From 28537b6a84ce51487f477e2619d1c177e08f399b Mon Sep 17 00:00:00 2001
From: VaiTon <eyadlorenzo@gmail.com>
Date: Thu, 6 Jul 2023 00:16:11 +0200
Subject: [PATCH] Better error message if .env is empty/does not exist.

---
 constants.py  | 2 ++
 ingest.py     | 7 ++++---
 privateGPT.py | 6 ++++--
 3 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/constants.py b/constants.py
index ca3b8a163..366a48a6d 100644
--- a/constants.py
+++ b/constants.py
@@ -6,6 +6,8 @@
 
 # Define the folder for storing database
 PERSIST_DIRECTORY = os.environ.get('PERSIST_DIRECTORY')
+if PERSIST_DIRECTORY is None:
+    raise Exception("Please set the PERSIST_DIRECTORY environment variable")
 
 # Define the Chroma settings
 CHROMA_SETTINGS = Settings(
diff --git a/ingest.py b/ingest.py
index 0ca80743f..825d73269 100755
--- a/ingest.py
+++ b/ingest.py
@@ -24,11 +24,12 @@
 from langchain.vectorstores import Chroma
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.docstore.document import Document
-from constants import CHROMA_SETTINGS
-
 
-load_dotenv()
+if not load_dotenv():
+    print("Could not load .env file or it is empty. Please check if it exists and is readable.")
+    exit(1)
 
+from constants import CHROMA_SETTINGS
 
 # Load environment variables
 persist_directory = os.environ.get('PERSIST_DIRECTORY')
diff --git a/privateGPT.py b/privateGPT.py
index 0e3b9d04d..74b3ca6f7 100755
--- a/privateGPT.py
+++ b/privateGPT.py
@@ -9,7 +9,9 @@
 import argparse
 import time
 
-load_dotenv()
+if not load_dotenv():
+    print("Could not load .env file or it is empty. Please check if it exists and is readable.")
+    exit(1)
 
 embeddings_model_name = os.environ.get("EMBEDDINGS_MODEL_NAME")
 persist_directory = os.environ.get('PERSIST_DIRECTORY')
@@ -39,7 +41,7 @@ def main():
         case _default:
             # raise exception if model_type is not supported
             raise Exception(f"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All")
-        
+
     qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents= not args.hide_source)
     # Interactive questions and answers
     while True: