validated business interaction

my_agent/utils/business_interaction.py

@@ -9,10 +9,7 @@ from .models_loader import llm,ST
 from .prompts import introduction_prompt , business_interaction_prompt, business_retrieval_prompt
 from .tools import retrieve_tool
 from langgraph.prebuilt import create_react_agent
-from langmem.short_term import SummarizationNode
-from langchain_core.messages.utils import count_tokens_approximately
-from langchain_core.messages import RemoveMessage
-from .data_loader import load_influencer_data
+from .utils import manual_retrieval
 
 
 
@@ -28,22 +25,9 @@ business_state = State()
 class BusinessInteractionChatbot:
     def __init__(self):
         self.messages = []
-
-        self.react_agent=create_react_agent(
-        model=llm.bind_tools([retrieve_tool]),
-        tools=[retrieve_tool]
-        )
-        self.summarization_model = llm.bind(max_tokens=128)
-
-        self.summarization_node = SummarizationNode(
-        token_counter=count_tokens_approximately,
-        model=self.summarization_model,
-        max_tokens=300,
-        max_tokens_before_summary=256,
-        max_summary_tokens=128,
-        )
+        self.business_details =  None
+        self.react_agent=create_react_agent(model=llm,tools=[])
         self.memory = MemorySaver()
-        # self.llm = ChatGroq(model_name="Gemma2-9b-It")
         self.workflow = self._initialize_workflow()
         self.interact_agent = self.workflow.compile(checkpointer=self.memory)
 
@@ -51,53 +35,33 @@ class BusinessInteractionChatbot:
     def _initialize_workflow(self):
         workflow = StateGraph(MessagesState)
         workflow.add_node("chatbot", self._call_model)
-        workflow.add_node("summarize",self.summarization_node)
         workflow.add_node("remove_message",self.delete_messages)
 
-        workflow.add_edge(START, "summarize")
-        workflow.add_edge("summarize", "chatbot")
+        workflow.add_edge(START, "chatbot")
         workflow.add_edge("chatbot","remove_message")
         workflow.add_edge("chatbot", END)
         return workflow
     
     def delete_messages(self,state):
         print('Entered message deletion....')
-        if len(self.messages) > 5:
+        if len(self.messages) > 4:
             print('satisfied...')
             self.messages = self.messages[2:]
 
-    def manual_retrieval(self):
-        embedded_query = ST.encode(str([msg['content'] for msg in self.messages if msg['role'] == 'user']))  # Embed each topic
-        data = load_influencer_data()
-        scores, retrieved_examples = data.get_nearest_examples("embeddings", embedded_query, k=1)
-
-        # Construct a list of dictionaries for this topic
-        result = [{user: story} for user, story in zip(retrieved_examples['username'], retrieved_examples['agentic_story'])]
-        return result
-
-
     def _call_model(self, state):
         print('Entered into callmodel')
-        template = business_interaction_prompt
+        retrievals = manual_retrieval(str([msg['content'] for msg in self.messages if msg['role'] == 'user']),self.business_details)
+        template = business_retrieval_prompt(str([msg['content'] for msg in self.messages if msg['role'] == 'user']),retrievals)
         messages = [SystemMessage(content=template)] + state["messages"]
-        # response = self.react_agent.invoke({'messages':messages})['messages'][-2]
-        response = self.react_agent.invoke({'messages':messages})['messages']
-        if response [-2].name == None:
-            print('Entered into manual retrieval')
-            retrievals = self.manual_retrieval()
-            template = business_retrieval_prompt(retrievals)
-            messages = [SystemMessage(content=template)] + state["messages"]
-            backup_response = self.react_agent.invoke({'messages':messages})['messages'][-1]
-            print('Backup response:',backup_response.content)
-            return {"messages": [backup_response.content]}
+        backup_response = self.react_agent.invoke({'messages':messages})['messages'][-1]
+        print('Backup response:',backup_response.content)
+        return {"messages": [backup_response.content]}
 
-        else:
-            return {"messages": [response[-1]]}
 
-    def chat(self, user_input: str):
+    def chat(self, user_input: str, business_details:dict):
         print('Entered into chat')
-        
-        self.messages.append({"role": "user", "content": user_input})
+        self.business_details=business_details
+        self.messages.append({"role": "user", "content": f'{user_input}'})
         config = {"configurable": {"thread_id": "2"}}
         response = self.interact_agent.invoke({"messages":self.messages}, config)['messages'][-1].content
         print('The response:',response)