Cloud Run docs for Go

docs-go/Makefile

@@ -1,10 +1,9 @@
 WEAVE=$(HOME)/go/bin/weave 
 
-all: $(WEAVE) get-started-go.md flows.md models.md prompts.md dotprompt.md pgvector.md
+all: $(WEAVE) get-started-go.md flows.md models.md prompts.md dotprompt.md pgvector.md cloud-run.md
 
 $(WEAVE): ../go/internal/cmd/weave/*.go
 	go -C ../go install ./internal/cmd/weave
 
 %.md: %.src
 	$(WEAVE) $< > $@
-

docs-go/cloud-run.md

@@ -0,0 +1,174 @@
+<!-- Autogenerated by weave; DO NOT EDIT -->
+
+# Firebase Genkit with Cloud Run
+
+You can deploy Firebase Genkit flows as web services using Cloud Run. This page,
+as an example, walks you through the process of deploying the default sample
+flow.
+
+1.  Install the [Google Cloud CLI](https://cloud.google.com/sdk/docs/install) if
+    you haven't already.
+
+1.  Create a new Google Cloud project using the
+    [Cloud console](https://console.cloud.google.com) or choose an existing one.
+    The project must be linked to a billing account.
+
+    After you create or choose a project, configure the Google Cloud CLI to use
+    it:
+
+    ```posix-terminal
+    gcloud auth login
+
+    gcloud init
+    ```
+
+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:
+
+    ```go
+    if err := genkit.Init(ctx,
+    	&genkit.Options{FlowAddr: ":3400"}, // Add this parameter.
+    ); err != nil {
+    	log.Fatal(err)
+    }
+    ```
+
+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 Cloud Run environment:
+
+          1.  In the Cloud console, enable the
+              [Secret Manager API](https://console.cloud.google.com/apis/library/secretmanager.googleapis.com?project=_).
+          1.  On the
+              [Secret Manager](https://console.cloud.google.com/security/secret-manager?project=_)
+              page, create a new secret containing your API key.
+          1.  After you create the secret, on the same page, grant your default
+              compute service account access to the secret with the
+              **Secret Manager Secret Accessor** role. (You can look up the name
+              of the default compute service account on the IAM page.)
+
+          In a later step, when you deploy your service, you will need to
+          reference the name of this secret.
+
+    - {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, ensure that the **Default compute service account** is granted
+          the **Vertex AI User** role.
+
+    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:
+
+    - {Gemini (Google AI)}
+
+      ```posix-terminal
+      gcloud run deploy --port 3400 \
+        --update-secrets=GOOGLE_GENAI_API_KEY=<your-secret-name>:latest
+      ```
+
+    - {Gemini (Vertex AI)}
+
+      ```posix-terminal
+      gcloud run deploy --port 3400 \
+        --set-env-vars GCLOUD_PROJECT=<your-gcloud-project> \
+        --set-env-vars GCLOUD_LOCATION=us-central1
+      ```
+
+      (`GCLOUD_LOCATION` configures the Vertex API region you want to use.)
+
+    Choose `N` when asked if you want to allow unauthenticated invocations.
+    Answering `N` will configure your service to require IAM credentials. See
+    [Authentication](https://cloud.google.com/run/docs/authenticating/overview)
+    in the Cloud Run docs for information on providing these credentials.
+
+After deployment finishes, the tool will print the service URL. You can test
+it with `curl`:
+
+```posix-terminal
+curl -X POST https://<service-url>/menuSuggestionFlow \
+  -H "Authorization: Bearer $(gcloud auth print-identity-token)" \
+  -H "Content-Type: application/json" -d '"banana"'
+```

docs-go/cloud-run.src

@@ -0,0 +1,166 @@
+# Firebase Genkit with Cloud Run
+
+You can deploy Firebase Genkit flows as web services using Cloud Run. This page,
+as an example, walks you through the process of deploying the default sample
+flow.
+
+1.  Install the [Google Cloud CLI](https://cloud.google.com/sdk/docs/install) if
+    you haven't already.
+
+1.  Create a new Google Cloud project using the
+    [Cloud console](https://console.cloud.google.com) or choose an existing one.
+    The project must be linked to a billing account.
+
+    After you create or choose a project, configure the Google Cloud CLI to use
+    it:
+
+    ```posix-terminal
+    gcloud auth login
+
+    gcloud init
+    ```
+
+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:
+
+    %include ../go/internal/doc-snippets/flows.go init
+
+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 Cloud Run environment:
+
+          1.  In the Cloud console, enable the
+              [Secret Manager API](https://console.cloud.google.com/apis/library/secretmanager.googleapis.com?project=_).
+          1.  On the
+              [Secret Manager](https://console.cloud.google.com/security/secret-manager?project=_)
+              page, create a new secret containing your API key.
+          1.  After you create the secret, on the same page, grant your default
+              compute service account access to the secret with the
+              **Secret Manager Secret Accessor** role. (You can look up the name
+              of the default compute service account on the IAM page.)
+
+          In a later step, when you deploy your service, you will need to
+          reference the name of this secret.
+
+    - {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, ensure that the **Default compute service account** is granted
+          the **Vertex AI User** role.
+
+    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:
+
+    - {Gemini (Google AI)}
+
+      ```posix-terminal
+      gcloud run deploy --port 3400 \
+        --update-secrets=GOOGLE_GENAI_API_KEY=<your-secret-name>:latest
+      ```
+
+    - {Gemini (Vertex AI)}
+
+      ```posix-terminal
+      gcloud run deploy --port 3400 \
+        --set-env-vars GCLOUD_PROJECT=<your-gcloud-project> \
+        --set-env-vars GCLOUD_LOCATION=us-central1
+      ```
+
+      (`GCLOUD_LOCATION` configures the Vertex API region you want to use.)
+
+    Choose `N` when asked if you want to allow unauthenticated invocations.
+    Answering `N` will configure your service to require IAM credentials. See
+    [Authentication](https://cloud.google.com/run/docs/authenticating/overview)
+    in the Cloud Run docs for information on providing these credentials.
+
+After deployment finishes, the tool will print the service URL. You can test
+it with `curl`:
+
+```posix-terminal
+curl -X POST https://<service-url>/menuSuggestionFlow \
+  -H "Authorization: Bearer $(gcloud auth print-identity-token)" \
+  -H "Content-Type: application/json" -d '"banana"'
+```

go/internal/doc-snippets/flows.go

@@ -157,3 +157,13 @@ func f4() {
 	// !-run
 
 }
+
+func deploy(ctx context.Context) {
+	//!+init
+	if err := genkit.Init(ctx,
+		&genkit.Options{FlowAddr: ":3400"}, // Add this parameter.
+	); err != nil {
+		log.Fatal(err)
+	}
+	//!-init
+}