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from langchain.chains import LLMChain
import streamlit as st
from decouple import config
from langchain.llms import OpenAI
from langchain.document_loaders import PyPDFLoader
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
from langchain.retrievers.document_compressors import LLMChainExtractor
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.evaluation.qa import QAGenerateChain
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import CSVLoader
from langchain.indexes import VectorstoreIndexCreator
from langchain.vectorstores import DocArrayInMemorySearch
from langchain.prompts import ChatPromptTemplate
from langchain.document_loaders.generic import GenericLoader
from langchain.document_loaders.parsers import OpenAIWhisperParser
from langchain.document_loaders.blob_loaders.youtube_audio import YoutubeAudioLoader
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain
import time
from htmlTemplates import css, bot_template, user_template
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
def timeit(func):
def wrapper(*args, **kwargs):
start_time = time.time() # Start time
result = func(*args, **kwargs) # Function execution
end_time = time.time() # End time
print(f"Function {func.__name__} took {end_time - start_time} seconds to execute.")
return result
return wrapper
@timeit
def get_llm():
return OpenAI(temperature=0.1)
@timeit
def get_memory():
return ConversationBufferMemory(
memory_key="chat_history",
return_messages=True
)
@timeit
def generate_response(question, vectordb, llm, memory,chat_history):
prompt = ChatPromptTemplate.from_template(
"You are a petroleum engineer specialist in hydralic fracture stimulation \
, please answer the question that surounded between the triple backtick \
```{question}```"
)
question_template = prompt.format_messages(question=question)
final_qa = question_template[0].content
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectordb.as_retriever(search_type="mmr",k=5, fetch_k=10),
memory=memory,
)
handle_userinput((qa({"question": question, "chat_history": chat_history})))
@timeit
def create_embeding_function():
embedding_func_all_mpnet_base_v2 = SentenceTransformerEmbeddings(
model_name="all-mpnet-base-v2")
# embedding_func_all_MiniLM_L6_v2 = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
return embedding_func_all_mpnet_base_v2
@timeit
def get_vector_db(embedding_function):
vector_db = Chroma(persist_directory="./final_db",
embedding_function=embedding_function)
return vector_db
def handle_userinput(user_question):
response = user_question
if chat_history not in st.session_state:
st.session_state.chat_history = []
st.session_state.chat_history = response['chat_history']
for i, message in enumerate(st.session_state.chat_history):
if i % 2 == 0:
st.write(user_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
else:
st.write(bot_template.replace(
"{{MSG}}", message.content), unsafe_allow_html=True)
if __name__ == "__main__":
st.set_page_config(page_title = "Hydraulic Fracture Stimulation Chat",page_icon=":books:")
st.write(css, unsafe_allow_html=True)
st.title("Hydraulic Fracture Stimulation Chat")
st.write(
"This is a chatbot that can answer questions related to petroleum engineering specially in hydraulic fracture stimulation.")
# get embeding function
embeding_function = create_embeding_function()
# get vector db
vector_db = get_vector_db(embeding_function)
# get llm
llm = get_llm()
# get memory
if 'memory' not in st.session_state:
st.session_state['memory'] = get_memory()
memory = st.session_state['memory']
# chat history
chat_history = []
prompt_question = st.chat_input("Please ask a question:")
if prompt_question:
generate_response(question= prompt_question, vectordb=vector_db, llm=llm, memory=memory,chat_history=chat_history)
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