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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.
# 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,
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# limitations under the License.
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# SPDX-License-Identifier: Apache-2.0
"""Vertex AI Vector Search with Firestore sample."""
import os
import time
import structlog
from google.cloud import aiplatform, firestore
from pydantic import BaseModel
from genkit.ai import Genkit
from genkit.blocks.document import Document
from genkit.core.typing import RetrieverResponse
from genkit.plugins.google_genai import VertexAI
from genkit.plugins.vertex_ai import define_vertex_vector_search_firestore
LOCATION = os.environ['LOCATION']
PROJECT_ID = os.environ['PROJECT_ID']
FIRESTORE_COLLECTION = os.environ['FIRESTORE_COLLECTION']
VECTOR_SEARCH_DEPLOYED_INDEX_ID = os.environ['VECTOR_SEARCH_DEPLOYED_INDEX_ID']
VECTOR_SEARCH_INDEX_ENDPOINT_PATH = os.environ['VECTOR_SEARCH_INDEX_ENDPOINT_PATH']
VECTOR_SEARCH_API_ENDPOINT = os.environ['VECTOR_SEARCH_API_ENDPOINT']
firestore_client = firestore.AsyncClient(project=PROJECT_ID)
aiplatform.init(project=PROJECT_ID, location=LOCATION)
logger = structlog.get_logger(__name__)
ai = Genkit(plugins=[VertexAI()])
# Define Vertex AI Vector Search with Firestore
define_vertex_vector_search_firestore(
ai,
name='my-vector-search',
embedder='vertexai/text-embedding-004',
embedder_options={
'task': 'RETRIEVAL_DOCUMENT',
'output_dimensionality': 128,
},
firestore_client=firestore_client,
collection_name=FIRESTORE_COLLECTION,
)
class QueryFlowInputSchema(BaseModel):
"""Input schema."""
query: str
k: int
class QueryFlowOutputSchema(BaseModel):
"""Output schema."""
result: list[dict]
length: int
time: int
@ai.flow(name='queryFlow')
async def query_flow(_input: QueryFlowInputSchema) -> QueryFlowOutputSchema:
"""Executes a vector search with VertexAI Vector Search."""
start_time = time.time()
query_document = Document.from_text(text=_input.query)
query_document.metadata = {
'api_endpoint': VECTOR_SEARCH_API_ENDPOINT,
'index_endpoint_path': VECTOR_SEARCH_INDEX_ENDPOINT_PATH,
'deployed_index_id': VECTOR_SEARCH_DEPLOYED_INDEX_ID,
}
result: RetrieverResponse = await ai.retrieve(
retriever='my-vector-search',
query=query_document,
options={'limit': 10},
)
end_time = time.time()
duration = int(end_time - start_time)
result_data = []
for doc in result.documents:
metadata = doc.metadata or {}
result_data.append({
'id': metadata.get('id'),
'text': doc.content[0].root.text if doc.content and doc.content[0].root.text else '',
'distance': metadata.get('distance', 0.0),
})
result_data = sorted(result_data, key=lambda x: x['distance'])
return QueryFlowOutputSchema(
result=result_data,
length=len(result_data),
time=duration,
)
async def main() -> None:
"""Main function."""
query_input = QueryFlowInputSchema(
query='Content for doc',
k=3,
)
await logger.ainfo(await query_flow(query_input))
if __name__ == '__main__':
ai.run_main(main())
