import os
import gradio as gr
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import ConversationalRetrievalChain
from langchain.memory import ConversationBufferMemory
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.llms import HuggingFaceEndpoint
# ------------------------------
# Configuration & LLM Selection
# ------------------------------
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]
# Token đọc từ Space secret
api_token = os.getenv("hf_token") # Space secret, không hardcode
# ------------------------------
# PDF Loading & Splitting
# ------------------------------
def load_doc(list_file_path):
pages = []
for file_path in list_file_path:
try:
loader = PyPDFLoader(file_path)
pages.extend(loader.load())
except Exception as e:
print(f"Error loading {file_path}: {e}")
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1024,
chunk_overlap=32
)
return text_splitter.split_documents(pages)
# ------------------------------
# Vector Database Creation
# ------------------------------
def create_db(doc_splits):
embeddings = HuggingFaceEmbeddings() # CPU-only
vectordb = FAISS.from_documents(doc_splits, embeddings)
return vectordb
# ------------------------------
# Initialize LLM + QA Chain
# ------------------------------
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db):
llm = HuggingFaceEndpoint(
repo_id=llm_model,
huggingfacehub_api_token=api_token,
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
# ------------------------------
# Database Initialization
# ------------------------------
def initialize_database(list_file_obj):
list_file_path = [x.name for x in list_file_obj if x is not None]
doc_splits = load_doc(list_file_path)
vector_db = create_db(doc_splits)
return vector_db, "Database created!"
# ------------------------------
# LLM Initialization
# ------------------------------
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db):
llm_name = list_llm[llm_option]
qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db)
return qa_chain, "QA chain initialized. Chatbot is ready!"
# ------------------------------
# Conversation Utilities
# ------------------------------
def format_chat_history(chat_history, max_messages=5):
formatted = []
for user_msg, bot_msg in chat_history[-max_messages:]:
formatted.append(f"User: {user_msg}")
formatted.append(f"Assistant: {bot_msg}")
return formatted
def conversation(qa_chain, message, history):
formatted_history = format_chat_history(history)
try:
response = qa_chain.invoke({"question": message, "chat_history": formatted_history})
answer = response["answer"]
if "Helpful Answer:" in answer:
answer = answer.split("Helpful Answer:")[-1]
sources = response["source_documents"]
top_sources = [(s.page_content.strip(), s.metadata.get("page", 0) + 1) for s in sources[:3]]
while len(top_sources) < 3:
top_sources.append(("", 0))
new_history = history + [(message, answer)]
return qa_chain, gr.update(value=""), new_history, *sum(top_sources, ())
except Exception as e:
print(f"Conversation error: {e}")
return qa_chain, gr.update(value=""), history, "", 0, "", 0, "", 0
# ------------------------------
# Gradio UI
# ------------------------------
def demo():
with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink")) as demo:
vector_db = gr.State()
qa_chain = gr.State()
gr.HTML("AERO RAG (CPU-only, Safe Secret)
")
gr.Markdown("Query your PDF documents! CPU-only mode. Token must be stored in Hugging Face Space secret `hf_token`.")
with gr.Row():
# Left Column
with gr.Column(scale=1):
document = gr.Files(file_count="multiple", file_types=[".pdf"], label="Upload PDFs")
db_btn = gr.Button("Create vector DB")
db_progress = gr.Textbox(value="Not initialized", show_label=False)
llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value=list_llm_simple[0], type="index")
slider_temperature = gr.Slider(0.01, 1.0, 0.5, 0.1, label="Temperature")
slider_maxtokens = gr.Slider(128, 4096, 1024, 128, label="Max New Tokens")
slider_topk = gr.Slider(1, 10, 3, 1, label="Top-K Tokens")
qachain_btn = gr.Button("Initialize QA Chatbot")
llm_progress = gr.Textbox(value="Not initialized", show_label=False)
# Right Column
with gr.Column(scale=8):
chatbot = gr.Chatbot(height=480)
doc_source1 = gr.Textbox(label="Reference 1", lines=2)
source1_page = gr.Number(label="Page")
doc_source2 = gr.Textbox(label="Reference 2", lines=2)
source2_page = gr.Number(label="Page")
doc_source3 = gr.Textbox(label="Reference 3", lines=2)
source3_page = gr.Number(label="Page")
msg = gr.Textbox(placeholder="Ask a question")
submit_btn = gr.Button("Submit")
clear_btn = gr.ClearButton([msg, chatbot], value="Clear")
# Event Bindings
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])
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])
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])
clear_btn.click(lambda: [None, "", 0, "", 0, "", 0],
inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page])
demo.queue().launch(debug=True)
if __name__ == "__main__":
demo()