modules/llama_func.py

import os
import logging

from llama_index import download_loader
from llama_index import (
    Document,
    LLMPredictor,
    PromptHelper,
    QuestionAnswerPrompt,
    RefinePrompt,
)
import colorama
import PyPDF2
from tqdm import tqdm

from modules.presets import *
from modules.utils import *

def get_index_name(file_src):
    file_paths = [x.name for x in file_src]
    file_paths.sort(key=lambda x: os.path.basename(x))

    md5_hash = hashlib.md5()
    for file_path in file_paths:
        with open(file_path, "rb") as f:
            while chunk := f.read(8192):
                md5_hash.update(chunk)

    return md5_hash.hexdigest()

def block_split(text):
    blocks = []
    while len(text) > 0:
        blocks.append(Document(text[:1000]))
        text = text[1000:]
    return blocks

def get_documents(file_src):
    documents = []
    logging.debug("Loading documents...")
    logging.debug(f"file_src: {file_src}")
    for file in file_src:
        filepath = file.name
        filename = os.path.basename(filepath)
        file_type = os.path.splitext(filepath)[1]
        logging.info(f"loading file: {filename}")
        if file_type == ".pdf":
            logging.debug("Loading PDF...")
            try:
                from modules.pdf_func import parse_pdf
                from modules.config import advance_docs
                two_column = advance_docs["pdf"].get("two_column", False)
                pdftext = parse_pdf(filepath, two_column).text
            except:
                pdftext = ""
                with open(filepath, 'rb') as pdfFileObj:
                    pdfReader = PyPDF2.PdfReader(pdfFileObj)
                    for page in tqdm(pdfReader.pages):
                        pdftext += page.extract_text()
            text_raw = pdftext
        elif file_type == ".docx":
            logging.debug("Loading Word...")
            DocxReader = download_loader("DocxReader")
            loader = DocxReader()
            text_raw = loader.load_data(file=filepath)[0].text
        elif file_type == ".epub":
            logging.debug("Loading EPUB...")
            EpubReader = download_loader("EpubReader")
            loader = EpubReader()
            text_raw = loader.load_data(file=filepath)[0].text
        elif file_type == ".xlsx":
            logging.debug("Loading Excel...")
            text_raw = excel_to_string(filepath)
        else:
            logging.debug("Loading text file...")
            with open(filepath, "r", encoding="utf-8") as f:
                text_raw = f.read()
        text = add_space(text_raw)
        # text = block_split(text)
        # documents += text
        documents += [Document(text)]
    logging.debug("Documents loaded.")
    return documents


def construct_index(
        api_key,
        file_src,
        max_input_size=4096,
        num_outputs=5,
        max_chunk_overlap=20,
        chunk_size_limit=600,
        embedding_limit=None,
        separator=" "
):
    from langchain.chat_models import ChatOpenAI
    from llama_index import GPTSimpleVectorIndex, ServiceContext

    os.environ["OPENAI_API_KEY"] = api_key
    chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
    embedding_limit = None if embedding_limit == 0 else embedding_limit
    separator = " " if separator == "" else separator

    llm_predictor = LLMPredictor(
        llm=ChatOpenAI(model_name="gpt-3.5-turbo-0301", openai_api_key=api_key)
    )
    prompt_helper = PromptHelper(max_input_size = max_input_size, num_output = num_outputs, max_chunk_overlap = max_chunk_overlap, embedding_limit=embedding_limit, chunk_size_limit=600, separator=separator)
    index_name = get_index_name(file_src)
    if os.path.exists(f"./index/{index_name}.json"):
        logging.info("找到了缓存的索引文件，加载中……")
        return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
    else:
        try:
            documents = get_documents(file_src)
            logging.info("构建索引中……")
            with retrieve_proxy():
                service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper, chunk_size_limit=chunk_size_limit)
                index = GPTSimpleVectorIndex.from_documents(
                    documents,  service_context=service_context
                )
            logging.debug("索引构建完成！")
            os.makedirs("./index", exist_ok=True)
            index.save_to_disk(f"./index/{index_name}.json")
            logging.debug("索引已保存至本地!")
            return index

        except Exception as e:
            logging.error("索引构建失败！", e)
            print(e)
            return None


def add_space(text):
    punctuations = {"，": "， ", "。": "。 ", "？": "？ ", "！": "！ ", "：": "： ", "；": "； "}
    for cn_punc, en_punc in punctuations.items():
        text = text.replace(cn_punc, en_punc)
    return text