Update app.py

app.py

@@ -5,8 +5,8 @@ from dotenv import load_dotenv
 
 from langchain.chains import ConversationalRetrievalChain
 from langchain_community.vectorstores import FAISS
-from langchain_community.chat_models import AzureChatOpenAI
 from langchain_community.embeddings import AzureOpenAIEmbeddings
+from langchain_community.chat_models import AzureChatOpenAI
 
 # Patch Gradio bug (schema parsing issue)
 import gradio_client.utils
@@ -17,25 +17,20 @@ load_dotenv()
 AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
 AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
 AZURE_OPENAI_LLM_DEPLOYMENT = os.getenv("AZURE_OPENAI_LLM_DEPLOYMENT")
-AZURE_OPENAI_EMBEDDING_DEPLOYMENT = os.getenv("AZURE_OPENAI_EMBEDDING_DEPLOYMENT")
-
-if not all([AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_LLM_DEPLOYMENT, AZURE_OPENAI_EMBEDDING_DEPLOYMENT]):
-    raise ValueError("Azure OpenAI environment variables missing.")
 
-print(f"Using embedding deployment: {AZURE_OPENAI_EMBEDDING_DEPLOYMENT}")
-print(f"Using LLM deployment: {AZURE_OPENAI_LLM_DEPLOYMENT}")
+if not all([AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_LLM_DEPLOYMENT]):
+    raise ValueError("Azure OpenAI environment variables are missing.")
 
 # Suppress warnings
 warnings.filterwarnings("ignore")
 
-# Initialize embedding model
+# Initialize embedding model (must be deployed separately)
 embeddings = AzureOpenAIEmbeddings(
-    azure_deployment=AZURE_OPENAI_EMBEDDING_DEPLOYMENT,
+    azure_deployment="text-embedding-ada-002",  # make sure this deployment exists
     azure_endpoint=AZURE_OPENAI_ENDPOINT,
     openai_api_key=AZURE_OPENAI_API_KEY,
     openai_api_version="2024-08-01-preview",
     chunk_size=1000
-    #validate_base_url=False
 )
 
 # Load FAISS vector store