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import gradio as gr | |
import os | |
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 spaces | |
from pathlib import Path | |
import chromadb | |
from unidecode import unidecode | |
from transformers import AutoTokenizer | |
import transformers | |
import torch | |
import tqdm | |
import accelerate | |
import re | |
# default_persist_directory = './chroma_HF/' | |
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/Meta-Llama-3.1-8B-Instruct", "microsoft/phi-2", \ | |
"TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \ | |
"google/flan-t5-xxl" | |
] | |
list_llm_simple = [os.path.basename(llm) for llm in list_llm] | |
huggingfacehub_api_token = os.environ.get('HUGGINGFACEHUB_API_TOKEN') | |
# Load PDF document and create doc splits | |
def load_doc(list_file_path, chunk_size, chunk_overlap): | |
# 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 = 600, chunk_overlap = 50) | |
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() | |
new_client = chromadb.EphemeralClient() | |
vectordb = Chroma.from_documents( | |
documents=splits, | |
embedding=embedding, | |
client=new_client, | |
collection_name=collection_name, | |
# persist_directory=default_persist_directory | |
) | |
return vectordb | |
# Load vector database | |
def load_db(): | |
embedding = HuggingFaceEmbeddings() | |
vectordb = Chroma( | |
# persist_directory=default_persist_directory, | |
embedding_function=embedding) | |
return vectordb | |
# Initialize langchain LLM chain | |
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
progress(0.1, desc="Initializing HF tokenizer...") | |
# HuggingFacePipeline uses local model | |
# Note: it will download model locally... | |
# tokenizer=AutoTokenizer.from_pretrained(llm_model) | |
# progress(0.5, desc="Initializing HF pipeline...") | |
# pipeline=transformers.pipeline( | |
# "text-generation", | |
# model=llm_model, | |
# tokenizer=tokenizer, | |
# torch_dtype=torch.bfloat16, | |
# trust_remote_code=True, | |
# device_map="auto", | |
# # max_length=1024, | |
# max_new_tokens=max_tokens, | |
# do_sample=True, | |
# top_k=top_k, | |
# num_return_sequences=1, | |
# eos_token_id=tokenizer.eos_token_id | |
# ) | |
# llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature}) | |
# HuggingFaceHub uses HF inference endpoints | |
progress(0.5, desc="Initializing HF Hub...") | |
# Use of trust_remote_code as model_kwargs | |
# Warning: langchain issue | |
# URL: https://github.com/langchain-ai/langchain/issues/6080 | |
if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1": | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
# model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True} | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_k = top_k, | |
load_in_8bit = True, | |
) | |
elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]: | |
raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint") | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_k = top_k, | |
) | |
elif llm_model == "microsoft/phi-2": | |
# raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...") | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
# model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"} | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_k = top_k, | |
trust_remote_code = True, | |
torch_dtype = "auto", | |
) | |
elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0": | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
# model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k} | |
temperature = temperature, | |
max_new_tokens = 250, | |
top_k = top_k, | |
) | |
elif llm_model == "meta-llama/Meta-Llama-3.1-8B-Instruct": | |
raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...") | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
# model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
huggingfacehub_api_token = huggingfacehub_api_token, | |
top_k = top_k, | |
) | |
else: | |
llm = HuggingFaceEndpoint( | |
repo_id=llm_model, | |
# model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"} | |
# model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} | |
temperature = temperature, | |
max_new_tokens = max_tokens, | |
top_k = top_k, | |
) | |
progress(0.75, desc="Defining buffer memory...") | |
memory = ConversationBufferMemory( | |
memory_key="chat_history", | |
output_key='answer', | |
return_messages=True | |
) | |
# retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3}) | |
retriever=vector_db.as_retriever() | |
progress(0.8, desc="Defining retrieval chain...") | |
qa_chain = ConversationalRetrievalChain.from_llm( | |
llm, | |
retriever=retriever, | |
chain_type="stuff", | |
memory=memory, | |
# combine_docs_chain_kwargs={"prompt": your_prompt}) | |
return_source_documents=True, | |
#return_generated_question=False, | |
verbose=False, | |
) | |
progress(0.9, desc="Done!") | |
return qa_chain | |
# Generate collection name for vector database | |
# - Use filepath as input, ensuring unicode text | |
def create_collection_name(filepath): | |
# Extract filename without extension | |
collection_name = Path(filepath).stem | |
# Fix potential issues from naming convention | |
## Remove space | |
collection_name = collection_name.replace(" ","-") | |
## ASCII transliterations of Unicode text | |
collection_name = unidecode(collection_name) | |
## Remove special characters | |
#collection_name = re.findall("[\dA-Za-z]*", collection_name)[0] | |
collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name) | |
## Limit length to 50 characters | |
collection_name = collection_name[:50] | |
## Minimum length of 3 characters | |
if len(collection_name) < 3: | |
collection_name = collection_name + 'xyz' | |
## Enforce start and end as alphanumeric character | |
if not collection_name[0].isalnum(): | |
collection_name = 'A' + collection_name[1:] | |
if not collection_name[-1].isalnum(): | |
collection_name = collection_name[:-1] + 'Z' | |
print('Filepath: ', filepath) | |
print('Collection name: ', collection_name) | |
return collection_name | |
# Initialize database | |
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): | |
# Create list of documents (when valid) | |
list_file_path = [x.name for x in list_file_obj if x is not None] | |
# Create collection_name for vector database | |
progress(0.1, desc="Creating collection name...") | |
collection_name = create_collection_name(list_file_path[0]) | |
progress(0.25, desc="Loading document...") | |
# Load document and create splits | |
doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap) | |
# Create or load vector database | |
progress(0.5, desc="Generating vector database...") | |
# global vector_db | |
vector_db = create_db(doc_splits, collection_name) | |
progress(0.9, desc="Done!") | |
return vector_db, collection_name, "Complete!" | |
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, "Complete!" | |
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) | |
#print("formatted_chat_history",formatted_chat_history) | |
# Generate response using QA chain | |
response = qa_chain({"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 | |
# print ('chat response: ', response_answer) | |
# print('DB source', response_sources) | |
# Append user message and response to chat history | |
new_history = history + [(message, response_answer)] | |
# return gr.update(value=""), new_history, response_sources[0], response_sources[1] | |
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) | |
# print(file_path) | |
# initialize_database(file_path, progress) | |
return list_file_path | |
def demo(): | |
with gr.Blocks(theme="base") as demo: | |
vector_db = gr.State() | |
qa_chain = gr.State() | |
collection_name = gr.State() | |
gr.Markdown( | |
"""<center><h2>PDF-based chatbot</center></h2> | |
<h3>Ask any questions about your PDF documents</h3>""") | |
gr.Markdown( | |
"""<b>Note:</b> This AI assistant, using Langchain and open-source LLMs, performs retrieval-augmented generation (RAG) from your PDF documents. \ | |
The user interface explicitely shows multiple steps to help understand the RAG workflow. | |
This chatbot takes past questions into account when generating answers (via conversational memory), and includes document references for clarity purposes.<br> | |
<br><b>Warning:</b> This space uses the free CPU Basic hardware from Hugging Face. Some steps and LLM models used below (free inference endpoints) can take some time to generate a reply. | |
""") | |
with gr.Tab("Step 1 - Upload PDF"): | |
with gr.Row(): | |
document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") | |
# upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1) | |
with gr.Tab("Step 2 - Process document"): | |
with gr.Row(): | |
db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database") | |
with gr.Accordion("Advanced options - Document text splitter", open=False): | |
with gr.Row(): | |
slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) | |
with gr.Row(): | |
slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) | |
with gr.Row(): | |
db_progress = gr.Textbox(label="Vector database initialization", value="None") | |
with gr.Row(): | |
db_btn = gr.Button("Generate vector database") | |
with gr.Tab("Step 3 - Initialize QA chain"): | |
with gr.Row(): | |
llm_btn = gr.Radio(list_llm_simple, \ | |
label="LLM models", value = list_llm_simple[0], type="index", info="Choose your LLM model") | |
with gr.Accordion("Advanced options - LLM model", open=False): | |
with gr.Row(): | |
slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) | |
with gr.Row(): | |
slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) | |
with gr.Row(): | |
slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True) | |
with gr.Row(): | |
llm_progress = gr.Textbox(value="None",label="QA chain initialization") | |
with gr.Row(): | |
qachain_btn = gr.Button("Initialize Question Answering chain") | |
with gr.Tab("Step 4 - Chatbot"): | |
chatbot = gr.Chatbot(height=300) | |
with gr.Accordion("Advanced - Document references", 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="Type message (e.g. 'What is this document about?')", container=True) | |
with gr.Row(): | |
submit_btn = gr.Button("Submit message") | |
clear_btn = gr.ClearButton([msg, chatbot], value="Clear conversation") | |
# Preprocessing events | |
#upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document]) | |
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]).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() | |