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 transformers import AutoTokenizer
import transformers
import torch
import tqdm
import accelerate
# 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 == "HuggingFaceH4/zephyr-7b-gemma-v0.1":
raise gr.Error("zephyr-7b-gemma-v0.1 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
# 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 = Path(list_file_path[0]).stem
# Fix potential issues from naming convention
## Remove space
collection_name = collection_name.replace(" ","-")
## Limit lenght to 50 characters
collection_name = collection_name[:50]
## Enforce start and end as alphanumeric character
if not collection_name[0].isalnum():
collection_name[0] = 'A'
if not collection_name[-1].isalnum():
collection_name[-1] = 'Z'
# print('list_file_path: ', list_file_path)
print('Collection name: ', collection_name)
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(
"""PDF-based chatbot (powered by LangChain and open-source LLMs)
Ask any questions about your PDF documents, along with follow-ups
Note: 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.
Warning: 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.
""")
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()