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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 presets import *
from utils import *
from 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...")
CJKPDFReader = download_loader("CJKPDFReader")
loader = CJKPDFReader()
text_raw = loader.load_data(file=filepath)[0].text
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
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