Upload app.py

app.py

@@ -31,7 +31,7 @@ def load_documents():
 
 
 def split_documents(documents):
-    text_splitter = SemanticChunker(OpenAIEmbeddings())
+    text_splitter = SemanticChunker(OpenAIEmbeddings(temperature=0))
     texts = text_splitter.split_documents(documents)
     return texts
 
@@ -39,7 +39,7 @@ def split_documents(documents):
 def embeddings_on_local_vectordb(texts):
     vectordb = Chroma.from_documents(
         texts,
-        embedding=OpenAIEmbeddings(),
+        embedding=OpenAIEmbeddings(temperature=0),
         persist_directory=LOCAL_VECTOR_STORE_DIR.as_posix(),
     )
     vectordb.persist()
@@ -52,7 +52,7 @@ def embeddings_on_local_vectordb(texts):
 
 def query_llm(retriever, query):
     qa_chain = ConversationalRetrievalChain.from_llm(
-        llm=OpenAIChat(),
+        llm=OpenAIChat(temperature=0),
         retriever=retriever,
         return_source_documents=True,
         chain_type="refine",