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webui_milvus.py
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webui_milvus.py
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import gradio as gr
import os
import shutil
from chains.serve_milvus import LocalDocQA
from configs.model_config import *
import nltk
from models.base import (BaseAnswer,
AnswerResult,
AnswerResultStream,
AnswerResultQueueSentinelTokenListenerQueue)
import models.shared as shared
from models.loader.args import parser
from models.loader import LoaderCheckPoint
from pymilvus import utility, connections
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
connections.connect(
alias="default",
user='username',
password='password',
host='10.48.0.92',
port='19530'
)
# get knowledge base list function
def get_vs_list():
lst_default = ["新建知识库"]
lst = utility.list_collections()
lst.sort()
return lst_default + lst
# knowledge base list
vs_list = get_vs_list()
# embedding model list
embedding_model_dict_list = list(embedding_model_dict.keys())
# llm model list
llm_model_dict_list = list(llm_model_dict.keys())
# answer question based on loacl knowledge base
local_doc_qa = LocalDocQA()
# gradio
flag_csv_logger = gr.CSVLogger()
def get_answer(query, vs_name, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
chunk_size=CHUNK_SIZE, streaming: bool = STREAMING):
if mode == "Bing搜索问答":
for resp, history in local_doc_qa.get_search_result_based_answer(
query=query, chat_history=history, streaming=streaming):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] <a href="{doc.metadata["source"]}" target="_blank">{doc.metadata["source"]}</a> </summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
elif mode == "知识库问答" and vs_name is not None:
print("****************************")
print(vs_name)
print("****************************")
for resp, history in local_doc_qa.get_knowledge_based_answer(
query=query, vs_name=vs_name, chat_history=history, streaming=streaming):
source = "\n\n"
source += "".join(
[f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history[-1][-1] += source
yield history, ""
elif mode == "知识库测试":
if os.path.exists(vs_name):
resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_name=vs_name,
score_threshold=score_threshold,
vector_search_top_k=vector_search_top_k,
chunk_conent=chunk_conent,
chunk_size=chunk_size)
if not resp["source_documents"]:
yield history + [[query,
"根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], ""
else:
source = "\n".join(
[
f"""<details open> <summary>【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】： {os.path.split(doc.metadata["source"])[-1]} </summary>\n"""
f"""{doc.page_content}\n"""
f"""</details>"""
for i, doc in
enumerate(resp["source_documents"])])
history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source])
yield history, ""
else:
yield history + [[query,
"请选择知识库后进行测试，当前未选择知识库。"]], ""
else:
for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
streaming=streaming):
resp = answer_result.llm_output["answer"]
history = answer_result.history
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.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_name={vs_name},mode={mode},history={history}")
flag_csv_logger.flag([query, vs_name, history, mode], username=FLAG_USER_NAME)
def init_model(llm_model: BaseAnswer = None):
try:
local_doc_qa.init_cfg(llm_model=llm_model)
generator = local_doc_qa.llm.generatorAnswer("你好")
for answer_result in generator:
print(answer_result.llm_output)
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"""
logger.info(reply)
return reply
except Exception as 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":
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:
logger.info(reply)
return reply
def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k, history):
try:
llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
llm_model_ins.history_len = llm_history_len
local_doc_qa.init_cfg(llm_model=llm_model_ins,
embedding_model=embedding_model,
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"""
logger.info(model_status)
except Exception as 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"""
logger.info(model_status)
return history + [[None, model_status]]
def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
vs_name = vs_id
vs_list = get_vs_list()
filelist = []
if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
if local_doc_qa.llm and local_doc_qa.embeddings:
if isinstance(files, list):
for file in files:
filename = os.path.split(file.name)[-1]
shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
vs_name, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_name, vs_list, sentence_size)
else:
vs_name, loaded_files = local_doc_qa.one_knowledge_add(vs_name, files, one_conent, one_content_segmentation,
sentence_size)
if len(loaded_files):
file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
else:
file_status = "文件未成功加载，请重新上传文件"
else:
file_status = "模型未完成加载，请先在加载模型后再导入文件"
vs_name = None
logger.info(file_status)
return vs_name, None, history + [[None, file_status]]
def change_vs_name_input(vs_id, history):
if vs_id == "新建知识库":
return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history
else:
file_status = f"已加载知识库{vs_id}，请开始提问"
'''
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH,
vs_id), history + [
[None, file_status]]
'''
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), vs_id, history + [[None, file_status]]
knowledge_base_test_mode_info = ("【注意】\n\n"
"1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，"
"并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n"
"2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。"
"""3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n"""
"4. 单条内容长度建议设置在100-150左右。\n\n"
"5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，"
"本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。"
"相关参数将在后续版本中支持本界面直接修改。")
def change_mode(mode, history):
if mode == "知识库问答":
return gr.update(visible=True), gr.update(visible=False), history
# + [[None, "【注意】：您已进入知识库问答模式，您输入的任何查询都将进行知识库查询，然后会自动整理知识库关联内容进入模型查询！！！"]]
elif mode == "知识库测试":
return gr.update(visible=True), gr.update(visible=True), [[None,
knowledge_base_test_mode_info]]
else:
return gr.update(visible=False), gr.update(visible=False), history
def change_chunk_conent(mode, label_conent, history):
conent = ""
if "chunk_conent" in label_conent:
conent = "搜索结果上下文关联"
elif "one_content_segmentation" in label_conent: # 这里没用上，可以先留着
conent = "内容分段入库"
if mode:
return gr.update(visible=True), history + [[None, f"【已开启{conent}】"]]
else:
return gr.update(visible=False), history + [[None, f"【已关闭{conent}】"]]
def add_vs_name(vs_name, vs_list, chatbot):
if vs_name in vs_list:
vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
chatbot = chatbot + [[None, vs_status]]
return gr.update(visible=True), vs_list, gr.update(visible=True), gr.update(visible=True), gr.update(
visible=False), chatbot
else:
vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """
chatbot = chatbot + [[None, vs_status]]
return gr.update(visible=True, choices=[vs_name] + vs_list, value=vs_name), [vs_name] + vs_list, gr.update(
visible=False), gr.update(visible=False), gr.update(visible=True), chatbot
block_css = """.importantButton {
background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
border: none !important;
}
.importantButton:hover {
background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
border: none !important;
}"""
webui_title = """
# 🎉langchain-ChatGLM WebUI🎉
👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
"""
default_vs = vs_list[0] if len(vs_list) > 1 else "为空"
init_message = f"""欢迎使用 langchain-ChatGLM Web UI！
请在右侧切换模式，目前支持直接与 LLM 模型对话或基于本地知识库问答。
知识库问答模式，选择知识库名称后，即可开始问答，当前知识库{default_vs}，如有需要可以在选择知识库名称后上传文件/文件夹至知识库。
知识库暂不支持文件删除，该功能将在后续版本中推出。
"""
# 初始化消息
args = None
args = parser.parse_args()
args_dict = vars(args)
print("shared.loaderCheckPoint")
shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
print("llm_model_ins")
llm_model_ins = shared.loaderLLM()
llm_model_ins.set_history_len(LLM_HISTORY_LEN)
print("model_status")
model_status = init_model(llm_model=llm_model_ins)
print("get_vector")
get_vector_store
default_theme_args = dict(
font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
)
with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo:
vs_path, file_status, model_status, vs_list = gr.State(
os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else ""), gr.State(""), gr.State(
model_status), gr.State(vs_list)
gr.Markdown(webui_title)
with gr.Tab("对话"):
with gr.Row():
with gr.Column(scale=10):
chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
elem_id="chat-box",
show_label=False).style(height=750)
query = gr.Textbox(show_label=False,
placeholder="请输入提问内容，按回车进行提交").style(container=False)
with gr.Column(scale=5):
mode = gr.Radio(["LLM 对话", "知识库问答", "Bing搜索问答"],
label="请选择使用模式",
value="知识库问答", )
knowledge_set = gr.Accordion("知识库设定", visible=False)
vs_setting = gr.Accordion("配置知识库")
mode.change(fn=change_mode,
inputs=[mode, chatbot],
outputs=[vs_setting, knowledge_set, chatbot])
with vs_setting:
select_vs = gr.Dropdown(vs_list.value,
label="请选择要加载的知识库",
interactive=True,
value=vs_list.value[0] if len(vs_list.value) > 0 else None
)
vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
lines=1,
interactive=True,
visible=True)
vs_add = gr.Button(value="添加至知识库选项", visible=True)
file2vs = gr.Column(visible=False)
with file2vs:
# load_vs = gr.Button("加载知识库")
gr.Markdown("向知识库中添加文件")
sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
label="文本入库分句长度限制",
interactive=True, visible=True)
with gr.Tab("上传文件"):
files = gr.File(label="添加文件",
file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="multiple",
show_label=False)
load_file_button = gr.Button("上传文件并加载知识库")
with gr.Tab("上传文件夹"):
folder_files = gr.File(label="添加文件",
# file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="directory",
show_label=False)
load_folder_button = gr.Button("上传文件夹并加载知识库")
vs_add.click(fn=add_vs_name,
inputs=[vs_name, vs_list, chatbot],
outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
select_vs.change(fn=change_vs_name_input,
inputs=[select_vs, chatbot],
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
load_file_button.click(get_vector_store,
show_progress=True,
inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add],
outputs=[vs_name, files, chatbot], )
load_folder_button.click(get_vector_store,
show_progress=True,
inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
vs_add],
outputs=[vs_name, folder_files, chatbot], )
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
query.submit(get_answer,
[query, vs_path, chatbot, mode],
[chatbot, query])
with gr.Tab("知识库测试 Beta"):
with gr.Row():
with gr.Column(scale=10):
chatbot = gr.Chatbot([[None, knowledge_base_test_mode_info]],
elem_id="chat-box",
show_label=False).style(height=750)
query = gr.Textbox(show_label=False,
placeholder="请输入提问内容，按回车进行提交").style(container=False)
with gr.Column(scale=5):
mode = gr.Radio(["知识库测试"], # "知识库问答",
label="请选择使用模式",
value="知识库测试",
visible=False)
knowledge_set = gr.Accordion("知识库设定", visible=True)
vs_setting = gr.Accordion("配置知识库", visible=True)
mode.change(fn=change_mode,
inputs=[mode, chatbot],
outputs=[vs_setting, knowledge_set, chatbot])
with knowledge_set:
score_threshold = gr.Number(value=VECTOR_SEARCH_SCORE_THRESHOLD,
label="知识相关度 Score 阈值，分值越低匹配度越高",
precision=0,
interactive=True)
vector_search_top_k = gr.Number(value=VECTOR_SEARCH_TOP_K, precision=0,
label="获取知识库内容条数", interactive=True)
chunk_conent = gr.Checkbox(value=False,
label="是否启用上下文关联",
interactive=True)
chunk_sizes = gr.Number(value=CHUNK_SIZE, precision=0,
label="匹配单段内容的连接上下文后最大长度",
interactive=True, visible=False)
chunk_conent.change(fn=change_chunk_conent,
inputs=[chunk_conent, gr.Textbox(value="chunk_conent", visible=False), chatbot],
outputs=[chunk_sizes, chatbot])
with vs_setting:
select_vs = gr.Dropdown(vs_list.value,
label="请选择要加载的知识库",
interactive=True,
value=vs_list.value[0] if len(vs_list.value) > 0 else None)
vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
lines=1,
interactive=True,
visible=True)
vs_add = gr.Button(value="添加至知识库选项", visible=True)
file2vs = gr.Column(visible=False)
with file2vs:
# load_vs = gr.Button("加载知识库")
gr.Markdown("向知识库中添加单条内容或文件")
sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
label="文本入库分句长度限制",
interactive=True, visible=True)
with gr.Tab("上传文件"):
files = gr.File(label="添加文件",
file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="multiple",
show_label=False
)
load_file_button = gr.Button("上传文件并加载知识库")
with gr.Tab("上传文件夹"):
folder_files = gr.File(label="添加文件",
# file_types=['.txt', '.md', '.docx', '.pdf'],
file_count="directory",
show_label=False)
load_folder_button = gr.Button("上传文件夹并加载知识库")
with gr.Tab("添加单条内容"):
one_title = gr.Textbox(label="标题", placeholder="请输入要添加单条段落的标题", lines=1)
one_conent = gr.Textbox(label="内容", placeholder="请输入要添加单条段落的内容", lines=5)
one_content_segmentation = gr.Checkbox(value=True, label="禁止内容分句入库",
interactive=True)
load_conent_button = gr.Button("添加内容并加载知识库")
# 将上传的文件保存到content文件夹下,并更新下拉框
vs_add.click(fn=add_vs_name,
inputs=[vs_name, vs_list, chatbot],
outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
select_vs.change(fn=change_vs_name_input,
inputs=[select_vs, chatbot],
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
load_file_button.click(get_vector_store,
show_progress=True,
inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add],
outputs=[vs_name, files, chatbot], )
load_folder_button.click(get_vector_store,
show_progress=True,
inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
vs_add],
outputs=[vs_name, folder_files, chatbot], )
load_conent_button.click(get_vector_store,
show_progress=True,
inputs=[select_vs, one_title, sentence_size, chatbot,
one_conent, one_content_segmentation],
outputs=[vs_name, files, chatbot], )
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
query.submit(get_answer,
[query, vs_path, chatbot, mode, score_threshold, vector_search_top_k, chunk_conent,
chunk_sizes],
[chatbot, query])
with gr.Tab("模型配置"):
llm_model = gr.Radio(llm_model_dict_list,
label="LLM 模型",
value=LLM_MODEL,
interactive=True)
no_remote_model = gr.Checkbox(shared.LoaderCheckPoint.no_remote_model,
label="加载本地模型",
interactive=True)
llm_history_len = gr.Slider(0, 10,
value=LLM_HISTORY_LEN,
step=1,
label="LLM 对话轮数",
interactive=True)
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
label="使用p-tuning-v2微调过的模型",
interactive=True)
use_lora = gr.Checkbox(USE_LORA,
label="使用lora微调的权重",
interactive=True)
embedding_model = gr.Radio(embedding_model_dict_list,
label="Embedding 模型",
value=EMBEDDING_MODEL,
interactive=True)
top_k = gr.Slider(1, 20, value=VECTOR_SEARCH_TOP_K, step=1,
label="向量匹配 top k", interactive=True)
load_model_button = gr.Button("重新加载模型")
load_model_button.click(reinit_model, show_progress=True,
inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora,
top_k, chatbot], outputs=chatbot)
(demo
.queue(concurrency_count=3)
.launch(server_name='0.0.0.0',
server_port=7861,
show_api=False,
share=False,
inbrowser=False))