Hybrid search through python code in Azure AI search.

Question

# Pure Vector Search
query = "how many rooms are there."
# generate_embeddings(query, model="model_nme")

search_client = SearchClient(search_service_endpoint, index_name, credential=credential)
vector_query = VectorizedQuery(text=query, k=1, fields="vector", exhaustive=True)

results = search_client.search(
    search_text=None,
    vector_queries= [vector_query],
    select=["title", "content", "category"],

)

# Display search results
for result in results:
    print(f"Title: {result['title']}")
    print(f"Score: {result['@search.score']}")
    print(f"Content: {result['content']}")
    print(f"Category: {result['category']}\n")

My Embedding are working fine. However, when I am running the above peice of code, its not showing me any results and neither showing any result. Please help me.

Answer 1

Hi @Smit Tiwary ,

I was able to successfully execute the VectorizedQuery. For the purpose of debugging the code, I had programatically generated the embeddings by setting the dimensionality to 1536 i.e. same as the dimensionality of the vector I created in the Azure portal. Please find below:

import random

# Set the dimensionality
dimensionality = 1536

# Generate a random query vector with values between -1 and 1
query_vector = [random.uniform(-1, 1) for _ in range(dimensionality)]

# Print or use the query vector as needed
print(query_vector)

Then I used this query_vector further in the piece of code that you were facing issue with and it is working fine for me now. Please find below:

import json
from azure.search.documents import SearchClient
from azure.search.documents.models import VectorizedQuery
from azure.core.credentials import AzureKeyCredential

# Set the values of these variables to your Azure Cognitive Search service, index, and credentials
search_service_endpoint = "****"
index_name = "langchain-vector-demo"
api_key = "****"


# Create an AzureKeyCredential object with the specified API key
credential = AzureKeyCredential(api_key)

# Create a SearchClient object with the specified endpoint, index name, and credentials
search_client = SearchClient(search_service_endpoint, index_name, credential)

# Create a VectorizedQuery object with the specified query vector and field name
vector_query = VectorizedQuery(vector=query_vector, fields="content_vector", exhaustive=True)

# Perform the vector search and retrieve the search results
results = search_client.search(
    search_text=None,
    vector_queries=[vector_query],
    select=["*"],
)

# Display search results
for result in results:
    print(result)

I found that the code is working as expected.

Below are the dimensions of the vector I created in the portal, just for testing, without depending on the GIT embeddings:

Note : Try to add more PRINT / debug statements for knowing the dimensionality, SELECT query columns etc need to match exactly in however way defined in the Azure portal.

1. Match the dimensionality Azure Portal Vs Python code.
2. Match column names Azure Portal Vs Python code.

Repro Output:

Hope this helps.

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Answer 2

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