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| 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 HuggingFaceHub | |
| from pathlib import Path | |
| import chromadb | |
| llm_names = ["mistralai/Mixtral-8x7B-Instruct-v0.1"] | |
| llm_names_simple = [os.path.basename(llm) for llm in llm_names] | |
| def load_doc(list_file_path, chunk_size, chunk_overlap): | |
| loaders = [PyPDFLoader(x) for x in list_file_path] | |
| pages = [loader.load() for loader in loaders] | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) | |
| doc_splits = text_splitter.split_documents(pages) | |
| return doc_splits | |
| 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, | |
| ) | |
| return vectordb | |
| def load_db(): | |
| embedding = HuggingFaceEmbeddings() | |
| vectordb = Chroma(embedding_function=embedding) | |
| return vectordb | |
| def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()): | |
| progress(0.1, desc="Initializing HF tokenizer...") | |
| progress(0.5, desc="Initializing HF Hub...") | |
| model_kwargs = {"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k} | |
| if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1": | |
| model_kwargs["load_in_8bit"] = True | |
| llm = HuggingFaceHub(repo_id=llm_model, model_kwargs=model_kwargs) | |
| 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 | |
| def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()): | |
| list_file_path = [x.name for x in list_file_obj if x is not None] | |
| collection_name = Path(list_file_path[0]).stem | |
| progress(0.25, desc="Loading document...") | |
| doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap) | |
| progress(0.5, desc="Generating vector database...") | |
| 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()): | |
| llm_name = llm_names[llm_option] | |
| 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 = [f"User: {user_message}\nAssistant: {bot_message}" for user_message, bot_message in chat_history] | |
| return formatted_chat_history | |
| def conversation(qa_chain, message, history): | |
| formatted_chat_history = format_chat_history(message, history) | |
| 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() | |
| response_source1_page = response_sources[0].metadata["page"] + 1 | |
| response_source2_page = response_sources[1].metadata["page"] + 1 | |
| new_history = history + [(message, response_answer)] | |
| 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 = [file_obj.name for _ in file_obj] | |
| 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("""<center><h2>ChatPDF</center></h2>""") | |
| with gr.Tab("Step 1 - Selezione PDF"): | |
| document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)") | |
| 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): | |
| slider_chunk_size = gr.Slider(minimum=100, maximum=1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True) | |
| slider_chunk_overlap = gr.Slider(minimum=10, maximum=200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True) | |
| db_progress = gr.Textbox(label="Vector database initialization", value="None") | |
| db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap], outputs=[vector_db, collection_name, db_progress]) | |
| with gr.Tab("Step 2 - Inizializzazione QA"): | |
| llm_btn = gr.Radio(llm_names_simple, label="LLM models", value=llm_names_simple[0], type="index", info="Choose your LLM model") | |
| 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) | |
| llm_progress = gr.Textbox(value="None", label="QA chain initialization") | |
| qachain_btn = gr.Button("Initialize question-answering chain...") | |
| 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], inputs=None, outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page], queue=False) | |
| with gr.Tab("Step 3 - Conversazione con Chatbot"): | |
| chatbot = gr.Chatbot(height=300) | |
| with gr.Accordion("Advanced - Document references", open=True): | |
| doc_source1 = gr.Textbox(label="Reference 1", lines=2, container=True, scale=20) | |
| source1_page = gr.Number(label="Page", scale=1) | |
| doc_source2 = gr.Textbox(label="Reference 2", lines=2, container=True, scale=20) | |
| source2_page = gr.Number(label="Page", scale=1) | |
| msg = gr.Textbox(placeholder="Type message", container=True) | |
| submit_btn = gr.Button("Submit") | |
| clear_btn = 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], 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() |