File size: 5,493 Bytes
b28a1a9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
632a25e
b28a1a9
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
import os
import logging

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 get_documents(file_src):
    from langchain.schema import Document
    from langchain.text_splitter import TokenTextSplitter
    text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=30)

    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(filename)[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", encoding="utf-8") as pdfFileObj:
                        pdfReader = PyPDF2.PdfReader(pdfFileObj)
                        for page in tqdm(pdfReader.pages):
                            pdftext += page.extract_text()
                texts = [Document(page_content=pdftext, metadata={"source": filepath})]
            elif file_type == ".docx":
                logging.debug("Loading Word...")
                from langchain.document_loaders import UnstructuredWordDocumentLoader
                loader = UnstructuredWordDocumentLoader(filepath)
                texts = loader.load()
            elif file_type == ".pptx":
                logging.debug("Loading PowerPoint...")
                from langchain.document_loaders import UnstructuredPowerPointLoader
                loader = UnstructuredPowerPointLoader(filepath)
                texts = loader.load()
            elif file_type == ".epub":
                logging.debug("Loading EPUB...")
                from langchain.document_loaders import UnstructuredEPubLoader
                loader = UnstructuredEPubLoader(filepath)
                texts = loader.load()
            elif file_type == ".xlsx":
                logging.debug("Loading Excel...")
                text_list = excel_to_string(filepath)
                texts = []
                for elem in text_list:
                    texts.append(Document(page_content=elem, metadata={"source": filepath}))
            else:
                logging.debug("Loading text file...")
                from langchain.document_loaders import TextLoader
                loader = TextLoader(filepath, "utf8")
                texts = loader.load()
        except Exception as e:
            import traceback
            logging.error(f"Error loading file: {filename}")
            traceback.print_exc()

        texts = text_splitter.split_documents(texts)
        documents.extend(texts)
    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.vectorstores import FAISS

    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

    index_name = get_index_name(file_src)
    index_path = f"./index/{index_name}"
    if local_embedding:
        from langchain.embeddings.huggingface import HuggingFaceEmbeddings
        embeddings = HuggingFaceEmbeddings(model_name = "sentence-transformers/distiluse-base-multilingual-cased-v2")
    else:
        from langchain.embeddings import OpenAIEmbeddings
        embeddings = OpenAIEmbeddings(openai_api_base=os.environ.get("OPENAI_API_BASE", None), openai_api_key=os.environ.get("OPENAI_EMBEDDING_API_KEY", api_key))
    if os.path.exists(index_path):
        logging.info("找到了缓存的索引文件,加载中……")
        return FAISS.load_local(index_path, embeddings)
    else:
        try:
            documents = get_documents(file_src)
            logging.info("构建索引中……")
            with retrieve_proxy():
                index = FAISS.from_documents(documents, embeddings)
            logging.debug("索引构建完成!")
            os.makedirs("./index", exist_ok=True)
            index.save_local(index_path)
            logging.debug("索引已保存至本地!")
            return index

        except Exception as e:
            import traceback
            logging.error("索引构建失败!%s", e)
            traceback.print_exc()
            return None