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from configs.model_config import *
from chains.local_doc_qa import LocalDocQA
import os
import nltk
from models.loader.args import parser
import models.shared as shared
from models.loader import LoaderCheckPoint
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
# Show reply with source text from input document
REPLY_WITH_SOURCE = True
def main():
llm_model_ins = shared.loaderLLM()
llm_model_ins.history_len = LLM_HISTORY_LEN
local_doc_qa = LocalDocQA()
local_doc_qa.init_cfg(llm_model=llm_model_ins,
embedding_model=EMBEDDING_MODEL,
embedding_device=EMBEDDING_DEVICE,
top_k=VECTOR_SEARCH_TOP_K)
vs_path = None
while not vs_path:
filepath = input("Input your local knowledge file path 请输入本地知识文件路径：")
# 判断 filepath 是否为空，如果为空的话，重新让用户输入,防止用户误触回车
if not filepath:
continue
vs_path, _ = local_doc_qa.init_knowledge_vector_store(filepath)
history = []
while True:
query = input("Input your question 请输入问题：")
last_print_len = 0
for resp, history in local_doc_qa.get_knowledge_based_answer(query=query,
vs_path=vs_path,
chat_history=history,
streaming=STREAMING):
if STREAMING:
print(resp["result"][last_print_len:], end="", flush=True)
last_print_len = len(resp["result"])
else:
print(resp["result"])
if REPLY_WITH_SOURCE:
source_text = [f"""出处 [{inum + 1}] {os.path.split(doc.metadata['source'])[-1]}：\n\n{doc.page_content}\n\n"""
# f"""相关度：{doc.metadata['score']}\n\n"""
for inum, doc in
enumerate(resp["source_documents"])]
print("\n\n" + "\n\n".join(source_text))
if __name__ == "__main__":
# # 通过cli.py调用cli_demo时需要在cli.py里初始化模型，否则会报错：
# langchain-ChatGLM: error: unrecognized arguments: start cli
# 为此需要先将
# args = None
# args = parser.parse_args()
# args_dict = vars(args)
# shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
# 语句从main函数里取出放到函数外部
# 然后在cli.py里初始化
args = None
args = parser.parse_args()
args_dict = vars(args)
shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
main()
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