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import gradio as gr | |
import shutil | |
from chains.local_doc_qa import LocalDocQA | |
from configs.model_config import * | |
import nltk | |
import models.shared as shared | |
from models.loader.args import parser | |
from models.loader import LoaderCheckPoint | |
import os | |
nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path | |
def get_vs_list(): | |
lst_default = ["新建知识库"] | |
if not os.path.exists(KB_ROOT_PATH): | |
return lst_default | |
lst = os.listdir(KB_ROOT_PATH) | |
if not lst: | |
return lst_default | |
lst.sort() | |
return lst_default + lst | |
embedding_model_dict_list = list(embedding_model_dict.keys()) | |
llm_model_dict_list = list(llm_model_dict.keys()) | |
local_doc_qa = LocalDocQA() | |
flag_csv_logger = gr.CSVLogger() | |
def get_answer(query, vs_path, 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_path is not None and os.path.exists(vs_path) and "index.faiss" in os.listdir( | |
vs_path): | |
for resp, history in local_doc_qa.get_knowledge_based_answer( | |
query=query, vs_path=vs_path, 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_path): | |
resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path, | |
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 | |
yield history, "" | |
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(): | |
args = parser.parse_args() | |
args_dict = vars(args) | |
shared.loaderCheckPoint = LoaderCheckPoint(args_dict) | |
llm_model_ins = shared.loaderLLM() | |
llm_model_ins.set_history_len(LLM_HISTORY_LEN) | |
try: | |
local_doc_qa.init_cfg(llm_model=llm_model_ins) | |
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_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store") | |
filelist = [] | |
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(KB_ROOT_PATH, vs_id, "content", filename)) | |
filelist.append(os.path.join(KB_ROOT_PATH, vs_id, "content", filename)) | |
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size) | |
else: | |
vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, 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_path = None | |
logger.info(file_status) | |
return vs_path, None, history + [[None, file_status]], \ | |
gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path) if vs_path else []) | |
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,\ | |
gr.update(choices=[]), gr.update(visible=False) | |
else: | |
vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store") | |
if "index.faiss" in os.listdir(vs_path): | |
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), \ | |
vs_path, history + [[None, file_status]], \ | |
gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), \ | |
gr.update(visible=True) | |
else: | |
file_status = f"已选择知识库{vs_id},当前知识库中未上传文件,请先上传文件后,再开始提问" | |
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), \ | |
vs_path, history + [[None, file_status]], \ | |
gr.update(choices=[], value=[]), gr.update(visible=True, value=[]) | |
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, chatbot): | |
if vs_name in get_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), gr.update(visible=True), gr.update(visible=True), gr.update( | |
visible=False), chatbot, gr.update(visible=False) | |
else: | |
# 新建上传文件存储路径 | |
if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "content")): | |
os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "content")) | |
# 新建向量库存储路径 | |
if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_name, "vector_store")): | |
os.makedirs(os.path.join(KB_ROOT_PATH, vs_name, "vector_store")) | |
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=get_vs_list(), value=vs_name), gr.update( | |
visible=False), gr.update(visible=False), gr.update(visible=True), chatbot, gr.update(visible=True) | |
# 自动化加载固定文件间中文件 | |
def reinit_vector_store(vs_id, history): | |
try: | |
shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id, "vector_store")) | |
vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store") | |
sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0, | |
label="文本入库分句长度限制", | |
interactive=True, visible=True) | |
vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(os.path.join(KB_ROOT_PATH, vs_id, "content"), | |
vs_path, sentence_size) | |
model_status = """知识库构建成功""" | |
except Exception as e: | |
logger.error(e) | |
model_status = """知识库构建未成功""" | |
logger.info(model_status) | |
return history + [[None, model_status]] | |
def refresh_vs_list(): | |
return gr.update(choices=get_vs_list()), gr.update(choices=get_vs_list()) | |
def delete_file(vs_id, files_to_delete, chatbot): | |
vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store") | |
content_path = os.path.join(KB_ROOT_PATH, vs_id, "content") | |
docs_path = [os.path.join(content_path, file) for file in files_to_delete] | |
status = local_doc_qa.delete_file_from_vector_store(vs_path=vs_path, | |
filepath=docs_path) | |
if "fail" not in status: | |
for doc_path in docs_path: | |
if os.path.exists(doc_path): | |
os.remove(doc_path) | |
rested_files = local_doc_qa.list_file_from_vector_store(vs_path) | |
if "fail" in status: | |
vs_status = "文件删除失败。" | |
elif len(rested_files)>0: | |
vs_status = "文件删除成功。" | |
else: | |
vs_status = f"文件删除成功,知识库{vs_id}中无已上传文件,请先上传文件后,再开始提问。" | |
logger.info(",".join(files_to_delete)+vs_status) | |
chatbot = chatbot + [[None, vs_status]] | |
return gr.update(choices=local_doc_qa.list_file_from_vector_store(vs_path), value=[]), chatbot | |
def delete_vs(vs_id, chatbot): | |
try: | |
shutil.rmtree(os.path.join(KB_ROOT_PATH, vs_id)) | |
status = f"成功删除知识库{vs_id}" | |
logger.info(status) | |
chatbot = chatbot + [[None, status]] | |
return gr.update(choices=get_vs_list(), value=get_vs_list()[0]), gr.update(visible=True), gr.update(visible=True), \ | |
gr.update(visible=False), chatbot, gr.update(visible=False) | |
except Exception as e: | |
logger.error(e) | |
status = f"删除知识库{vs_id}失败" | |
chatbot = chatbot + [[None, status]] | |
return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), \ | |
gr.update(visible=True), chatbot, gr.update(visible=True) | |
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 = get_vs_list()[0] if len(get_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 | |
model_status = init_model() | |
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 = gr.State( | |
os.path.join(KB_ROOT_PATH, get_vs_list()[0], "vector_store") if len(get_vs_list()) > 1 else ""), gr.State(""), gr.State( | |
model_status) | |
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: | |
vs_refresh = gr.Button("更新已有知识库选项") | |
select_vs = gr.Dropdown(get_vs_list(), | |
label="请选择要加载的知识库", | |
interactive=True, | |
value=get_vs_list()[0] if len(get_vs_list()) > 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) | |
vs_delete = gr.Button("删除本知识库", visible=False) | |
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', '.png', '.jpg', ".csv"], | |
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_count="directory", | |
show_label=False) | |
load_folder_button = gr.Button("上传文件夹并加载知识库") | |
with gr.Tab("删除文件"): | |
files_to_delete = gr.CheckboxGroup(choices=[], | |
label="请从知识库已有文件中选择要删除的文件", | |
interactive=True) | |
delete_file_button = gr.Button("从知识库中删除选中文件") | |
vs_refresh.click(fn=refresh_vs_list, | |
inputs=[], | |
outputs=select_vs) | |
vs_add.click(fn=add_vs_name, | |
inputs=[vs_name, chatbot], | |
outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete]) | |
vs_delete.click(fn=delete_vs, | |
inputs=[select_vs, chatbot], | |
outputs=[select_vs, vs_name, vs_add, file2vs, chatbot, vs_delete]) | |
select_vs.change(fn=change_vs_name_input, | |
inputs=[select_vs, chatbot], | |
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot, files_to_delete, vs_delete]) | |
load_file_button.click(get_vector_store, | |
show_progress=True, | |
inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add], | |
outputs=[vs_path, files, chatbot, files_to_delete], ) | |
load_folder_button.click(get_vector_store, | |
show_progress=True, | |
inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add, | |
vs_add], | |
outputs=[vs_path, folder_files, chatbot, files_to_delete], ) | |
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged") | |
query.submit(get_answer, | |
[query, vs_path, chatbot, mode], | |
[chatbot, query]) | |
delete_file_button.click(delete_file, | |
show_progress=True, | |
inputs=[select_vs, files_to_delete, chatbot], | |
outputs=[files_to_delete, chatbot]) | |
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: | |
vs_refresh = gr.Button("更新已有知识库选项") | |
select_vs_test = gr.Dropdown(get_vs_list(), | |
label="请选择要加载的知识库", | |
interactive=True, | |
value=get_vs_list()[0] if len(get_vs_list()) > 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_refresh.click(fn=refresh_vs_list, | |
inputs=[], | |
outputs=select_vs_test) | |
vs_add.click(fn=add_vs_name, | |
inputs=[vs_name, chatbot], | |
outputs=[select_vs_test, vs_name, vs_add, file2vs, chatbot]) | |
select_vs_test.change(fn=change_vs_name_input, | |
inputs=[select_vs_test, chatbot], | |
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot]) | |
load_file_button.click(get_vector_store, | |
show_progress=True, | |
inputs=[select_vs_test, files, sentence_size, chatbot, vs_add, vs_add], | |
outputs=[vs_path, files, chatbot], ) | |
load_folder_button.click(get_vector_store, | |
show_progress=True, | |
inputs=[select_vs_test, folder_files, sentence_size, chatbot, vs_add, | |
vs_add], | |
outputs=[vs_path, folder_files, chatbot], ) | |
load_conent_button.click(get_vector_store, | |
show_progress=True, | |
inputs=[select_vs_test, one_title, sentence_size, chatbot, | |
one_conent, one_content_segmentation], | |
outputs=[vs_path, 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) | |
# load_knowlege_button = gr.Button("重新构建知识库") | |
# load_knowlege_button.click(reinit_vector_store, show_progress=True, | |
# inputs=[select_vs, chatbot], outputs=chatbot) | |
demo.load( | |
fn=refresh_vs_list, | |
inputs=None, | |
outputs=[select_vs, select_vs_test], | |
queue=True, | |
show_progress=False, | |
) | |
(demo | |
.queue(concurrency_count=3) | |
.launch(server_name='0.0.0.0', | |
server_port=7860, | |
show_api=False, | |
share=False, | |
inbrowser=False)) | |