Update SDK (#1241)

Author: pamelafox

app/backend/approaches/approach.py

@@ -5,9 +5,9 @@
 import aiohttp
 from azure.search.documents.aio import SearchClient
 from azure.search.documents.models import (
-    CaptionResult,
+    QueryCaptionResult,
     QueryType,
-    RawVectorQuery,
+    VectorizedQuery,
     VectorQuery,
 )
 from openai import AsyncOpenAI
@@ -27,7 +27,7 @@ class Document:
     sourcefile: Optional[str]
     oids: Optional[List[str]]
     groups: Optional[List[str]]
-    captions: List[CaptionResult]
+    captions: List[QueryCaptionResult]
 
     def serialize_for_results(self) -> dict[str, Any]:
         return {
@@ -146,7 +146,7 @@ async def search(
                         sourcefile=document.get("sourcefile"),
                         oids=document.get("oids"),
                         groups=document.get("groups"),
-                        captions=cast(List[CaptionResult], document.get("@search.captions")),
+                        captions=cast(List[QueryCaptionResult], document.get("@search.captions")),
                     )
                 )
         return documents
@@ -186,7 +186,7 @@ async def compute_text_embedding(self, q: str):
             input=q,
         )
         query_vector = embedding.data[0].embedding
-        return RawVectorQuery(vector=query_vector, k=50, fields="embedding")
+        return VectorizedQuery(vector=query_vector, k_nearest_neighbors=50, fields="embedding")
 
     async def compute_image_embedding(self, q: str, vision_endpoint: str, vision_key: str):
         endpoint = f"{vision_endpoint}computervision/retrieval:vectorizeText"
@@ -200,7 +200,7 @@ async def compute_image_embedding(self, q: str, vision_endpoint: str, vision_key
             ) as response:
                 json = await response.json()
                 image_query_vector = json["vector"]
-        return RawVectorQuery(vector=image_query_vector, k=50, fields="imageEmbedding")
+        return VectorizedQuery(vector=image_query_vector, k_nearest_neighbors=50, fields="imageEmbedding")
 
     async def run(
         self, messages: list[dict], stream: bool = False, session_state: Any = None, context: dict[str, Any] = {}

app/backend/requirements.in

@@ -4,7 +4,7 @@ quart-cors
 openai[datalib]>=1.3.7
 tiktoken
 tenacity
-azure-search-documents==11.4.0b11
+azure-search-documents==11.6.0b1
 azure-storage-blob
 uvicorn
 aiohttp

app/backend/requirements.txt

@@ -44,7 +44,7 @@ azure-monitor-opentelemetry==1.2.0
     # via -r requirements.in
 azure-monitor-opentelemetry-exporter==1.0.0b21
     # via azure-monitor-opentelemetry
-azure-search-documents==11.4.0b11
+azure-search-documents==11.6.0b1
     # via -r requirements.in
 azure-storage-blob==12.19.0
     # via -r requirements.in

scripts/prepdocslib/searchmanager.py

@@ -3,19 +3,18 @@
 from typing import List, Optional
 
 from azure.search.documents.indexes.models import (
+    HnswAlgorithmConfiguration,
     HnswParameters,
-    HnswVectorSearchAlgorithmConfiguration,
-    PrioritizedFields,
     SearchableField,
     SearchField,
     SearchFieldDataType,
     SearchIndex,
     SemanticConfiguration,
     SemanticField,
-    SemanticSettings,
+    SemanticPrioritizedFields,
+    SemanticSearch,
     SimpleField,
     VectorSearch,
-    VectorSearchAlgorithmKind,
     VectorSearchProfile,
 )
 
@@ -74,7 +73,7 @@ async def create_index(self):
                     sortable=False,
                     facetable=False,
                     vector_search_dimensions=1536,
-                    vector_search_profile="embedding_config",
+                    vector_search_profile_name="embedding_config",
                 ),
                 SimpleField(name="category", type="Edm.String", filterable=True, facetable=True),
                 SimpleField(name="sourcepage", type="Edm.String", filterable=True, facetable=True),
@@ -102,35 +101,34 @@ async def create_index(self):
                         sortable=False,
                         facetable=False,
                         vector_search_dimensions=1024,
-                        vector_search_profile="embedding_config",
+                        vector_search_profile_name="embedding_config",
                     ),
                 )
 
             index = SearchIndex(
                 name=self.search_info.index_name,
                 fields=fields,
-                semantic_settings=SemanticSettings(
+                semantic_search=SemanticSearch(
                     configurations=[
                         SemanticConfiguration(
                             name="default",
-                            prioritized_fields=PrioritizedFields(
-                                title_field=None, prioritized_content_fields=[SemanticField(field_name="content")]
+                            prioritized_fields=SemanticPrioritizedFields(
+                                title_field=None, content_fields=[SemanticField(field_name="content")]
                             ),
                         )
                     ]
                 ),
                 vector_search=VectorSearch(
                     algorithms=[
-                        HnswVectorSearchAlgorithmConfiguration(
+                        HnswAlgorithmConfiguration(
                             name="hnsw_config",
-                            kind=VectorSearchAlgorithmKind.HNSW,
                             parameters=HnswParameters(metric="cosine"),
                         )
                     ],
                     profiles=[
                         VectorSearchProfile(
                             name="embedding_config",
-                            algorithm="hnsw_config",
+                            algorithm_configuration_name="hnsw_config",
                         ),
                     ],
                 ),

scripts/requirements.in

@@ -1,7 +1,7 @@
 pypdf
 aiohttp
 azure-identity
-azure-search-documents==11.4.0b11
+azure-search-documents==11.6.0b1
 azure-ai-formrecognizer
 azure-storage-blob
 azure-storage-file-datalake

scripts/requirements.txt

@@ -36,7 +36,7 @@ azure-identity==1.15.0
     # via -r requirements.in
 azure-keyvault-secrets==4.7.0
     # via -r requirements.in
-azure-search-documents==11.4.0b11
+azure-search-documents==11.6.0b1
     # via -r requirements.in
 azure-storage-blob==12.19.0
     # via

tests/test_chatvisionapproach.py

@@ -3,7 +3,7 @@
 import pytest
 from azure.search.documents.indexes.models import SearchField, SearchIndex
 from azure.search.documents.models import (
-    RawVectorQuery,
+    VectorizedQuery,
 )
 from openai.types.chat import ChatCompletion
 
@@ -139,7 +139,7 @@ async def test_compute_text_embedding(chat_approach, openai_client, mock_openai_
 
     result = await chat_approach.compute_text_embedding("test query")
 
-    assert isinstance(result, RawVectorQuery)
+    assert isinstance(result, VectorizedQuery)
     assert result.vector == [0.0023064255, -0.009327292, -0.0028842222]
-    assert result.k == 50
+    assert result.k_nearest_neighbors == 50
     assert result.fields == "embedding"