More flexible generateJson prompt.

README.md

@@ -50,18 +50,20 @@ for await (const textFragment of textStream) {
 ```ts
 const json = await generateJson(
   new OpenAIChatModel({ model: "gpt-3.5-turbo", maxTokens: 1000 }),
-  [
-    OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-    OpenAIChatMessage.user("A robot learning to love"),
-  ],
-  {
-    name: "story",
-    description: "Write the story",
-    parameters: z.object({
-      title: z.string().describe("The title of the story"),
-      content: z.string().describe("The content of the story"),
-    }),
-  }
+  new OpenAIChatSingleFunctionPrompt({
+    messages: [
+      OpenAIChatMessage.system("You are a story writer. Write a story about:"),
+      OpenAIChatMessage.user("A robot learning to love"),
+    ],
+    fn: {
+      name: "story",
+      description: "Write the story",
+      parameters: z.object({
+        title: z.string().describe("The title of the story"),
+        content: z.string().describe("The content of the story"),
+      }),
+    },
+  })
 );
 ```
 

docs/concept/function/generate-json.md

@@ -12,28 +12,29 @@ Generates JSON using a prompt and a structure.
 
 [generateJson API](/api/modules#generatejson)
 
-Generates JSON using a prompt and a structure.
+Generates JSON using a prompt.
 The prompt format depends on the model.
-The structure is a JSON object that describes the desired output.
-The parameters must be a Zod object schema.
+It contains the information required to parse the generated JSON.
 
 #### With OpenAI chat model
 
 ```ts
 const story = await generateJson(
   new OpenAIChatModel(/* ... */),
-  [
-    OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-    OpenAIChatMessage.user("A robot learning to love"),
-  ],
-  {
-    name: "story",
-    description: "Write the story",
-    parameters: z.object({
-      title: z.string().describe("The title of the story"),
-      content: z.string().describe("The content of the story"),
-    }),
-  }
+  new OpenAIChatSingleFunctionPrompt({
+    messages: [
+      OpenAIChatMessage.system("You are a story writer. Write a story about:"),
+      OpenAIChatMessage.user("A robot learning to love"),
+    ],
+    fn: {
+      name: "story",
+      description: "Write the story",
+      parameters: z.object({
+        title: z.string().describe("The title of the story"),
+        content: z.string().describe("The content of the story"),
+      }),
+    },
+  })
 );
 ```
 
@@ -50,18 +51,23 @@ The input signature of the prompt templates becomes the call signature of the ge
 ```ts
 const generateStoryAbout = generateJsonAsFunction(
   new OpenAITextGenerationModel(/* ... */),
-  async (theme: string) => [
-    OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-    OpenAIChatMessage.user(theme),
-  ],
-  {
-    name: "story",
-    description: "Write the story",
-    parameters: z.object({
-      title: z.string().describe("The title of the story"),
-      content: z.string().describe("The content of the story"),
-    }),
-  }
+  async (theme: string) =>
+    new OpenAIChatSingleFunctionPrompt({
+      messages: [
+        OpenAIChatMessage.system(
+          "You are a story writer. Write a story about:"
+        ),
+        OpenAIChatMessage.user(theme),
+      ],
+      fn: {
+        name: "story",
+        description: "Write the story",
+        parameters: z.object({
+          title: z.string().describe("The title of the story"),
+          content: z.string().describe("The content of the story"),
+        }),
+      },
+    })
 );
 
 const story = await generateStoryAbout("A robot learning to love");

docs/integration/model-provider/openai.md

@@ -105,28 +105,32 @@ for await (const textFragment of textStream) {
 JSON generation uses the [OpenAI GPT function calling API](https://platform.openai.com/docs/guides/gpt/function-calling). It provides a single function specification and instructs the model to provide parameters for calling the function. The result is returned as parsed JSON.
 
 [OpenAIChatModel API](/api/classes/OpenAIChatModel)
+[OpenAIChatSingleFunctionPrompt API](/api/classes/OpenAIChatSingleFunctionPrompt)
 
 ```ts
-import { OpenAIChatMessage, OpenAIChatModel, generateJson } from "ai-utils.js";
+import {
+  OpenAIChatMessage,
+  OpenAIChatModel,
+  OpenAIChatSingleFunctionPrompt,
+  generateJson,
+} from "ai-utils.js";
 
 const json = await generateJson(
-  new OpenAIChatModel({
-    model: "gpt-3.5-turbo",
-    temperature: 0.7,
-    maxTokens: 1000,
-  }),
-  [
-    OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-    OpenAIChatMessage.user("A robot learning to love"),
-  ],
-  {
-    name: "story",
-    description: "Write the story",
-    parameters: z.object({
-      title: z.string().describe("The title of the story"),
-      content: z.string().describe("The content of the story"),
-    }),
-  }
+  new OpenAIChatModel({ model: "gpt-3.5-turbo", maxTokens: 1000 }),
+  new OpenAIChatSingleFunctionPrompt({
+    messages: [
+      OpenAIChatMessage.system("You are a story writer. Write a story about:"),
+      OpenAIChatMessage.user("A robot learning to love"),
+    ],
+    fn: {
+      name: "story",
+      description: "Write the story",
+      parameters: z.object({
+        title: z.string().describe("The title of the story"),
+        content: z.string().describe("The content of the story"),
+      }),
+    },
+  })
 );
 ```
 

docs/recipe/sentiment-analysis.md

@@ -21,25 +21,28 @@ const analyzeSentiment = generateJsonAsFunction(
     temperature: 0, // remove randomness
     maxTokens: 500, // enough tokens for reasoning and sentiment
   }),
-  async (productReview: string) => [
-    OpenAIChatMessage.system(
-      "You are a sentiment evaluator. " +
-        "Analyze the sentiment of the following product review:"
-    ),
-    OpenAIChatMessage.user(productReview),
-  ],
-  {
-    name: "sentiment",
-    description: "Write the sentiment analysis",
-    parameters: z.object({
-      // Reason first to improve results:
-      reasoning: z.string().describe("Reasoning to explain the sentiment."),
-      // Report sentiment after reasoning:
-      sentiment: z
-        .enum(["positive", "neutral", "negative"])
-        .describe("Sentiment."),
-    }),
-  }
+  async (productReview: string) =>
+    new OpenAIChatSingleFunctionPrompt({
+      messages: [
+        OpenAIChatMessage.system(
+          "You are a sentiment evaluator. " +
+            "Analyze the sentiment of the following product review:"
+        ),
+        OpenAIChatMessage.user(productReview),
+      ],
+      fn: {
+        name: "sentiment",
+        description: "Write the sentiment analysis",
+        parameters: z.object({
+          // Reason first to improve results:
+          reasoning: z.string().describe("Reasoning to explain the sentiment."),
+          // Report sentiment after reasoning:
+          sentiment: z
+            .enum(["positive", "neutral", "negative"])
+            .describe("Sentiment."),
+        }),
+      },
+    })
 );
 ```
 

examples/basic/src/model-provider/openai/openai-chat-generate-json-example.ts

@@ -1,28 +1,33 @@
-import { OpenAIChatMessage, OpenAIChatModel, generateJson } from "ai-utils.js";
+import {
+  OpenAIChatMessage,
+  OpenAIChatModel,
+  OpenAIChatSingleFunctionPrompt,
+  generateJson,
+} from "ai-utils.js";
 import dotenv from "dotenv";
 import { z } from "zod";
 
 dotenv.config();
 
 (async () => {
   const json = await generateJson(
-    new OpenAIChatModel({
-      model: "gpt-3.5-turbo",
-      temperature: 0.7,
-      maxTokens: 1000,
-    }),
-    [
-      OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-      OpenAIChatMessage.user("A robot learning to love"),
-    ],
-    {
-      name: "story",
-      description: "Write the story",
-      parameters: z.object({
-        title: z.string().describe("The title of the story"),
-        content: z.string().describe("The content of the story"),
-      }),
-    }
+    new OpenAIChatModel({ model: "gpt-3.5-turbo", maxTokens: 1000 }),
+    new OpenAIChatSingleFunctionPrompt({
+      messages: [
+        OpenAIChatMessage.system(
+          "You are a story writer. Write a story about:"
+        ),
+        OpenAIChatMessage.user("A robot learning to love"),
+      ],
+      fn: {
+        name: "story",
+        description: "Write the story",
+        parameters: z.object({
+          title: z.string().describe("The title of the story"),
+          content: z.string().describe("The content of the story"),
+        }),
+      },
+    })
   );
 
   console.log(JSON.stringify(json, null, 2));

examples/basic/src/recipes/sentiment-analysis.ts

@@ -1,6 +1,7 @@
 import {
   OpenAIChatMessage,
   OpenAIChatModel,
+  OpenAIChatSingleFunctionPrompt,
   generateJsonAsFunction,
 } from "ai-utils.js";
 import dotenv from "dotenv";
@@ -15,24 +16,30 @@ dotenv.config();
       temperature: 0, // remove randomness
       maxTokens: 500, // enough tokens for reasoning and sentiment
     }),
-    async (productReview: string) => [
-      OpenAIChatMessage.system(
-        "You are a sentiment evaluator. Analyze the sentiment of the following product review:"
-      ),
-      OpenAIChatMessage.user(productReview),
-    ],
-    {
-      name: "sentiment",
-      description: "Write the sentiment analysis",
-      parameters: z.object({
-        // Reason first to improve results:
-        reasoning: z.string().describe("Reasoning to explain the sentiment."),
-        // Report sentiment after reasoning:
-        sentiment: z
-          .enum(["positive", "neutral", "negative"])
-          .describe("Sentiment."),
-      }),
-    }
+    async (productReview: string) =>
+      new OpenAIChatSingleFunctionPrompt({
+        messages: [
+          OpenAIChatMessage.system(
+            "You are a sentiment evaluator. " +
+              "Analyze the sentiment of the following product review:"
+          ),
+          OpenAIChatMessage.user(productReview),
+        ],
+        fn: {
+          name: "sentiment",
+          description: "Write the sentiment analysis",
+          parameters: z.object({
+            // Reason first to improve results:
+            reasoning: z
+              .string()
+              .describe("Reasoning to explain the sentiment."),
+            // Report sentiment after reasoning:
+            sentiment: z
+              .enum(["positive", "neutral", "negative"])
+              .describe("Sentiment."),
+          }),
+        },
+      })
   );
 
   const result1 = await analyzeSentiment(

examples/basic/src/text/generate-json-as-function-example.ts

@@ -1,6 +1,7 @@
 import {
   OpenAIChatMessage,
   OpenAIChatModel,
+  OpenAIChatSingleFunctionPrompt,
   generateJsonAsFunction,
 } from "ai-utils.js";
 import dotenv from "dotenv";
@@ -15,18 +16,23 @@ dotenv.config();
       temperature: 0.7,
       maxTokens: 1000,
     }),
-    async (theme: string) => [
-      OpenAIChatMessage.system("You are a story writer. Write a story about:"),
-      OpenAIChatMessage.user(theme),
-    ],
-    {
-      name: "story",
-      description: "Write the story",
-      parameters: z.object({
-        title: z.string().describe("The title of the story"),
-        content: z.string().describe("The content of the story"),
-      }),
-    }
+    async (theme: string) =>
+      new OpenAIChatSingleFunctionPrompt({
+        messages: [
+          OpenAIChatMessage.system(
+            "You are a story writer. Write a story about:"
+          ),
+          OpenAIChatMessage.user(theme),
+        ],
+        fn: {
+          name: "story",
+          description: "Write the story",
+          parameters: z.object({
+            title: z.string().describe("The title of the story"),
+            content: z.string().describe("The content of the story"),
+          }),
+        },
+      })
   );
 
   const story = await generateStoryAbout("A robot learning to love");