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GPT-Gradio-Agent.py
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import gradio as gr
import openai
import time
import os
from dotenv import load_dotenv
import pandas
import importlib
# Customized Modules
from vecstore.vecstore import *
from vecstore.Agent import *
from vecstore.search_engine import *
from vecstore.reranker import *
from gga_utils.common import *
from gga_utils.theme import *
from vecstore.template import *
from gga_utils.vec_utils import *
from pic_gen.pic_gen import *
from Agent.agent_tools import *
from Agent.agent import *
# import langchain to chat with file
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders.unstructured import UnstructuredFileLoader
from langchain.chains import RetrievalQA
load_dotenv()
# initialize the embedding model setting
model_type_choice = ["OpenAI","Hugging Face(local)"]
openai_embedding_model = ["text-embedding-ada-002"]
local_embedding_model = ['bge-base-zh-v1.5','bge-base-en-v1.5',
'bge-large-zh-v1.5','bge-large-en-v1.5']
# initialize the chat model setting
model_source_choice = ["OpenAI","Ollama"]
openai_chat_model = ["gpt-35-turbo","gpt-35-turbo-16k","gpt-4","gpt-4-32k","gpt-4-1106-preview","gpt-4-vision-preview"]
ollama_chat_model = get_ollama_model_list()
# 初始化主题
set_theme = adjust_theme()
#gr.Chatbot.postprocess = format_io
# Initialize language
i18n = I18nAuto()
# Initialize knowledge base
kb = GRKnowledgeBase()
# 获取所有 tools
module = importlib.import_module('Agent.agent_tools')
# 使用列表表达式将所有工具添加到 tools 列表中
# 遍历模块的__dict__属性，并筛选出函数工具
tools = [name for name, obj in module.__dict__.items() if callable(obj) and not name.startswith('__') and name != "tool"]
# <---------- set environmental parameters --------->
# TODO:增加自定义模型的max_token,并且记得增加对其他自定义参数的适配
def model_token_correct(model_choice:str):
'''Different model has different max tokens, this is to correct the max_token slider right.'''
model_maxtoken_dic = {
"gpt-35-turbo":3000,
"gpt-35-turbo-16k":15000,
"gpt-4":7000,
"gpt-4-32k":30000,
"gpt-4-1106-preview":128000,
"gpt-4-vision-preview":128000,
"qwen:7b-chat":32000
}
return model_maxtoken_dic[model_choice]
def stream(history_list:list,chat_history:list[dict],if_agent_mode:bool=False):
'''
Used to make LLM output looks like stream(Not real stream output).
'''
bot_message = chat_history[-1]['content']
history_list[-1][1] = ""
for character in bot_message:
history_list[-1][1] += character
time.sleep(0.02)
yield history_list
def upload_file(file_obj,
split_tmp,
progress=gr.Progress()
):
'''
Upload your file to chat
Args:
file_obj: the file you want to upload.注：这个将改为文件列表
split_tmp: the list of splitted files.
return:
list of files are splitted.
'''
from pdf2image.exceptions import PDFInfoNotInstalledError
split_docs = choose_text_splitter(file_obj)
split_tmp.append(split_docs)
progress(1, desc="Dealing...")
gr.Info("Processing completed.")
return split_tmp,gr.File(label="The file you want to chat with")
def file_ask_stream(file_ask_history_list:list[list],file_answer:list):
'''
Used to make file-answer looks like stream;\n
'file_ask_history_list' will be transfered to chatbot
'''
try:
bot_message = file_answer[0]["answer"]
except TypeError:
raise gr.Error("No model response obtained")
ref_result = get_accordion(res=file_answer[0],response=file_answer[0]["answer"])
file_ask_history_list[-1][1] = ""
for character in bot_message:
file_ask_history_list[-1][1] += character
time.sleep(0.02)
yield file_ask_history_list
file_ask_history_list[-1][1] += ref_result
yield file_ask_history_list
def sum_stream(summarize_result,chatbot):
'''
Used to make summarized result be outputed as stream.
'''
chatbot[-1][1] = ""
for character in summarize_result:
chatbot[-1][1] += character
time.sleep(0.02)
yield chatbot
# <---------- GUI ---------->
with gr.Blocks(title="GPT-Gradio-Agent",
theme=set_theme,
css='style\style.css') as demo:
gr.Markdown(
'''
# <center>GPT AGENT<center>
<center>Use the agent make your work and life much more efficient.<center>
<center>📁 means *knowledgebase* in the interface.<center>
'''
)
usr_msg = gr.State()
chat_his = gr.State([])
with gr.Row(elem_classes="col-container"):
with gr.Column(elem_id="history"):
with gr.Row():
add_dialog = gr.ClearButton(
components=[chat_his],
icon=r"icon\add_dialog.png",
#variant="primary",
value=i18n("New Dialog"),
min_width=5,
elem_id="btn_transparent",
size="sm"
)
delete_dialog = gr.Button(
icon=r"icon\delete_dialog.png",
value=i18n("Delete Dialog"),
min_width=5,
elem_id="btn_transparent",
size="sm",
)
His_choice_cache = get_all_conversation_names()
Historylist = gr.Radio(
#label="Dialog Box",
show_label=False,
interactive=True,
value=list_vali_check(His_choice_cache),
choices=His_choice_cache,
elem_id="history-select-dropdown",
)
with gr.Column(scale=4):
with gr.Row():
chat_model_type = gr.Radio(label=i18n("Chat Model Type"),
choices=model_source_choice,
value="Ollama",
interactive=True)
model_choice = gr.Radio(choices=ollama_chat_model,
value=ollama_chat_model[0],
label=i18n("Model"),
info=i18n("Model info"),
interactive=True)
with gr.Tab(label=i18n("ChatInterface")):
with gr.Group():
with gr.Row():
chat_name = gr.Textbox(label=i18n("Chatbot name"),
interactive=True,
value=get_last_conversation_name(),
info=i18n("Chatbot info"),
scale=2)
if_agent_mode = gr.Checkbox(label=i18n("Agent Mode"),
value=False,
info=i18n("If you want llm to be an Agent, and use efficient tools to help you, enable this option."),
visible=False,
scale=1
)
def get_chat_model_select(evt: gr.SelectData):
if evt.value == 'OpenAI':
return gr.Dropdown(choices=openai_chat_model,
value=openai_chat_model[0]
),gr.Checkbox(visible=True)
elif evt.value == 'Ollama':
return gr.Dropdown(choices=ollama_chat_model,
value=ollama_chat_model[0]
),gr.Checkbox(value=False,visible=False)
chat_model_type.select(get_chat_model_select,outputs=[model_choice,if_agent_mode])
agent_tools_list = gr.CheckboxGroup(choices=tools,
label=i18n("Usable Agent Tools"),
visible=False,
interactive=True)
def get_agent_mode_select(evt: gr.SelectData):
if evt.selected:
return gr.CheckboxGroup(visible=True)
else:
return gr.CheckboxGroup(visible=False)
if_agent_mode.select(get_agent_mode_select,outputs=agent_tools_list)
chat_bot = gr.Chatbot(height=600,
value=get_last_conversation_content(),
show_label=False,
show_copy_button=True,
bubble_full_width=False,
render_markdown=True,)
with gr.Row():
message = gr.Textbox(label=i18n("Input your prompt"),
info=i18n("'Shift + Enter' to begin an new line. Press 'Enter' can also send your Prompt to the LLM."),
scale=7)
export_his = gr.Button(value=i18n("Export Chat History"),scale=1)
with gr.Row():
chat_with_file = gr.Button(value=i18n("Chat with file (Valid for 📁)"))
with gr.Column(scale=3):
send = gr.Button(i18n("Send"),variant='primary',elem_id="btn")
with gr.Row():
delete_latest_round_button = gr.Button(i18n("Delete previous round"),size="sm")
regenerate_button = gr.Button(i18n("Regenerate"),size="sm")
clear = gr.ClearButton([message, chat_bot,chat_his],value=i18n("Clear"))
with gr.Tab(label=i18n("Knowledge Base Info Interface")):
kb_vector_content = gr.DataFrame(visible=False,interactive=False,)
with gr.Row():
kb_path = gr.Textbox(label=i18n("Knowledge Base path"),
scale=3)
kb_file_list = gr.Dropdown(interactive=True, # The contents are exactly the same as file_list
# allow_custom_value=True,
label=i18n("File list"),
scale=3)
with gr.Column(scale=1):
refresh_kb_info = gr.Button(value=i18n("Refresh"))
advance_kb_info = gr.Checkbox(label=i18n("Show document details"))
kb_info = gr.HTML(value=i18n("Knowledge base not loaded"))
with gr.Tab(label="Dall-E"):
with gr.Row(equal_height=True):
pic_gen_prompt = gr.Textbox(label=i18n("Input your prompt"),
info=i18n("'Shift + Enter' to begin an new line. "),
scale=5)
pic_gen_button = gr.Button(value=i18n("Generate"),
variant="primary",
elem_id="btn",
scale=1)
with gr.Accordion(label=i18n("image setting"),
elem_id="Accordion",
open=False):
with gr.Row():
t2p_model = gr.Radio(choices=["Dall-E-3"],
value="Dall-E-3",
label=i18n("Model"),
scale=2)
image_style_radio = gr.Radio(choices=[(i18n('natural'),'natural'),
(i18n('vivid'),'vivid')],
label=i18n("Image style"),
value='natural',
scale=1,)
with gr.Row():
image_size_radio = gr.Radio(choices=['1024x1024','1792x1024', '1024x1792'],
label=i18n("Image size"),
value='1024x1024',
scale=2,)
image_quality_dropdown = gr.Dropdown(choices=[(i18n('standard'),'standard'),
i18n('hd'),'hd'],
label=i18n("Image quality"),
value='standard',
scale=1,)
img = gr.Image()
actual_prompt = gr.Textbox(label=i18n("Actual prompt"),
info=i18n("The Prompt you enter is not actually accepted by Dall-E-3, but will be embellished and amplified by ChatGPT before it is entered."),
show_copy_button=True,
interactive=False,
scale=1)
open_dir = gr.Button(value=i18n("Open output directory"))
with gr.Tab(i18n("RAG Search")):
with gr.Tab(i18n("Search")):
search_result_title = gr.HTML(value=search_Answer_icon,
visible=False)
search_result = gr.HTML(visible=False)
search_source_title = gr.HTML(value=search_quote_icon,
visible=False)
search_source = gr.HTML(visible=False)
with gr.Row():
search_query = gr.Textbox(label=i18n("Query Prompt"),
scale=3)
search_btn = gr.Button(value=i18n("Search"),
scale=1)
rag_engine = RAGSearchEngine()
with gr.Tab(i18n("Search History")):
with gr.Row():
search_history_dropdown = gr.Dropdown(label=i18n("Library"),
choices=rag_engine.get_search_history(),
scale=4)
refresh_search_history_list_btn = gr.Button(value=i18n("refresh"))
search_history_content = gr.HTML()
search_history_sources = gr.HTML()
with gr.Column():
with gr.Tab(i18n("Chat")):
with gr.Row():
with gr.Column():
with gr.Accordion(i18n("Commom Setting"),
elem_id="Accordion"):
System_Prompt = gr.Textbox("You are a helpful AI.", label=i18n("System Prompt"),
info=i18n("'Shift + Enter' to begin an new line."))
Context_length = gr.Slider(0, 32, value=4, step=1, label=i18n("Context length"),
info=i18n("The number of historical messages carried per request"))
save_chat_config = gr.Button(value=i18n("Save All of Chat Configs"))
with gr.Accordion(i18n("Additional Setting"),
elem_id="Accordion"):
max_tokens = gr.Slider(0, model_token_correct("gpt-35-turbo"), value=1200, step=1, label="max_tokens",
info=i18n("Maximum number of tokens carrying context interactions."))
Temperature = gr.Slider(0, 2, value=0.5, step=0.1, label="Temperature",
info=i18n("Randomness: the larger the value, the more random the response is"))
top_p = gr.Slider(0, 1, value=1, step=0.1, label="top_p",
info=i18n("Nuclear sampling: Similar to randomness, but not modified with randomness"))
frequency_penalty = gr.Slider(-2, 2, value=0, step=0.1, label=i18n("frequency_penalty"),
info=i18n("Frequency penalty: the larger the value, the less likely it is to be a repeated word"))
presence_penalty = gr.Slider(-2, 2, value=0, step=0.1, label=i18n("presence_penalty"),
info=i18n("Topic freshness: the larger the value, the more likely it is to expand to new topics"))
with gr.Tab("RAG",elem_id="chat-tab-in-column"):
split_tmp = gr.State(['0'])
sum_result = gr.State()
# set a element to aviod indexerror
file_answer = gr.State(['0'])
embedding_model_type = gr.Dropdown(choices=model_type_choice,
value='Hugging Face(local)',
label=i18n("Embedding Model Type"),
interactive=True)
embedding_model = gr.Dropdown(choices=local_embedding_model,
value=local_embedding_model[0],
label=i18n("Embedding Model"),
interactive=True,
visible=True,
allow_custom_value=True)
def get_select_value(evt: gr.SelectData):
if evt.value == 'OpenAI':
return gr.Dropdown(choices=openai_embedding_model,
value=openai_embedding_model[0])
elif evt.value == 'Hugging Face(local)':
return gr.Dropdown(choices=local_embedding_model,
value=local_embedding_model[0])
embedding_model_type.select(get_select_value,outputs=[embedding_model])
with gr.Column():
with gr.Accordion(label=i18n("RAG Basic Setting"),
elem_id="Accordion"):
with gr.Group():
file = gr.File(label=i18n("The file you want to chat with"),
file_types=[".eml", ".html", ".json", ".md", ".msg", ".rst", ".rtf", ".txt", ".xml",# Plaintext
# ".jpeg", ".png",# images
".csv", ".doc", ".docx", ".epub", ".odt", ".pdf", ".ppt", ".pptx", ".tsv", ".xlsx"# Documents
],
file_count="multiple"
# height=150
)
summarize = gr.Button(value=i18n("Summarize file content"),visible=False)
with gr.Row():
estimate_cost = gr.Text(label=i18n("Estimated cost:"),
info=i18n("Estimated cost of embed file"),
scale=2)
refresh_file_cost = gr.Button(value=i18n("Refresh file and estimate cost"),
scale=1)
with gr.Group():
vector_path = gr.Text(label=i18n("Knowledge base save path"),
info=i18n("Choose the folder you want to save, and PASTE THE ABSOLUTE PATH here"),
visible=False
)
with gr.Accordion(label=i18n("Create/Delete Knowledge base"), open=False,
elem_id="Accordion_sec"):
vector_name = gr.Textbox(label=i18n("Knowledge base name"),
elem_id="text_sec")
create_vec_but = gr.Button(value=i18n("Create a new knowledge base 📁"))
# delete_vectorstore_button = gr.Button(value=i18n("Delete knowledge base"),
# variant='primary',
# elem_id="btn_danger")
vector_list = gr.Dropdown(label=i18n("Knowledge base list"),
choices=kb.knowledge_bases)
vector_content = gr.DataFrame(#label="Knowledge Base Document Catalog",
value = pd.DataFrame(columns=['文件名称']),
visible=False,
interactive=False,
)
file_list = gr.Dropdown(interactive=True,
# allow_custom_value=True,
label=i18n("File list"))
with gr.Column():
# `load_complete_vec_button` created in v0.12, is used to reload `vector_list`
load_complete_vec_button = gr.Button(value=i18n("Load your 📁 "),variant='primary',elem_id="btn")
# `load_vec` is was used in version previous v0.12 to load vector store in specific path.
# DEPRECATED
load_vec = gr.Button(value=i18n("Load your 📁 "),variant='primary',elem_id="btn",visible=False)
with gr.Row():
add_file = gr.Button(value=i18n("Add it (The file uploaded) to 📁"),
scale=1)
delete_file = gr.Button(value=i18n("Delete it (Selected in dropdown) from 📁"),
scale=1)
with gr.Accordion(i18n("File chat setting"),
open=False,
elem_id="Accordion"):
if_hybrid_retrieve = gr.Checkbox(label=i18n("Hybrid Retrieve"),
value=True,
info=i18n("Hybrid search achieves complementarity between multiple search techniques by combining multiple search systems."))
hybrid_retrieve_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.5, label=i18n("Hybrid Retrieve Weight"),info=i18n("Using BM25 as the sparse retriever, the higher the value, the more inclined the sparse retriever results."))
if_rerank = gr.Checkbox(label=i18n("Rerank"),
value=True,
info=i18n("Re-ranking the content of retrieved files"))
filter_choice = gr.Radio(choices=["All", "Selected file"],
value="All",
label=i18n("Search scope"),
info=i18n("“All” means whole knowledge base;“Selected file” means the file selected in dropdown"))
sum_type = gr.Radio(choices=[(i18n("small file"),"stuff"),
(i18n("large file(refine)"),"refine"),
(i18n("large file(map reduce)"),"map_reduce"),
(i18n("large file(map rerank, for chat)"),"map_rerank")],
value="refine",
label=i18n("File size type"),
info=i18n("Only works on the file selected in the file box. If the number of words to be summarized is large, select 'lagre size' (selecting 'small size' may result in exceeding the GPT's maximum Token)."),
visible=False)
with gr.Tab("Agent"):
with gr.Tab(i18n("Web Request")):
sum_url = gr.Textbox(label=i18n("URL"),
info=i18n("Paste the link to the page you want to request here."))
web_template = gr.Textbox(label=i18n("Prompt Template"),
info=i18n("Input the template you want to use here."))
sum_url_button = gr.Button(value=i18n("Request URL"),
variant='primary',
elem_id="btn",
scale=2)
template_example = gr.Examples([sum_wechat_gzh,
sina_test],
inputs=[web_template],
)
# Radio control
add_dialog.click(
add_conversation_to_json,
inputs=[chat_name,chat_bot]
).success(
lambda:gr.Radio(choices=get_all_conversation_names(),
value=get_last_conversation_name()),
outputs=Historylist
).success(
lambda: gr.Chatbot(value=''),
[],
[chat_bot]
).success(
lambda: gr.Textbox(value=get_last_conversation_name()),
[],
[chat_name]
).then(
save_all_settings,
inputs=[chat_name,System_Prompt,Context_length,Temperature,max_tokens,top_p,frequency_penalty,presence_penalty]
)
delete_dialog.click(delete_conversation_from_json,
inputs=[chat_name]
).success(lambda: gr.Radio(choices=get_all_conversation_names(),
value=get_last_conversation_name()),
outputs=[Historylist]
).success(get_last_conversation_content,
[],
[chat_bot]).success(lambda Historylist:gr.Textbox(value=Historylist),
[Historylist],
[chat_name])
Historylist.select(lambda:gr.Radio(),[],[Historylist]
).success(lambda Historylist: gr.Textbox(value=Historylist),
[Historylist],[chat_name]
).success(get_selected_conversation_content,
[chat_name],
[chat_bot]).success(reload_memory,
[chat_bot,Context_length]
).success(lambda: gr.Info(i18n("Load dialog memory success!")))
chat_name.blur(
modify_conversation_name,
inputs=[Historylist,chat_name],
outputs=[chat_name]
).success(lambda chat_name: gr.Radio(
show_label=False,
interactive=True,
value=chat_name,
choices=get_all_conversation_names(),
),
inputs=chat_name,outputs=[Historylist]
)
# Merge all handles that require input and output.
input_param = [message, chat_model_type,model_choice, chat_his, chat_bot, System_Prompt,
Context_length, Temperature,max_tokens,top_p,frequency_penalty,
presence_penalty, if_agent_mode]
output_param = [chat_bot, usr_msg, chat_his]
# update model max_token
model_choice.change(lambda model_choice: gr.Slider(maximum=model_token_correct(model_choice),value=1200),
inputs=[model_choice],
outputs=[max_tokens]
)
# Knowledge base refresh button event
refresh_kb_info.click(load_vectorstore,
inputs=[kb_path,embedding_model_type,embedding_model],
outputs=[kb_vector_content,kb_file_list]
).success(get_chroma_info,
[kb_path,kb_file_list,advance_kb_info],
[kb_info])
kb_file_list.change(get_chroma_info,
[kb_path,kb_file_list,advance_kb_info],
[kb_info])
advance_kb_info.select(get_chroma_info,
[kb_path,kb_file_list,advance_kb_info],
[kb_info])
'''
chatbot button event
'''
message.submit(
reload_memory,
[chat_bot,Context_length],
).success(
deliver,
input_param,
output_param,
queue=False
).then(
lambda: gr.Textbox(value=''),
[],
[message]
).success(
stream,
[chat_bot,chat_his,if_agent_mode]
,chat_bot
).success(
update_conversation_to_json,
[chat_name,chat_bot]
)
send.click(reload_memory,
[chat_bot,Context_length],
).success(deliver,
input_param,
output_param,
queue=False
).then(lambda: gr.Textbox(value=''), [],[message]
).success(stream,
[chat_bot,chat_his,if_agent_mode],
chat_bot
).success(update_conversation_to_json,
[chat_name,chat_bot])
regenerate_button.click(remove_last_chat,
[chat_his,chat_bot],
[chat_his,chat_bot,message]
).success(lambda: gr.Button(interactive=False),[],[send]
).success(lambda: gr.Button(interactive=False),[],[chat_with_file]
).success(reload_memory,
[chat_bot,Context_length],
).success(deliver,
input_param,
output_param,
queue=False
).then(lambda: gr.Textbox(value=''), [],[message]
).success(stream,
[chat_bot,chat_his,if_agent_mode],
chat_bot
).success(lambda: gr.Button(interactive=True),[],[send]
).success(lambda: gr.Button(interactive=True),[],[chat_with_file]
).success(update_conversation_to_json,
[chat_name,chat_bot])
clear.click(rst_mem,
inputs=chat_his,
outputs=chat_his
).success(update_conversation_to_json,
[chat_name,chat_bot])
delete_latest_round_button.click(remove_last_chat,
[chat_his,chat_bot],
[chat_his,chat_bot]
).success(update_conversation_to_json,
[chat_name,chat_bot]).success(lambda: gr.Info(i18n("Successfully delete current round!")))
export_his.click(export_to_markdown,[chat_bot,chat_name])
save_chat_config.click(save_all_settings,
inputs=[chat_name,System_Prompt,Context_length,Temperature,max_tokens,top_p,frequency_penalty,presence_penalty])
'''
chat_file button event
'''
file.upload(upload_file,inputs=[file,split_tmp],outputs=[split_tmp,file],show_progress="full")#.then(cal_token_cost,[split_tmp],[estimate_cost])
# file.clear(lambda:gr.Textbox(value=''),[],[estimate_cost])
# refresh_file_cost.click(lambda:gr.Text(),[],[estimate_cost]).then(lambda:gr.File(),[],[file]).then(lambda:gr.Text(),[],[estimate_cost])
chat_with_file.click(ask_file,
inputs=[chat_bot,message,file_answer,chat_model_type,model_choice,sum_type,vector_path,
file_list,filter_choice,if_rerank,if_hybrid_retrieve,hybrid_retrieve_weight],
outputs=[chat_bot,file_answer]
).then(file_ask_stream,
[chat_bot,file_answer],
[chat_bot]
).success(update_conversation_to_json,
[chat_name,chat_bot])
summarize.click(summarize_file,
inputs=[split_tmp,chat_bot,model_choice,sum_type],
outputs=[sum_result,chat_bot]
).then(sum_stream,
[sum_result,chat_bot],
[chat_bot]
).success(update_conversation_to_json,
[chat_name,chat_bot])
chat_with_file.click(lambda: gr.Textbox(value=''), [],[message])
summarize.click(lambda: gr.Textbox(value=''), [],[message])
# Manage vectorstore event
create_vec_but.click(
create_vec_in_specific_path,
inputs=[vector_name,embedding_model_type,embedding_model]
).success(
create_kb_info_in_config,[vector_name,embedding_model_type,embedding_model],[]
).then(
lambda: gr.Textbox(value=None),[],[vector_name]
).success(
kb.reinitialize,[],[]
).success(
lambda: gr.Dropdown(choices=kb.knowledge_bases),[],[vector_list]
)
# 未找到解除知识库的占用的方法，搁置
# delete_vectorstore_button.click(delete_vec_in_specific_path,
# inputs=[vector_list]
# ).then(lambda: gr.Dropdown(),[],[vector_list])
# TODO:后续将暂时隐藏或取消该按钮（原用于加载自定义路径的知识库）
# 新增一个用于刷新知识库列表的按钮（浏览器刷新无法真正刷新）
load_vec.click(load_vectorstore,
inputs=[vector_path,embedding_model_type,embedding_model],
outputs=[vector_content,file_list]
).then(lambda vector_path:gr.Textbox(value=vector_path),
[vector_path],
[kb_path]
).then(load_vectorstore,
[vector_path,embedding_model_type,embedding_model],
[kb_vector_content,kb_file_list])
load_complete_vec_button.click(kb.reinitialize,[],[]
).success(
lambda: gr.Dropdown(choices=kb.knowledge_bases),[],[vector_list]
)
vector_list.select(kb.get_persist_vec_path,
[vector_list],
[vector_path]
).then(load_vectorstore,
inputs=[vector_path,embedding_model_type,embedding_model],
outputs=[vector_content,file_list]
).then(lambda vector_path:gr.Textbox(value=vector_path),
[vector_path],
[kb_path]
).then(load_vectorstore,
[vector_path,embedding_model_type,embedding_model],
[kb_vector_content,kb_file_list]
).then(
# 然后更新知识库的嵌入类型和模型，并使锁死 embedding_model
lambda vector_list:kb.get_embedding_model(vector_list),
[vector_list],
[embedding_model_type,embedding_model]
)
#file_list.change(refresh_file_list,inputs=[vector_content],outputs=file_list)
add_file.click(add_file_in_vectorstore,
inputs=[vector_path,split_tmp,embedding_model_type,embedding_model,file],
outputs=[vector_content,file_list,file]
).then(load_vectorstore,
inputs=[vector_path,embedding_model_type,embedding_model],
outputs=[vector_content,file_list]
)
delete_file.click(delete_flie_in_vectorstore,inputs=file_list).then(load_vectorstore,inputs=[vector_path,embedding_model_type,embedding_model],outputs=[vector_content,file_list])
# Agent button event
sum_url_button.click(url_request_chain,
inputs=[model_choice,sum_url,chat_bot,web_template],
outputs=[chat_bot]).success(update_conversation_to_json,
[chat_name,chat_bot])
'''
Dall-e-3
'''
pic_gen_button.click(generate_dall3_image,
inputs=[pic_gen_prompt,image_size_radio,image_quality_dropdown,image_style_radio],
outputs=[img,actual_prompt])
open_dir.click(fn = open_dir_func)
'''
RAG-search
'''
search_btn.click(
lambda:gr.HTML(visible=True),[],[search_result_title]
).then(
lambda:gr.HTML(visible=True),[],[search_result]
).then(
lambda:gr.HTML(visible=True),[],[search_source]
).then(
lambda:gr.HTML(value=""),[],[search_source]
).then(
rag_engine.query_function,
inputs=[search_query,model_choice],
outputs=[search_result]
).then(
rag_engine.gen_html_page,
outputs=[search_source]
).then(lambda:gr.Textbox(value=""),[],[search_query])
# TODO:增加一个将 search_query 设置为 HTML 标题的处理函数
refresh_search_history_list_btn.click(lambda:gr.Dropdown(choices=rag_engine.get_search_history()),[],[search_history_dropdown])
search_history_dropdown.change(rag_engine.get_search_history_by_key,
[search_history_dropdown],[search_history_content,search_history_sources])
demo.queue().launch(inbrowser=True,debug=True,show_api=False
#auth=[("admin","123456")],auth_message="欢迎使用 GPT-Gradio-Agent ,请输入用户名和密码"
)