grab date with fn inside runnable lambda

chain.py

@@ -160,9 +160,13 @@ class ChainBuilder:
         # vector_search_as_retriever = self.get_retriever()
         # query_rewrite_prompt = self.get_query_rewrite_prompt()
 
+        def get_date():
+            return {"today": datetime.datetime.now().strftime("%B %d, %Y")}
+
         # RAG Chain
         chain = (
             {
+                "date_str": RunnableLambda(get_date), # date to be passed to system prompt for context of when knowledge base was last updated
                 "question": itemgetter("messages") | RunnableLambda(self.extract_user_query_string), # set 'question' to the result of: grabbing the ["messages"] component of the dict we 'invoke()' or 'stream()', then passing to extract_user_query_string()
                 "chat_history": itemgetter("messages") | RunnableLambda(self.extract_chat_history),
                 "formatted_chat_history": itemgetter("messages")
@@ -180,8 +184,7 @@ class ChainBuilder:
                 | self.get_retriever() # set 'context' to the result of passing either the base question, or the reformatted question to the retriever for semantic search
                 | RunnableLambda(self.format_context), 
                 "formatted_chat_history": itemgetter("formatted_chat_history"),
-                "question": itemgetter("question"),
-                "date_str": datetime.datetime.now().strftime("%B %d, %Y"), # date to be passed to system prompt for context of when knowledge base was last updated
+                "question": itemgetter("question")
             }
             | self.get_prompt() # 'context', 'formatted_chat_history', and 'question' passed to prompt
             | self.get_model() # prompt passed to model