fix vectorstore use

app.py

@@ -7,7 +7,7 @@ from azure.storage.fileshare import ShareServiceClient
 # Import custom modules
 from climateqa.engine.embeddings import get_embeddings_function
 from climateqa.engine.llm import get_llm
-from climateqa.engine.vectorstore import get_vectorstore
+from climateqa.engine.vectorstore import get_azure_search_vectorstore
 from climateqa.engine.reranker import get_reranker
 from climateqa.engine.graph import make_graph_agent, make_graph_agent_poc
 from climateqa.engine.chains.retrieve_papers import find_papers
@@ -67,9 +67,9 @@ user_id = create_user_id()
 # Create vectorstore and retriever
 embeddings_function = get_embeddings_function()
 
-vectorstore = get_vectorstore(provider="azure_search", embeddings=embeddings_function, index_name="climateqa-ipx")
-vectorstore_graphs = get_vectorstore(provider="azure_search", embeddings=embeddings_function, index_name="climateqa-owid", text_key="description")
-vectorstore_region = get_vectorstore(provider="azure_search", embeddings=embeddings_function, index_name="climateqa-v2")
+vectorstore = get_azure_search_vectorstore(embeddings=embeddings_function, index_name="climateqa-ipx")
+vectorstore_graphs = get_azure_search_vectorstore(embeddings=embeddings_function, index_name="climateqa-owid", text_key="description")
+vectorstore_region = get_azure_search_vectorstore(embeddings=embeddings_function, index_name="climateqa-v2")
 
 
 llm = get_llm(provider="openai", max_tokens=1024, temperature=0.0)

climateqa/engine/chains/retrieve_documents.py

@@ -19,7 +19,7 @@ from ..llm import get_llm
 from .prompts import retrieve_chapter_prompt_template
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_core.output_parsers import StrOutputParser
-from ..vectorstore import get_vectorstore
+from ..vectorstore import get_azure_search_vectorstore
 from ..embeddings import get_embeddings_function
 import ast
 
@@ -134,7 +134,7 @@ def get_ToCs(version: str) :
         "version": version
     }
     embeddings_function = get_embeddings_function()
-    vectorstore = get_vectorstore(provider="qdrant", embeddings=embeddings_function, index_name="climateqa")
+    vectorstore = get_azure_search_vectorstore(embeddings=embeddings_function, index_name="climateqa")
     tocs = vectorstore.similarity_search_with_score(query="",filter = filters_text)
 
     # remove duplicates or almost duplicates