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import streamlit as st
from langchain_community.document_loaders.pdf import PyPDFDirectoryLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceHub
def make_vectorstore(embeddings):
loader = PyPDFDirectoryLoader("data")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=200, chunk_overlap=0)
texts = text_splitter.split_documents(documents)
docsearch = FAISS.from_documents(texts, embeddings)
return docsearch
def get_conversation(vectorstore, model):
memory = ConversationBufferMemory(memory_key="messages", return_messages=True)
conversation_chain = RetrievalQA.from_llm(
llm=model,
retriever=vectorstore.as_retriever(),
memory=memory)
return conversation_chain
def get_response(conversation_chain, query):
# get the response
response = conversation_chain.invoke(query)
response = response["result"]
answer = response.split('\nHelpful Answer: ')[1]
return answer
def main():
st.title("Chat LLM")
print("Downloading Embeddings Model")
with st.spinner('Downloading Embeddings Model...'):
embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base", model_kwargs = {'device': 'cpu'})
print("Loading LLM from HuggingFace")
with st.spinner('Loading LLM from HuggingFace...'):
llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature":0.7, "max_new_tokens":512, "top_p":0.95, "top_k":50},)
# multiple pdfs uploader in the side bar
st.sidebar.title("Upload PDFs")
uploaded_files = st.sidebar.file_uploader("Upload PDFs", accept_multiple_files=True)
if uploaded_files:
for file in uploaded_files:
with open(f"data/{file.name}", "wb") as f:
f.write(file.getbuffer())
st.sidebar.success("PDFs uploaded successfully")
else:
st.sidebar.warning("Please upload PDFs")
# add a clear chat button which will clear the session state and the conversation history
if "messages" not in st.session_state:
st.session_state.messages = []
for message in st.session_state.messages:
if message["role"] == "user":
st.chat_message("user").markdown(message["content"])
else:
st.chat_message("bot").markdown(message["content"])
with st.spinner('making a vectorstore database...'):
vectorstore = make_vectorstore(embeddings)
with st.spinner('making a conversation chain...'):
conversation_chain = get_conversation(vectorstore, llm)
user_prompt = st.chat_input("ask a question", key="user")
if user_prompt:
st.chat_message("user").markdown(user_prompt)
st.session_state.messages.append({"role": "user", "content": user_prompt})
response = get_response(conversation_chain, user_prompt)
st.chat_message("bot").markdown(response)
st.session_state.messages.append({"role": "bot", "content": response})
if __name__ == "__main__":
main() |