Generic deployment docs for Go (#636)

docs-go/_guides.yaml

@@ -33,6 +33,8 @@ toc:
   - heading: Deploying AI workflows
   - title: Deploy with Cloud Run
     path: /docs/genkit-go/cloud-run
+  - title: Deploy with any hosting service
+    path: /docs/genkit-go/deploy
 
   # - heading: Writing plugins
   # - title: Overview

docs-go/deploy.md

@@ -0,0 +1,139 @@
+# Deploy flows to any app hosting platform
+
+You can deploy Firebase Genkit flows as web services using any service that can
+host a Go binary.
+This page, as an example, walks you through the general process of deploying the
+default sample flow, and points out where you must take provider-specific
+actions.
+
+1.  Create a directory for the Genkit sample project:
+
+    ```posix-terminal
+    mkdir -p ~/tmp/genkit-cloud-project
+
+    cd ~/tmp/genkit-cloud-project
+    ```
+
+    If you're going to use an IDE, open it to this directory.
+
+1.  Initialize a Go module in your project directory:
+
+    ```posix-terminal
+    go mod init example/cloudrun
+    ```
+
+1.  Initialize Genkit in your project:
+
+    ```posix-terminal
+    genkit init
+    ```
+
+    Select the model provider you want to use.
+
+    Accept the defaults for the remaining prompts. The `genkit` tool will create
+    a sample source file to get you started developing your own AI flows.
+    For the rest of this tutorial, however, you'll just deploy the sample flow.
+
+1.  Edit the sample file (`main.go` or `genkit.go`) to explicitly specify the
+    port the flow server should listen on:
+
+    ```golang
+    {% includecode github_path="firebase/genkit/go/internal/doc-snippets/flows.go" region_tag="init" adjust_indentation="auto" %}
+    ```
+
+    If your provider requires you to listen on a specific port, be sure to
+    configure Genkit accordingly.
+
+1.  Implement some form of authentication and authorization to gate access to
+    the flows you plan to deploy.
+
+    Because most generative AI services are metered, you most likely do not want
+    to allow open access to any endpoints that call them. Some hosting services
+    provide an authentication layer as a frontend to apps deployed on them,
+    which you can use for this purpose.
+
+1.  Make API credentials available to your deployed function. Do one of the
+    following, depending on the model provider you chose:
+
+    - {Gemini (Google AI)}
+
+      1.  Make sure Google AI is
+          [available in your region](https://ai.google.dev/available_regions).
+
+      1.  [Generate an API key](https://aistudio.google.com/app/apikey) for the
+          Gemini API using Google AI Studio.
+
+      1.  Make the API key available in the deployed environment.
+
+          Most app hosts provide some system for securely handling secrets such
+          as API keys. Often, these secrets are available to your app in the
+          form of environment variables. If you can assign your API key to the
+          `GOOGLE_GENAI_API_KEY` variable, Genkit will use it automatically.
+          Otherwise, you need to modify the `googleai.Init()` call to explicitly
+          set the key. (But don't embed the key directly in code! Use the secret
+          management facilities provided by your hosting provider.)
+
+    - {Gemini (Vertex AI)}
+
+      1.  In the Cloud console,
+          [Enable the Vertex AI API](https://console.cloud.google.com/apis/library/aiplatform.googleapis.com?project=_)
+          for your project.
+
+      1.  On the [IAM](https://console.cloud.google.com/iam-admin/iam?project=_)
+          page, create a service account for accessing the Vertex AI API if you
+          don't alreacy have one.
+
+          Grant the account the **Vertex AI User** role.
+
+      1.  [Set up Application Default Credentials](https://cloud.google.com/docs/authentication/provide-credentials-adc#on-prem)
+          in your hosting environment.
+
+      1.  Configure the plugin with your Google Cloud project ID and the Vertex
+          AI API location you want to use. You can do so either by setting the
+          `GCLOUD_PROJECT` and `GCLOUD_LOCATION` environment variables in your
+          hosting environment, or in your `vertexai.Init()` call.
+
+    The only secret you need to set up for this tutorial is for the model
+    provider, but in general, you must do something similar for each service
+    your flow uses.
+
+1.  **Optional**: Try your flow in the developer UI:
+
+    1.  Set up your local environment for the model provider you chose:
+
+        - {Gemini (Google AI)}
+
+          ```posix-terminal
+          export GOOGLE_GENAI_API_KEY=<your API key>
+          ```
+
+        - {Gemini (Vertex AI)}
+
+          ```posix-terminal
+          export GCLOUD_PROJECT=<your project ID>
+
+          export GCLOUD_LOCATION=us-central1
+
+          gcloud auth application-default login
+          ```
+
+    1.  Start the UI:
+
+        ```posix-terminal
+        genkit start
+        ```
+
+    1.  In the developer UI (http://localhost:4000/), run the flow:
+
+        1.  Click **menuSuggestionFlow**.
+
+        1.  On the **Input JSON** tab, provide a subject for the model:
+
+            ```json
+            "banana"
+            ```
+
+        1.  Click **Run**.
+
+1.  If everything's working as expected so far, you can build and deploy the
+    flow using your provider's tools.