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import gradio as gr | |
import os | |
import re | |
from pathlib import Path | |
from langchain_community.vectorstores import Chroma | |
from langchain.chains import ConversationalRetrievalChain | |
from langchain_community.embeddings import HuggingFaceEmbeddings | |
from langchain_community.llms import HuggingFaceEndpoint | |
from pathlib import Path | |
import chromadb | |
from unidecode import unidecode | |
# List of allowed models | |
allowed_llms = [ | |
"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" | |
] | |
list_llm_simple = [os.path.basename(llm) for llm in allowed_llms] | |
# Load PDF document and create doc splits | |
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, collection_name): | |
embedding = HuggingFaceEmbeddings() | |
embedding=embedding, | |
client=new_client, | |
collection_name=collection_name, | |
) | |
return vectordb | |
# Initialize langchain LLM chain | |
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
temperature=temperature, | |
max_new_tokens=max_tokens, | |
top_k=top_k, | |
load_in_8bit=True, | |
) | |
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 | |
# Generate collection name for vector database | |
def create_collection_name(filepath): | |
collection_name = Path(filepath).stem | |
collection_name = unidecode(collection_name).replace(" ", "-") | |
collection_name = re.sub('[^A-Za-z0-9]+', '-', 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 | |
# 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] | |
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) | |
return vector_db, collection_name, "Complete!" | |
# Initialize LLM | |
def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
llm_name = allowed_llms[llm_option] | |
qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress) | |
return qa_chain, "Complete!" | |
# Format chat history | |
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 | |
# Conversation handling | |
def conversation(qa_chain, message, history): | |
formatted_chat_history = format_chat_history(message, history) | |
response = qa_chain({"question": message, "chat_history": formatted_chat_history}) | |
response_answer = response["answer"].split("Helpful Answer:")[-1] | |
response_sources = response["source_documents"] | |
new_history = history + [(message, response_answer)] | |
response_details = [(src.page_content.strip(), src.metadata["page"] + 1) for src in response_sources[:3]] | |
return qa_chain, gr.update(value=""), new_history, *sum(response_details, ()) | |
# Gradio Interface | |
def demo(): | |
with gr.Blocks(theme="default") as demo: | |
vector_db = gr.State() | |
qa_chain = gr.State() | |
collection_name = gr.State() | |
gr.Markdown( | |
"""<center><h2>PDF-based Chatbot</h2></center> | |
<h3>Ask any questions about your PDF documents</h3>""") | |
with gr.Tab("Upload PDF"): | |
document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF Documents") | |
with gr.Tab("Process Document"): | |
db_btn = gr.Radio(["ChromaDB"], label="Vector Database", value="ChromaDB", type="index") | |
with gr.Accordion("Advanced Options", open=False): | |
slider_chunk_size = gr.Slider(100, 1000, 600, 20, label="Chunk Size", interactive=True) | |
slider_chunk_overlap = gr.Slider(10, 200, 40, 10, label="Chunk Overlap", interactive=True) | |
db_progress = gr.Textbox(label="Database Initialization Status", value="None") | |
db_btn = gr.Button("Generate Database") | |
with gr.Tab("Initialize QA Chain"): | |
llm_btn = gr.Radio(list_llm_simple, label="LLM Models", value=list_llm_simple[0], type="index") | |
with gr.Accordion("Advanced Options", open=False): | |
slider_temperature = gr.Slider(0.01, 1.0, 0.7, 0.1, label="Temperature", interactive=True) | |
slider_maxtokens = gr.Slider(224, 4096, 1024, 32, label="Max Tokens", interactive=True) | |
slider_topk = gr.Slider(1, 10, 3, 1, label="Top-k Samples", interactive=True) | |
llm_progress = gr.Textbox(value="None", label="QA Chain Initialization Status") | |
qachain_btn = gr.Button("Initialize QA Chain") | |
with gr.Tab("Chatbot"): | |
chatbot = gr.Chatbot(height=300) | |
with gr.Accordion("Document References", open=False): | |
for i in range(1, 4): | |
gr.Row([gr.Textbox(label=f"Reference {i}", lines=2, container=True, scale=20), gr.Number(label="Page", scale=1)]) | |
msg = gr.Textbox(placeholder="Type message here...", container=True) | |
gr.Row([gr.Button("Submit"), gr.Button("Clear Conversation")]) | |
# Define Interactions | |
db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, 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] + [None] * 6) | |
demo.launch(debug=True) | |
if __name__ == "__main__": | |
demo() |