File size: 4,030 Bytes
feed15f |
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 |
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:
print("注意输入的路径是完整的文件路径，例如knowledge_base/`knowledge_base_id`/content/file.md，多个路径用英文逗号分割")
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
# 支持加载多个文件
filepath = filepath.split(",")
# filepath错误的返回为None, 如果直接用原先的vs_path,_ = local_doc_qa.init_knowledge_vector_store(filepath)
# 会直接导致TypeError: cannot unpack non-iterable NoneType object而使得程序直接退出
# 因此需要先加一层判断，保证程序能继续运行
temp,loaded_files = local_doc_qa.init_knowledge_vector_store(filepath)
if temp is not None:
vs_path = temp
# 如果loaded_files和len(filepath)不一致，则说明部分文件没有加载成功
# 如果是路径错误，则应该支持重新加载
if len(loaded_files) != len(filepath):
reload_flag = eval(input("部分文件加载失败，若提示路径不存在，可重新加载，是否重新加载，输入True或False: "))
if reload_flag:
vs_path = None
continue
print(f"the loaded vs_path is 加载的vs_path为: {vs_path}")
else:
print("load file failed, re-input your local knowledge file path 请重新输入本地知识文件路径")
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()
|