chatpdf.py

# -*- coding: utf-8 -*-
"""
@author:XuMing(xuming624@qq.com)
@description: 
"""
import argparse
from typing import Union, List

import torch
from loguru import logger
from peft import PeftModel
from similarities import Similarity
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
from transformers.generation.utils import GenerationConfig

PROMPT_TEMPLATE = """基于以下已知信息，简洁和专业的来回答用户的问题。
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。

已知内容:
{context_str}

问题:
{query_str}
"""


class ChatPDF:
    def __init__(
            self,
            sim_model_name_or_path: str = "shibing624/text2vec-base-chinese",
            gen_model_type: str = "baichuan",
            gen_model_name_or_path: str = "baichuan-inc/Baichuan-13B-Chat",
            lora_model_name_or_path: str = None,
            device: str = None,
            int8: bool = False,
            int4: bool = False,
    ):
        default_device = 'cpu'
        if torch.cuda.is_available():
            default_device = 'cuda'
        elif torch.backends.mps.is_available():
            default_device = 'mps'
        self.device = device or default_device
        self.sim_model = Similarity(model_name_or_path=sim_model_name_or_path, device=self.device)
        self.gen_model, self.tokenizer = self._init_gen_model(
            gen_model_type,
            gen_model_name_or_path,
            peft_name=lora_model_name_or_path,
            int8=int8,
            int4=int4,
        )
        self.history = None
        self.doc_files = None

    def _init_gen_model(
            self,
            gen_model_type: str,
            gen_model_name_or_path: str,
            peft_name: str = None,
            int8: bool = False,
            int4: bool = False,
    ):
        """Init generate model."""
        if int8 or int4:
            device_map = None
        else:
            device_map = "auto"
        if gen_model_type == "chatglm":
            model = AutoModel.from_pretrained(
                gen_model_name_or_path,
                torch_dtype=torch.float16,
                device_map=device_map,
                trust_remote_code=True
            )
        else:
            model = AutoModelForCausalLM.from_pretrained(
                gen_model_name_or_path,
                torch_dtype=torch.float16,
                device_map=device_map,
                trust_remote_code=True
            )
        if int4:
            model = model.quantize(4).cuda()
        elif int8:
            model = model.quantize(8).cuda()
        model.generation_config = GenerationConfig.from_pretrained(gen_model_name_or_path)
        tokenizer = AutoTokenizer.from_pretrained(
            gen_model_name_or_path,
            use_fast=False,
            trust_remote_code=True
        )
        if peft_name:
            model = PeftModel.from_pretrained(
                model,
                peft_name,
                torch_dtype=torch.float16,
            )
            logger.info(f"Loaded peft model from {peft_name}")
        return model, tokenizer

    @torch.inference_mode()
    def generate_answer(
            self,
            prompt,
            max_new_tokens=512,
            temperature=0.7,
            top_k=40,
            top_p=0.9,
            do_sample=True,
            repetition_penalty=1.0,
            context_len=2048
    ):
        generation_config = dict(
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            do_sample=do_sample,
            repetition_penalty=repetition_penalty,
        )
        input_ids = self.tokenizer(prompt).input_ids
        max_src_len = context_len - max_new_tokens - 8
        input_ids = input_ids[-max_src_len:]
        generation_output = self.gen_model.generate(
            input_ids=torch.as_tensor([input_ids]).to(self.device),
            **generation_config,
        )
        output_ids = generation_output[0]
        output = self.tokenizer.decode(output_ids, skip_special_tokens=False)
        stop_str = self.tokenizer.eos_token
        l_prompt = len(self.tokenizer.decode(input_ids, skip_special_tokens=False))
        pos = output.rfind(stop_str, l_prompt)
        if pos != -1:
            output = output[l_prompt:pos]
        else:
            output = output[l_prompt:]
        return output.strip()

    def load_doc_files(self, doc_files: Union[str, List[str]]):
        """Load document files."""
        if isinstance(doc_files, str):
            doc_files = [doc_files]
        for doc_file in doc_files:
            if doc_file.endswith('.pdf'):
                corpus = self.extract_text_from_pdf(doc_file)
            elif doc_file.endswith('.docx'):
                corpus = self.extract_text_from_docx(doc_file)
            elif doc_file.endswith('.md'):
                corpus = self.extract_text_from_markdown(doc_file)
            else:
                corpus = self.extract_text_from_txt(doc_file)
            self.sim_model.add_corpus(corpus)
        self.doc_files = doc_files

    @staticmethod
    def extract_text_from_pdf(file_path: str):
        """Extract text content from a PDF file."""
        import PyPDF2
        contents = []
        with open(file_path, 'rb') as f:
            pdf_reader = PyPDF2.PdfReader(f)
            for page in pdf_reader.pages:
                page_text = page.extract_text().strip()
                raw_text = [text.strip() for text in page_text.splitlines() if text.strip()]
                new_text = ''
                for text in raw_text:
                    new_text += text
                    if text[-1] in ['.', '!', '?', '。', '！', '？', '…', ';', '；', ':', '：', '”', '’', '）', '】', '》', '」',
                                    '』', '〕', '〉', '》', '〗', '〞', '〟', '»', '"', "'", ')', ']', '}']:
                        contents.append(new_text)
                        new_text = ''
                if new_text:
                    contents.append(new_text)
        return contents

    @staticmethod
    def extract_text_from_txt(file_path: str):
        """Extract text content from a TXT file."""
        contents = []
        with open(file_path, 'r', encoding='utf-8') as f:
            contents = [text.strip() for text in f.readlines() if text.strip()]
        return contents

    @staticmethod
    def extract_text_from_docx(file_path: str):
        """Extract text content from a DOCX file."""
        import docx
        document = docx.Document(file_path)
        contents = [paragraph.text.strip() for paragraph in document.paragraphs if paragraph.text.strip()]
        return contents

    @staticmethod
    def extract_text_from_markdown(file_path: str):
        """Extract text content from a Markdown file."""
        import markdown
        from bs4 import BeautifulSoup
        with open(file_path, 'r', encoding='utf-8') as f:
            markdown_text = f.read()
        html = markdown.markdown(markdown_text)
        soup = BeautifulSoup(html, 'html.parser')
        contents = [text.strip() for text in soup.get_text().splitlines() if text.strip()]
        return contents

    @staticmethod
    def _add_source_numbers(lst):
        """Add source numbers to a list of strings."""
        return [f'[{idx + 1}]\t "{item}"' for idx, item in enumerate(lst)]

    def query(
            self,
            query: str,
            topn: int = 5,
            max_length: int = 512,
            max_input_size: int = 1024,
    ):
        """Query from corpus."""

        sim_contents = self.sim_model.most_similar(query, topn=topn)

        reference_results = []
        for query_id, id_score_dict in sim_contents.items():
            for corpus_id, s in id_score_dict.items():
                reference_results.append(self.sim_model.corpus[corpus_id])
        if not reference_results:
            return '没有提供足够的相关信息', reference_results
        reference_results = self._add_source_numbers(reference_results)
        context_str = '\n'.join(reference_results)[:(max_input_size - len(PROMPT_TEMPLATE))]

        prompt = PROMPT_TEMPLATE.format(context_str=context_str, query_str=query)
        response = self.generate_answer(prompt, max_new_tokens=max_length)
        return response, reference_results

    def save_index(self, index_path=None):
        """Save model."""
        if index_path is None:
            index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json'
        self.sim_model.save_index(index_path)

    def load_index(self, index_path=None):
        """Load model."""
        if index_path is None:
            index_path = '.'.join(self.doc_files.split('.')[:-1]) + '_index.json'
        self.sim_model.load_index(index_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--sim_model", type=str, default="shibing624/text2vec-base-chinese")
    parser.add_argument("--gen_model_type", type=str, default="baichuan")
    parser.add_argument("--gen_model", type=str, default="baichuan-inc/Baichuan-13B-Chat")
    parser.add_argument("--lora_model", type=str, default=None)
    parser.add_argument("--device", type=str, default=None)
    parser.add_argument("--int4", action='store_true', help="use int4 quantization")
    parser.add_argument("--int8", action='store_true', help="use int8 quantization")
    args = parser.parse_args()
    print(args)
    m = ChatPDF(
        sim_model_name_or_path=args.sim_model,
        gen_model_type=args.gen_model_type,
        gen_model_name_or_path=args.gen_model,
        lora_model_name_or_path=args.lora_model,
        device=args.device,
        int4=args.int4,
        int8=args.int8
    )
    m.load_doc_files(doc_files='sample.pdf')
    response = m.query('自然语言中的非平行迁移是指什么？')
    print(response)
    print(response[0])
    response = m.query('本文作者是谁？')
    print(response)