Update app.py

app.py

@@ -10,7 +10,6 @@ import io
 from huggingface_hub import HfApi, login
 from PyPDF2 import PdfReader
 from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import Chroma
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain_groq import ChatGroq
 from dotenv import load_dotenv
@@ -246,7 +245,7 @@ def embed_documents_into_vectorstore(chunks, model_name, persist_directory):
     print("Start setup_vectorstore_function")
     embedding_model = HuggingFaceEmbeddings(model_name=model_name)   
     vectorstore = get_chroma_vectorstore(embedding_model, persist_directory)
-    vectorstore.add_documents(chunks)
+    vector_store.add_documents(chunks)
     return vectorstore
 
 # Setup LLM
@@ -334,8 +333,8 @@ def rag_workflow(query):
 
 
     
-    kadi_apy_docs = retrieve_within_kadiApy_docs (docstore, query, k = 5)
-    kadi_apy_library_docs = retrieve_within_kadiApy_library (docstore, query, k = 10)
+    kadi_apy_docs = retrieve_within_kadiApy_docs (vectorstore, query, k = 5)
+    kadi_apy_library_docs = retrieve_within_kadiApy_library (vectorstore, query, k = 10)
 
     doc_context = format_kadi_api_doc_context(kadi_apy_docs)
     code_context = format_kadi_apy_library_context(kadi_apy_library_docs)
@@ -391,7 +390,7 @@ def rag_workflow(query):
 
 
 def initialize():
-    global docstore, codestore, chunks, llm
+    global vectore_store, chunks, llm
 
     download_gitlab_project_by_version()
     #download_gitlab_repo()
@@ -417,11 +416,11 @@ def initialize():
     #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)
 
-    embed_documents_into_vectorstore(
-        chunks=kadiAPY_code_chunks + kadiAPY_doc_chunks, 
-        model_name= EMBEDDING_MODEL_NAME,  # Correcting the variable name
-        persist_directory= PERSIST_DOC_DIRECTORY  # Correcting the variable name
-    )
+    vectorstore = embed_documents_into_vectorstore(
+                    chunks=kadiAPY_code_chunks + kadiAPY_doc_chunks, 
+                    model_name= EMBEDDING_MODEL_NAME, 
+                    persist_directory= PERSIST_DOC_DIRECTORY 
+                    )
     
     llm = setup_llm(LLM_MODEL_NAME, LLM_TEMPERATURE, GROQ_API_KEY)