# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
import json
import os
from case_03.chats import (
ChatHistoryStore,
ChatSessionInputSchema,
ChatSessionOutputSchema,
)
from menu_ai import ai
from genkit.core.typing import Message, Role, TextPart
from genkit.plugins.google_genai import googleai_name
from genkit.plugins.google_genai.models.gemini import GoogleAIGeminiVersion as GeminiVersion
menu_json_path = os.path.join(os.path.dirname(__file__), '..', '..', 'data', 'menu.json')
with open(menu_json_path) as f:
menu_data = json.load(f)
formatted_menu_data = '\n'.join([f'- {r["title"]} ${r["price"]}\n{r["description"]}' for r in menu_data])
preamble = [
Message(
role=Role.USER,
content=[
TextPart(text="Hi. What's on the menu today?"),
],
),
Message(
role=Role.MODEL,
content=[
TextPart(
text=f"""I am Walt, a helpful AI assistant here at the restaurant.
I can answer questions about the food on the menu or any other questions
you have about food in general. I probably can't help you with anything else.
Here is today's menu:
{formatted_menu_data}
Do you have any questions about the menu?"""
),
],
),
]
chat_history_store = ChatHistoryStore(
preamble=preamble,
)
@ai.flow(name='s03_multiTurnChat')
async def s03_multiTurnChatFlow(
my_input: ChatSessionInputSchema,
) -> ChatSessionOutputSchema:
history = chat_history_store.read(my_input.session_id)
llm_response = await ai.generate(
model=googleai_name(GeminiVersion.GEMINI_3_FLASH_PREVIEW),
messages=history,
prompt=[TextPart(text=my_input.question)],
)
history = llm_response.messages
chat_history_store.write(my_input.session_id, history)
return ChatSessionOutputSchema(
session_id=my_input.session_id,
history=history,
)
