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pdf_bot.py
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pdf_bot.py
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import os
import streamlit as st
from langchain.chains import RetrievalQA
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.base import BaseCallbackHandler
from langchain.vectorstores.neo4j_vector import Neo4jVector
from streamlit.logger import get_logger
from chains import (
load_embedding_model,
load_llm,
)
# load api key lib
from dotenv import load_dotenv
load_dotenv(".env")
url = os.getenv("NEO4J_URI")
username = os.getenv("NEO4J_USERNAME")
password = os.getenv("NEO4J_PASSWORD")
ollama_base_url = os.getenv("OLLAMA_BASE_URL")
embedding_model_name = os.getenv("EMBEDDING_MODEL")
llm_name = os.getenv("LLM")
# Remapping for Langchain Neo4j integration
os.environ["NEO4J_URL"] = url
logger = get_logger(__name__)
embeddings, dimension = load_embedding_model(
embedding_model_name, config={"ollama_base_url": ollama_base_url}, logger=logger
)
class StreamHandler(BaseCallbackHandler):
def __init__(self, container, initial_text=""):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.markdown(self.text)
llm = load_llm(llm_name, logger=logger, config={"ollama_base_url": ollama_base_url})
# Streamlit UI
styl = f"""
<style>
.stChatFloatingInputContainer {{
bottom: 20px;
}}
</style>
"""
st.markdown(styl, unsafe_allow_html=True)
# create separate functions for the chat history and the chat input
def display_chat_history():
if "user_input" not in st.session_state:
st.session_state["user_input"] = []
if "generated_output" not in st.session_state:
st.session_state["generated_output"] = []
if st.session_state[f"generated_output"]:
size = len(st.session_state[f"generated_output"])
# Display only the last three exchanges
for i in range(max(size - 3, 0), size):
with st.chat_message("user"):
st.write(st.session_state[f"user_input"][i])
with st.chat_message("assistant"):
st.write(st.session_state[f"generated_output"][i])
with st.container():
st.write(" ")
def chat_input(qa):
query = st.chat_input("Ask questions about related your upload pdf files")
if query:
with st.chat_message("user"):
st.write(query)
with st.chat_message("assistant"):
stream_handler = StreamHandler(st.empty())
qa.run(query, callbacks=[stream_handler])
# add the query to user input
st.session_state[f"user_input"].append(query)
# add the answer to generated output
text = stream_handler.text
st.session_state[f"generated_output"].append(text)
def main():
st.header("📄Chat with your pdf files with history")
# upload a your pdf file
pdf = st.file_uploader("Upload your PDF", type="pdf")
if pdf is not None:
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
# langchain_textspliter
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000, chunk_overlap=200, length_function=len
)
chunks = text_splitter.split_text(text=text)
# Store the chunks part in db (vector)
vectorstore = Neo4jVector.from_texts(
chunks,
url=url,
username=username,
password=password,
embedding=embeddings,
index_name="pdf_bot",
node_label="PdfBotChunk",
pre_delete_collection=True, # Delete existing PDF data
)
qa = RetrievalQA.from_chain_type(
llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever()
)
# # Accept user questions/query
# query = st.text_input("Ask questions about your PDF file")
# if query:
# stream_handler = StreamHandler(st.empty())
# qa.run(query, callbacks=[stream_handler])
display_chat_history()
chat_input(qa)
if __name__ == "__main__":
main()