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import gradio as gr | |
import os | |
api_token = os.getenv("HF_TOKEN") | |
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 | |
list_llm = ["meta-llama/Meta-Llama-3-8B-Instruct", "mistralai/Mistral-7B-Instruct-v0.2"] | |
list_llm_simple = [os.path.basename(llm) for llm in list_llm] | |
# Load and split PDF document | |
def load_doc(list_file_path): | |
# Processing for one document only | |
# loader = PyPDFLoader(file_path) | |
# pages = loader.load() | |
loaders = [PyPDFLoader(x) for x in list_file_path] | |
pages = [] | |
for loader in loaders: | |
pages.extend(loader.load()) | |
text_splitter = RecursiveCharacterTextSplitter( | |
chunk_size = 1024, | |
chunk_overlap = 64 | |
) | |
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, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
if llm_model == "meta-llama/Meta-Llama-3-8B-Instruct": | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
huggingfacehub_api_token = api_token, | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_k = top_k, | |
) | |
else: | |
llm = HuggingFaceEndpoint( | |
huggingfacehub_api_token = api_token, | |
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 | |
# Initialize database | |
def initialize_database(list_file_obj, progress=gr.Progress()): | |
# Create a list of documents (when valid) | |
list_file_path = [x.name for x in list_file_obj if x is not None] | |
# Load document and create splits | |
doc_splits = load_doc(list_file_path) | |
# Create or load vector database | |
vector_db = create_db(doc_splits) | |
return vector_db, "Database created!" | |
# Initialize LLM | |
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
# print("llm_option",llm_option) | |
llm_name = list_llm[llm_option] | |
print("llm_name: ",llm_name) | |
qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) | |
return qa_chain, "QA chain initialized. Chatbot is ready!" | |
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) | |
# Generate response using QA chain | |
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() | |
# Langchain sources are zero-based | |
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 | |
# Append user message and response to chat history | |
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="sky")) as demo: | |
with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo: | |
vector_db = gr.State() | |
qa_chain = gr.State() | |
gr.HTML("<center><h1>RAG PDF chatbot</h1><center>") | |
gr.Markdown("""<b>Query your PDF documents!</b> This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \ | |
<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 RAG pipeline</b>") | |
with gr.Row(): | |
document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents") | |
with gr.Row(): | |
db_btn = gr.Button("Create vector database") | |
with gr.Row(): | |
db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", | |
gr.Markdown("<style>body { font-size: 16px; }</style><b>Select Large Language Model (LLM) and input parameters</b>") | |
with gr.Row(): | |
llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False | |
with gr.Row(): | |
with gr.Accordion("LLM input parameters", open=False): | |
with gr.Row(): | |
slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True) | |
with gr.Row(): | |
slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True) | |
with gr.Row(): | |
slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True) | |
with gr.Row(): | |
qachain_btn = gr.Button("Initialize Question Answering Chatbot") | |
with gr.Row(): | |
llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", | |
with gr.Column(scale = 200): | |
gr.Markdown("<b>Step 2 - Chat with your Document</b>") | |
chatbot = gr.Chatbot(height=505) | |
with gr.Accordion("Relevent 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], \ | |
outputs=[vector_db, db_progress]) | |
qachain_btn.click(initialize_LLM, \ | |
inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, 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() |