Update rag.py

rag.py

@@ -42,7 +42,7 @@ def generate_embeddings_and_vectorstore(path):
         #print('docs')
         #print(docs)
 
-        fc_embeddings = OpenAIEmbeddings(openai_api_key=os.getenv('OPENAI_KEY'))
+        fc_embeddings = OpenAIEmbeddings(openai_api_key=os.environ['OPENAI_KEY'])
         vectorstore = Chroma.from_documents(docs, fc_embeddings)
 
         return vectorstore
@@ -71,7 +71,7 @@ class Rag:
         self.qa = ConversationalRetrievalChain.from_llm(
                     llm=ChatOpenAI(model_name="gpt-3.5-turbo",
                                    temperature=0.3,
-                                   openai_api_key=os.getenv('OPENAI_KEY'),
+                                   openai_api_key=os.environ['OPENAI_KEY'],
                                    max_tokens=int(int(max_words) + (int(max_words) / 2))), #número máximo de tokens para a resposta
                     memory=self.memory,
                     retriever=vectorstore.as_retriever(), #search_kwargs={'k': 3}