Update app.py

app.py

@@ -17,6 +17,7 @@ from dotenv import load_dotenv
 from langchain.docstore.document import Document
 from langchain.schema import Document
 from chunk_python_code import chunk_python_code_with_metadata
+from vectorstore.py import get_chroma_vectorstore
 
 # Load environment variables from .env file
 load_dotenv()
@@ -241,16 +242,13 @@ def split_into_chunks(texts, references, chunk_size, chunk_overlap):
     return chunks
 
 # Setup Vectorstore
-def setup_vectorstore(chunks, model_name, persist_directory):
+def embed_documents_into_vectorstore(chunks, model_name, persist_directory):
     print("Start setup_vectorstore_function")
     embedding_model = HuggingFaceEmbeddings(model_name=model_name)   
-    vectorstore = Chroma.from_documents(chunks, embedding=embedding_model, persist_directory=persist_directory)
+    vectorstore = get_chroma_vectorstore(embedding_model)
+    vectorstore.add_documents(chunks)
     return vectorstore
 
-
-
-
-
 # Setup LLM
 def setup_llm(model_name, temperature, api_key):
     llm = ChatGroq(model=model_name, temperature=temperature, api_key=api_key)
@@ -365,8 +363,8 @@ def initialize():
     print(f"Total number of code_chunks: {len(kadiAPY_code_chunks)}")
     print(f"Total number of doc_chunks: {len(kadiAPY_doc_chunks)}")
 
-    docstore = setup_vectorstore(kadiAPY_code_chunks, EMBEDDING_MODEL_NAME, PERSIST_DOC_DIRECTORY)
-    codestore = setup_vectorstore(kadiAPY_doc_chunks, EMBEDDING_MODEL_NAME, PERSIST_CODE_DIRECTORY)
+    docstore = embed_documents_into_vectorstore(kadiAPY_code_chunks, EMBEDDING_MODEL_NAME, PERSIST_DOC_DIRECTORY)
+    codestore = embed_documents_into_vectorstore(kadiAPY_doc_chunks, EMBEDDING_MODEL_NAME, PERSIST_CODE_DIRECTORY)
     
     llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY)