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feat(py): py menu sample
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‎py/plugins/dev-local-vector-store/pyproject.toml

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dependencies = ["genkit-ai", "pytest-mock", "pytest-asyncio"]
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description = "Genkit Local Vector Store Plugin"
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license = { text = "Apache-2.0" }
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name = "genkit-dev-local-vectorstore-plugin"
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name = "vectorstore-plugin"
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readme = "README.md"
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requires-python = ">=3.12"
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version = "0.1.0"

‎py/samples/menu/data/menu.jpeg

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‎py/samples/menu/data/menu.json

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[
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{
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"title": "Mozzarella Sticks",
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"price": 8,
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"description": "Crispy fried mozzarella sticks served with marinara sauce."
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},
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{
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"title": "Chicken Wings",
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"price": 10,
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"description": "Crispy fried chicken wings tossed in your choice of sauce."
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},
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{
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"title": "Nachos",
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"price": 12,
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"description": "Crispy tortilla chips topped with melted cheese, chili, sour cream, and salsa."
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},
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{
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"title": "Onion Rings",
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"price": 7,
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"description": "Crispy fried onion rings served with ranch dressing."
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},
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{
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"title": "French Fries",
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"price": 5,
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"description": "Crispy fried french fries."
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},
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{
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"title": "Mashed Potatoes",
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"price": 6,
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"description": "Creamy mashed potatoes."
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},
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{
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"title": "Coleslaw",
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"price": 4,
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"description": "Homemade coleslaw."
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},
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{
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"title": "Classic Cheeseburger",
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"price": 12,
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"description": "A juicy beef patty topped with melted American cheese, lettuce, tomato, and onion on a toasted bun."
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},
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{
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"title": "Bacon Cheeseburger",
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"price": 14,
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"description": "A classic cheeseburger with the addition of crispy bacon."
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},
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{
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"title": "Mushroom Swiss Burger",
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"price": 15,
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"description": "A beef patty topped with sautéed mushrooms, melted Swiss cheese, and a creamy horseradish sauce."
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},
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{
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"title": "Chicken Sandwich",
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"price": 13,
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"description": "A crispy chicken breast on a toasted bun with lettuce, tomato, and your choice of sauce."
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},
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{
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"title": "Pulled Pork Sandwich",
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"price": 14,
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"description": "Slow-cooked pulled pork on a toasted bun with coleslaw and barbecue sauce."
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},
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{
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"title": "Reuben Sandwich",
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"price": 15,
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"description": "Thinly sliced corned beef, Swiss cheese, sauerkraut, and Thousand Island dressing on rye bread."
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},
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{
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"title": "House Salad",
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"price": 8,
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"description": "Mixed greens with your choice of dressing."
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},
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{
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"title": "Caesar Salad",
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"price": 9,
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"description": "Romaine lettuce with croutons, Parmesan cheese, and Caesar dressing."
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},
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{
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"title": "Greek Salad",
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"price": 10,
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"description": "Mixed greens with feta cheese, olives, tomatoes, cucumbers, and red onions."
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},
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{
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"title": "Chocolate Lava Cake",
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"price": 8,
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"description": "A warm, gooey chocolate cake with a molten chocolate center."
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},
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{
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"title": "Apple Pie",
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"price": 7,
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"description": "A classic apple pie with a flaky crust and warm apple filling."
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},
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{
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"title": "Cheesecake",
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"price": 8,
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"description": "A creamy cheesecake with a graham cracker crust."
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}
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]

‎py/samples/menu/src/__init__.py

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# Copyright 2025 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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# 01
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from case_01.prompts import s01_staticMenuDotPrompt, s01_vanillaPrompt
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from case_02.flows import s02_menuQuestionFlow
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from case_02.prompts import s02_dataMenuPrompt
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# 02
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from case_02.tools import menu_tool
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# 03
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from case_03.flows import s03_multiTurnChatFlow
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from case_03.prompts import s03_chatPreamblePrompt
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# 04
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# TODO: uncomment once implemented
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# from case_04.flows import s04_indexMenuItemsFlow, s04_ragMenuQuestionFlow
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# from case_04.prompts import s04_ragDataMenuPrompt
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# 05
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from case_05.flows import (
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s05_readMenuFlow,
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s05_textMenuQuestionFlow,
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s05_visionMenuQuestionFlow,
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)
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from case_05.prompts import s05_readMenuPrompt, s05_textMenuPrompt
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print('All prompts and flows loaded, use the Developer UI to test them out')

‎py/samples/menu/src/case_01/__init__.py

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# Copyright 2025 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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‎py/samples/menu/src/case_01/example.json

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{
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"input": {
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"question": "Which of your burgers would you recommend for someone like me who loves bacon?"
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}
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}

‎py/samples/menu/src/case_01/prompts.py

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# Copyright 2025 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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from menu_ai import ai
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from menu_schemas import MenuQuestionInputSchema
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from genkit.plugins.google_genai.models.gemini import gemini15Flash
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s01_vanillaPrompt = ai.define_prompt(
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variant='s01_vanillaPrompt',
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input_schema=MenuQuestionInputSchema,
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system="""You are acting as a helpful AI assistant named "Walt" that can answer questions about the food available on the menu at Walt's Burgers.""",
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config={'temperature': 0.3},
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)
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s01_staticMenuDotPrompt = ai.define_prompt(
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variant='s01_staticMenuDotPrompt',
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model=gemini15Flash.versions[0],
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output_format='text',
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input_schema=MenuQuestionInputSchema,
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system="""
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You are acting as a helpful AI assistant named "Walt" that can answer
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questions about the food available on the menu at Walt's Burgers.
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Here is today's menu:
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- The Regular Burger $12
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The classic charbroiled to perfection with your choice of cheese
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- The Fancy Burger $13
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Classic burger topped with bacon & Blue Cheese
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- The Bacon Burger $13
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Bacon cheeseburger with your choice of cheese.
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- Everything Burger $14
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Heinz 57 sauce, American cheese, bacon, fried egg & crispy onion bits
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- Chicken Breast Sandwich $12
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Tender juicy chicken breast on a brioche roll.
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Grilled, blackened, or fried
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Our fresh 1/2 lb. beef patties are made using choice cut
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brisket, short rib & sirloin. Served on a toasted
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brioche roll with chips. Served with lettuce, tomato & pickles.
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Onions upon request. Substitute veggie patty $2
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Answer this customer's question, in a concise and helpful manner,
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as long as it is about food.
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Question:
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{{question}} ?""",
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)

‎py/samples/menu/src/case_02/__init__.py

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# Copyright 2025 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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‎py/samples/menu/src/case_02/example.json

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{
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"question": "I'd like to try something spicy. What do you recommend?"
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}

‎py/samples/menu/src/case_02/flows.py

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# Copyright 2025 Google LLC
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# SPDX-License-Identifier: Apache-2.0
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from menu_ai import ai
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from menu_schemas import AnswerOutputSchema, MenuQuestionInputSchema
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from .prompts import s02_dataMenuPrompt
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@ai.flow(name='s02_menuQuestion')
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def s02_menuQuestionFlow(
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my_input: MenuQuestionInputSchema,
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) -> AnswerOutputSchema:
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text = s02_dataMenuPrompt({'question': my_input.question})
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return AnswerOutputSchema(
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answer=text,
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)

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