|
from paddleocr import PaddleOCR |
|
|
|
import gradio as gr |
|
import shutil |
|
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 |
|
|
|
from chains.local_doc_qa import LocalDocQA |
|
|
|
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) |
|
print(torch.cuda.is_available()) |
|
|
|
|
|
def init_model(): |
|
print("start 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 |
|
|
|
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: |
|
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: |
|
|
|
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")) |
|
|
|
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) |
|
|
|
|
|
|
|
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 = """ |
|
# 🎉张平的专属知识库 |
|
""" |
|
default_vs = get_vs_list()[0] if len(get_vs_list()) > 1 else "为空" |
|
init_message = f"""欢迎使用 张平的专属知识库！ |
|
|
|
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，如有需要可以上传文件/文件夹至知识库。 |
|
知识库暂不支持文件删除。 |
|
""" |
|
|
|
|
|
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: |
|
|
|
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"], |
|
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: |
|
|
|
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_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") |
|
|
|
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) |
|
|
|
|
|
|
|
demo.load( |
|
fn=refresh_vs_list, |
|
inputs=None, |
|
outputs=[select_vs, select_vs_test], |
|
queue=True, |
|
show_progress=False, |
|
) |
|
|
|
|
|
|
|
(demo.queue().launch(server_port=7880,share=True, inbrowser=True)) |
|
|
