changes to app and config

app.py

@@ -10,9 +10,6 @@ from src.document_retrieval import DocumentRetrieval
 from utils.parsing.sambaparse import parse_doc_universal # added
 from utils.vectordb.vector_db import VectorDb
 
-CONFIG_PATH = os.path.join(current_dir,'config.yaml')
-PERSIST_DIRECTORY = os.path.join(current_dir,f"data/my-vector-db") # changed to current_dir
-
 def handle_userinput(user_question, conversation_chain, history):
     if user_question:
         try:
@@ -46,12 +43,6 @@ def process_documents(files, collection_name, document_retrieval, vectorstore, c
     except Exception as e:
         return conversation_chain, vectorstore, document_retrieval, collection_name, f"An error occurred while processing: {str(e)}"
 
-# Read config file
-with open(CONFIG_PATH, 'r') as yaml_file:
-    config = yaml.safe_load(yaml_file)
-    
-prod_mode = config.get('prod_mode', False)
-
 caution_text = """⚠️ Note: depending on the size of your document, this could take several minutes.
 """
 
@@ -66,7 +57,7 @@ with gr.Blocks() as demo:
     
     gr.Markdown("Powered by LLama3.1-8B-Instruct on SambaNova Cloud. Get your API key [here](https://cloud.sambanova.ai/apis).")
 
-    #api_key = gr.Textbox(label="API Key", type="password", placeholder="(Optional) Enter your API key here for more availability")
+    api_key = gr.Textbox(label="API Key", type="password", placeholder="(Optional) Enter your API key here for more availability")
 
     # Step 1: Add PDF file   
     gr.Markdown("## 1️⃣ Upload PDF")

config.yaml

@@ -1,9 +1,9 @@
 api: "sncloud" #  set either sambastudio or sncloud
 
 embedding_model: 
-    "type": "cpu" # set either sambastudio or cpu
+    "type": "sambastudio" # set either sambastudio or cpu
     "batch_size": 1 #set depending of your endpoint configuration (1 if CoE embedding expert)
-    "coe": True #set true if using Sambastudio embeddings in a CoE endpoint 
+    "coe": False #set true if using Sambastudio embeddings in a CoE endpoint 
     "select_expert": "e5-mistral-7b-instruct" #set if using SambaStudio CoE embedding expert
 
 llm: