Change link to dataset (#20)

* enable deep memory

* point to dev vector store with tensor_db

* rename blocks to demo for autoreload without args

app.py

@@ -87,9 +87,8 @@ def get_answer(history, sources: Optional[list[str]] = None):
         yield history, completion
 
 
-block = gr.Blocks()
-
-with block:
+demo = gr.Blocks()
+with demo:
     with gr.Row():
         gr.Markdown(
             "<h3><center>Buster 🤖: A Question-Answering Bot for your documentation</center></h3>"
@@ -132,5 +131,5 @@ with block:
     ).then(add_sources, inputs=[chatbot, response], outputs=[chatbot])
 
 
-block.queue(concurrency_count=16)
-block.launch(debug=True, share=False)
+demo.queue(concurrency_count=16)
+demo.launch(debug=True, share=False)

cfg.py

@@ -17,7 +17,7 @@ ACTIVELOOP_TOKEN = os.getenv("ACTIVELOOP_TOKEN")
 if ACTIVELOOP_TOKEN is None:
     logger.warning("No activeloop token found, you will not be able to fetch data.")
 
-DEEPLAKE_DATASET = os.getenv("DEEPLAKE_DATASET", "wiki_tai_langchain")
+DEEPLAKE_DATASET = os.getenv("DEEPLAKE_DATASET", "dev_vector_store")
 DEEPLAKE_ORG = os.getenv("DEEPLAKE_ORG", "towards_ai")
 
 # if you want to use a local dataset, set the env. variable, it overrides all others
@@ -70,6 +70,7 @@ A user will now submit a question. Respond 'true' if it is valid, respond 'false
         "embedding_model": "text-embedding-ada-002",
         "exec_option": "compute_engine",
         "use_tql": True,
+        "deep_memory": True,
     },
     documents_answerer_cfg={
         "no_documents_message": "No blog posts are available for this question.",