Based on the vectorizing and indexing images, the following executes a query on the index to retrieve results.

#! /usr/bin/python

import json

import sys

import os

import requests

from azure.core.credentials import AzureKeyCredential

from azure.identity import DefaultAzureCredential

from azure.search.documents import SearchClient

from azure.search.documents.indexes import SearchIndexClient

from azure.search.documents.models import (

    RawVectorQuery,

    VectorizableTextQuery

)

sys.path.insert(0, os.path.abspath(".."))

from visionprocessor.vectorizer import vectorize_image

search_endpoint = os.getenv("AZURE_SEARCH_SERVICE_ENDPOINT")

index_name = os.getenv("AZURE_SEARCH_INDEX_NAME")

api_version = os.getenv("AZURE_SEARCH_API_VERSION")

search_api_key = os.getenv("AZURE_SEARCH_ADMIN_KEY")

vision_api_key = os.getenv("AZURE_AI_VISION_API_KEY")

vision_api_version = os.getenv("AZURE_AI_VISION_API_VERSION")

vision_region = os.getenv("AZURE_AI_VISION_REGION")

vision_endpoint = os.getenv("AZURE_AI_VISION_ENDPOINT")

credential = AzureKeyCredential(search_api_key)

search_client = SearchClient(endpoint=search_endpoint, index_name=index_name, credential=credential)

"""

# Each of the search cases commented out is valid and successful but we will keep it simple

# start with a vector search

blob_url = "https://saravinoteblogs.blob.core.windows.net/playground/vision/query/RedCar4.jpg?sp=racwdle&st=2025-05-26T23:54:09Z&se=2025-05-27T07:54:09Z&spr=https&sv=2024-11-04&sr=d&sig=9RRmmtlBnEiFsOGHJ2d%2ByEkBz2gxXOrQEc%2B4uf%2Fd6ao%3D&sdd=2"

vector = vectorize_image(blob_url, vision_api_key, "eastus")

print(f"len={len(vector)}")

print("search_client created")

vector_query = RawVectorQuery(vector=vector,

                              k=3,

                              fields = "image_vector")

results = search_client.search(

    search_text=None,

    vector_queries= [vector_query],

    select=["id", "description"]

)

# and simple text multimodal

results = search_client.search(query_type='simple',

    search_text="green street crossing mark for bicycles" ,

    select='id,description',

    include_total_count=True,

    top=10)

"""

# and effect of alternate jargon

results = search_client.search(query_type='simple',

    search_text="red car" ,

    select='id,description',

    include_total_count=True,

    top=5)

"""

# and semantic search

results = search_client.search(query_type='semantic', semantic_configuration_name='my-semantic-config',

    search_text="green crossing for bicycles at street intersection",

    select='id,description', query_caption='extractive')

# and vectorizable text query

query="Do bicycles have a dedicated crossing at street intersections?"

vector_query = VectorizableTextQuery(text=query, k_nearest_neighbors=50, fields="image_vector")

# Set up the search results and the chat thread.

# Retrieve the selected fields from the search index related to the question.

# Search results are limited to the top 5 matches. Limiting top can help you stay under LLM quotas.

results = search_client.search(

    search_text=query,

    vector_queries= [vector_query],

    select=["id", "description"],

    include_total_count=True,

    top=5,

)

# this one returns Message: Field 'image_vector' does not have a vectorizer defined in it's vector profile.

"""

print(repr(results))

if results:

    print(f"Number of results: {results.get_count()}")

    for result in results:

         if result:

            # print(repr(result))

            print(f"{result['id']}")

            # print("\n")

            # break

Output:

<iterator object azure.core.paging.ItemPaged at 0x2604c3fa510>

Number of results: 12305

017760

017761

017754

004391

014962