Prompt minor changes (#20)

* update: using chat function workflows

* fix: add config.json boilerplate

* fix: test chat function name

* revert to non workflows until backend ready

* minor updates readme

* debugging input

* fix: prompt parsing

* introduce deepseek api option

* update conversationalStyle for agent

* minor prompt updates

* minor debugging

* fix: system prompt location summary

* fix system message location

Author: neagualexa

index.py

@@ -9,7 +9,7 @@ def handler(event, context):
     Lambda handler function
     """
     # Log the input event for debugging purposes
-    # print("Received event:", json.dumps(event, indent=2))
+    print("Received event:", json.dumps(event, indent=2))
 
     if "message" not in event:
         return {

src/agents/base_agent/base_agent.py

@@ -64,15 +64,15 @@ def call_model(self, state: State, config: RunnableConfig) -> str:
         # Adding external student progress and question context details from data queries
         question_response_details = config["configurable"].get("question_response_details", "")
         if question_response_details:
-            system_message += f"## Known Question Materials: {question_response_details} \n\n"
+            system_message += f"\n\n ## Known Question Materials: {question_response_details} \n\n"
 
         # Adding summary and conversational style to the system message
         summary = state.get("summary", "")
         conversationalStyle = state.get("conversationalStyle", "")
         if summary:
             system_message += summary_system_prompt.format(summary=summary)
         if conversationalStyle:
-            system_message += f"## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
+            system_message += f"\n\n ## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
 
         messages = [SystemMessage(content=system_message)] + state['messages']
 
@@ -120,12 +120,12 @@ def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
             conversationalStyle_message = self.conversation_preference_prompt
 
         # STEP 1: Summarize the conversation
-        messages = state["messages"][:-1] + [SystemMessage(content=summary_message)] 
+        messages = [SystemMessage(content=summary_message)] + state["messages"][:-1]
         valid_messages = self.check_for_valid_messages(messages)
         summary_response = self.summarisation_llm.invoke(valid_messages)
 
         # STEP 2: Analyze the conversational style
-        messages = state["messages"][:-1] + [SystemMessage(content=conversationalStyle_message)]
+        messages = [SystemMessage(content=conversationalStyle_message)] + state["messages"][:-1]
         valid_messages = self.check_for_valid_messages(messages)
         conversationalStyle_response = self.summarisation_llm.invoke(valid_messages)
 
@@ -184,7 +184,7 @@ def invoke_base_agent(query: str, conversation_history: list, summary: str, conv
     Call an agent that has no conversation memory and expects to receive all past messages in the params and the latest human request in the query.
     If conversation history longer than X, the agent will summarize the conversation and will provide a conversational style analysis.
     """
-    print(f'in invoke_base_agent(), query = {query}, thread_id = {session_id}')
+    print(f'in invoke_base_agent(), thread_id = {session_id}')
 
     config = {"configurable": {"thread_id": session_id, "summary": summary, "conversational_style": conversationalStyle, "question_response_details": question_response_details}}
     response_events = agent.app.invoke({"messages": conversation_history, "summary": summary, "conversational_style": conversationalStyle}, config=config, stream_mode="values") #updates

src/agents/google_learnLM_agent/google_learnLM_agent.py

@@ -68,15 +68,15 @@ def call_model(self, state: State, config: RunnableConfig) -> str:
         # Adding external student progress and question context details from data queries
         question_response_details = config["configurable"].get("question_response_details", "")
         if question_response_details:
-            system_message += f"## Known Question Materials: {question_response_details} \n\n"
+            system_message += f"\n\n ## Known Question Materials: {question_response_details} \n\n"
 
         # Adding summary and conversational style to the system message
         summary = state.get("summary", "")
         conversationalStyle = state.get("conversationalStyle", "")
         if summary:
             system_message += summary_system_prompt.format(summary=summary)
         if conversationalStyle:
-            system_message += f"## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
+            system_message += f"\n\n ## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
 
         messages = [SystemMessage(content=system_message)] + state['messages']
 
@@ -124,12 +124,12 @@ def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
             conversationalStyle_message = self.conversation_preference_prompt
 
         # STEP 1: Summarize the conversation
-        messages = state["messages"][:-1] + [SystemMessage(content=summary_message)] 
+        messages = [SystemMessage(content=summary_message)] + state["messages"][:-1]
         valid_messages = self.check_for_valid_messages(messages)
         summary_response = self.summarisation_llm.invoke(valid_messages)
 
         # STEP 2: Analyze the conversational style
-        messages = state["messages"][:-1] + [SystemMessage(content=conversationalStyle_message)]
+        messages = [SystemMessage(content=conversationalStyle_message)] + state["messages"][:-1]
         valid_messages = self.check_for_valid_messages(messages)
         conversationalStyle_response = self.summarisation_llm.invoke(valid_messages)
 

src/agents/informational_agent/informational_agent.py

@@ -66,15 +66,15 @@ def call_model(self, state: State, config: RunnableConfig) -> str:
         # Adding external student progress and question context details from data queries
         question_response_details = config["configurable"].get("question_response_details", "")
         if question_response_details:
-            system_message += f"## Known Learning Materials: {question_response_details} \n\n"
+            system_message += f"\n\n ## Known Learning Materials: {question_response_details} \n\n"
 
         # Adding summary and conversational style to the system message
         summary = state.get("summary", "")
         conversationalStyle = state.get("conversationalStyle", "")
         if summary:
             system_message += summary_system_prompt.format(summary=summary)
         # if conversationalStyle:
-        #     system_message += f"## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
+        #     system_message += f"\n\n ## Known conversational style and preferences of the student for this conversation: {conversationalStyle}. \n\nYour answer must be in line with this conversational style."
 
         messages = [SystemMessage(content=system_message)] + state['messages']
 
@@ -101,7 +101,7 @@ def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
 
         summary = state.get("summary", "")
         previous_summary = config["configurable"].get("summary", "")
-        # previous_conversationalStyle = config["configurable"].get("conversational_style", "")
+        previous_conversationalStyle = config["configurable"].get("conversational_style", "")
         if previous_summary:
             summary = previous_summary
 
@@ -113,29 +113,29 @@ def summarize_conversation(self, state: State, config: RunnableConfig) -> dict:
         else:
             summary_message = self.summary_prompt
 
-        # if previous_conversationalStyle:
-        #     conversationalStyle_message = (
-        #         f"This is the previous conversational style of the student for this conversation: {previous_conversationalStyle}\n\n" +
-        #         self.update_conversation_preference_prompt
-        #     )
-        # else:
-        #     conversationalStyle_message = self.conversation_preference_prompt
+        if previous_conversationalStyle:
+            conversationalStyle_message = (
+                f"This is the previous conversational style of the student for this conversation: {previous_conversationalStyle}\n\n" +
+                self.update_conversation_preference_prompt
+            )
+        else:
+            conversationalStyle_message = self.conversation_preference_prompt
 
         # STEP 1: Summarize the conversation
-        messages = state["messages"][:-1] + [SystemMessage(content=summary_message)] 
+        messages = [SystemMessage(content=summary_message)] + state["messages"][:-1]
         valid_messages = self.check_for_valid_messages(messages)
         summary_response = self.summarisation_llm.invoke(valid_messages)
 
         # STEP 2: Analyze the conversational style
-        # messages = state["messages"][:-1] + [SystemMessage(content=conversationalStyle_message)]
-        # valid_messages = self.check_for_valid_messages(messages)
-        # conversationalStyle_response = self.summarisation_llm.invoke(valid_messages)
+        messages = [SystemMessage(content=conversationalStyle_message)] + state["messages"][:-1]
+        valid_messages = self.check_for_valid_messages(messages)
+        conversationalStyle_response = self.summarisation_llm.invoke(valid_messages)
 
         # Delete messages that are no longer wanted, except the last ones
-        delete_messages: list[AllMessageTypes] = [RemoveMessage(id=m.id) for m in state["messages"][:-5]]
+        delete_messages: list[AllMessageTypes] = [RemoveMessage(id=m.id) for m in state["messages"][:-3]]
 
-        # return {"summary": summary_response.content, "conversationalStyle": conversationalStyle_response.content, "messages": delete_messages}
-        return {"summary": summary_response.content, "messages": delete_messages}
+        return {"summary": summary_response.content, "conversationalStyle": conversationalStyle_response.content, "messages": delete_messages}
+        # return {"summary": summary_response.content, "messages": delete_messages}
 
     def should_summarize(self, state: State) -> str:
         """
@@ -183,7 +183,7 @@ def pretty_response_value(self, event: dict) -> str:
 
 agent = InformationalAgent()
 def invoke_informational_agent(query: str, conversation_history: list, summary: str, conversationalStyle: str, question_response_details: str, session_id: str) -> InvokeAgentResponseType:
-    print(f'in invoke_informational_agent(), query = {query}, thread_id = {session_id}')
+    print(f'in invoke_informational_agent(), thread_id = {session_id}')
 
     config = {"configurable": {"thread_id": session_id, "summary": summary, "conversational_style": conversationalStyle, "question_response_details": question_response_details}}
     response_events = agent.app.invoke({"messages": conversation_history, "summary": summary, "conversational_style": conversationalStyle}, config=config, stream_mode="values") #updates