Invoke a model in modus quickstart

modus/quickstart.mdx

@@ -84,31 +84,63 @@
     ```
 
   </Step>
-  <Step title="Add a function">
+  <Step title="Add a model">
+    Modus also supports AI models. You can define new models in your `modus.json` file. Let's add a new meta-llama model:
+
+      ```json
+        "models": {
+          "text-generator": {
+            "sourceModel": "meta-llama/Llama-3.2-3B-Instruct",
+            "provider": "hugging-face",
+            "connection": "hypermode"
+          }
+        },
+      ```
+
+  </Step>
+  <Step title="Install the Hyp CLI and log in">
+    Next, install the Hyp CLI. This allows you to access hosted models on the Hypermode platform.
+
+    ```sh
+      npm install -g @hypermode/hyp-cli
+    ```
+
+    You can now use the `hyp login` command to log in to the Hyp CLI.
+    This links your project to the Hypermode platform, allowing you to leverage the model in your modus app.
+
+  </Step>
+
+  <Step title="Add a function with AI integration">
     Functions are the building blocks of your app. Let's add a function that fetches a random quote from
-    the ZenQuotes connection you just created.
+    the ZenQuotes connection and uses AI to generate a summary for the quote.
 
     <Tabs>
       <Tab title="Go">
-        To add a function, create a new file in the root directory with the following code:
+        Create a new file in the root directory with the following code:
 
         ```go quotes.go
         package main
 
         import (
           "errors"
           "fmt"
+          "strings"
 
           "github.com/hypermodeinc/modus/sdk/go/pkg/http"
+          "github.com/hypermodeinc/modus/sdk/go/pkg/models"
+          "github.com/hypermodeinc/modus/sdk/go/pkg/models/openai"
         )
 
         type Quote struct {
-          Quote  string `json:"q"`
-          Author string `json:"a"`
+          Quote   string `json:"q"`
+          Author  string `json:"a"`
+          Summary string `json:"summary,omitempty"`
         }
 
-        // this function makes a request to an API that returns data in JSON format, and
-        // returns an object representing the data
+        const modelName = "text-generator"
+
+        // this function makes a request to an API that returns data in JSON format,
+        // and returns a single quote with AI-generated summary
         func GetRandomQuote() (*Quote, error) {
           request := http.NewRequest("https://zenquotes.io/api/random")
 
@@ -117,25 +149,72 @@
             return nil, err
           }
           if !response.Ok() {
-            return nil, fmt.Errorf("Failed to fetch quote. Received: %d %s", response.Status, response.StatusText)
+            return nil, fmt.Errorf("failed to fetch quote. Received: %d %s", response.Status, response.StatusText)
           }
 
-          // the API returns an array of quotes, but we only want the first one
+          // the API returns an array of quotes, but we only need the first one
           var quotes []Quote
           response.JSON(&quotes)
           if len(quotes) == 0 {
             return nil, errors.New("expected at least one quote in the response, but none were found")
           }
-          return &quotes[0], nil
+
+          // Get the first (and only) quote
+          quote := quotes[0]
+
+          // Generate AI summary for the quote
+          summary, err := summarizeQuote(quote.Quote, quote.Author)
+          if err != nil {
+            fmt.Printf("Warning: failed to summarize quote by %s: %v\n", quote.Author, err)
+            quote.Summary = "Summary unavailable"
+          } else {
+            quote.Summary = summary
+          }
+
+          return &quote, nil
+        }
+
+        // summarizeQuote uses the AI model to generate a concise summary of the quote
+        func summarizeQuote(quote, author string) (string, error) {
+          model, err := models.GetModel[openai.ChatModel](modelName)
+          if err != nil {
+            return "", err
+          }
+
+          instruction := "You are a helpful assistant that summarizes quotes. Provide a brief, insightful summary that captures the essence and meaning of the quote in 1-2 sentences."
+          prompt := fmt.Sprintf("Quote: \"%s\" - %s", quote, author)
+
+          input, err := model.CreateInput(
+            openai.NewSystemMessage(instruction),
+            openai.NewUserMessage(prompt),
+          )
+          if err != nil {
+            return "", err
+          }
+
+          // Set temperature for consistent but creative responses
+          input.Temperature = 0.7
+
+          output, err := model.Invoke(input)
+          if err != nil {
+            return "", err
+          }
+
+          return strings.TrimSpace(output.Choices[0].Message.Content), nil
         }
         ```
       </Tab>
 
       <Tab title="AssemblyScript">
-        To add a function, create a new file in the `assembly` directory with the following code:
+        Create a new file in the `assembly` directory with the following code:
 
         ```ts quotes.ts
-        import { http } from "@hypermode/modus-sdk-as";
+        import { http, models } from "@hypermode/modus-sdk-as";
+        import {
+          OpenAIChatModel,
+          SystemMessage,
+          UserMessage,
+        } from "@hypermode/modus-sdk-as/models/openai/chat";
 
         @json
         class Quote {
@@ -144,10 +223,14 @@
 
           @alias("a")
           author!: string;
+
+          summary?: string;
         }
 
-        // this function makes a request to an API that returns data in JSON format, and
-        // returns an object representing the data
+        const modelName: string = "text-generator";
+
+        // this function makes a request to an API that returns data in JSON format,
+        // and returns a single quote with AI-generated summary
         export function getRandomQuote(): Quote {
           const request = new http.Request("https://zenquotes.io/api/random");
 
@@ -158,8 +241,43 @@
             );
           }
 
-          // the API returns an array of quotes, but we only want the first one
-          return response.json<Quote[]>()[0];
+          // the API returns an array of quotes, but we only need the first one
+          const quotes = response.json<Quote[]>();
+          if (quotes.length === 0) {
+            throw new Error("Expected at least one quote in the response, but none were found");
+          }
+
+          // Get the first (and only) quote
+          const quote = quotes[0];
+
+          // Generate AI summary for the quote
+          try {
+            quote.summary = summarizeQuote(quote.quote, quote.author);
+          } catch (error) {
+            console.log(`Warning: failed to summarize quote by ${quote.author}: ${error}`);
+            quote.summary = "Summary unavailable";
+          }
+
+          return quote;
+        }
+
+        // summarizeQuote uses the AI model to generate a concise summary of the quote
+        function summarizeQuote(quote: string, author: string): string {
+          const model = models.getModel<OpenAIChatModel>(modelName);
+
+          const instruction = "You are a helpful assistant that summarizes quotes. Provide a brief, insightful summary that captures the essence and meaning of the quote in 1-2 sentences.";
+          const prompt = `Quote: "${quote}" - ${author}`;
+
+          const input = model.createInput([
+            new SystemMessage(instruction),
+            new UserMessage(prompt),
+          ]);
+
+          // Set temperature for consistent but creative responses
+          input.temperature = 0.7;
+
+          const output = model.invoke(input);
+          return output.choices[0].message.content.trim();
         }
         ```
 
@@ -172,32 +290,26 @@
 
       </Tab>
     </Tabs>
-    After adding your function, you can use the API Explorer interface to test the `GetRandomQuote` function.
 
   </Step>
-  <Step title="Add a model">
-    Modus also supports AI models. You can define new models in your `modus.json` file. Let's add a new meta-llama model:
-
-      ```json
-        "models": {
-          "text-generator": {
-            "sourceModel": "meta-llama/Llama-3.2-3B-Instruct",
-            "provider": "hugging-face",
-            "connection": "hypermode"
-          }
-        },
-      ```
 
-  </Step>
-  <Step title="Install the Hyp CLI and log in">
-    Next, install the Hyp CLI. This allows you to access hosted models on the Hypermode platform.
+  <Step title="Make your first AI call">
+    Now that you've integrated the AI model, let's test it! After adding your function, restart your development server:
 
     ```sh
-      npm install -g @hypermode/hyp-cli
+    modus dev
     ```
 
-    You can now use the `hyp login` command to log in to the Hyp CLI.
-    This links your project to the Hypermode platform, allowing you to leverage the model in your modus app.
+    Navigate to the API Explorer at `http://localhost:8686/explorer` and you'll see your `randomQuote` function available to test.
+
+    When you call the function, you'll notice that the quote includes three fields:
+    - `quote`: The original quote text
+    - `author`: The author's name
+    - `summary`: An AI-generated summary that captures the essence of the quote
+
+    The AI model analyzes the quote and provides insightful context about its meaning, making your app more engaging and informative for users.
+
+    Try calling the function multiple times to see how the AI generates different summaries for various quotes!
 
   </Step>
 
@@ -213,6 +325,12 @@
 
     ![local model tracing](../images/observe-functions/local-inference-history.png)
 
+    You can now see detailed information about each AI model call, including:
+    - Input prompts sent to the model
+    - Generated responses
+    - Performance metrics like response time
+    - Token usage and costs
+
   </Step>
 </Steps>