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from src.utils.models_loader import reply_llm
from .prompts import prompt
from .schemas import ReplyFormatter
from .state import State
from langchain_core.messages import SystemMessage, HumanMessage, FunctionMessage
from langgraph.graph import StateGraph, START, END
from langgraph.checkpoint.memory import MemorySaver
class Generate:
def __init__(self):
self.llm = reply_llm
def run(self, state:State):
messages = [SystemMessage(content=prompt),
FunctionMessage(name='conversation-context',content=state['conversation_chat']),
HumanMessage(content=f'''The required tone: {state["tones"]}''')]
reply = reply_llm.with_structured_output(ReplyFormatter).invoke(messages)
print('The reply is:',reply)
print(reply.model_dump())
return {
'messages':[{'role': 'assistant', 'content': f'''The generated replies are: {str(reply.model_dump())}'''}],
'replies': reply.model_dump()
}
class Graph:
def __init__(self):
self.memory = MemorySaver()
def run(self):
workflow = StateGraph(State)
workflow.add_node('generate_reply', Generate().run)
workflow.add_edge(START,'generate_reply')
workflow.add_edge('generate_reply',END)
return workflow.compile(checkpointer=self.memory)
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