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import os
from groq import Groq
from langchain.memory import ConversationTokenBufferMemory
from langdetect import detect
from deep_translator import GoogleTranslator
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.vectorstores import FAISS
import time
class Comsatsbot:
def __init__(self, hf, llm, api_key, chats_collection, paths, index_path='faiss_kb'):
self.llm = llm
self.client = Groq(api_key=api_key)
# Initialize memory buffer and MongoDB connection
self.memory = ConversationTokenBufferMemory(llm=self.llm, max_token_limit=4000)
self.chats_collection = chats_collection
self.index_path = index_path
self.hf = hf
self.faiss_index = None
self.faiss_retriever = None
self.paths = paths
self.initialize_faiss_index()
def load_data(self, paths):
documents = []
for path in paths:
loader = CSVLoader(file_path=path)
data = loader.load()
documents.extend(data)
return documents
def initialize_faiss_index(self):
if os.path.exists(self.index_path):
self.faiss_index = FAISS.load_local(self.index_path, self.hf, allow_dangerous_deserialization=True)
self.faiss_retriever = self.faiss_index.as_retriever(search_kwargs={"k": 5})
else:
documents = self.load_data(self.paths)
self.faiss_index = FAISS.from_documents(documents, self.hf)
self.faiss_index.save_local(self.index_path)
self.faiss_retriever = self.faiss_index.as_retriever(search_kwargs={"k": 5})
def retrieve_answer(self, query):
if self.faiss_retriever:
return self.faiss_retriever.invoke(query)
else:
print("FAISS retriever is not initialized. Please create or load an index.")
return None
def create_chat_record(self, chat_id):
self.chats_collection.insert_one({
"_id": chat_id,
"history": []
})
def update_chat(self, chat_id, question, answer):
self.chats_collection.update_one(
{"_id": chat_id},
{"$push": {"history": {"question": question, "answer": answer}}}
)
def load_chat(self, chat_id):
chat_record = self.chats_collection.find_one({"_id": chat_id})
if not chat_record:
raise KeyError(f"Chat ID {chat_id} does not exist.")
return chat_record.get('history', [])
def new_chat(self, chat_id):
# Check if chat ID already exists
if self.chats_collection.find_one({"_id": chat_id}):
raise KeyError(f"Chat ID {chat_id} exist already.")
# Create a new chat record if it doesn't exist
self.create_chat_record(chat_id)
return "success"
def delete_chat(self, chat_id):
# Check if the chat ID exists
if not self.chats_collection.find_one({"_id": chat_id}):
raise KeyError(f"Chat ID {chat_id} does not exist.")
# Delete the chat if it exists
self.chats_collection.delete_one({"_id": chat_id})
return "success"
def generate_response(self, question, history, context, detected_language):
models = ["llama3-groq-70b-8192-tool-use-preview", "llama-3.1-70b-versatile", "llama3-70b-8192", "mixtral-8x7b-32768", "gemma2-9b-it"]
if detected_language == 'ur':
language = 'Urdu'
else:
language = 'English'
while True:
for model in models:
try:
chat_completion = self.client.chat.completions.create(
messages=[
{
"role": "user",
"content": f'''
You are a conversational and helpfull agent to help the comsats university attock campus students, and your task is to provide concise and direct answers to the questions asked in {language} Language. kindly answer in correct way and to the point in {language} language.
Dont need to explain irrelevant things and use the correct {language} in response.
Kindly genrate emojis when user is asking questions (funny, happy, sad etc) then add the anger, sad, smile, happy, surprise and sleeping emojis when it is required in conversation. Use these kind of emojis when needed it in response otherwise dont use the emojis in response.
Please follow these guidelines when answering:
Dont need to explain and repeat the prompt in response. Answer in {language} language.
if you provide any link so this link should be clickable when i display your response your provided links should be clickable kindly dont provide link in text form.
1. If the question is in {language}, answer in {language}.
2. Dont need to explain irrelevant explanation and use the proper emojis in answer if required in response.
3. Always respond in a *human-like tone* and keep your answers concise, to the point, and friendly.
4. If the question is *conversational* (like greetings, need any converstaion etc), respond in a warm, conversational tone.
5. Always consider the provided *context* and *chat history* to formulate your response.
6. If you don’t know the answer to the question or you did not find the answer from the context, kindly respond with "I don't know the answer to this." without adding irrelevant explanations.
7. KIndly generate a perfect and to the point answer. Dont use any irrelevant text explanation and i want full concise and to the point answer.
8. Kindly answer in {language} and dont need to generate a response in other language and i want answer in this language {language}.
*Question:* {question}
*Use the following context to answer:*
Comsats Attock Campus Provide BSomputerScience, BSSoftwareEngineer BSArtificialIntelligence BSEnglish BSmath BSElectricalEngineering BSComputerEngineering BSBBA
Has three departments CS(CS, AI, SE), Math(math, BBA, english) and EE(EE, CE).
It has cricket ground and football ground and two canteens. First near math and ee department and second near cs department. There is also mosque near cs department. CS department has threater liker rooms lt and total 9 theaters called lt and math has classroom cr and ee has labs.
They accept the nts test for admission and provide the cgpa for 4 on 85 percent and 3.66 between 79 to 84 and many more.
{context}
*Consider the following chat history for additional context to answer the question:*
{history}
'''
}
],
model=model,
)
response_content = chat_completion.choices[0].message.content
return response_content
except Exception as e:
time.sleep(2)
continue
def response(self, question, chat_id):
chat_history = self.load_chat(chat_id)
# Load the previous conversation into memory
for entry in chat_history:
self.memory.save_context({"input": entry["question"]}, {"output": entry["answer"]})
language = detect(question)
if language == 'ur':
question_translation = GoogleTranslator(source='ur', target='en').translate(question)
context = self.faiss_retriever.invoke(question_translation)
else:
context = self.faiss_retriever.invoke(question)
all_content = ''
for document in context:
page_content = document.page_content
all_content += page_content + '\n' # Add a line break between contents for readability
answer = self.generate_response(question, self.memory.load_memory_variables({})['history'], all_content, language)
self.update_chat(chat_id, question, answer)
return answer |