Update app.py

app.py

@@ -8,7 +8,7 @@ from dotenv import load_dotenv
 
 from chunking import chunk_pythoncode_and_add_metadata
 from chunking import chunk_text_and_add_metadata
-from vectorstore import get_chroma_vectorstore
+from vectorstore import setup_vectorstore
 from download_repo import download_gitlab_repo_to_hfspace
 from process_repo import extract_repo_files
 from ragchain import RAGChain
@@ -58,8 +58,8 @@ def rag_workflow(query):
     """
     RAGChain class to perform the complete RAG workflow.
     """
-    # Assume 'llm' and 'vector_store' are already initialized instances
-    rag_chain = RAGChain(llm, vector_store)
+    # Assume 'llm' and 'vectorstore' are already initialized instances
+    rag_chain = RAGChain(llm, vectorstore)
 
 
     """
@@ -102,7 +102,7 @@ def rag_workflow(query):
 
 
 def initialize():
-    global vector_store, chunks, llm
+    global vectorstore, chunks, llm
 
     
     download_gitlab_repo_to_hfspace(GITLAB_API_URL, GITLAB_PROJECT_ID, GITLAB_PROJECT_VERSION)
@@ -120,7 +120,7 @@ def initialize():
     print(f"Total number of code_chunks: {len(code_chunks)}")
     print(f"Total number of doc_chunks: {len(doc_chunks)}")
 
-    vector_store = embed_documents_into_vectorstore(doc_chunks + code_chunks, EMBEDDING_MODEL_NAME, VECTORSTORE_DIRECTORY)
+    vectorstore = setup_vectorstore(doc_chunks + code_chunks, EMBEDDING_MODEL_NAME, VECTORSTORE_DIRECTORY)
     llm = get_groq_llm(LLM_MODEL_NAME, LLM_MODEL_TEMPERATURE, GROQ_API_KEY)
 
     from langchain_community.document_loaders import TextLoader