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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# SPDX-License-Identifier: Apache-2.0
"""Flows for case 04."""
import json
import os
from pydantic import BaseModel, Field
from genkit.blocks.document import Document
from ..menu_ai import ai
from ..menu_schemas import AnswerOutputSchema, MenuItemSchema, MenuQuestionInputSchema
from .prompts import s04_rag_data_menu_prompt
class IndexMenuItemsOutputSchema(BaseModel):
"""Output schema for indexing items."""
rows: int = Field(...)
@ai.flow(name='s04_index_menu_items')
async def s04_index_menu_items_flow(
menu_items: list[MenuItemSchema],
) -> IndexMenuItemsOutputSchema:
"""Index menu items for retrieval.
Args:
menu_items: List of menu items to index.
Returns:
Number of items indexed.
Example:
>>> await s04_index_menu_items_flow([MenuItemSchema(title='Burger', price=10.0, description='Yum')])
IndexMenuItemsOutputSchema(rows=1)
"""
# If empty list provided (e.g., from Dev UI default), load from example file
if not menu_items:
example_file = os.path.join(os.path.dirname(__file__), 'example.indexMenuItems.json')
with open(example_file) as f:
menu_data = json.load(f)
menu_items = [MenuItemSchema(**item) for item in menu_data]
documents = [
Document.from_text(f'{item.title} {item.price} \n {item.description}', metadata=item.model_dump())
for item in menu_items
]
await ai.index(
indexer='menu-items',
documents=documents,
)
return IndexMenuItemsOutputSchema(rows=len(menu_items))
@ai.flow(name='s04_rag_menu_question')
async def s04_rag_menu_question_flow(
my_input: MenuQuestionInputSchema,
) -> AnswerOutputSchema:
"""Answer a question using RAG on menu items.
Args:
my_input: Input containing the question.
Returns:
The answer.
Example:
>>> await s04_rag_menu_question_flow(MenuQuestionInputSchema(question='Do you have burgers?'))
AnswerOutputSchema(answer="Yes, we have...")
"""
# Retrieve the 3 most relevant menu items for the question
docs = await ai.retrieve(
retriever='menu-items',
query=my_input.question,
options={'limit': 3},
)
menu_data = [MenuItemSchema(**doc.metadata) for doc in docs.documents if doc.metadata]
# Generate the response
response = await s04_rag_data_menu_prompt({'menuData': menu_data, 'question': my_input.question})
return AnswerOutputSchema(answer=response.text)
