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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate |
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from langchain_core.messages import HumanMessage, AIMessage |
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from langchain_groq import ChatGroq |
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from typing import List |
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import os |
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from src.services.prompts import ASSISTANT_PROMPT |
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from langchain.memory import ConversationSummaryMemory |
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from dotenv import load_dotenv |
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load_dotenv() |
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os.environ["GROQ_API_KEY"]=os.getenv("GROQ_API_KEY") |
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class ConversationHandler: |
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def __init__(self, model_name="llama-3.3-70b-versatile", temperature=0.7): |
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self.chat_model = ChatGroq( |
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model_name=model_name, |
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temperature=temperature |
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) |
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self.prompt = ChatPromptTemplate.from_messages([ |
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("system", ASSISTANT_PROMPT)]) |
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self.memory=ConversationSummaryMemory( |
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llm=self.chat_model, |
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max_token_limit=2000, |
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return_messages=True, |
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memory_key="chat_history" |
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) |
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async def give_response(self,user_input): |
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chain= self.prompt|self.chat_model |
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memory_variables = self.memory.load_memory_variables({}) |
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response=await chain.ainvoke( |
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{ |
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"user_query": user_input, |
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"chat_history": memory_variables["chat_history"] |
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} |
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) |
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self.memory.save_context( |
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{"input": user_input}, |
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{"output": response.content} |
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) |
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return response.content |
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async def summarize_conversation(self) -> str: |
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memory_variables = self.memory.load_memory_variables({}) |
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return self.memory.predict_new_summary( |
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messages=memory_variables["chat_history"], |
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existing_summary="" |
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) |
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async def clear_memory(self): |
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await self.memory.clear() |
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if __name__ == "__main__": |
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import asyncio |
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async def main(): |
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handler = ConversationHandler() |
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response1 = await handler.give_response("What is machine learning?by the way my name is ravi and my home town is ilam.") |
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print("Response 1:", response1.content) |
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response2 = await handler.give_response("Can you give me an example of machine learning mathematics?") |
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print("Response 2:", response2.content) |
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response2 = await handler.give_response("Can you tell me more example? and also tell me from which country i am?") |
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print("Response 2:", response2.content) |
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summary = await handler.summarize_conversation() |
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print("\nConversation Summary:", summary) |
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asyncio.run(main()) |
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