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import gradio as gr
import os
from langchain_community.vectorstores import FAISS
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 HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint
import torch
api_token = os.getenv("HF_TOKEN")
list_llm = ["microsoft/Phi-3-mini-4k-instruct", "mistralai/Mistral-7B-Instruct-v0.3"]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]
# Load and split PDF document
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
# Create vector database
def create_db(splits):
embeddings = HuggingFaceEmbeddings()
vectordb = FAISS.from_documents(splits, embeddings)
return vectordb
# Initialize langchain LLM chain
def initialize_llmchain(llm_model, vector_db, progress=gr.Progress()):
llm = HuggingFaceEndpoint(
huggingfacehub_api_token=api_token,
repo_id=llm_model,
temperature=0.1,
max_new_tokens=2000,
top_k=3,
)
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
# Initialize database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
list_file_path = [x.name for x in list_file_obj if x is not None]
doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
vector_db = create_db(doc_splits)
if vector_db is None:
print("Vector database creation failed")
else:
print("Embedding database created successfully")
return vector_db, "Embedding database created!"
# Initialize LLM
def initialize_LLM(llm_option, vector_db, progress=gr.Progress()):
if vector_db is None:
print("Vector database is None")
return None, "Failed to initialize RAG System: Vector database is None"
llm_name = list_llm[llm_option]
qa_chain = initialize_llmchain(llm_name, vector_db, progress)
return qa_chain, "RAG System initialized!"
def format_chat_history(message, chat_history):
formatted_chat_history = []
for user_message, bot_message in chat_history:
formatted_chat_history.append(f"User: {user_message}")
formatted_chat_history.append(f"Assistant: {bot_message}")
return formatted_chat_history
def conversation(qa_chain, message, history):
formatted_chat_history = format_chat_history(message, history)
response = qa_chain.invoke({"question": message, "chat_history": formatted_chat_history})
response_answer = response["answer"]
if response_answer.find("Helpful Answer:") != -1:
response_answer = response_answer.split("Helpful Answer:")[-1]
response_sources = response["source_documents"]
response_source1 = response_sources[0].page_content.strip()
response_source2 = response_sources[1].page_content.strip()
response_source3 = response_sources[2].page_content.strip()
response_source1_page = response_sources[0].metadata["page"] + 1
response_source2_page = response_sources[1].metadata["page"] + 1
response_source3_page = response_sources[2].metadata["page"] + 1
new_history = history + [(message, response_answer)]
return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
def upload_file(file_obj):
list_file_path = []
for idx, file in enumerate(file_obj):
file_path = file_obj.name
list_file_path.append(file_path)
return list_file_path
def demo():
with gr.Blocks(theme=gr.themes.Default(primary_hue="green")) as demo:
vector_db = gr.State()
qa_chain = gr.State()
gr.HTML("<center><h1>RAG System</h1><center>")
gr.Markdown("""This App is designed to perform retrieval augmented generation (RAG) on PDF documents. \
<b>Please do not upload confidential documents.</b>
""")
with gr.Row():
with gr.Column(scale=86):
gr.Markdown("<b>Step 1 - Upload PDF documents and Initialize the RAG system</b>")
with gr.Row():
document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents")
with gr.Row():
slider_chunk_size = gr.Slider(minimum=10, maximum=1000, value=200, step=5, label="Chunk Size")
slider_chunk_overlap = gr.Slider(minimum=0, maximum=512, value=20, step=5, label="Chunk Overlap")
with gr.Row():
db_btn = gr.Button("Create Embeddings")
with gr.Row():
db_progress = gr.Textbox(value="Not initialized", show_label=False)
gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM)</b>")
with gr.Row():
llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
with gr.Row():
qachain_btn = gr.Button("Initialize RAG system")
with gr.Row():
llm_progress = gr.Textbox(value="Not initialized", show_label=False)
with gr.Column(scale=200):
gr.Markdown("<b>Step 2 - Chat with your Document</b>")
chatbot = gr.Chatbot(height=505)
with gr.Accordion("Similar context from the source document", open=False):
with gr.Row():
doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20)
source1_page = gr.Number(label="Page", scale=1)
with gr.Row():
doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20)
source2_page = gr.Number(label="Page", scale=1)
with gr.Row():
doc_source3 = gr.Textbox(label="Reference 3", lines=2, container=True, scale=20)
source3_page = gr.Number(label="Page", scale=1)
with gr.Row():
msg = gr.Textbox(placeholder="Ask a question", container=True)
with gr.Row():
submit_btn = gr.Button("Submit")
clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
# Preprocessing events
db_btn.click(initialize_database,
inputs=[document, slider_chunk_size, slider_chunk_overlap],
outputs=[vector_db, db_progress])
qachain_btn.click(initialize_LLM,
inputs=[llm_btn, vector_db],
outputs=[qa_chain, llm_progress]).then(lambda:[None, "", 0, "", 0, "", 0],
inputs=None,
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False)
# Chatbot events
msg.submit(conversation,
inputs=[qa_chain, msg, chatbot],
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False)
submit_btn.click(conversation,
inputs=[qa_chain, msg, chatbot],
outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False)
clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
inputs=None,
outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page],
queue=False)
demo.queue().launch(debug=True)
if __name__ == "__main__":
demo()
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