import gradio as gr import os from langchain.document_loaders import PyPDFLoader from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Chroma from langchain.chains import ConversationalRetrievalChain from langchain.embeddings import HuggingFaceEmbeddings from langchain.llms import HuggingFacePipeline from langchain.chains import ConversationChain from langchain.memory import ConversationBufferMemory from langchain.llms import HuggingFaceHub from pathlib import Path import chromadb from transformers import AutoTokenizer import transformers import torch import tqdm import accelerate #Set parameters llm_model = 'mistralai/Mixtral-8x7B-Instruct-v0.1' list_file_path = '/home/user/app/pdfs' chunk_size = 1024 chunk_overlap = 128 temperature = 0.1 max_tokens = 6000 top_k = 3 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(vector_db): llm = HuggingFaceHub(repo_id = llm_model, model_kwargs={"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 vector_db, collection_name = initialize_database() #list_file_obj = document # Initialize database def initialize_database(list_file_obj): # 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 def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db): # print("llm_option",llm_option) llm_name = llm_model qa_chain = initialize_llmchain(llm_name, temperature, max_tokens, top_k, vector_db) return qa_chain 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 document = os.listdir(list_file_path) vector_db, collection_name = initialize_database(document) qa_chain = initialize_LLM(vector_db) def demo(): with gr.Blocks(theme='base') as demo: vector_db = gr.State() qa_chain = gr.State() collection_name = gr.State() chatbot = gr.Chatbot(height=300) with gr.Accordion('References', open=True): 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 = 'Ask your question', container = True) with gr.Row(): submit_btn = gr.Button('Submit') clear_button = gr.ClearButton([msg, chatbot]) 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()