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jinchat-server
Unverified 提交 47e9bdb1 作者: CHANGXUBO 提交者： GitHub
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logging: 格式日志输出 (#268) 

* logging: 统一日志格式输出

---------

Co-authored-by: Bob Chang <bob.chang@amway.com>
Co-authored-by: imClumsyPanda <littlepanda0716@gmail.com>
上级 7f25c9fa
正在显示 包含 40 行增加 和 26 行删除
cli_demo.py
......@@ -31,13 +31,13 @@ if __name__ == "__main__":
chat_history=history,
streaming=STREAMING):
if STREAMING:
print(resp["result"][last_print_len:], end="", flush=True)
logger.info(resp["result"][last_print_len:], end="", flush=True)
last_print_len = len(resp["result"])
else:
print(resp["result"])
logger.info(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))
logger.info("\n\n" + "\n\n".join(source_text))
configs/model_config.py
import torch.cuda
import torch.backends
import os
import logging
import uuid
LOG_FORMAT = "%(levelname) -5s %(asctime)s" "-1d: %(message)s"
logger = logging.getLogger()
logger.setLevel(logging.INFO)
logging.basicConfig(format=LOG_FORMAT)
embedding_model_dict = {
"ernie-tiny": "nghuyong/ernie-3.0-nano-zh",
......@@ -63,3 +70,13 @@ LLM_HISTORY_LEN = 3
VECTOR_SEARCH_TOP_K = 5
NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")
FLAG_USER_NAME = uuid.uuid4().hex
logger.info(f"""
loading model config
llm device: {LLM_DEVICE}
embedding device: {EMBEDDING_DEVICE}
dir: {os.path.dirname(os.path.dirname(__file__))}
flagging username: {FLAG_USER_NAME}
""")
\ No newline at end of file
models/chatglm_llm.py
......@@ -125,9 +125,8 @@ class ChatGLM(LLM):
prefix_encoder_file.close()
model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
model_config.prefix_projection = prefix_encoder_config['prefix_projection']
except Exception as e:
print(e)
print("加载PrefixEncoder config.json失败")
except Exception as e:
logger.error(f"加载PrefixEncoder config.json失败: {e}")
self.model = AutoModel.from_pretrained(model_name_or_path, config=model_config, trust_remote_code=True,
**kwargs)
if LLM_LORA_PATH and use_lora:
......@@ -164,9 +163,8 @@ class ChatGLM(LLM):
new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
self.model.transformer.prefix_encoder.float()
except Exception as e:
print(e)
print("加载PrefixEncoder模型参数失败")
except Exception as e:
logger.error(f"加载PrefixEncoder模型参数失败:{e}")
self.model = self.model.eval()
......@@ -177,8 +175,8 @@ if __name__ == "__main__":
llm_device=LLM_DEVICE, )
last_print_len = 0
for resp, history in llm._call("你好", streaming=True):
print(resp[last_print_len:], end="", flush=True)
logger.info(resp[last_print_len:], end="", flush=True)
last_print_len = len(resp)
for resp, history in llm._call("你好", streaming=False):
print(resp)
logger.info(resp)
pass
webui.py
......@@ -4,8 +4,7 @@ import shutil
from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
import nltk
import uuid
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
def get_vs_list():
......@@ -27,9 +26,7 @@ llm_model_dict_list = list(llm_model_dict.keys())
local_doc_qa = LocalDocQA()
logger = gr.CSVLogger()
username = uuid.uuid4().hex
flag_csv_logger = gr.CSVLogger()
def get_answer(query, vs_path, history, mode,
streaming: bool = STREAMING):
......@@ -54,23 +51,24 @@ def get_answer(query, vs_path, history, mode,
history[-1][-1] = resp + (
"\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
yield history, ""
logger.flag([query, vs_path, history, mode], username=username)
logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}")
flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)
def init_model():
try:
local_doc_qa.init_cfg()
local_doc_qa.llm._call("你好")
reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
print(reply)
logger.info(reply)
return reply
except Exception as e:
print(e)
logger.error(e)
reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
if str(e) == "Unknown platform: darwin":
print("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
" https://github.com/imClumsyPanda/langchain-ChatGLM")
else:
print(reply)
logger.info(reply)
return reply
......@@ -83,11 +81,11 @@ def reinit_model(llm_model, embedding_model, llm_history_len, use_ptuning_v2, us
use_lora=use_lora,
top_k=top_k, )
model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
print(model_status)
logger.info(model_status)
except Exception as e:
print(e)
logger.error(e)
model_status = """模型未成功重新加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
print(model_status)
logger.info(model_status)
return history + [[None, model_status]]
......@@ -109,7 +107,7 @@ def get_vector_store(vs_id, files, history):
else:
file_status = "模型未完成加载，请先在加载模型后再导入文件"
vs_path = None
print(file_status)
logger.info(file_status)
return vs_path, None, history + [[None, file_status]]
......@@ -235,7 +233,7 @@ with gr.Blocks(css=block_css) as demo:
inputs=[select_vs, folder_files, chatbot],
outputs=[vs_path, folder_files, chatbot],
)
logger.setup([query, vs_path, chatbot, mode], "flagged")
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
query.submit(get_answer,
[query, vs_path, chatbot, mode],
[chatbot, query])
......
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