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| import gradio as gr | |
| import os | |
| import tqdm | |
| 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 | |
| VECTOR_DIR = './chroma_HF/' | |
| MODEL_NAME = "mistralai/Mistral-7B-Instruct-v0.2" | |
| CHUNK_SIZE = 600 | |
| CHUNK_OVERLAP = 40 | |
| # Load PDF document and create doc splits | |
| def load_doc(list_file_path): | |
| loaders = [PyPDFLoader(x) for x in list_file_path] | |
| pages = [] | |
| for loader in loaders: | |
| pages.extend(loader.load()) | |
| 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): | |
| embedding = HuggingFaceEmbeddings() | |
| vectordb = Chroma.from_documents( | |
| documents=splits, | |
| embedding=embedding, | |
| ) | |
| return vectordb | |
| # Load vector database | |
| def load_db(): | |
| embedding = HuggingFaceEmbeddings() | |
| vectordb = Chroma( | |
| persist_directory=VECTOR_DIR, | |
| embedding_function=embedding, | |
| ) | |
| return vectordb | |
| # Initialize langchain LLM chain | |
| def initialize_llmchain(temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| progress(0.1, desc="Initializing HF tokenizer...") | |
| # HuggingFaceHub uses HF inference endpoints | |
| progress(0.5, desc="Initializing HF Hub...") | |
| llm = HuggingFaceHub( | |
| repo_id=MODEL_NAME, | |
| model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k,\ | |
| "trust_remote_code": True, "torch_dtype": "auto"} | |
| ) | |
| progress(0.75, desc="Defining buffer memory...") | |
| memory = ConversationBufferMemory( | |
| memory_key="chat_history", | |
| output_key='answer', | |
| return_messages=True | |
| ) | |
| 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, | |
| return_source_documents=True, | |
| ) | |
| progress(0.9, desc="Done!") | |
| return qa_chain | |
| # Initialize database | |
| def initialize_database(list_file_obj, 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] | |
| # print('list_file_path', list_file_path) | |
| progress(0.25, desc="Loading document...") | |
| # Load document and create splits | |
| doc_splits = load_doc(list_file_path) | |
| # Create or load Vector database | |
| progress(0.5, desc="Generating vector database...") | |
| # global vector_db | |
| vector_db = create_db(doc_splits) | |
| progress(0.9, desc="Done!") | |
| return vector_db, "Complete!" | |
| def initialize_LLM(llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| qa_chain = initialize_llmchain(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) | |
| # Generate response using QA chain | |
| response = qa_chain({"question": message, "chat_history": formatted_chat_history}) | |
| response_answer = response["answer"] | |
| response_sources = response["source_documents"] | |
| response_source1 = response_sources[0].page_content.strip() | |
| response_source2 = response_sources[1].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 | |
| # 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 | |
| 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="base") as demo: | |
| vector_db = gr.State() | |
| qa_chain = gr.State() | |
| gr.Markdown( | |
| """<center><h2>Mistral 7B Document Chat</center></h2> | |
| <h3>Ask any questions about your PDF documents, along with follow-ups</h3> | |
| <br/> | |
| <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> | |
| <br><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.<br> | |
| """) | |
| 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)") | |
| with gr.Row(): | |
| db_progress = gr.Textbox(label="Vector database initialization", value="None") | |
| with gr.Row(): | |
| db_btn = gr.Button("Generating vector database...") | |
| with gr.Tab("Step 2 - QA chain initialization"): | |
| with gr.Accordion("Advanced options - LLM model", open=False): | |
| slider_temperature = gr.Slider(minimum = 0.0, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True) | |
| slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True) | |
| 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("Initializing 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(): | |
| msg = gr.Textbox(placeholder="Type message", container=True) | |
| with gr.Row(): | |
| submit_btn = gr.Button("Submit") | |
| clear_btn = gr.ClearButton([msg, chatbot]) | |
| # Preprocessing events | |
| db_btn.click(initialize_database, \ | |
| inputs=[document], \ | |
| outputs=[vector_db, db_progress]) | |
| qachain_btn.click(initialize_LLM, \ | |
| inputs=[slider_temperature, slider_maxtokens, slider_topk, vector_db], \ | |
| outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0], \ | |
| inputs=None, \ | |
| outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_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], \ | |
| queue=False) | |
| submit_btn.click(conversation, \ | |
| inputs=[qa_chain, msg, chatbot], \ | |
| outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page], \ | |
| queue=False) | |
| clear_btn.click(lambda:[None,"",0,"",0], \ | |
| inputs=None, \ | |
| outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], \ | |
| queue=False) | |
| demo.queue().launch(debug=True) | |
| if __name__ == "__main__": | |
| demo() | |