import gradio as gr import os api_token = os.getenv("HF_TOKEN") from langchain.llms.base import LLM from transformers import AutoTokenizer from huggingface_hub import HfApi import requests from langchain_community.vectorstores import FAISS 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 HuggingFaceHub, HuggingFaceEndpoint import torch from langchain.llms.base import LLM from transformers import AutoTokenizer from huggingface_hub import HfApi import requests list_llm = ["meta-llama/Llama-3.1-8B-Instruct"] # , "HuggingFaceH4/zephyr-7b-beta"] # "mistralai/Mistral-7B-Instruct-v0.2" # meta-llama/Meta-Llama-3-8B-Instruct list_llm_simple = [os.path.basename(llm) for llm in list_llm] # class ZephyrLLM(LLM): # def __init__(self, repo_id, huggingfacehub_api_token, max_new_tokens=512, temperature=0.7, **kwargs): # super().__init__(**kwargs) # self.repo_id = repo_id # self.api_token = huggingfacehub_api_token # self.api_url = f"https://api-inference.huggingface.co/models/{repo_id}" # self.headers = {"Authorization": f"Bearer {huggingfacehub_api_token}"} # self.tokenizer = AutoTokenizer.from_pretrained(repo_id) # self.max_new_tokens = max_new_tokens # self.temperature = temperature # def _call(self, prompt, stop=None): # # Format as chat message # messages = [{"role": "user", "content": prompt}] # # Apply Zephyr's chat template # formatted_prompt = self.tokenizer.apply_chat_template( # messages, tokenize=False, add_generation_prompt=True # ) # # Send request to Hugging Face Inference API # payload = { # "inputs": formatted_prompt, # "parameters": { # "max_new_tokens": self.max_new_tokens, # "temperature": self.temperature # } # } # response = requests.post(self.api_url, headers=self.headers, json=payload) # if response.status_code == 200: # full_response = response.json()[0]["generated_text"] # # Extract the assistant reply from the full response # # After <|assistant|>\n, everything is the model's answer # if "<|assistant|>" in full_response: # return full_response.split("<|assistant|>")[-1].strip() # else: # return full_response.strip() # else: # raise Exception(f"Failed call [{response.status_code}]: {response.text}") # @property # def _llm_type(self) -> str: # return "zephyr-custom" # Load and split PDF document def load_doc(list_file_path): # 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 = 1024, chunk_overlap = 64 ) doc_splits = text_splitter.split_documents(pages) return doc_splits # Create vector database def create_db(splits): embeddings = HuggingFaceEmbeddings() vectordb = FAISS.from_documents(splits, embeddings) return vectordb # Initialize langchain LLM chain def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): # if llm_model == "HuggingFaceH4/zephyr-7b-beta": # llm = ZephyrLLM( # repo_id=llm_model, # huggingfacehub_api_token=api_token, # temperature=temperature, # max_new_tokens=max_tokens, # ) if llm_model == "meta-llama/Llama-3.1-8B-Instruct": llm = HuggingFaceEndpoint( repo_id=llm_model, task="text-generation", huggingfacehub_api_token = api_token, temperature = temperature, max_new_tokens = max_tokens, top_k = top_k, ) # llm = HuggingFaceHub( # repo_id="mistralai/Mistral-7B-Instruct-v0.2", # huggingfacehub_api_token=api_token, # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens} # ) else: llm = HuggingFaceEndpoint( huggingfacehub_api_token = api_token, repo_id=llm_model, task="text-generation", temperature = temperature, max_new_tokens = max_tokens, top_k = top_k, ) 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 # Initialize database def initialize_database(list_file_obj, progress=gr.Progress()): # Create a list of documents (when valid) list_file_path = [x.name for x in list_file_obj if x is not None] # Load document and create splits doc_splits = load_doc(list_file_path) # Create or load vector database vector_db = create_db(doc_splits) return vector_db, "Database created!" # Initialize LLM 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, "QA chain initialized. Chatbot is ready!" 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) # Generate response using QA chain response = qa_chain.invoke({"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 # Append user message and response to chat history new_history = history + [(message, response_answer)] 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) return list_file_path def demo(): # with gr.Blocks(theme=gr.themes.Default(primary_hue="sky")) as demo: with gr.Blocks(theme=gr.themes.Default(primary_hue="red", secondary_hue="pink", neutral_hue = "sky")) as demo: vector_db = gr.State() qa_chain = gr.State() gr.HTML("

RAG PDF chatbot

") gr.Markdown("""Query your PDF documents! This AI agent is designed to perform retrieval augmented generation (RAG) on PDF documents. The app is hosted on Hugging Face Hub for the sole purpose of demonstration. \ Please do not upload confidential documents. """) with gr.Row(): with gr.Column(scale = 86): gr.Markdown("Step 1 - Upload PDF documents and Initialize RAG pipeline") with gr.Row(): document = gr.Files(height=300, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload PDF documents") with gr.Row(): db_btn = gr.Button("Create vector database") with gr.Row(): db_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Vector database status", gr.Markdown("Select Large Language Model (LLM) and input parameters") with gr.Row(): llm_btn = gr.Radio(list_llm_simple, label="Available LLMs", value = list_llm_simple[0], type="index") # info="Select LLM", show_label=False with gr.Row(): with gr.Accordion("LLM input parameters", open=False): with gr.Row(): slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.5, step=0.1, label="Temperature", info="Controls randomness in token generation", interactive=True) with gr.Row(): slider_maxtokens = gr.Slider(minimum = 128, maximum = 9192, value=4096, step=128, label="Max New Tokens", info="Maximum number of tokens to be generated",interactive=True) with gr.Row(): slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k", info="Number of tokens to select the next token from", interactive=True) with gr.Row(): qachain_btn = gr.Button("Initialize Question Answering Chatbot") with gr.Row(): llm_progress = gr.Textbox(value="Not initialized", show_label=False) # label="Chatbot status", with gr.Column(scale = 200): gr.Markdown("Step 2 - Chat with your Document") chatbot = gr.Chatbot(height=505) with gr.Accordion("Relevent context from the source document", 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="Ask a question", container=True) with gr.Row(): submit_btn = gr.Button("Submit") clear_btn = gr.ClearButton([msg, chatbot], value="Clear") # Preprocessing events db_btn.click(initialize_database, \ inputs=[document], \ outputs=[vector_db, 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()