Merge branch 'next' into improve-cloud-telemetry-errors

.github/workflows/builder.yml

@@ -27,10 +27,10 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - uses: pnpm/action-setup@v3
-    - name: Set up node v20
+    - name: Set up node v21
       uses: actions/setup-node@v4
       with:
-        node-version: 20.x
+        node-version: 21.x
         cache: 'pnpm'
     - name: Install dependencies
       run: pnpm install

.github/workflows/e2e-tests.yml

@@ -29,7 +29,7 @@ jobs:
     - name: Set up node v20
       uses: actions/setup-node@v4
       with:
-        node-version: 20.x
+        node-version: 21.x
         cache: 'pnpm'
     - name: Install dependencies
       run: pnpm install

.github/workflows/formatter.yml

@@ -27,10 +27,10 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - uses: pnpm/action-setup@v3
-    - name: Set up node v20
+    - name: Set up node v21
       uses: actions/setup-node@v4
       with:
-        node-version: 20.x
+        node-version: 21.x
         cache: 'pnpm'
     - name: Install dependencies
       run: pnpm install

.github/workflows/tests.yml

@@ -27,10 +27,10 @@ jobs:
     steps:
     - uses: actions/checkout@v3
     - uses: pnpm/action-setup@v3
-    - name: Set up node v20
+    - name: Set up node v21
       uses: actions/setup-node@v4
       with:
-        node-version: 20.x
+        node-version: 21.x
         cache: 'pnpm'
     - name: Install dependencies and build
       run: pnpm build:genkit-tools

README.md

@@ -88,9 +88,14 @@ Find excellent examples of community-built plugins for OpenAI, Anthropic, Cohere
 
 ## Try Genkit on IDX
 
-<img src="docs/resources/idx-logo.png" width="50" alt="Project IDX logo">
-
-Want to try Genkit without a local setup? [Explore it on Project IDX](https://idx.google.com/new/genkit), Google's AI-assisted workspace for full-stack app development in the cloud.
+Want to skip the local setup? Click below to try out Genkit using [Project IDX](https://idx.dev), Google's AI-assisted workspace for full-stack app development in the cloud.
+
+<a href="https://idx.google.com/import?url=">
+  <img
+    height="32"
+    alt="Try in IDX"
+    src="https://cdn.idx.dev/btn/try_purple_32.svg">
+</a>
 
 ## Sample apps
 

docs/dotprompt.md

@@ -53,7 +53,7 @@ const result = await greetingPrompt.generate({
   },
 });
 
-console.log(result.text());
+console.log(result.text);
 ```
 
 Dotprompt's syntax is based on the [Handlebars](https://handlebarsjs.com/guide/)
@@ -183,7 +183,7 @@ const myPrompt = promptRef("myPrompt");
 const result = await myPrompt.generate<typeof MySchema>({...});
 
 // now strongly typed as MySchema
-result.output();
+result.output;
 ```
 
 ## Overriding Prompt Metadata
@@ -237,7 +237,7 @@ const menu = await createMenuPrompt.generate({
   },
 });
 
-console.log(menu.output());
+console.log(menu.output);
 ```
 
 Output conformance is achieved by inserting additional instructions into the
@@ -340,7 +340,7 @@ const result = await describeImagePrompt.generate({
   },
 });
 
-console.log(result.text());
+console.log(result.text);
 ```
 
 ## Partials

docs/evaluation.md

@@ -204,7 +204,7 @@ export const synthesizeQuestions = defineFlow(
           text: `Generate one question about the text below: ${chunks[i]}`,
         },
       });
-      questions.push(qResponse.text());
+      questions.push(qResponse.text);
     }
     return questions;
   }

docs/get-started.md

@@ -131,7 +131,7 @@ so that it can be used outside of a Node project.
         // Handle the response from the model API. In this sample, we just convert
         // it to a string, but more complicated flows might coerce the response into
         // structured output or chain the response into another LLM call, etc.
-        return llmResponse.text();
+        return llmResponse.text;
       }
     );
 

docs/models.md

@@ -109,7 +109,7 @@ configureGenkit(/* ... */);
     prompt: 'Invent a menu item for a pirate themed restaurant.',
   });
 
-  console.log(await llmResponse.text());
+  console.log(await llmResponse.text);
 })();
 ```
 
@@ -339,7 +339,7 @@ object's `output()` method:
 ```ts
 type MenuItem = z.infer<typeof MenuItemSchema>;
 
-const output: MenuItem | null = llmResponse.output();
+const output: MenuItem | null = llmResponse.output;
 ```
 
 #### Handling errors
@@ -425,7 +425,7 @@ Handle each of these chunks as they become available:
 ```ts
 for await (const responseChunkData of llmResponseStream.stream()) {
   const responseChunk = responseChunkData as GenerateResponseChunk;
-  console.log(responseChunk.text());
+  console.log(responseChunk.text);
 }
 ```
 
@@ -454,7 +454,7 @@ const llmResponseStream = await generateStream({
 for await (const responseChunkData of llmResponseStream.stream()) {
   const responseChunk = responseChunkData as GenerateResponseChunk<Menu>;
   // output() returns an object representing the entire output so far
-  const output: Menu | null = responseChunk.output();
+  const output: Menu | null = responseChunk.output;
   console.log(output);
 }
 ```
@@ -605,15 +605,15 @@ your users will not be interacting directly with the model in this way, the
 conversational style of prompting is a powerful way to influence the output
 generated by an AI model.
 
-To generate message history from a model response, call the `toHistory()`
+To generate message history from a model response, call the `.messages`
 method:
 
 ```ts
 let response = await generate({
   model: gemini15Flash,
   prompt: "How do you say 'dog' in French?",
 });
-let history = response.toHistory();
+let history = response.messages;
 ```
 
 You can serialize this history and persist it in a database or session storage.
@@ -625,7 +625,7 @@ response = await generate({
   prompt: 'How about in Spanish?',
   history,
 });
-history = response.toHistory();
+history = response.messages;
 ```
 
 If the model you're using supports the `system` role, you can use the initial

docs/plugin-authoring-evaluator.md

@@ -110,9 +110,9 @@ export async function deliciousnessScore<
   });
 
   // Parse the output
-  const parsedResponse = response.output();
+  const parsedResponse = response.output;
   if (!parsedResponse) {
-    throw new Error(`Unable to parse evaluator response: ${response.text()}`);
+    throw new Error(`Unable to parse evaluator response: ${response.text}`);
   }
 
   // Return a scored response

docs/plugins/google-cloud.md

@@ -221,32 +221,84 @@ Common dimensions include:
 - `topK` - the inference topK [value](https://ai.google.dev/docs/concepts#model-parameters).
 - `topP` - the inference topP [value](https://ai.google.dev/docs/concepts#model-parameters).
 
-### Flow-level metrics
+### Feature-level metrics
+
+Features are the top-level entry-point to your Genkit code. In most cases, this
+will be a flow, but if you do not use flows, this will be the top-most span in a trace.
+
+| Name                    | Type      | Description             |
+| ----------------------- | --------- | ----------------------- |
+| genkit/feature/requests | Counter   | Number of requests      |
+| genkit/feature/latency  | Histogram | Execution latency in ms |
+
+Each feature-level metric contains the following dimensions:
+
+| Name          | Description                                                                      |
+| ------------- | -------------------------------------------------------------------------------- |
+| name          | The name of the feature. In most cases, this is the top-level Genkit flow        |
+| status        | 'success' or 'failure' depending on whether or not the feature request succeeded |
+| error         | Only set when `status=failure`. Contains the error type that caused the failure  |
+| source        | The Genkit source language. Eg. 'ts'                                             |
+| sourceVersion | The Genkit framework version                                                     |
 
-| Name                 | Dimensions                           |
-| -------------------- | ------------------------------------ |
-| genkit/flow/requests | flow_name, error_code, error_message |
-| genkit/flow/latency  | flow_name                            |
 
 ### Action-level metrics
 
-| Name                   | Dimensions                           |
-| ---------------------- | ------------------------------------ |
-| genkit/action/requests | flow_name, error_code, error_message |
-| genkit/action/latency  | flow_name                            |
+Actions represent a generic step of execution within Genkit. Each of these steps
+will have the following metrics tracked:
+
+| Name                    | Type      | Description                                   |
+| ----------------------- | --------- | --------------------------------------------- |
+| genkit/action/requests  | Counter   | Number of times this action has been executed |
+| genkit/action/latency   | Histogram | Execution latency in ms                       |
+
+Each action-level metric contains the following dimensions:
+
+| Name          | Description                                                                                          |
+| ------------- | ---------------------------------------------------------------------------------------------------- |
+| name          | The name of the action                                                                               |
+| featureName   | The name of the parent feature being executed                                                        |
+| path          | The path of execution from the feature root to this action. eg. '/myFeature/parentAction/thisAction' |
+| status        | 'success' or 'failure' depending on whether or not the action succeeded                              |
+| error         | Only set when `status=failure`. Contains the error type that caused the failure                      |
+| source        | The Genkit source language. Eg. 'ts'                                                                 |
+| sourceVersion | The Genkit framework version                                                                         |
 
 ### Generate-level metrics
 
-| Name                                 | Dimensions                                                           |
-| ------------------------------------ | -------------------------------------------------------------------- |
-| genkit/ai/generate                   | flow_path, model, temperature, topK, topP, error_code, error_message |
-| genkit/ai/generate/input_tokens      | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/output_tokens     | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/input_characters  | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/output_characters | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/input_images      | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/output_images     | flow_path, model, temperature, topK, topP                            |
-| genkit/ai/generate/latency           | flow_path, model, temperature, topK, topP, error_code, error_message |
+These are special action metrics relating to actions that interact with a model.
+In addition to requests and latency, input and output are also tracked, with model
+specific dimensions that make debugging and configuration tuning easier.
+
+| Name                                 | Type      | Description                                |
+| ------------------------------------ | --------- | ------------------------------------------ |
+| genkit/ai/generate/requests          | Counter   | Number of times this model has been called |
+| genkit/ai/generate/latency           | Histogram | Execution latency in ms                    |
+| genkit/ai/generate/input/tokens      | Counter   | Input tokens                               |
+| genkit/ai/generate/output/tokens     | Counter   | Output tokens                              |
+| genkit/ai/generate/input/characters  | Counter   | Input characters                           |
+| genkit/ai/generate/output/characters | Counter   | Output characters                          |
+| genkit/ai/generate/input/images      | Counter   | Input images                               |
+| genkit/ai/generate/output/images     | Counter   | Output images                              |
+| genkit/ai/generate/input/audio       | Counter   | Input audio files                          |
+| genkit/ai/generate/output/audio      | Counter   | Output audio files                         |
+
+Each generate-level metric contains the following dimensions:
+
+| Name            | Description                                                                                          |
+| --------------- | ---------------------------------------------------------------------------------------------------- |
+| modelName       | The name of the model                                                                                |
+| featureName     | The name of the parent feature being executed                                                        |
+| path            | The path of execution from the feature root to this action. eg. '/myFeature/parentAction/thisAction' |
+| temperature     | The temperature parameter passed to the model                                                        |
+| maxOutputTokens | The maxOutputTokens parameter passed to the model                                                    |
+| topK            | The topK parameter passed to the model                                                               |
+| topP            | The topP parameter passed to the model                                                               |
+| latencyMs       | The response time taken by the model                                                                 |
+| status          | 'success' or 'failure' depending on whether or not the feature request succeeded                     |
+| error           | Only set when `status=failure`. Contains the error type that caused the failure                      |
+| source          | The Genkit source language. Eg. 'ts'                                                                 |
+| sourceVersion   | The Genkit framework version                                                                         |
 
 Visualizing metrics can be done through the Metrics Explorer. Using the side menu, select 'Logging' and click 'Metrics explorer'
 

docs/prompts.md

@@ -100,7 +100,7 @@ const response = await (threeGreetingsPrompt.generate<typeof outputSchema>(
   { input: { name: 'Fred' } }
 ));
 
-response.output()?.likeAPirate
+response.output?.likeAPirate
 // "Ahoy there, Fred!  May the winds be ever in your favor!"
 ```
 

docs/rag.md

@@ -296,7 +296,7 @@ export const menuQAFlow = defineFlow(
       context: docs,
     });
 
-    const output = llmResponse.text();
+    const output = llmResponse.text;
     return output;
   }
 );
@@ -333,7 +333,7 @@ defineSimpleRetriever({
   // and several keys to use as metadata
   metadata: ['from', 'to', 'subject'],
 } async (query, config) => {
-  const result = await searchEmails(query.text(), {limit: config.limit});
+  const result = await searchEmails(query.text, {limit: config.limit});
   return result.data.emails;
 });
 ```
@@ -433,7 +433,7 @@ export const rerankFlow = defineFlow(
     });
 
     return rerankedDocuments.map((doc) => ({
-      text: doc.text(),
+      text: doc.text,
       score: doc.metadata.score,
     }));
   }

docs/tool-calling.md

@@ -169,7 +169,7 @@ while (true) {
           throw Error('Tool not found');
         }
       }));
-    generateOptions.history = llmResponse.toHistory();
+    generateOptions.history = llmResponse.messages;
     generateOptions.prompt = toolResponses;
 }
 ```

genkit-tools/cli/config/firebase.index.ts.template

@@ -59,6 +59,6 @@ export const menuSuggestionFlow = onFlow(
 		// convert it to a string, but more complicated flows might coerce the
 		// response into structured output or chain the response into another
 		// LLM call, etc.
-    return llmResponse.text();
+    return llmResponse.text;
   }
 );

genkit-tools/cli/config/nextjs.genkit.ts.template

@@ -39,7 +39,7 @@ const menuSuggestionFlow = ai.defineFlow(
     // convert it to a string, but more complicated flows might coerce the
     // response into structured output or chain the response into another
     // LLM call, etc.
-    return llmResponse.text();
+    return llmResponse.text;
   }
 );
 

genkit-tools/cli/config/nodejs.index.ts.template

@@ -39,6 +39,6 @@ export const menuSuggestionFlow = ai.defineFlow(
 		// Handle the response from the model API. In this sample, we just convert
     // it to a string, but more complicated flows might coerce the response into
     // structured output or chain the response into another LLM call, etc.
-    return llmResponse.text();
+    return llmResponse.text;
   }
 );

genkit-tools/cli/package.json

@@ -1,6 +1,6 @@
 {
   "name": "genkit-cli",
-  "version": "0.6.0-dev.2",
+  "version": "0.9.0-dev.1",
   "description": "CLI for interacting with the Google Genkit AI framework",
   "license": "Apache-2.0",
   "keywords": [
@@ -28,7 +28,7 @@
   "dependencies": {
     "@genkit-ai/tools-common": "workspace:*",
     "@genkit-ai/telemetry-server": "workspace:*",
-    "axios": "^1.6.7",
+    "axios": "^1.7.7",
     "colorette": "^2.0.20",
     "commander": "^11.1.0",
     "extract-zip": "^2.0.1",