fix auth bug with a new embedding endpoint

app.py

@@ -7,6 +7,7 @@ from spinoza_project.source.backend.llm_utils import (
     get_llm,
     get_llm_api,
     get_vectorstore,
+    get_vectorstore_api,
 )
 from spinoza_project.source.backend.document_store import pickle_to_document_store
 from spinoza_project.source.backend.get_prompts import get_qa_prompts
@@ -46,6 +47,7 @@ llm = get_llm_api()
 
 ## Loading_tools
 print("Loading Databases")
+bdd_presse = get_vectorstore_api("presse")
 qdrants = {
     tab: pickle_to_document_store(
         hf_hub_download(
@@ -204,7 +206,7 @@ def answer_questions(*questions_sources, config=config):
         ]
 
 
-def get_sources(questions, bdd_presse, qdrants=qdrants, config=config):
+def get_sources(questions, qdrants=qdrants, bdd_presse=bdd_presse, config=config):
     k = config["num_document_retrieved"]
     min_similarity = config["min_similarity"]
     formated = []
@@ -258,9 +260,8 @@ def get_sources(questions, bdd_presse, qdrants=qdrants, config=config):
     return formated, text
 
 
-def retrieve_sources(*questions, qdrants=qdrants, config=config):
-    bdd_presse = get_vectorstore("presse")
-    formated_sources, text_sources = get_sources(questions, bdd_presse, qdrants, config)
+def retrieve_sources(*questions, qdrants=qdrants, bdd_presse=bdd_presse, config=config):
+    formated_sources, text_sources = get_sources(questions, qdrants, bdd_presse, config)
 
     return (formated_sources, *text_sources)
 

spinoza_project/source/backend/llm_utils.py

@@ -1,4 +1,3 @@
-from tracemalloc import stop
 from langchain_openai import AzureChatOpenAI
 from msal import ConfidentialClientApplication
 from langchain_openai import AzureOpenAIEmbeddings
@@ -97,3 +96,23 @@ def get_vectorstore(index_name, model="text-embedding-ada-002"):
     )
 
     return vector_store
+
+
+def get_vectorstore_api(index_name):
+    aoai_embeddings = AzureOpenAIEmbeddings(
+        model="text-embedding-ada-002",
+        api_key=os.getenv("OPENAI_API_KEY"),
+        azure_endpoint=os.environ["AZURE_ENDPOINT_API"],
+        openai_api_version=os.getenv("OPENAI_API_VERSION"),
+    )
+
+    os.environ["AZURE_OPENAI_API_KEY"] = get_token()
+
+    vector_store: AzureSearch = AzureSearch(
+        azure_search_endpoint=os.getenv("VECTOR_STORE_ADDRESS"),
+        azure_search_key=os.getenv("VECTOR_STORE_PASSWORD"),
+        index_name=index_name,
+        embedding_function=aoai_embeddings.embed_query,
+    )
+
+    return vector_store