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jinchat-server
提交 3187423e 作者: imClumsyPanda
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update cli_demo.py

上级 5d2055c6
正在显示 包含 121 行增加 和 99 行删除
chains/local_doc_qa.py
......@@ -8,6 +8,7 @@ import sentence_transformers
import os
from configs.model_config import *
import datetime
from typing import List
# return top-k text chunk from vector store
VECTOR_SEARCH_TOP_K = 10
......@@ -42,25 +43,35 @@ class LocalDocQA:
self.top_k = top_k
def init_knowledge_vector_store(self,
filepath: str):
if not os.path.exists(filepath):
print("路径不存在")
return None
elif os.path.isfile(filepath):
file = os.path.split(filepath)[-1]
try:
loader = UnstructuredFileLoader(filepath, mode="elements")
docs = loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
filepath: str or List[str]):
if isinstance(filepath, str):
if not os.path.exists(filepath):
print("路径不存在")
return None
elif os.path.isdir(filepath):
elif os.path.isfile(filepath):
file = os.path.split(filepath)[-1]
try:
loader = UnstructuredFileLoader(filepath, mode="elements")
docs = loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
return None
elif os.path.isdir(filepath):
docs = []
for file in os.listdir(filepath):
fullfilepath = os.path.join(filepath, file)
try:
loader = UnstructuredFileLoader(fullfilepath, mode="elements")
docs += loader.load()
print(f"{file} 已成功加载")
except:
print(f"{file} 未能成功加载")
else:
docs = []
for file in os.listdir(filepath):
fullfilepath = os.path.join(filepath, file)
for file in filepath:
try:
loader = UnstructuredFileLoader(fullfilepath, mode="elements")
loader = UnstructuredFileLoader(file, mode="elements")
docs += loader.load()
print(f"{file} 已成功加载")
except:
......@@ -74,7 +85,7 @@ class LocalDocQA:
def get_knowledge_based_answer(self,
query,
vs_path,
chat_history=[],):
chat_history=[], ):
prompt_template = """基于以下已知信息，简洁和专业的来回答用户的问题。
如果无法从中得到答案，请说 "根据已知信息无法回答该问题" 或 "没有提供足够的相关信息"，不允许在答案中添加编造成分，答案请使用中文。
......
webui.py
import gradio as gr
import os
import shutil
import cli_demo as kb
from chains.local_doc_qa import LocalDocQA
from configs.model_config import *
def get_file_list():
......@@ -12,9 +13,11 @@ def get_file_list():
file_list = get_file_list()
embedding_model_dict_list = list(kb.embedding_model_dict.keys())
embedding_model_dict_list = list(embedding_model_dict.keys())
llm_model_dict_list = list(kb.llm_model_dict.keys())
llm_model_dict_list = list(llm_model_dict.keys())
local_doc_qa = LocalDocQA()
def upload_file(file):
......@@ -27,9 +30,9 @@ def upload_file(file):
return gr.Dropdown.update(choices=file_list, value=filename)
def get_answer(query, vector_store, history):
resp, history = kb.get_knowledge_based_answer(
query=query, vector_store=vector_store, chat_history=history)
def get_answer(query, vs_path, history):
resp, history = local_doc_qa.get_knowledge_based_answer(
query=query, vs_path=vs_path, chat_history=history)
return history, history
......@@ -41,6 +44,25 @@ def get_file_status(history):
return history + [[None, "文档已完成加载，请开始提问"]]
def init_model():
try:
local_doc_qa.init_cfg()
return """模型已成功加载，请选择文件后点击"加载文件"按钮"""
except:
return """模型未成功加载，请重新选择后点击"加载模型"按钮"""
def reinit_model(llm_model, embedding_model, llm_history_len, top_k):
local_doc_qa.init_cfg(llm_model=llm_model,
embedding_model=embedding_model,
llm_history_len=llm_history_len,
top_k=top_k),
model_status = gr.State()
history = gr.State([])
vs_path = gr.State()
model_status = init_model()
with gr.Blocks(css="""
.importantButton {
background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
......@@ -63,89 +85,78 @@ with gr.Blocks(css="""
with gr.Row():
with gr.Column(scale=2):
chatbot = gr.Chatbot([[None, """欢迎使用 langchain-ChatGLM Web UI，开始提问前，请依次如下 3 个步骤：
1. 选择语言模型、Embedding 模型及相关参数后点击"step.1: setting"，并等待加载完成提示
2. 上传或选择已有文件作为本地知识文档输入后点击"step.2 loading"，并等待加载完成提示
3. 输入要提交的问题后点击"step.3 asking" """]],
1. 选择语言模型、Embedding 模型及相关参数后点击"重新加载模型"，并等待加载完成提示
2. 上传或选择已有文件作为本地知识文档输入后点击"重新加载文档"，并等待加载完成提示
3. 输入要提交的问题后，点击回车提交 """], [None, str(model_status)]],
elem_id="chat-box",
show_label=False).style(height=600)
with gr.Column(scale=1):
with gr.Column():
llm_model = gr.Radio(llm_model_dict_list,
label="llm model",
value="chatglm-6b",
interactive=True)
LLM_HISTORY_LEN = gr.Slider(0,
10,
value=3,
step=1,
label="LLM history len",
interactive=True)
embedding_model = gr.Radio(embedding_model_dict_list,
label="embedding model",
value="text2vec",
interactive=True)
VECTOR_SEARCH_TOP_K = gr.Slider(1,
20,
value=6,
step=1,
label="vector search top k",
interactive=True)
load_model_button = gr.Button("step.1：setting")
load_model_button.click(lambda *args:
kb.init_cfg(args[0], args[1], args[2], args[3]),
show_progress=True,
api_name="init_cfg",
inputs=[llm_model, embedding_model, LLM_HISTORY_LEN,VECTOR_SEARCH_TOP_K]
).then(
get_model_status, chatbot, chatbot
)
with gr.Column():
with gr.Tab("select"):
selectFile = gr.Dropdown(file_list,
label="content file",
interactive=True,
value=file_list[0] if len(file_list) > 0 else None)
with gr.Tab("upload"):
file = gr.File(label="content file",
file_types=['.txt', '.md', '.docx', '.pdf']
).style(height=100)
# 将上传的文件保存到content文件夹下,并更新下拉框
file.upload(upload_file,
inputs=file,
outputs=selectFile)
history = gr.State([])
vector_store = gr.State()
load_button = gr.Button("step.2：loading")
load_button.click(lambda fileName:
kb.init_knowledge_vector_store(
"content/" + fileName),
show_progress=True,
api_name="init_knowledge_vector_store",
inputs=selectFile,
outputs=vector_store
).then(
get_file_status,
chatbot,
chatbot,
show_progress=True,
)
with gr.Row():
with gr.Column(scale=2):
query = gr.Textbox(show_label=False,
placeholder="Prompts",
placeholder="请提问",
lines=1,
value="用200字总结一下"
).style(container=False)
with gr.Column(scale=1):
generate_button = gr.Button("step.3：asking",
elem_classes="importantButton")
generate_button.click(get_answer,
[query, vector_store, chatbot],
[chatbot, history],
api_name="get_knowledge_based_answer"
)
llm_model = gr.Radio(llm_model_dict_list,
label="LLM 模型",
value="chatglm-6b",
interactive=True)
llm_history_len = gr.Slider(0,
10,
value=3,
step=1,
label="LLM history len",
interactive=True)
embedding_model = gr.Radio(embedding_model_dict_list,
label="Embedding 模型",
value="text2vec",
interactive=True)
top_k = gr.Slider(1,
20,
value=6,
step=1,
label="向量匹配 top k",
interactive=True)
load_model_button = gr.Button("重新加载模型")
# with gr.Column():
with gr.Tab("select"):
selectFile = gr.Dropdown(file_list,
label="content file",
interactive=True,
value=file_list[0] if len(file_list) > 0 else None)
with gr.Tab("upload"):
file = gr.File(label="content file",
file_types=['.txt', '.md', '.docx', '.pdf']
) # .style(height=100)
load_button = gr.Button("重新加载文件")
load_model_button.click(reinit_model,
show_progress=True,
api_name="init_cfg",
inputs=[llm_model, embedding_model, llm_history_len, top_k]
).then(
get_model_status, chatbot, chatbot
)
# 将上传的文件保存到content文件夹下,并更新下拉框
file.upload(upload_file,
inputs=file,
outputs=selectFile)
# load_button.click(local_doc_qa.init_knowledge_vector_store,
# show_progress=True,
# api_name="init_knowledge_vector_store",
# inputs=selectFile,
# outputs=vs_path
# ).then(
# get_file_status,
# chatbot,
# chatbot,
# show_progress=True,
# )
# query.submit(get_answer,
# [query, vs_path, chatbot],
# [chatbot, history],
# api_name="get_knowledge_based_answer"
# )
demo.queue(concurrency_count=3).launch(
server_name='0.0.0.0', share=False, inbrowser=False)
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