Azure认知向量搜索（ACS）索引创建与向量查询报错求助

我想基于向量查询实现一个Azure认知搜索（ACS）的课程目录检索功能，目前已经有一个名为courses_pd的Pandas DataFrame，包含content（课程内容）和embeddings（我用SentenceTransformer的all-MiniLM-L6-v2模型编码生成的向量）两列。

我已经在Azure Databricks notebook中编写了创建ACS索引和上传文档的代码，具体如下：

创建索引的代码片段 #

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
)
from azure.core.credentials import AzureKeyCredential


# 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.")

上传文档的代码片段 #

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.")

查询时遇到的问题 #

现在我尝试查询时遇到了多个错误。不管是直接用原始字符串查询，还是先把查询字符串用模型编码成向量再查询，都会返回<iterator object azure.core.paging.ItemPaged at 0x7fcf9f086220>，同时日志显示查询失败。

我的查询代码如下：

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']}")

具体错误信息 #

运行上述查询代码后，我得到了以下错误：

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.

我尝试在VectorQuery中添加kind = 'vector'参数，但仍然报错说kind未设置！

我已经在Azure门户中确认索引和文档都已成功创建，索引结构显示正常（能看到定义的id、content和embedding字段）。

我应该是在索引创建或者查询的方式上出了问题，查了官方文档和GitHub代码库都没找到相关的解决方法，我是刚接触这个技术，希望能得到社区的帮助，谢谢！

备注：内容来源于stack exchange，提问作者Strayhorn