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
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/Llama-2-7b-chat-hf", "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]
# 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/Llama-2-7b-chat-hf":
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,
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 (powered by LangChain and open-source Large Language Models)</h2>
<h3>Ask any questions about your PDF documents, along with follow-ups.</h3>
<b>Note:</b> This AI assistant performs retrieval-augmented generation from your PDF documents.
When generating answers, it takes past questions into account (via conversational memory), and includes document references for clarity purposes.</i>
<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 an output.
</center>
"""
)
with gr.Tab("Step 1 - Document pre-processing"):
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.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 2 - QA chain initialization"):
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 = 0.0, 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 3 - Conversation with 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", container=True)
with gr.Row():
submit_btn = gr.Button("Submit")
clear_btn = gr.ClearButton([msg, chatbot])
# 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()