[Python] Azure Cognitive Vector search query and index creation

I wanted to create a Azure Cognitive Search to query course catalogue using vectors. I have pandas dataframe called courses_pd and it has two columns, 'content' and 'embeddings' which is the embedding I have created using model = SentenceTransformer('all-MiniLM-L6-v2') and then model.encode(x).

Below is the python code-snippet which creates the index in ACS and uploades the documents from Azure databricks notebook.

from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
SimpleField,
SearchFieldDataType,
SearchableField,
SearchField,
VectorSearch,
HnswAlgorithmConfiguration,
VectorSearchProfile,
SemanticConfiguration,
SemanticPrioritizedFields,
SemanticField,
SemanticSearch,
SearchIndex,
AzureOpenAIVectorizer,
AzureOpenAIVectorizerParameters
)


# Azure Cognitive Search setup
service_endpoint = "https://yourserviceendpoint.search.windows.net"
admin_key = "ABC"
index_name = "courses-index"

# Wrap admin_key in AzureKeyCredential
credential = AzureKeyCredential(admin_key)

# Create the index client with AzureKeyCredential
index_client = SearchIndexClient(endpoint=service_endpoint, credential=credential)

# Define the index schema
fields = [
SimpleField(name="id", type="Edm.String", key=True),
SimpleField(name="content", type="Edm.String"),
SearchField(
name="embedding",
type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
searchable=True,
vector_search_dimensions=384,
vector_search_profile_name="myHnswProfile"
)
# SearchField(name="embedding", type='Collection(Edm.Single)', searchable=True)
]

# Configure the vector search configuration
vector_search = VectorSearch(
algorithms=[
HnswAlgorithmConfiguration(
name="myHnsw"
)
],
profiles=[
VectorSearchProfile(
name="myHnswProfile",
algorithm_configuration_name="myHnsw"
)
]
)

# Create the index
index = SearchIndex(
name=index_name,
fields=fields,
vector_search=vector_search
)

# Send the index creation request
index_client.create_index(index)

print(f"Index '{index_name}' created successfully.")


And then upload the document using the below code:

from azure.search.documents import SearchClient

# Generate embeddings and upload data
search_client = SearchClient(endpoint=service_endpoint, index_name=index_name, credential=credential)

documents = []
for i, row in courses_pd.iterrows():
document = {
"id": str(i),
"content": row["content"],
"embedding": row["embeddings"] # Ensure embeddings are a list of floats
}
documents.append(document)

# Upload documents to the index
search_client.upload_documents(documents=documents)
print(f"Uploaded {len(documents)} documents to Azure Cognitive Search.")


Now, when I am querying the search_client, I am getting multiple errors, if i search using raw string, or after doing a model.encode(str), it return a <iterator object azure.core.paging.ItemPaged at 0x7fcf9f086220> but search is getting failed in the log.

from azure.search.documents.models import VectorQuery

# Generate embedding for the query
query = "machine learning"
query_embedding = model.encode(query).tolist() # Convert to list of floats

# Create a VectorQuery
vector_query = VectorQuery(
vector=query_embedding,
k=3, # Number of nearest neighbors
fields="embedding" # Name of the field where embeddings are stored
)

# Perform the search
results = search_client.search(
vector_queries=[vector_query],
select=["id", "content"]
)

# Print the results
for result in results:
print(f"ID: {result['id']}, Content: {result['content']}")


The error then says:

vector is not a known attribute of class <class 'azure.search.documents._generated.models._models_py3.VectorQuery'> and will be ignored
k is not a known attribute of class <class 'azure.search.documents._generated.models._models_py3.VectorQuery'> and will be ignored
HttpResponseError: (InvalidRequestParameter) The vector query's 'kind' parameter is not set.


Then tried with providing kind = 'vector' as a parameter in VectorQuery, then it says kind is not set!

Documents are getting uploaded and index is created as I can see in the portal.

I must be doing something wrong, either the way I have setup the search index or the way I am querying the index, the documentation and github codebase is not providing anything around this, so seeking help from the community, new to this.

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