From fbcc5be9187dfa413c14412507a46c30dc724f25 Mon Sep 17 00:00:00 2001
From: Jayant Verma <98758438+jayantverma2809@users.noreply.github.com>
Date: Fri, 2 Feb 2024 03:26:58 +0530
Subject: [PATCH] Fixed ImportError - cannot import name 'Vector' from
'azure.search.documents.models' (#10399)
---
llama_index/vector_stores/cogsearch.py | 8 ++++----
1 file changed, 4 insertions(+), 4 deletions(-)
diff --git a/llama_index/vector_stores/cogsearch.py b/llama_index/vector_stores/cogsearch.py
index ed5e5a413..fe4cf203f 100644
--- a/llama_index/vector_stores/cogsearch.py
+++ b/llama_index/vector_stores/cogsearch.py
@@ -627,12 +627,12 @@ class AzureQueryResultSearchBase:
class AzureQueryResultSearchDefault(AzureQueryResultSearchBase):
def _create_query_vector(self) -> Optional[List[Any]]:
"""Query vector store."""
- from azure.search.documents.models import Vector
+ from azure.search.documents.models import VectorizedQuery
if not self._query.query_embedding:
raise ValueError("Query missing embedding")
- vector = Vector(
+ vector = VectorizedQuery(
value=self._query.query_embedding,
k=self._query.similarity_top_k,
fields=self._field_mapping["embedding"],
@@ -666,13 +666,13 @@ class AzureQueryResultSearchHybrid(
class AzureQueryResultSearchSemanticHybrid(AzureQueryResultSearchHybrid):
def _create_query_vector(self) -> Optional[List[Any]]:
"""Query vector store."""
- from azure.search.documents.models import Vector
+ from azure.search.documents.models import VectorizedQuery
if not self._query.query_embedding:
raise ValueError("Query missing embedding")
# k is set to 50 to align with the number of accept document in azure semantic reranking model.
# https://learn.microsoft.com/en-us/azure/search/semantic-search-overview
- vector = Vector(
+ vector = VectorizedQuery(
value=self._query.query_embedding,
k=50,
fields=self._field_mapping["embedding"],
--
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