better fix for chromadb issue

from here: https://github.com/langchain-ai/langchain/issues/26884

app_gradio.py

@@ -27,6 +27,7 @@ from langchain_core.output_parsers import StrOutputParser
 from langchain.callbacks import FileCallbackHandler
 from langchain.callbacks.manager import CallbackManager
 from langchain.schema import Document
+import chromadb
 
 import instructor
 from pydantic import BaseModel, Field
@@ -313,12 +314,12 @@ def run_rag_qa(query, papers_df, question_type):
         doc = Document(page_content=content, metadata=metadata)
         documents.append(doc)
 
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=50, add_start_index=True)
-    
     try:
         del vectorstore, splits
+        chromadb.api.client.SharedSystemClient.clear_system_cache()
     except:
         print('no vectorstore found, initializing')
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=150, chunk_overlap=50, add_start_index=True)
     splits = text_splitter.split_documents(documents)
     vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings, collection_name='retdoc4')
     retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": len(documents)})