Spaces:
Running
Running
File size: 5,629 Bytes
b28a1a9 4dfaeff b28a1a9 4dfaeff b28a1a9 4dfaeff b28a1a9 4dfaeff b28a1a9 4dfaeff b28a1a9 4dfaeff b28a1a9 4dfaeff 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 |
import os
import logging
import hashlib
import PyPDF2
from tqdm import tqdm
from modules.presets import *
from modules.utils import *
from modules.config import local_embedding
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") 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_file_hash(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
if os.environ.get("OPENAI_API_TYPE", "openai") == "openai":
embeddings = OpenAIEmbeddings(openai_api_base=os.environ.get(
"OPENAI_API_BASE", None), openai_api_key=os.environ.get("OPENAI_EMBEDDING_API_KEY", api_key))
else:
embeddings = OpenAIEmbeddings(deployment=os.environ["AZURE_EMBEDDING_DEPLOYMENT_NAME"], openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
model=os.environ["AZURE_EMBEDDING_MODEL_NAME"], openai_api_base=os.environ["AZURE_OPENAI_API_BASE_URL"], openai_api_type="azure")
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
|