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from langchain.chains import RetrievalQA
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.manager import CallbackManager
#from langchain_community.llms import Ollama
#from langchain_community.embeddings.ollama import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationBufferMemory
import streamlit as st
import os
import time
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.embeddings import HuggingFaceEmbeddings
model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {'device': 'cpu'}
encode_kwargs = {'normalize_embeddings': False}
embeddings = HuggingFaceEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs
)
if not os.path.exists('files'):
os.mkdir('files')
if not os.path.exists('jj'):
os.mkdir('jj')
if 'template' not in st.session_state:
st.session_state.template = """You are a knowledgeable chatbot, here to help with questions of the user. Your tone should be professional and informative.Try to give answer in tabular and shortcut.
Context: {context}
History: {history}
User: {question}
Chatbot:"""
if 'prompt' not in st.session_state:
st.session_state.prompt = PromptTemplate(
input_variables=["history", "context", "question"],
template=st.session_state.template,
)
if 'memory' not in st.session_state:
st.session_state.memory = ConversationBufferMemory(
memory_key="history",
return_messages=True,
input_key="question")
if 'vectorstore' not in st.session_state:
#st.session_state.vectorstore = Chroma(persist_directory='jj', embedding_function=OllamaEmbeddings(base_url='http://localhost:11434',model="mistral")
st.session_state.vectorstore = Chroma(persist_directory='jj', embedding_function=embeddings)
if 'llm' not in st.session_state:
#st.session_state.llm = Ollama(base_url="http://localhost:11434",model="mistral",verbose=True,callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),)
st.session_state.llm = HuggingFaceEndpoint(repo_id="mistralai/Mistral-7B-Instruct-v0.3", Temperature=0.2)
# Initialize session state
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
st.title("PDF Chatbot")
# Upload a PDF file
uploaded_file = st.file_uploader("Upload your PDF", type='pdf')
for message in st.session_state.chat_history:
with st.chat_message(message["role"]):
st.markdown(message["message"])
if uploaded_file is not None:
if not os.path.isfile("files/"+uploaded_file.name+".pdf"):
with st.status("Analyzing your document..."):
bytes_data = uploaded_file.read()
f = open("files/"+uploaded_file.name+".pdf", "wb")
f.write(bytes_data)
f.close()
loader = PyPDFLoader("files/"+uploaded_file.name+".pdf")
data = loader.load()
# Initialize text splitter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1500,
chunk_overlap=0,
length_function=len
)
all_splits = text_splitter.split_documents(data)
# Create and persist the vector store
#st.session_state.vectorstore = Chroma.from_documents(documents=all_splits,embedding=OllamaEmbeddings(model="mistral"))
st.session_state.vectorstore = Chroma.from_documents(documents=all_splits,embedding=embeddings)
st.session_state.vectorstore.persist()
st.session_state.retriever = st.session_state.vectorstore.as_retriever()
# Initialize the QA chain
if 'qa_chain' not in st.session_state:
st.session_state.qa_chain = RetrievalQA.from_chain_type(
llm=st.session_state.llm,
chain_type='stuff',
retriever=st.session_state.retriever,
verbose=True,
chain_type_kwargs={
"verbose": True,
"prompt": st.session_state.prompt,
"memory": st.session_state.memory,
}
)
# Chat input
if user_input := st.chat_input("You:", key="user_input"):
user_message = {"role": "user", "message": user_input}
st.session_state.chat_history.append(user_message)
with st.chat_message("user"):
st.markdown(user_input)
with st.chat_message("assistant"):
with st.spinner("Assistant is typing..."):
response = st.session_state.qa_chain(user_input)
message_placeholder = st.empty()
full_response = ""
for chunk in response['result'].split():
full_response += chunk + " "
time.sleep(0.05)
# Add a blinking cursor to simulate typing
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
chatbot_message = {"role": "assistant", "message": response['result']}
st.session_state.chat_history.append(chatbot_message)
else:
st.write("Please upload a PDF file.") |