Update app.py

app.py

@@ -45,7 +45,7 @@ development_texts = [
 # --- Chroma Client Settings (in-memory) ---
 client_settings = Settings(
     chroma_api_impl="local",
-    persist_directory=None  # Set to None for ephemeral in-memory DB
+    persist_directory=None  # Use None for ephemeral in-memory DB; or specify a folder path to persist data.
 )
 
 # --- Preprocessing & Embeddings ---
@@ -55,7 +55,7 @@ development_docs = splitter.create_documents(development_texts)
 
 embeddings = OpenAIEmbeddings(
     model="text-embedding-3-large",
-    openai_api_key=os.environ.get("OPENAI_API_KEY")  # Make sure your HF Space secret is set
+    openai_api_key=os.environ.get("OPENAI_API_KEY")  # Set this in your HF Secrets.
 )
 
 # Create vector stores using local in-memory Chroma
@@ -89,8 +89,8 @@ development_tool = create_retriever_tool(
 tools = [research_tool, development_tool]
 
 # --- Agent and Workflow Functions ---
+# Note: Using only AIMessage and HumanMessage for message types.
 class AgentState(TypedDict):
-    # Using only AIMessage | HumanMessage
     messages: Annotated[Sequence[AIMessage | HumanMessage], add_messages]
 
 def agent(state: AgentState):
@@ -127,8 +127,6 @@ Otherwise, just answer directly.
     if response.status_code == 200:
         response_text = response.json()['choices'][0]['message']['content']
         logger.info(f"DeepSeek response: {response_text}")
-
-        # Format the response to call the right tool
         if "SEARCH_RESEARCH:" in response_text:
             query = response_text.split("SEARCH_RESEARCH:")[1].strip()
             results = research_retriever.invoke(query)