUNNEL_NAME
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devtunnel port create -p 5244 $TUNNEL_NAME
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```
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and once we have the tunnel created we can just start forwarding with the following command:
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```bash
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devtunnel host $TUNNEL_NAME
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```
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Copy the local address (it will look something like https://yout_tunnel_name.euw.devtunnels.ms) and append `/api/github/webhooks` at the end. Using this value, update the Github App's webhook URL and you are ready to go!
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Before you go and have the best of times, there is one last thing left to do [load the WAF into the vector DB](#load-the-waf-into-qdrant)
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Also, since this project is relying on Orleans for the Agents implementation, there is a [dashboard](https://github.com/OrleansContrib/OrleansDashboard) available at https://yout_tunnel_name.euw.devtunnels.ms/dashboard, with useful metrics and stats related to the running Agents.
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## How do I deploy the azure bits?
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This repo is setup to use [azd](https://learn.microsoft.com/en-us/azure/developer/azure-developer-cli/overview) to work with the Azure bits. `azd` is installed in the codespace.
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Let's start by logging in to Azure using
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```bash
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azd auth login
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```
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After we've logged in, we need to create a new environment provision the azure bits.
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```bash
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ENVIRONMENT=_name_of_your_env
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azd env new $ENVIRONMENT
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azd provision -e $ENVIRONMENT
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```
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After the provisioning is done, you can inspect the outputs with the following command
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```bash
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azd env get-values -e dev
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```
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As the last step, we also need to [load the WAF into the vector DB](#load-the-waf-into-qdrant)
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### Load the WAF into Qdrant.
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If you are running the app locally, we have [Qdrant](https://qdrant.tech/) setup in the Codespace and if you are running in Azure, Qdrant is deployed to ACA.
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The loader is a project in the `src\apps` folder, called `seed-memory`. We need to fill in the `appsettings.json` file in the `config` folder with the OpenAI details and the Qdrant endpoint, then just run the loader with `dotnet run` and you are ready to go. |