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import shutil |
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import os |
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import gradio as gr |
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import torch |
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from uuid import uuid4 |
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from huggingface_hub.file_download import http_get |
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from langchain.document_loaders import ( |
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CSVLoader, |
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EverNoteLoader, |
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PDFMinerLoader, |
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TextLoader, |
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UnstructuredEmailLoader, |
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UnstructuredEPubLoader, |
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UnstructuredHTMLLoader, |
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UnstructuredMarkdownLoader, |
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UnstructuredODTLoader, |
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UnstructuredPowerPointLoader, |
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UnstructuredWordDocumentLoader, |
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) |
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from langchain.text_splitter import RecursiveCharacterTextSplitter |
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from langchain.docstore.document import Document |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import cos_sim |
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from llama_cpp import Llama |
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SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им." |
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LOADER_MAPPING = { |
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".csv": (CSVLoader, {}), |
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".doc": (UnstructuredWordDocumentLoader, {}), |
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".docx": (UnstructuredWordDocumentLoader, {}), |
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".enex": (EverNoteLoader, {}), |
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".epub": (UnstructuredEPubLoader, {}), |
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".html": (UnstructuredHTMLLoader, {}), |
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".md": (UnstructuredMarkdownLoader, {}), |
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".odt": (UnstructuredODTLoader, {}), |
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".pdf": (PDFMinerLoader, {}), |
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".ppt": (UnstructuredPowerPointLoader, {}), |
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".pptx": (UnstructuredPowerPointLoader, {}), |
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".txt": (TextLoader, {"encoding": "utf8"}), |
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} |
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def load_model( |
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directory: str = ".", |
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model_name: str = "model-q4_K.gguf", |
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model_url: str = "https://huggingface.co/IlyaGusev/saiga2_13b_gguf/resolve/main/model-q4_K.gguf" |
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): |
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final_model_path = os.path.join(directory, model_name) |
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print("Downloading all files...") |
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if not os.path.exists(final_model_path): |
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with open(final_model_path, "wb") as f: |
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http_get(model_url, f) |
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os.chmod(final_model_path, 0o777) |
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print("Files downloaded!") |
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model = Llama( |
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model_path=final_model_path, |
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n_ctx=2000, |
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n_parts=1, |
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) |
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print("Model loaded!") |
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return model |
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MAX_NEW_TOKENS = 1500 |
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EMBEDDER = SentenceTransformer("sentence-transformers/paraphrase-multilingual-mpnet-base-v2") |
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MODEL = load_model() |
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def get_uuid(): |
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return str(uuid4()) |
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def load_single_document(file_path: str) -> Document: |
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ext = "." + file_path.rsplit(".", 1)[-1] |
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assert ext in LOADER_MAPPING |
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loader_class, loader_args = LOADER_MAPPING[ext] |
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loader = loader_class(file_path, **loader_args) |
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return loader.load()[0] |
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def get_message_tokens(model, role, content): |
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content = f"{role}\n{content}\n</s>" |
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content = content.encode("utf-8") |
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return model.tokenize(content, special=True) |
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def get_system_tokens(model): |
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system_message = {"role": "system", "content": SYSTEM_PROMPT} |
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return get_message_tokens(model, **system_message) |
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def process_text(text): |
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lines = text.split("\n") |
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lines = [line for line in lines if len(line.strip()) > 2] |
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text = "\n".join(lines).strip() |
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if len(text) < 10: |
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return None |
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return text |
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def upload_files(files, file_paths): |
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file_paths = [f.name for f in files] |
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return file_paths |
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def build_index(file_paths, db, chunk_size, chunk_overlap, file_warning): |
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documents = [load_single_document(path) for path in file_paths] |
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap) |
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documents = text_splitter.split_documents(documents) |
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print("Documents after split:", len(documents)) |
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fixed_documents = [] |
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for doc in documents: |
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doc.page_content = process_text(doc.page_content) |
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if not doc.page_content: |
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continue |
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fixed_documents.append(doc) |
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print("Documents after processing:", len(fixed_documents)) |
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texts = [doc.page_content for doc in fixed_documents] |
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embeddings = EMBEDDER.encode(texts, convert_to_tensor=True) |
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db = {"docs": texts, "embeddings": embeddings} |
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print("Embeddings calculated!") |
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file_warning = f"Загружено {len(fixed_documents)} фрагментов! Можно задавать вопросы." |
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return db, file_warning |
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def retrieve(history, db, retrieved_docs, k_documents): |
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retrieved_docs = "" |
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if db: |
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last_user_message = history[-1][0] |
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query_embedding = EMBEDDER.encode(last_user_message, convert_to_tensor=True) |
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scores = cos_sim(query_embedding, db["embeddings"])[0] |
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top_k_idx = torch.topk(scores, k=k_documents)[1] |
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top_k_documents = [db["docs"][idx] for idx in top_k_idx] |
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retrieved_docs = "\n\n".join(top_k_documents) |
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return retrieved_docs |
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def user(message, history, system_prompt): |
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new_history = history + [[message, None]] |
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return "", new_history |
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def bot( |
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history, |
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system_prompt, |
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conversation_id, |
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retrieved_docs, |
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top_p, |
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top_k, |
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temp |
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): |
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if not history: |
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return |
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tokens = get_system_tokens(MODEL)[:] |
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tokens.append(LINEBREAK_TOKEN) |
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for user_message, bot_message in history[:-1]: |
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message_tokens = get_message_tokens(model=MODEL, role="user", content=user_message) |
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tokens.extend(message_tokens) |
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if bot_message: |
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message_tokens = get_message_tokens(model=MODEL, role="bot", content=bot_message) |
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tokens.extend(message_tokens) |
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last_user_message = history[-1][0] |
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if retrieved_docs: |
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last_user_message = f"Контекст: {retrieved_docs}\n\nИспользуя контекст, ответь на вопрос: {last_user_message}" |
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message_tokens = get_message_tokens(model=MODEL, role="user", content=last_user_message) |
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tokens.extend(message_tokens) |
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role_tokens = [MODEL.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN] |
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tokens.extend(role_tokens) |
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generator = MODEL.generate( |
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tokens, |
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top_k=top_k, |
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top_p=top_p, |
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temp=temp |
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) |
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partial_text = "" |
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for i, token in enumerate(generator): |
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if token == MODEL.token_eos() or (MAX_NEW_TOKENS is not None and i >= MAX_NEW_TOKENS): |
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break |
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partial_text += MODEL.detokenize([token]).decode("utf-8", "ignore") |
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history[-1][1] = partial_text |
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yield history |
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with gr.Blocks( |
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theme=gr.themes.Soft() |
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) as demo: |
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db = gr.State(None) |
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conversation_id = gr.State(get_uuid) |
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favicon = '<img src="https://cdn.midjourney.com/b88e5beb-6324-4820-8504-a1a37a9ba36d/0_1.png" width="48px" style="display: inline">' |
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gr.Markdown( |
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f"""<h1><center>{favicon}Saiga 13B llama.cpp: retrieval QA</center></h1> |
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""" |
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) |
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with gr.Row(): |
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with gr.Column(scale=5): |
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file_output = gr.File(file_count="multiple", label="Загрузка файлов") |
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file_paths = gr.State([]) |
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file_warning = gr.Markdown(f"Фрагменты ещё не загружены!") |
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with gr.Column(min_width=200, scale=3): |
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with gr.Tab(label="Параметры нарезки"): |
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chunk_size = gr.Slider( |
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minimum=50, |
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maximum=2000, |
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value=250, |
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step=50, |
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interactive=True, |
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label="Размер фрагментов", |
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) |
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chunk_overlap = gr.Slider( |
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minimum=0, |
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maximum=500, |
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value=30, |
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step=10, |
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interactive=True, |
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label="Пересечение" |
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) |
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with gr.Row(): |
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k_documents = gr.Slider( |
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minimum=1, |
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maximum=10, |
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value=2, |
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step=1, |
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interactive=True, |
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label="Кол-во фрагментов для контекста" |
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) |
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with gr.Row(): |
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retrieved_docs = gr.Textbox( |
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lines=6, |
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label="Извлеченные фрагменты", |
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placeholder="Появятся после задавания вопросов", |
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interactive=False |
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) |
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with gr.Row(): |
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with gr.Column(scale=5): |
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system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT, interactive=False) |
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chatbot = gr.Chatbot(label="Диалог").style(height=400) |
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with gr.Column(min_width=80, scale=1): |
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with gr.Tab(label="Параметры генерации"): |
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top_p = gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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value=0.9, |
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step=0.05, |
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interactive=True, |
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label="Top-p", |
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) |
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top_k = gr.Slider( |
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minimum=10, |
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maximum=100, |
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value=30, |
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step=5, |
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interactive=True, |
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label="Top-k", |
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) |
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temp = gr.Slider( |
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minimum=0.0, |
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maximum=2.0, |
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value=0.1, |
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step=0.1, |
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interactive=True, |
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label="Temp" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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msg = gr.Textbox( |
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label="Отправить сообщение", |
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placeholder="Отправить сообщение", |
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show_label=False, |
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).style(container=False) |
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with gr.Column(): |
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with gr.Row(): |
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submit = gr.Button("Отправить") |
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stop = gr.Button("Остановить") |
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clear = gr.Button("Очистить") |
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upload_event = file_output.change( |
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fn=upload_files, |
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inputs=[file_output, file_paths], |
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outputs=[file_paths], |
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queue=True, |
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).success( |
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fn=build_index, |
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inputs=[file_paths, db, chunk_size, chunk_overlap, file_warning], |
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outputs=[db, file_warning], |
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queue=True |
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) |
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submit_event = msg.submit( |
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fn=user, |
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inputs=[msg, chatbot, system_prompt], |
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outputs=[msg, chatbot], |
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queue=False, |
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).success( |
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fn=retrieve, |
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inputs=[chatbot, db, retrieved_docs, k_documents], |
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outputs=[retrieved_docs], |
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queue=True, |
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).success( |
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fn=bot, |
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inputs=[ |
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chatbot, |
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system_prompt, |
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conversation_id, |
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retrieved_docs, |
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top_p, |
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top_k, |
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temp |
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], |
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outputs=chatbot, |
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queue=True, |
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) |
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submit_click_event = submit.click( |
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fn=user, |
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inputs=[msg, chatbot, system_prompt], |
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outputs=[msg, chatbot], |
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queue=False, |
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).success( |
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fn=retrieve, |
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inputs=[chatbot, db, retrieved_docs, k_documents], |
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outputs=[retrieved_docs], |
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queue=True, |
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).success( |
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fn=bot, |
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inputs=[ |
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chatbot, |
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system_prompt, |
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conversation_id, |
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retrieved_docs, |
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top_p, |
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top_k, |
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temp |
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], |
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outputs=chatbot, |
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queue=True, |
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) |
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stop.click( |
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fn=None, |
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inputs=None, |
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outputs=None, |
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cancels=[submit_event, submit_click_event], |
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queue=False, |
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) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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demo.queue(max_size=128, concurrency_count=1) |
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demo.launch(show_error=True) |
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