কাঠামোগত আউটপুট

from google import genai
from pydantic import BaseModel, Field
from typing import List, Optional

class Ingredient(BaseModel):
    name: str = Field(description="Name of the ingredient.")
    quantity: str = Field(description="Quantity of the ingredient, including units.")

class Recipe(BaseModel):
    recipe_name: str = Field(description="The name of the recipe.")
    prep_time_minutes: Optional[int] = Field(description="Optional time in minutes to prepare the recipe.")
    ingredients: List[Ingredient]
    instructions: List[str]

client = genai.Client()

prompt = """
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
"""

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Recipe.model_json_schema()
    },
)

recipe = Recipe.model_validate_json(interaction.steps[-1].content[0].text)
print(recipe)

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const ingredientSchema = z.object({
  name: z.string().describe("Name of the ingredient."),
  quantity: z.string().describe("Quantity of the ingredient, including units."),
});

const recipeSchema = z.object({
  recipe_name: z.string().describe("The name of the recipe."),
  prep_time_minutes: z.number().optional().describe("Optional time in minutes to prepare the recipe."),
  ingredients: z.array(ingredientSchema),
  instructions: z.array(z.string()),
});

const client = new GoogleGenAI({});

const prompt = `
Please extract the recipe from the following text.
The user wants to make delicious chocolate chip cookies.
They need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,
1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,
3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.
For the best part, they'll need 2 cups of semisweet chocolate chips.
First, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,
baking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar
until light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry
ingredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons
onto ungreased baking sheets and bake for 9 to 11 minutes.
`;

const interaction = await client.interactions.create({
  model: "gemini-3-flash-preview",
  input: prompt,
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(recipeSchema)
  },
});

const recipe = recipeSchema.parse(JSON.parse(interaction.steps.at(-1).content[0].text));
console.log(recipe);

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
    -H "x-goog-api-key: $GEMINI_API_KEY" \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "gemini-3-flash-preview",
      "input": "Please extract the recipe from the following text.\nThe user wants to make delicious chocolate chip cookies.\nThey need 2 and 1/4 cups of all-purpose flour, 1 teaspoon of baking soda,\n1 teaspoon of salt, 1 cup of unsalted butter (softened), 3/4 cup of granulated sugar,\n3/4 cup of packed brown sugar, 1 teaspoon of vanilla extract, and 2 large eggs.\nFor the best part, they will need 2 cups of semisweet chocolate chips.\nFirst, preheat the oven to 375°F (190°C). Then, in a small bowl, whisk together the flour,\nbaking soda, and salt. In a large bowl, cream together the butter, granulated sugar, and brown sugar\nuntil light and fluffy. Beat in the vanilla and eggs, one at a time. Gradually beat in the dry\ningredients until just combined. Finally, stir in the chocolate chips. Drop by rounded tablespoons\nonto ungreased baking sheets and bake for 9 to 11 minutes.",
      "response_format": {
        "type": "text",
        "mime_type": "application/json",
        "schema": {
          "type": "object",
          "properties": {
            "recipe_name": {
              "type": "string",
              "description": "The name of the recipe."
            },
            "prep_time_minutes": {
                "type": "integer",
                "description": "Optional time in minutes to prepare the recipe."
            },
            "ingredients": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "name": { "type": "string", "description": "Name of the ingredient."},
                  "quantity": { "type": "string", "description": "Quantity of the ingredient, including units."}
                },
                "required": ["name", "quantity"]
              }
            },
            "instructions": {
              "type": "array",
              "items": { "type": "string" }
            }
          },
          "required": ["recipe_name", "ingredients", "instructions"]
        }
      }
      }
    }'

from google import genai
from pydantic import BaseModel
from typing import Literal

class Feedback(BaseModel):
    sentiment: Literal["positive", "neutral", "negative"]
    summary: str

client = genai.Client()
prompt = "The new UI is incredibly intuitive. Add a very long summary to test streaming!"

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input=prompt,
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": Feedback.model_json_schema()
    },
    stream=True
)
for event in stream:
    if event.event_type == "step.delta" and event.delta.text:
        print(event.delta.text, end="")

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const client = new GoogleGenAI({});
const feedbackSchema = z.object({
  sentiment: z.enum(["positive", "neutral", "negative"]),
  summary: z.string(),
});

const stream = await client.interactions.create({
  model: "gemini-3-flash-preview",
  input: "The new UI is incredibly intuitive. Add a very long summary!",
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(feedbackSchema)
  },
  stream: true,
});

for await (const event of stream) {
  if (event.type === "step.delta" && event.delta?.text) {
    process.stdout.write(event.delta.text);
  }
}

from google import genai
from pydantic import BaseModel, Field
from typing import List

class MatchResult(BaseModel):
    winner: str = Field(description="The name of the winner.")
    final_match_score: str = Field(description="The final match score.")
    scorers: List[str] = Field(description="The name of the scorer.")

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.1-pro-preview",
    input="Search for all details for the latest Euro.",
    tools=[{"type": "google_search"}, {"type": "url_context"}],
    response_format={
        "type": "text",
        "mime_type": "application/json",
        "schema": MatchResult.model_json_schema()
    },
)

result = MatchResult.model_validate_json(interaction.steps[-1].content[0].text)
print(result)

import { GoogleGenAI } from "@google/genai";
import { z } from "zod";
import { zodToJsonSchema } from "zod-to-json-schema";

const client = new GoogleGenAI({});
const matchSchema = z.object({
  winner: z.string().describe("The name of the winner."),
  final_match_score: z.string().describe("The final score."),
  scorers: z.array(z.string()).describe("The name of the scorer.")
});

const interaction = await client.interactions.create({
  model: "gemini-3.1-pro-preview",
  input: "Search for all details for the latest Euro.",
  tools: [{type: "google_search"}, {type: "url_context"}],
  response_format: {
    type: 'text',
    mime_type: 'application/json',
    schema: zodToJsonSchema(matchSchema)
  },
});

const match = matchSchema.parse(JSON.parse(interaction.steps.at(-1).content[0].text));
console.log(match);

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "gemini-3.1-pro-preview",
    "input": "Search for all details for the latest Euro.",
    "tools": [{"type": "google_search"}, {"type": "url_context"}],
    "response_format": {
      "type": "text",
      "mime_type": "application/json",
      "schema": {
        "type": "object",
        "properties": {
            "winner": {"type": "string"},
            "final_match_score": {"type": "string"},
            "scorers": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["winner", "final_match_score", "scorers"]
      }
    }
  }'