use chroma db from disk

app.py

@@ -15,12 +15,12 @@ from langchain.vectorstores import Chroma
 
 from langchain.chains import RetrievalQA
 
-web_links = ["https://www.databricks.com/","https://help.databricks.com","https://docs.databricks.com","https://kb.databricks.com/","http://docs.databricks.com/getting-started/index.html","http://docs.databricks.com/introduction/index.html","http://docs.databricks.com/getting-started/tutorials/index.html","http://docs.databricks.com/machine-learning/index.html","http://docs.databricks.com/sql/index.html"]
-loader = WebBaseLoader(web_links)
-documents = loader.load()
+#web_links = ["https://www.databricks.com/","https://help.databricks.com","https://docs.databricks.com","https://kb.databricks.com/","http://docs.databricks.com/getting-started/index.html","http://docs.databricks.com/introduction/index.html","http://docs.databricks.com/getting-started/tutorials/index.html","http://docs.databricks.com/machine-learning/index.html","http://docs.databricks.com/sql/index.html"]
+#loader = WebBaseLoader(web_links)
+#documents = loader.load()
      
-texts = text_splitter.split_documents(documents)
-db = Chroma.from_documents(texts, embeddings)
+#texts = text_splitter.split_documents(documents)
+db = Chroma.from_documents(persist_directory=chroma_db, embedding_function=embeddings)
 retriever = db.as_retriever()
 global qa 
 qa = RetrievalQA.from_chain_type(llm=model_id, chain_type="stuff", retriever=retriever, return_source_documents=True)