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import streamlit as st
import os
from pathlib import Path
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from unidecode import unidecode
import chromadb
import re
list_llm = [
"mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1",
"mistralai/Mistral-7B-Instruct-v0.1", "google/gemma-7b-it", "google/gemma-2b-it",
"HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1",
"meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2",
"TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct",
"google/flan-t5-xxl"
]
def load_doc(list_file_path, chunk_size, chunk_overlap):
loaders = [PyPDFLoader(x) for x in list_file_path]
pages = []
for loader in loaders:
pages.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
doc_splits = text_splitter.split_documents(pages)
return doc_splits
def create_db(splits, collection_name):
embedding = HuggingFaceEmbeddings()
new_client = chromadb.EphemeralClient()
vectordb = Chroma.from_documents(
documents=splits,
embedding=embedding,
client=new_client,
collection_name=collection_name
)
return vectordb
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
llm = HuggingFaceEndpoint(repo_id=llm_model, temperature=temperature, max_new_tokens=max_tokens, top_k=top_k)
memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
retriever = vector_db.as_retriever()
qa_chain = ConversationalRetrievalChain.from_llm(
llm,
retriever=retriever,
chain_type="stuff",
memory=memory,
return_source_documents=True,
verbose=False
)
return qa_chain
def create_collection_name(file_path):
collection_name = Path(file_path).stem
collection_name = unidecode(collection_name)
collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
collection_name = collection_name[:50]
if len(collection_name) < 3:
collection_name = collection_name + 'xyz'
if not collection_name[0].isalnum():
collection_name = 'A' + collection_name[1:]
if not collection_name[-1].isalnum():
collection_name = collection_name[:-1] + 'Z'
return collection_name
def main():
st.title("PDF-based Chatbot")
uploaded_files = st.file_uploader("Upload PDF documents (single or multiple)", type="pdf", accept_multiple_files=True)
if uploaded_files:
chunk_size = st.slider("Chunk size", min_value=100, max_value=1000, value=600, step=20)
chunk_overlap = st.slider("Chunk overlap", min_value=10, max_value=200, value=40, step=10)
if st.button("Generate Vector Database"):
list_file_path = [file.name for file in uploaded_files]
collection_name = create_collection_name(list_file_path[0])
doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
vector_db = create_db(doc_splits, collection_name)
llm_model = st.selectbox("Choose LLM Model", list_llm)
temperature = st.slider("Temperature", min_value=0.01, max_value=1.0, value=0.7, step=0.1)
max_tokens = st.slider("Max Tokens", min_value=224, max_value=4096, value=1024, step=32)
top_k = st.slider("Top-K Samples", min_value=1, max_value=10, value=3, step=1)
if st.button("Initialize QA Chain"):
qa_chain = initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db)
st.header("Chatbot")
message = st.text_input("Type your message")
if st.button("Submit"):
response = qa_chain({"question": message, "chat_history": []})
st.write("Assistant:", response["answer"])
if __name__ == "__main__":
main()
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