Spaces:
No application file
No application file
File size: 2,726 Bytes
88f42a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | import os
import streamlit as st
from langchain.chains import RetrievalQA
from PyPDF2 import PdfReader
from langchain.callbacks.base import BaseCallbackHandler
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores 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})
def main():
st.header("📄Chat with your pdf file")
# 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])
if __name__ == "__main__":
main()
|