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 == "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 # 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' 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( """PDF-based chatbot (by Dr. Aloke Upadhaya)