import gradio as gr from uuid import uuid4 from huggingface_hub import snapshot_download from langchain.document_loaders import ( CSVLoader, EverNoteLoader, PDFMinerLoader, TextLoader, UnstructuredEmailLoader, UnstructuredEPubLoader, UnstructuredHTMLLoader, UnstructuredMarkdownLoader, UnstructuredODTLoader, UnstructuredPowerPointLoader, UnstructuredWordDocumentLoader, ) from langchain.text_splitter import RecursiveCharacterTextSplitter from langchain.vectorstores import Chroma from langchain.embeddings import HuggingFaceEmbeddings from langchain.docstore.document import Document from chromadb.config import Settings from llama_cpp import Llama SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им." SYSTEM_TOKEN = 1788 USER_TOKEN = 1404 BOT_TOKEN = 9225 LINEBREAK_TOKEN = 13 ROLE_TOKENS = { "user": USER_TOKEN, "bot": BOT_TOKEN, "system": SYSTEM_TOKEN } LOADER_MAPPING = { ".csv": (CSVLoader, {}), ".doc": (UnstructuredWordDocumentLoader, {}), ".docx": (UnstructuredWordDocumentLoader, {}), ".enex": (EverNoteLoader, {}), ".epub": (UnstructuredEPubLoader, {}), ".html": (UnstructuredHTMLLoader, {}), ".md": (UnstructuredMarkdownLoader, {}), ".odt": (UnstructuredODTLoader, {}), ".pdf": (PDFMinerLoader, {}), ".ppt": (UnstructuredPowerPointLoader, {}), ".pptx": (UnstructuredPowerPointLoader, {}), ".txt": (TextLoader, {"encoding": "utf8"}), } repo_name = "IlyaGusev/saiga_13b_lora_llamacpp" model_name = "ggml-model-q4_1.bin" embedder_name = "sentence-transformers/paraphrase-multilingual-mpnet-base-v2" snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name) model = Llama( model_path=model_name, n_ctx=2000, n_parts=1, ) max_new_tokens = 1500 embeddings = HuggingFaceEmbeddings(model_name=embedder_name) def get_uuid(): return str(uuid4()) def load_single_document(file_path: str) -> Document: ext = "." + file_path.rsplit(".", 1)[-1] assert ext in LOADER_MAPPING loader_class, loader_args = LOADER_MAPPING[ext] loader = loader_class(file_path, **loader_args) return loader.load()[0] def get_message_tokens(model, role, content): message_tokens = model.tokenize(content.encode("utf-8")) message_tokens.insert(1, ROLE_TOKENS[role]) message_tokens.insert(2, LINEBREAK_TOKEN) message_tokens.append(model.token_eos()) return message_tokens def get_system_tokens(model): system_message = {"role": "system", "content": SYSTEM_PROMPT} return get_message_tokens(model, **system_message) def upload_files(files, file_paths): file_paths = [f.name for f in files] return file_paths def process_text(text): lines = text.split("\n") lines = [line for line in lines if len(line.strip()) > 2] text = "\n".join(lines).strip() if len(text) < 10: return None return text def build_index(file_paths, db, chunk_size, chunk_overlap, file_warning): documents = [load_single_document(path) for path in file_paths] text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) documents = text_splitter.split_documents(documents) fixed_documents = [] for doc in documents: doc.page_content = process_text(doc.page_content) if not doc.page_content: continue fixed_documents.append(doc) db = Chroma.from_documents( fixed_documents, embeddings, client_settings=Settings( anonymized_telemetry=False ) ) file_warning = f"Загружено {len(fixed_documents)} фрагментов! Можно задавать вопросы." return db, file_warning def user(message, history, system_prompt): new_history = history + [[message, None]] return "", new_history def retrieve(history, db, retrieved_docs, k_documents): context = "" if db: last_user_message = history[-1][0] retriever = db.as_retriever(search_kwargs={"k": k_documents}) docs = retriever.get_relevant_documents(last_user_message) retrieved_docs = "\n\n".join([doc.page_content for doc in docs]) return retrieved_docs def bot( history, system_prompt, conversation_id, retrieved_docs, top_p, top_k, temp ): if not history: return tokens = get_system_tokens(model)[:] tokens.append(LINEBREAK_TOKEN) for user_message, bot_message in history[:-1]: message_tokens = get_message_tokens(model=model, role="user", content=user_message) tokens.extend(message_tokens) if bot_message: message_tokens = get_message_tokens(model=model, role="bot", content=bot_message) tokens.extend(message_tokens) last_user_message = history[-1][0] if retrieved_docs: last_user_message = f"Контекст: {retrieved_docs}\n\nИспользуя контекст, ответь на вопрос: {last_user_message}" message_tokens = get_message_tokens(model=model, role="user", content=last_user_message) tokens.extend(message_tokens) role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN] tokens.extend(role_tokens) generator = model.generate( tokens, top_k=top_k, top_p=top_p, temp=temp ) partial_text = "" for i, token in enumerate(generator): if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens): break partial_text += model.detokenize([token]).decode("utf-8", "ignore") history[-1][1] = partial_text yield history with gr.Blocks( theme=gr.themes.Soft() ) as demo: db = gr.State(None) conversation_id = gr.State(get_uuid) favicon = '' gr.Markdown( f"""

{favicon}Saiga 13B llama.cpp: retrieval QA

""" ) with gr.Row(): with gr.Column(scale=5): file_output = gr.File(file_count="multiple", label="Загрузка файлов") file_paths = gr.State([]) file_warning = gr.Markdown(f"Фрагменты ещё не загружены!") with gr.Column(min_width=200, scale=3): with gr.Tab(label="Параметры нарезки"): chunk_size = gr.Slider( minimum=50, maximum=2000, value=250, step=50, interactive=True, label="Размер фрагментов", ) chunk_overlap = gr.Slider( minimum=0, maximum=500, value=30, step=10, interactive=True, label="Пересечение" ) with gr.Row(): k_documents = gr.Slider( minimum=1, maximum=10, value=2, step=1, interactive=True, label="Кол-во фрагментов для контекста" ) with gr.Row(): retrieved_docs = gr.Textbox( lines=6, label="Извлеченные фрагменты", placeholder="Появятся после задавания вопросов", interactive=False ) with gr.Row(): with gr.Column(scale=5): system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT, interactive=False) chatbot = gr.Chatbot(label="Диалог").style(height=400) with gr.Column(min_width=80, scale=1): with gr.Tab(label="Параметры генерации"): top_p = gr.Slider( minimum=0.0, maximum=1.0, value=0.9, step=0.05, interactive=True, label="Top-p", ) top_k = gr.Slider( minimum=10, maximum=100, value=30, step=5, interactive=True, label="Top-k", ) temp = gr.Slider( minimum=0.0, maximum=2.0, value=0.1, step=0.1, interactive=True, label="Temp" ) with gr.Row(): with gr.Column(): msg = gr.Textbox( label="Отправить сообщение", placeholder="Отправить сообщение", show_label=False, ).style(container=False) with gr.Column(): with gr.Row(): submit = gr.Button("Отправить") stop = gr.Button("Остановить") clear = gr.Button("Очистить") # Upload files upload_event = file_output.change( fn=upload_files, inputs=[file_output, file_paths], outputs=[file_paths], queue=True, ).success( fn=build_index, inputs=[file_paths, db, chunk_size, chunk_overlap, file_warning], outputs=[db, file_warning], queue=True ) # Pressing Enter submit_event = msg.submit( fn=user, inputs=[msg, chatbot, system_prompt], outputs=[msg, chatbot], queue=False, ).success( fn=retrieve, inputs=[chatbot, db, retrieved_docs, k_documents], outputs=[retrieved_docs], queue=True, ).success( fn=bot, inputs=[ chatbot, system_prompt, conversation_id, retrieved_docs, top_p, top_k, temp ], outputs=chatbot, queue=True, ) # Pressing the button submit_click_event = submit.click( fn=user, inputs=[msg, chatbot, system_prompt], outputs=[msg, chatbot], queue=False, ).success( fn=retrieve, inputs=[chatbot, db, retrieved_docs, k_documents], outputs=[retrieved_docs], queue=True, ).success( fn=bot, inputs=[ chatbot, system_prompt, conversation_id, retrieved_docs, top_p, top_k, temp ], outputs=chatbot, queue=True, ) # Stop generation stop.click( fn=None, inputs=None, outputs=None, cancels=[submit_event, submit_click_event], queue=False, ) # Clear history clear.click(lambda: None, None, chatbot, queue=False) demo.queue(max_size=128, concurrency_count=1) demo.launch()