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import os
from dotenv import load_dotenv
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder, HumanMessagePromptTemplate
from langchain.chains import ConversationChain
from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
from langchain_openai import ChatOpenAI
from langchain_groq import ChatGroq
load_dotenv()
class ChatBot:
def __init__(self, session_id):
self.session_id = session_id
self.mongo_conn_str = "mongodb+srv://dhara732002:6M2rikdwZxvwMzN0@cluster0.pbzipls.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0"
def create_llm_chain(self):
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant.You should give respnse in 1-2 lines without new line."),
MessagesPlaceholder(variable_name="history"),
HumanMessagePromptTemplate.from_template("{input}"),
]
)
message_history = MongoDBChatMessageHistory(connection_string=self.mongo_conn_str, session_id=self.session_id)
memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=message_history, return_messages=True, k=3)
conversation_chain = ConversationChain(
llm=ChatGroq(temperature=0, groq_api_key=os.getenv("GROQ_API_KEY"), model_name="llama3-70b-8192"),
prompt=prompt,
verbose=True,
memory=memory,
)
self.conversation_chain = conversation_chain
return "Chain created successfully"
def get_response(self, question):
ans= self.conversation_chain.predict(input=question)
return ans
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