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| 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 * | |
| from modules.config import local_embedding | |
| 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}") | |
| try: | |
| 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_list = excel_to_string(filepath) | |
| for elem in text_list: | |
| documents.append(Document(elem)) | |
| continue | |
| else: | |
| logging.debug("Loading text file...") | |
| with open(filepath, "r", encoding="utf-8") as f: | |
| text_raw = f.read() | |
| except Exception as e: | |
| logging.error(f"Error loading file: {filename}") | |
| pass | |
| 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 langchain.embeddings.huggingface import HuggingFaceEmbeddings | |
| from llama_index import GPTSimpleVectorIndex, ServiceContext, LangchainEmbedding, OpenAIEmbedding | |
| if api_key: | |
| os.environ["OPENAI_API_KEY"] = api_key | |
| else: | |
| # 由于一个依赖的愚蠢的设计，这里必须要有一个API KEY | |
| os.environ["OPENAI_API_KEY"] = "sk-xxxxxxx" | |
| 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 | |
| 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) | |
| if local_embedding: | |
| embed_model = LangchainEmbedding(HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2")) | |
| else: | |
| embed_model = OpenAIEmbedding() | |
| logging.info("构建索引中……") | |
| with retrieve_proxy(): | |
| service_context = ServiceContext.from_defaults( | |
| prompt_helper=prompt_helper, | |
| chunk_size_limit=chunk_size_limit, | |
| embed_model=embed_model, | |
| ) | |
| 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 | |